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        "idx": 1,
        "result": "supported"
    },
    {
        "idx": 2,
        "result": "unsupported"
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<reference>
arXiv:2510.05374v1 [eess.SY] 6 Oct 2025

D IGITAL T WINS FOR I NTELLIGENT I NTERSECTIONS :
A L ITERATURE R EVIEW

Alben Rome Bagabaldo, Jürgen Hackl
Department of Civil and Environmental Engineering
Princeton University
Princeton, NJ, USA 08544
{alben, hackl}@princeton.edu

A BSTRACT
Intelligent intersections play a pivotal role in urban mobility, demanding innovative solutions such
as digital twins to enhance safety and efficiency. This literature review investigates the integration
and application of digital twins for intelligent intersections, a critical area within smart urban traffic
systems. The review systematically categorizes existing research into five key thematic areas: (i)
Digital Twin Architectures and Frameworks; (ii) Data Processing and Simulation Techniques; (iii)
Artificial Intelligence and Machine Learning for Adaptive Traffic Control; (iv) Safety and Protection
of Vulnerable Road Users; and (v) Scaling from Localized Intersections to Citywide Traffic Networks.
Each theme is explored comprehensively, highlighting significant advancements, current challenges,
and critical insights. The findings reveal that multi-layered digital twin architectures incorporating
real-time data fusion and AI-driven decision-making enhances traffic efficiency and safety. Advanced
simulation techniques combined with sophisticated AI/ML algorithms demonstrate notable improvements in real-time responsiveness and predictive accuracy for traffic management. Additionally, the
integration of digital twins has shown substantial promise in safeguarding vulnerable road users
through proactive and adaptive safety strategies. Despite these advancements, key challenges persist,
including interoperability of diverse data sources, scalability of digital twins for extensive traffic networks, and managing uncertainty within dynamic urban environments. Addressing these challenges
will be essential for the future development and deployment of intelligent, adaptive, and sustainable
intersection management systems.
Keywords digital twins · traffic management · artificial intelligence · safety · smart infrastructure · intersections ·
review

1

Introduction

Road infrastructure plays a foundational role in modern society by connecting communities, driving economic growth,
and supporting access to essential services. In the United States alone, public roads span approximately 4.19 million
miles (FHWA, 2023) and include over 15.8 million intersections (Streetsblog USA, 2022), with 255 million drivers
undertaking around 227 billion driving trips daily and spending an average of 60.2 minutes on the road (Steinbach and
Tefft, 2023). Despite their ubiquity, intersections account for a disproportionate share of traffic incidents, responsible
for over 40% of crashes (Azimi et al., 2014; Namazi et al., 2019; Choi, 2010) and 27% of traffic fatalities (NCSA,
2024). High rates of intersection-related crashes stem from factors such as driver errors, poor visibility, and adverse
weather conditions, while pedestrians face an even greater risk, with pedestrian fatalities rising by 75% since 2010
(Smart Growth America, 2024). In 2023, fatal and severe traffic crashes in the U.S. resulted in $1.85 trillion in total
losses, including $460 billion in direct economic costs and $1.4 trillion in quality-of-life impacts (TRIP, 2024).
There are many attempts to address these underlying issues for traffic intersections, including modernization of signal
systems and geometric improvements (Chandler et al., 2013). Among these, digital twin (DT) technology has emerged
as particularly promising (Tao et al., 2019). A digital twin represents a virtual counterpart of a physical system or
process, continuously updated through real-time sensor data, simulations, and algorithms (Fu et al., 2025). The concept

Digital Twins for Intelligent Intersections: A Literature Review

of digital twins was first introduced in 2003 within Michael Grieves’s Product Lifecycle Management framework at
University of Michigan (Grieves, 2014). Subsequent adoption of the term by NASA in “Digital Twin Paradigm for
Future NASA and U.S. Air Force Vehicles” (Glaessgen and Stargel, 2012) further spurred interest in this area. Since
then, digital twins have provided a powerful framework to address challenges, such as reducing crash risk, improving
pedestrian safety, mitigating driver errors, and adapting to adverse weather conditions, by leveraging real-time sensor
data, advanced simulation tools, and machine learning algorithms.
Virtual models that mirror physical intersections allow stakeholders to predict traffic behavior, identify hazards, and
implement adaptive traffic control strategies (Abdelrahman et al., 2025; NCSA, 2024). Moreover, this innovation
has the potential to significantly improve safety for vulnerable road users (VRUs), such as pedestrians, cyclists, and
micromobility device users (Townsend et al., 2023). By offering a dynamic, data-driven approach to analyze and
optimize intersection performance, digital twins can transform urban traffic systems to be safer, more efficient, and
more sustainable (Xu et al., 2025a).

2

Methodology

This section outlines our structured, multi-stage review process, modeled after best practices in systematic literature
reviews, to assemble and analyze research on digital twins for intelligent intersections. We organized papers into five
themes mentioned in Section 1-Introduction.
Parallel to the developments in digital twins, the significant growth of scholarly literature has driven the development
of innovative approaches to synthesizing research findings. Recent studies have shown that although academics are
reading more articles than ever before, the average time spent per article has decreased, indicating a shift toward
more superficial engagement with the literature (Tenopir et al., 2015). To manage this increasing information load,
LLMs have emerged as valuable tools to automate and expedite systematic literature reviews. For instance, LLMs can
efficiently perform tasks such as title and abstract screening and preliminary data synthesis, thereby reducing the human
workload and enhancing the comprehensiveness of reviews (Dennstädt et al., 2024; Luo et al., 2024). Despite concerns
regarding potential hallucinations and the need for human oversight (Chelli et al., 2024; Khraisha et al., 2024), the
integration of LLMs represents a significant methodological advancement. In our work, we leverage these capabilities
to systematically integrate insights on digital twin applications in intelligent intersections.
2.1

Literature Search and Screening

We queried ScienceDirect using broad terms: “digital twins,” “intelligent intersections,” and “transportation” for
publications from January 2015 to May 2025 and selecting only papers that are Open Access. Following database
retrieval, all full texts were processed for automated summarization using Gemini 2.0 Pro, which ingests complete
PDFs and outputs structured summaries under a schema capturing scope, methods, intersection relevance, and thematic
signals.
Each summary was screened by asking the LLM prompt: “Is this paper relevant to digital twins for traffic intersections?”
with rationale. Outputs were then manually reviewed. Papers without relevance to cross roads or intersections or
focused outside transportation were excluded. To enhance thematic depth, we manually inspected references from
all included papers. Works presenting foundational frameworks or significant empirical advances were incorporated
through a snowballing process, which extended the corpus beyond database searches to include both seminal and
emergent contributions. This snowballing process includes those papers that were identified from the supplementary
thematic search described in subsection 2.2.
During the search, we initially identified 544 papers, this number includes the papers identified in the snowballing
process. After applying LLM-based filtering and skimming through the content, this number was reduced to 85 unique
articles, which were included in this review. As shown in Figure 1, the distribution of papers by publication year reveals
a clear upward trend. From 2019 to 2022, between 1 and 6 relevant studies were identified annually (1 in 2019, 5 in
2020, 2 in 2021, and 8 in 2022). A remarkable increase begins in 2023 with 26 papers, followed by a further rise to 34
papers in 2024. Although the literature search was cut off in May 2025, 11 papers were already identified for that year,
indicating that 2025 is on pace for continued substantial contributions. This trend highlights the increasing significance
and rapid development of this research domain.
2.2

Thematic Classification and Keyword-Augmented Searches

All these relevant papers were manually classified into the aforementioned themes. We assigned each paper to the theme
reflecting its primary contribution. Many papers overlapped themes, and were documented accordingly. To ensure
2

Digital Twins for Intelligent Intersections: A Literature Review

comprehensive coverage, we also conducted theme-specific supplementary searches using refined keyword strings or
keywords shown in Table 1.
Table 1: Themes, additional keywords used, and number of unique papers considered.
Theme
Digital Twin Architectures and Frameworks
Data Processing and Simulation Techniques
AI/ML in Traffic Control
Safety and Protection of Vulnerable Road Users
Scaling to Citywide Traffic Networks

Keywords
digital twins architecture, interoperability, digital
twin design
data fusion, sensor integration, simulation
reinforcement learning traffic, AI signal control,
multi-agent control, deep learning
pedestrian digital twin, vulnerable road user safety,
agent-based safety simulation, ethics in traffic AI
urban scale digital twins, distributed control, smartcity infrastructure, IoT-enabled twins

Final No. of Papers
18
19
23
15
35

This process led to the inclusion of additional papers with distinct relevance to each theme. We organized the papers
into theme-specific folders and used Gemini 2.0 Pro to generate initial syntheses for each category. Several studies
contributed to more than one theme, reflecting the interdisciplinary and interconnected nature of digital twin research in
transportation. The number of papers associated with each theme is as follows: 18, 19, 23, 15, and 35, respectively as
shown in Table 1. The synthesis prompts guided the extraction of shared findings, key technical advancements, current
limitations, and emerging research directions. These LLM-generated summaries served as thematic scaffolding, which
we subsequently refined through manual review, writing, and editing into the coherent narrative sections presented in
this paper.
40

Number of Papers

34
30
26
20
11
10

8
5
2

1

25
20

24
20

20
23

22
20

21
20

20
20

20

19

0
Publication Year
Figure 1: Number of papers by publication year.

3

Digital Twin Architectures and Frameworks

The concepts of architectures and frameworks are important in structuring and implementing effective digital twins. A
digital twin architecture defines the structured organization of components and their relationships within a digital twin
system. This architecture is essential for enabling interoperability and data exchange with both the physical system
and external applications (Cavalieri and Gambadoro, 2023). Frameworks, on the other hand, constitute the conceptual
and architectural foundation that supports the integration of heterogeneous data sources, computational infrastructures,
and analytical models. They offer standardized practices that enhance interoperability and scalability across a range of
applications (Nativi et al., 2021).
To address the question, “What architectural and interoperability frameworks are necessary to develop scalable,
modular, and interoperable digital twins for intelligent intersections?”, we conducted a thorough search and filtering
process and identified 18 scholarly articles specifically focused on digital twin architectures and frameworks relevant to
3

Digital Twins for Intelligent Intersections: A Literature Review

the topic. These selected studies offer valuable insights into current practices and innovations in the field. The search
parameters and inclusion criteria are detailed in Section 2 – Methodology.
This section surveys the architectural foundations and enabling technologies driving digital twins for intelligent
intersections. The discussion is organized into two subsections: Architectures, which covers real-time synchronization
mechanisms, data-driven and optimization-centric systems, AI-enhanced structures, and domain-specific configurations;
and Frameworks and Enabling Technologies, which explores supporting computational methods, interoperability
protocols, and deployment strategies that facilitate scalable and adaptive digital twin implementations. Together, these
approaches illustrate the diverse, multi-layered, and rapidly evolving ecosystem of digital twin solutions for traffic
management and intersection intelligence.
3.1

Architectures

Recent advancements in digital twin architectures showcase diverse system designs that integrate real-time sensing,
communication protocols, and simulation tools to support intelligent intersections. A zero calibration, satellite ground
mapping architecture is presented in literature, which fuses hierarchical spatiotemporal video analysis, on-the-fly
3D bounding box estimation, and deep convolutional neural networks (CNNs) to support real-time environment
modeling (Rezaei et al., 2023). Likewise, a continuously synchronized framework is adopted, combining live traffic
feeds, vehicle-to-everything (V2X) protocols such as Dedicated Short-Range Communications (DSRC) (Kenney, 2011),
Wireless Access in Vehicular Environments (WAVE) (Jiang and Zhu, 2019), and Cellular V2X (C-V2X) over 5G New
Radio (the fifth generation cellular standard), along with a Simulation of Urban MObility (SUMO)-based microscopic
simulator (Lopez et al., 2018) via the Traffic Control Interface (TraCI) API (Wegener et al., 2008) (Kušić et al., 2023).
Hardware-in-the-Loop (HIL) simulation and mixed reality virtual layers further refine control logic (Wágner et al.,
2023), while another work integrates roadside units (RSUs), millimeter-wave (mmWave) radar, Light Detection and
Ranging (LiDAR), weather stations, and edge computing microservices into a cloud native co-simulation platform (An
et al., 2023). Co-simulation refers to the coordinated execution of two or more simulation tools, often running in parallel
and exchanging data in real time, to realistically represent complex, multi-domain systems such as traffic flow and
vehicle behavior (Yilmaz-Niewerth et al., 2024).
An intelligent digital twin system is outlined, featuring integrated sensor data, a Remote Dictionary Server (Redis)
database backbone (Redis Labs, 2025), and Kalman-filtered prediction modules. This architecture enables client-side
visualization while avoiding reliance on direct simulation engines, supporting a lightweight forecasting layer (Liu
et al., 2024a). Redis is a high-performance, open-source, in-memory NoSQL data structure store that can be used as a
database, cache, and message broker.
Data-driven and optimization-centric architecture emphasize model accuracy, robustness to missing data, and automated
decision-making. A digital twin is presented that leverages Gaussian mixture clustering, expectation–maximization
(EM) algorithms, regression analysis, and singular value decomposition (SVD) for iterative engine-performance
indexing (Taghavi and Perera, 2024). Meanwhile, a temporal neighboring interpolation and missing data imputation
routines to reconstruct sparse traffic volumes in connected corridors is also introduced (Saroj et al., 2023). In the
rail domain, a method is presented that automatically generates track geometry from airborne LiDAR point clouds
using Industry Foundation Classes (IFC)-based parametric assemblies and mesh-based reconstruction. The dataset
also includes challenging scenarios such as where a railway line crosses a road, demonstrating the system’s ability
to handle complex geometries (Ariyachandra and Brilakis, 2023). Meanwhile, an architecture is structured around a
monitoring and visualization dashboard that feeds an Optimal Level Crossing (OLC) optimization model, supported by
a physical-to-virtual data layer that ensures safe rail crossing sequences (Djordjević et al., 2024).
Certain literature also focuses on layered AI and generative frameworks, coupling foundational models with feedback
loops. A three-layer digital twin is synthesized, comprising an AI core, bidirectional physical-to-virtual and virtualto-physical channels for sensor ingestion and actuator commands, and RL agents for adaptive control (Kreuzer et al.,
2024). Building on this, a GenAI-enabled large-flow model is introduced that unifies Generative Adversarial Networks
(GANs), Variational Autoencoders (VAEs), transformers, diffusion models, and foundation models for adaptive urban
flow simulation (Huang et al., 2025). Another approach integrates graph neural networks (GNNs) and spatial–temporal
graph attention networks (STGATs) to blend static infrastructure features with dynamic traffic states, driving preventive
maintenance assessments rather than continuous clustering or optimization routines (Lu et al., 2025). On the other hand,
a pipeline style architectural framework is proposed, centered on context-aware segmentation of LIDAR point clouds,
3D meshing, semantic labeling, and asset management integration, prioritizing geometric fidelity over deep generative
reasoning (Davletshina et al., 2024).
Domain specific communication and representation frameworks further diversify digital twin architectures. A dual
vehicle digital twin system (head and assistant) is implemented using V2X reservation intervals and Operational
4

Digital Twins for Intelligent Intersections: A Literature Review

Design Domain (ODD) constraints (Du et al., 2024). Another implementation embeds SPaT/MAP protocols within a
PLC-based control and HIL simulation environment, augmented by mixed-reality visualization (Wágner et al., 2023). A
complementary cloud-native digital twin architecture integrates edge computing, multi-modal sensing, and open service
APIs to manage distributed sensor arrays in support of vehicle-infrastructure cooperative intelligent driving (An et al.,
2023). Finally, a scan-to-graph pipeline converts segmented point clouds through lane polynomial fitting and Frenet
transformations into a graph representation, offering a graph-centric perspective on highway digital twins (Pan et al.,
2024).
Many surveyed studies adopt a multilayer architecture, where layer refers to a distinct functional component within
the system’s design, each responsible for specific tasks. The architecture can be divided into five key layers: (i) the
data acquisition layer, which ingests heterogeneous sensor streams such as video, LiDAR, radar, loop detectors, and
weather stations (Rezaei et al., 2023; Kušić et al., 2023; Pan et al., 2024); (ii) the communication layer, which leverages
V2X protocols (DSRC/WAVE, C-V2X), 5G New Radio, Message Queuing Telemetry Transport (MQTT), and edge
microservices for low-latency data transfer (Wágner et al., 2023; Du et al., 2024; Aguiar et al., 2022); (iii) the processing
layer, which employs simulation engines such as SUMO (Kušić et al., 2023) and HIL setups (Wágner et al., 2023),
clustering and optimization algorithms including Gaussian Mixture Models(GMMs) and EM (Taghavi and Perera,
2024) and Optimal Level Crossing optimization (Djordjević et al., 2024), AI modules such as GNNs and STGATs (Lu
et al., 2025), GANs and VAEs (Huang et al., 2025), and RL agents for adaptive control (Kreuzer et al., 2024); (iv)
the modeling layer, which integrates physics-based solvers, foundation-model reasoning, and parametric assemblies
like IFC and mesh-based reconstruction to support predictive analytics and maintenance planning (Liu et al., 2024a);
and (v) the visualization layer, which delivers web dashboards, Building Information Modeling (BIM) interfaces, and
mixed-reality environments for user interaction and asset management (Sabato et al., 2023; An et al., 2023; Wágner
et al., 2023). This multilayered architecture is shown in the Figure 2.

Figure 2: Layered digital twin architecture for intelligent intersections, shown left to right: (i) Data Acquisition Layer
ingests heterogeneous real-time inputs (e.g., video, LiDAR, radar, loop detectors, weather stations); (ii) Communication
Layer ensures low-latency, reliable transfer via technologies such as DSRC/WAVE, C-V2X, 5G NR, MQTT, and
edge microservices; (iii) Processing Layer performs simulation, AI inference, data fusion and optimization (e.g.,
SUMO, GMM & EM, GNNs, generative models); (iv) Modeling Layer constructs predictive and physics-informed
representations including foundation models, mesh reconstruction, and parametric assemblies; and (v) Visualization
Layer supports operator interaction and asset management through web dashboards, BIM viewers, and mixed-reality
interfaces. The directional flow highlights how raw sensing progresses through communication and computation into
actionable models and user-facing insights.
3.2

Frameworks and Enabling Technologies

The diverse architectures presented in the literature are enabled by frameworks that coordinate interactions between
layers, components, and technologies. These frameworks typically incorporate specific computational, communication,
and coordination technologies.
5

Digital Twins for Intelligent Intersections: A Literature Review

A key enabler across these frameworks is the integration of AI/ML techniques. These are often employed not only
for data analysis and imputation, such as GMMs, expectation–maximization, and GNNs, but also for real-time
decision-making and predictive control. For instance, clustering and regression techniques are used to generate
robust engine performance indices in maritime digital twins through GMMs, EM algorithms, and singular value
decomposition (Taghavi and Perera, 2024). In the urban traffic domain, generative AI models unify GANs, VAEs, and
transformers to simulate adaptive traffic flow under varying city conditions, offering a scalable and sustainable approach
to mobility forecasting (Huang et al., 2025). AI-enhanced digital twin architectures for smart intersections also employ
RL and context-aware control mechanisms to coordinate infrastructure and vehicle behavior in real time (Wágner et al.,
2023). In support of road asset maintenance, STGATs and GNNs are integrated to fuse infrastructure features and traffic
states for condition prediction and preventive maintenance (Lu et al., 2025). Finally, deep learning and context-aware
segmentation pipelines are applied to point cloud data to automate the geometric modeling of road assets, prioritizing
meshing accuracy and semantic labeling over generative simulation (Davletshina et al., 2024).
Edge computing and cloud-native deployment strategies are also central to many frameworks. These enable distributed
processing of sensor data and the scalability needed for citywide deployments. Technologies such as Docker containers,
MQTT protocols (a lightweight, publish-subscribe network protocol designed for connecting remote devices with
resource constraints or limited network bandwidth), and microservice-based architectures support modularity, fault
tolerance, and efficient data handling. For instance, one system utilizes edge computing microservices and multi-modal
sensing to manage distributed data (An et al., 2023) , while another one integrates a Redis database and Kalman
filter-based forecasting to enable lightweight, real-time traffic predictions (Liu et al., 2024a). Similarly, the MobiWise
architecture provides a microservice-based, IoT-leveraged framework for eco-routing decisions using MQTT and
containerized services (Aguiar et al., 2022). Another foundational element is the use of standardized data formats and
interoperability protocols. Tools like SUMO and the TraCI API are used to ensure consistent simulation and control
integration, as demonstrated in real-time traffic synchronization frameworks (Kušić et al., 2023).
Visualization frameworks play a pivotal role in enabling interaction with digital twins. These range from web dashboards
to immersive mixed-reality interfaces and BIM-integrated viewers. Systems such as BIM-integrated visualization for
smart intersections offer intuitive interfaces for real-time monitoring, coordination, and decision-making (Sabato et al.,
2023).
Beyond conventional architectural and control-oriented perspectives, recent work has also introduced the concept of
“Fused Twins,” which refers to digital twins that are embedded within their physical environments through situated
analytics. These twins integrate the physical, data, analytical, virtual, and connection environments into in-situ overlays,
enabling real-world objects to be augmented with digital layers for enhanced context-awareness. This approach
encourages embodied interfaces, such as AR/VR overlays that are spatially grounded in the physical world, and calls
for empirical studies evaluating cognitive benefits such as reduced mental workload in real deployments (Grübel et al.,
2022). In transportation, a related survey of digital twin applications for autonomous driving proposed a test framework
based on V2X communication for real-time simulation, but underscored the lack of clear definitions and real-world
validation in current digital twin-based testbeds (Niaz et al., 2021).

4

Data Processing and Simulation Techniques

Effective digital twins for intelligent intersections rely on two interconnected pillars: (i) robust data processing pipelines
and (ii) high fidelity simulation platforms. Data processing pipelines must integrate and fuse heterogeneous sensor
streams, such as loop detectors, LiDAR and radar scanners, and connected vehicle broadcasts, through data ingestion,
cleaning, and fusion steps. Standardizing and combining these diverse inputs in real time enables big data analytics to
forecast traffic conditions and support traffic demand management (Torre-Bastida et al., 2018). Simulation techniques
span microscopic traffic models (e.g., SUMO (Lopez et al., 2018)), dynamic traffic assignment (Wang et al., 2018), and
co-simulation frameworks that integrate autonomous vehicle (AV) simulators (e.g., CARLA (Dosovitskiy et al., 2017))
with traffic flow models (Varga et al., 2023; Yilmaz-Niewerth et al., 2024). These platforms also support calibration and
validation of virtual sensor models and AI-driven synthetic data generation for enhanced digital twin realism.
This section synthesizes 19 key studies that explore data processing and simulation strategies essential for developing
digital twins for intelligent intersections, with a focus on addressing the question: “What data fusion strategies and
uncertainty modeling approaches can enhance the reliability, accuracy, and real-time decision-making capabilities
of digital twins?” Table C.1 (in the appendices) contrasts chosen digital twin works based on their data fusion or
processing approaches and corresponding simulation environments.
The remainder of this section is organized into two subsections. Real-Time Data Fusion and Integration focuses on
heterogeneous data fusion methods, including techniques for combining sensor inputs such as loop detectors, LiDAR,
radar, and connected vehicle broadcasts. It also highlights structured fusion frameworks, missing data restoration, and
6

Digital Twins for Intelligent Intersections: A Literature Review

predictive modules that support proactive traffic management. Simulation Platforms examines digital twin environments,
emphasizing co-simulation, calibration and validation of virtual sensor models, and AI-driven synthetic data generation.
4.1

Real-time Data Integration and Heterogeneous Data Fusion

Digital twins for intelligent intersections rely on a diverse range of data sources to faithfully replicate real-world
dynamics and support decision-making in real time. These sources include structured data from loop detectors, LiDAR
and radar sensors, GPS traces from connected vehicles, traffic signal states, and simulation logs from platforms such
as SUMO and CARLA (Kušić et al., 2023; Yilmaz-Niewerth et al., 2024; Aguiar et al., 2022). Unstructured and
semi-structured data, such as textual incident reports, GIS metadata, and social movement patterns, are increasingly
fused with sensor inputs to capture contextual and behavioral dimensions of urban mobility (Katsumbe et al., 2024;
Villanueva-Merino et al., 2024). Additionally, high fidelity perception data, such as annotated images, point clouds,
and acoustic signals, enhance simulation realism and feed machine learning models for predictive analytics (Lv et al.,
2022a; Bamminger et al., 2023; Yadav and Agarwal, 2025). Examples of data collection and ingestion include the
low-latency integration of heterogeneous sources such as loop detectors, radar, and floating car data into unified twin
environments (Kušić et al., 2023), while real-time energy analytics and eco-routing rely on dynamic script-driven
updates within simulation platforms like SUMO (Angelina et al., 2024; Aguiar et al., 2022). These data streams
typically undergo preprocessing steps such as cleaning, normalization, and transformation to enable downstream tasks
like forecasting and decision support. Despite the richness of these inputs, missing or incomplete data remains a
persistent challenge. Sensor outages, transmission delays, or coverage gaps can degrade the performance of digital
twins by introducing uncertainty into traffic flow predictions and safety assessments. To address this, recent studies
have applied techniques such as k-means clustering and temporal-neighbor interpolation to restore missing traffic
volume counts with measurable improvements in output fidelity (Saroj et al., 2023). Structured fusion frameworks
and forecasting models also play a critical role in mitigating data sparsity by inferring plausible values and enabling
proactive system responses (Louati, 2025a; Hassani et al., 2024).
As outlined in the preceding section, a foundational pillar of digital twins for intelligent intersections is the integration
and fusion of heterogeneous data streams in real time. Systems continuously ingest roadside sensor and vehicle data into
unified, low-latency twin environments (Kušić et al., 2023; Aguiar et al., 2022). Data gaps in traffic volume counts can
be characterized and imputed (e.g., through k-means clustering of loss patterns and temporal-neighbor interpolation)
to maintain application performance (Saroj et al., 2023). Energy analytics integrated with SUMO via Python scripts
enable estimation of consumption under varying scenarios (Angelina et al., 2024). Pavement management twins merge
heterogeneous logs using spatiotemporal graph attention networks for condition prediction (Lu et al., 2025). Event
detection can be performed in a lightweight, online manner by combining PCA with DBSCAN to identify accidents
from GPS traces (Papadopoulos et al., 2024). In parallel, Logistics 4.0 applications employ connected tracking and
tracing devices to provide real-time, end-to-end visibility (Helo and Thai, 2024).
Structured fusion frameworks further support decision-making. Common strategies, like complementary, redundant,
and cooperative fusion, operate at the signal/raw, feature/intermediate, and decision levels, often organized under
established taxonomies such as the Joint Directors of Laboratories (JDL), Luo–Kay, or Dasarathy models (White,
1991; Hassani et al., 2024). Beyond sensor streams, unstructured text can be mined via natural language processing
(NLP) methods (e.g., Word2Vec) to extract salient system elements and relationships for urban and transport modeling,
subsequently informing quantitative indicators (Katsumbe et al., 2024). Visual fusion in transportation twins leverages
memory-augmented modules to segment evolving scenes, particularly in self-driving contexts (Lv et al., 2022a).
Cross-domain pipelines unify IoT inputs, multi-objective optimization outputs, and microservice architectures, often
coupled to simulation twins, to address data sparsity (Aguiar et al., 2022). Hybrid approaches pair statistical tools with
machine learning methods (e.g., PCA with DBSCAN for online event detection and ARIMA/ANN for forecasting
safety and energy trends) to synthesize diverse indicators into coherent insights (Papadopoulos et al., 2024; Louati,
2025a).
4.2

Simulation Platforms

Intersection twins often require concurrent traffic and AV simulations. Digital twins of intersections increasingly
depend on co-simulation frameworks that synchronize traffic flow models with AV simulators to test control, sensing,
and coordination strategies. For example, CARLA and SUMO are linked in lock step co-simulation by exchanging
vehicle state vectors at each time step to analyze AV performance under mixed traffic conditions (Yilmaz-Niewerth
et al., 2024). To scale corridor-level traffic studies, mesoscopic dynamic traffic assignment (DTA) engines run in
tandem with microscopic SUMO routines, allowing efficient signal-timing evaluation across large urban networks
(Kuraksin et al., 2020). For energy-aware modeling, detailed consumption profiles are scripted into SUMO using
fine-grained variables (Angelina et al., 2024). Specialized testbeds like CupCarbon support ambient noise stress
7

Digital Twins for Intelligent Intersections: A Literature Review

testing, employing least squares–predicted acoustic signals and multi-criteria asynchronous classifiers to simulate smart
accident management (Yadav and Agarwal, 2025). Digital twins for eco-routing leverage real-time IoT loops within
the MobiWise framework, feeding sensor data into microscopic simulators for continual optimization (Aguiar et al.,
2022). To ensure perceptual realism, high fidelity sensor models (LiDAR, radar, camera) are validated in multi-body
simulation platforms based on experimental benchmarks (Bamminger et al., 2023). Meanwhile, simulation performance
tuning is explored through downscaling strategies that introduce mesoscopic traffic simulation to offload non-critical
traffic dynamics from microscopic AV simulators, improving computation time by up to 500% (Varga et al., 2023).
Communication standards are also integrated: a co-simulation testbed coupling SUMO, Veins, and OMNeT++ validates
SPaT/MAP V2X protocols between traffic lights and vehicles to support intersection control in connected environments
(Wágner et al., 2023). Even path-following control laws are tested through co-simulation environments like CarSim and
MATLAB/Simulink; for instance, the Pure Pursuit algorithm has been optimized using proportional-derivative (PD)
error corrections to reduce overshoot on high-curvature segments (Cao et al., 2025).
High fidelity perception within twins requires rigorous calibration and validation. Building on the need for accurate
simulation of intersection dynamics, advanced perception modeling ensures that digital twins can faithfully represent
real-world conditions. High-resolution perception models are calibrated using field-recorded data and simulation
platforms such as CarMaker, where tools like Scenario Record,Replay,Rearrange (RRR) enable injection of real
scenarios; this is combined with Mixture Density Networks to close the sim-to-reality gap by over 30%(Bamminger
et al., 2023). Memory-augmented segmentation networks allow digital twins to self-tune visual fidelity by aligning
outputs with ground-truth image sequences, dynamically adapting to environmental changes(Lv et al., 2022a). Pavement
twins further exploit STGATs to harmonize predicted road states with sensor observations in highway infrastructure
management (Lu et al., 2025). Urban-scale pedestrian flow models are refined using social simulation kernels that
blend GIS datasets with movement patterns of local populations, enabling inclusive digital twin testing for age-friendly
infrastructure (Villanueva-Merino et al., 2024). In acoustics, asynchronous sound recordings from field deployments
are used to validate smart accident detection models driven by multi-criteria classifiers embedded within CupCarbon
simulators (Yadav and Agarwal, 2025).

5

Artificial Intelligence and Machine Learning in Traffic Control

Leveraging digital twin architectures and continuous data assimilation discussed in Sections 3 and 4, we can see how
AI/ML further empower these twins to improve traffic control. By integrating AI/ML algorithms, these digital replicas,
which serve as virtual mirrors of physical systems, are empowered to simulate and intelligently analyze and predict
evolving traffic conditions.
In the context of intelligent intersections, a digital twin uses live data from vehicles, traffic signals, and sensors to
dynamically map the real world into a virtual environment, ingesting multiple sources such as vehicle counts, speeds,
signal states, and V2X messages (Lv et al., 2022b; Mi et al., 2024). This integration enables enhanced visualization,
adaptive decision-making, and optimized performance in real time, allowing operators or autonomous controllers to
react swiftly to fluctuations in demand. Within the virtual model, scenarios can be tested and optimized continuously;
for example, signal timing strategies or lane configurations can be tested before deployment, and the digital twin can
predict queue buildups or spillovers to enable preventive adjustments to signals or traveler information systems.
Traditional traffic control relies on fixed-time or rule-based plans that cannot adjust to real-time fluctuations. In contrast,
AI-powered adaptive systems digest streaming data from multiple sources and make split-second decisions to optimize
flow, reduce congestion, and improve safety (Aulia et al., 2024). Techniques such as RL and neural network–based
prediction enable controllers to learn optimal responses (e.g., when to extend a green light or trigger an alert) from
experience and data rather than static programs, addressing the complexity of urban traffic where patterns change hourly
and incidents require immediate response (Lai et al., 2024; Yao et al., 2023).
This section addresses the question, “How can AI/ML methods, such as RL and DL, enhance adaptive traffic control
within digital twins?” This synthesis covers 23 recent studies and is structured into four subsections: Traditional
ML Methods for Traffic Control, which discusses early AI applications such as random forests, SVMs, and time
series models for forecasting and classification; RL and Multi-Agent Coordination, which explores adaptive signal
control using RL agents and collaborative strategies for network-level optimization; Deep Learning for Prediction,
Perception, and Control, which highlights CNNs, GNNs, and hybrid models for spatio-temporal forecasting and
incident detection; and AI/ML-Driven Edge-to-Cloud Architectures and Immersive Decision Interfaces, which presents
emerging paradigms including distributed inference, quantum-accelerated optimization, explainable AI, and immersive
stakeholder engagement.
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Digital Twins for Intelligent Intersections: A Literature Review

5.1

Traditional ML Methods for Traffic Control

AI/ML models embedded in digital twins learn from historical and real-time data to refine predictions and control
policies, while the digital twin provides a realistic environment linking decisions to simulated outcomes (Lai et al.,
2024; Yao et al., 2023). Regression and classification algorithms provide foundational analysis of traffic dynamics; for
example, regression-based simulators capture complex spatiotemporal dependencies and support what-if exploration for
policy and congestion estimation (Mostafi et al., 2022). Linear regression has been shown to yield accurate volume
estimates on low volume roadways when appropriate explanatory variables are used (Yeboah et al., 2023). Classification
models including random forests and support vector machines (SVM) are known to be useful in classifying traffic
states and detecting anomalies, while time series forecasting models like autoregressive integrated moving average
(ARIMA) model forecast short-term flows, accident hot spots, and energy consumption, as demonstrated in sustainable
management frameworks (Nautiyal et al., 2025; Louati, 2025b). Together, digital twins and AI/ML enable proactive
adaptation by sensing contextual changes at intersections (e.g., sudden influxes or emergency vehicles) and dynamically
adjusting signal phases or traveler advisories (Liu et al., 2023). These capabilities are further strengthened by ongoing
advances in AI/ML algorithms, significantly enhancing traffic management.
5.2

Reinforcement Learning and Multi-Agent Coordination

As part of proactive traffic management, RL (Sutton and Barto, 2018) has emerged as a leading AI approach for
adaptive traffic signal control. In an RL setup, traffic signal controllers are considered agents that monitor various traffic
conditions, including queue lengths, waiting times, and current signal phases, enabling them to execute actions such as
adjusting the signal lights. This agent-environment interaction aims to maximize a predefined reward, often tied to
performance metrics like minimizing vehicular delays and improving overall traffic throughput (Mousavi et al., 2017;
Wei et al., 2021). Over time, the RL agent “learns” an optimal or near-optimal policy by trying different actions and
receiving feedback in the form of reward signals. This approach enables self-optimization that can adjust to complex
traffic patterns without being explicitly programmed for every scenario (Aslani et al., 2017). Reward functions in the
literature often reflect traffic performance metrics, such as vehicle waiting time, queue length, or throughput(Egea
et al., 2020). For example, if long queues form on one approach, the RL agent might learn to extend the green light
to dissipate the queue, as this yields a higher cumulative reward due to reduced delays. Based on this, the agent is
trained using a simulation and over many simulation cycles, the agent refines its timing decisions to optimally respond
to demand fluctuations. Studies have demonstrated that RL controllers can outperform pre-timed or actuated controllers
by reacting more adaptively to incidents and daily traffic variations (Lai et al., 2024).
In early implementations, simple RL algorithms like Q-learning were used to adjust a single intersection’s lights,
showing significant reductions in delay and stops. More recently, deep RL methods have been applied, where deep
neural networks approximate the optimal policy or value function. This approach allows handling larger state spaces
(for example, using camera images or data from multiple intersections). Robust RL policies trained in digital twin
environments adapt to disruptions like lane closures or incidents, enhancing urban grid resilience and informing future
resilience models (Bubicz et al., 2023). Overall, RL provides a flexible and powerful toolkit for adaptive signal control,
though its field deployment requires robust simulation training (often using digital twins) and careful attention to safety
and fairness (Louati, 2025b).
At the network level, MARL strategies are being pursued. One study optimized signals across a city grid by coordinating agents at each intersection, leading to smoother traffic progression. Another innovative paradigm, dubbed
“democratizing traffic control,” trained multiple RL agents, each with a different objective (e.g., minimizing travel time
or emissions), and then had them collectively vote on the final signal plan in real time (Korecki et al., 2024).
MARL extends RL by coordinating agents at intersections via shared rewards or messages to optimize total travel
time or balanced queues (Louati, 2025b). Cooperative adaptable lanes dynamically reallocate lanes using multiple
RL agents, cutting average waiting times by almost 50% compared to static allocations (Dubey et al., 2024). Agents
with distinct objectives vote on signal plans, balancing travel time, emissions, and safety in a participatory MARL
framework (Korecki et al., 2024).
5.3

Deep Learning for Prediction, Perception, and Control

Deep learning significantly contributes to adaptive traffic management, particularly by capturing complex spatial and
temporal traffic patterns using CNNs, recurrent neural networks (RNNs), and GNNs (Ju et al., 2024; Jiang and Luo,
2022).
Deep learning methods (LeCun et al., 2015), excel at capturing complex spatial and temporal patterns in traffic
data (Scarselli et al., 2009). Hybrid models integrating YOLOv4 (Bochkovskiy et al., 2020) detection with LSTM layers
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Digital Twins for Intelligent Intersections: A Literature Review

predict time incremental flows, enabling preemptive signal adjustments with higher accuracy (Shepelev et al., 2023).
Spatial–temporal GNNs encode road networks as graphs, preserving topology for superior flow prediction and control
recommendations (Li et al., 2023). Generative spatial AI, such as foundation-based Large Flow Models, fills missing
traffic data and forecasts multimodal urban dynamics within digital twins, improving data fidelity (Huang et al., 2025).
Cooperative perception fuses camera and LiDAR data across connected autonomous vehicles and infrastructure via
CNN or graph-based models, enhancing object detection and trajectory forecasts for incident-aware control (Yu et al.,
2022). Meanwhile, 3D-Net uses monocular cameras to detect and track road users in 3D, rapidly flagging incidents for
downstream control adjustments (Rezaei et al., 2023). DL-driven predictions feed into adaptive loops, such as dynamic
green extensions or diversion alerts, closing the feedback loop in the digital twin framework (Liu et al., 2023).
5.4

AI/ML-Driven Edge-to-Cloud Architectures and Immersive Decision Interfaces

AI/ML-enabled digital twins leverage an edge–fog–cloud continuum to satisfy strict latency and compute requirements
for continuous model updates and real-time inference (Al-Dulaimy et al., 2024). Complementary Narrow-Band
IoT–linked digital twins employ lightweight ML models at the network edge to maintain energy efficient, scalable
sensor fusion for large-scale deployments (Dangana et al., 2024). Meanwhile, economic–environmental frameworks
automatically partition AI workloads to allocate preprocessing, inference, and training tasks between edge nodes and
cloud servers, to optimize throughput, cost, and carbon footprint (Liu et al., 2024b). Emerging quantum computing
techniques promise to accelerate combinatorial optimization subroutines in AI traffic signal controllers, enabling
sustainable, circular transport strategies (Jami and Haleem, 2025). Although originally developed for maritime
operations, systems-driven methods integrate explainable AI modules within digital twins to generate operator-centric
recommendations, validate safety constraints, and support human-in-the-loop tuning prior to field deployment (Zhang
et al., 2025). Finally, gamified VR dashboards and metaverse interfaces harness interactive ML visualizations to engage
planners and the public to allow immersive exploration, scenario analysis, and participatory tuning of twin-based control
strategies (Reynoso Vanderhorst et al., 2024).

6

Safety and Protection of Vulnerable Road Users

Building on the previous section’s discussion of how AI/ML integrated with digital twins can enhance traffic control,
this section answers the question, “What methodologies enable a digital twin to detect, predict, and mitigate risks for
vulnerable road users?”
Vulnerable road users (VRUs), such as pedestrians and cyclists, face disproportionate risks with the highest crash rates
per distance traveled and rising fatality trends in some regions (Tengilimoglu et al., 2023). Enhancing digital twin
models to better capture human behavior is critical, as it can reveal potential risks and enable proactive interventions
to prevent crashes. For example, a recent work demonstrates how generative spatial AI can complete and predict
flow data for pedestrians and vehicles alike, offering a powerful tool for pre-crash scenario analysis and VRU risk
assessment (Huang et al., 2025) . At a higher architectural level, a layered IoT–edge–cloud framework can support
low-latency, distributed sensing and analytics for VRU protection in digital twins(Al-Dulaimy et al., 2024).
In total, this section reviews 15 papers that are related to vulnerable road user protection within digital twins. The
discussion is organized into three subsections: Sensing, Modeling, and Predictive Analytics, which addresses data
acquisition methods and AI/ML-based pedestrian behavior prediction; Behavioral and Infrastructure Simulations,
which examines simulation frameworks for VRU behavior and infrastructural interventions; and Ethical, Privacy, and
Systems Perspectives, which explores privacy concerns, bias mitigation, and system-level governance for safe and
equitable digital twin deployments.
6.1

Sensing, Modeling, and Predictive Analytics

Modern sensing techniques provide rich data on both road users and the surrounding environment. For example,
unmanned aerial vehicles (UAVs) equipped with photogrammetry can rapidly survey intersections and generate detailed
3D models, identifying hazards such as foliage obstructing sight lines (Congress et al., 2021). On-vehicle multimodal
sensor suites, combined with infrastructure-mounted sensors, ensure robust detection of pedestrians and cyclists by
compensating for the limitations of any single sensor modality. A complementary approach is found in the zero
calibration 3D object recognition system which uses only monocular cameras and GPS information to estimate 3D
bounding boxes and trajectories for vehicles, cyclists, and pedestrians in real time (Rezaei et al., 2023). On the
other hand, emergency vehicles with edge computing sensors to detect blind spot obstacles, including VRUs, at the
vehicle perimeter, providing millisecond scale alerts to drivers (Mukhopadhyay et al., 2024) . The deployment of
roadside sensor networks, comprising of cameras, LiDAR, millimeter-wave radar, and weather stations, for cooperative
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Digital Twins for Intelligent Intersections: A Literature Review

intelligent driving has been shown to form the backbone of intelligent intersections by enabling global scene sensing
and collaborative VRU detection in digital twin environments (An et al., 2023).
Machine learning models learn pedestrian crossing behaviors or cyclist speed profiles from data, improving the
prediction of potential conflict points. In vehicle-to-digital-twin systems, real-time AI-driven analytics can flag
anomalous behavior (e.g., a pedestrian jaywalking) and assess collision risks. Notably, while camera-only systems
detect less than 30% of potential pedestrian collisions, fused sensor systems (integrating cameras, LiDAR, and radar)
can prevent over 90% of them (Tengilimoglu et al., 2023). Furthermore, these predictive capabilities are also extended
by enabling flow completion in unobserved locations and forecasting the evolution of VRU movements under different
infrastructure scenarios (Huang et al., 2025) . Acoustic signal-based detection systems further augment these digital
twins, a multi-criterion asynchronous classifier is used to detect accident hot spots and provide early alerts for VRUs,
outperforming traditional fixed detector methods in accuracy and responsiveness (Yadav and Agarwal, 2025).
6.2

Behavioral and Infrastructure Simulations

Several methodologies have been proposed to capture and predict VRU behavior within traffic digital twins. One
approach integrates continuous real-time calibration of microscopic simulators, such as SUMO, with live sensor data,
enabling digital twins to dynamically adapt to changing traffic conditions and pedestrian movements (Kušić et al.,
2023). Another methodology uses agent-based pedestrian network digital twins, which focus on discrete network
graphs to simulate individual pedestrian rerouting behaviors, effectively capturing the stochastic nature of route choice
(Jabbari et al., 2023). MARL has also been used to dynamically reallocate lane widths and right-of-way permissions,
significantly reducing queue lengths and enhancing safety in mixed traffic scenarios (Dubey et al., 2024). Meanwhile,
probabilistic pedestrian crossing models combined with Monte Carlo simulations provide insights into pedestrian risk
behaviors under dynamic traffic conditions, emphasizing critical safety thresholds related to vehicle proximity and
traffic signal phases (Bagabaldo and Hackl, 2025). Additionally, infrastructure simulations underscore the importance
of modeling interactions between autonomous vehicle-centric infrastructure changes and VRU movements to ensure
safety in mixed traffic environments (Tengilimoglu et al., 2023).
Digital twins also incorporate detailed road infrastructure attributes, ranging from intersection geometries to signal
phasing and crosswalk types. Simulation frameworks can test modifications, such as converting an unsignalized
crosswalk to a signalized one or adding a pedestrian refuge island, while remote sensing and GIS-based methods
(e.g., high-resolution 3D digital surface models from UAV surveys) simulate drivers’ and pedestrians’ sight lines
(Congress et al., 2021). Combining spatial crash models (e.g., Geographically Weighted Poisson Regression, which is a
spatial regression model for count processes wiht local variation, with macroscopic statistical models (e.g., Negative
Binomial) within a digital twin architecture improves hot spot identification accuracy by over 10% relative to single
model approaches (Rua et al., 2024).
6.3

Ethical, Privacy, and Systems Perspectives

Eventually, human behavior in traffic is influenced by social, cultural, and ethical factors. Methodologies incorporating expert knowledge and human factors are vital, ensuring that digital twins respect privacy, avoid algorithmic
discrimination, and support socially acceptable interventions. Delphi studies, which is a structured, iterative survey
technique that uses multiple rounds of anonymized questionnaires and controlled feedback among a panel of experts, and
multi-stakeholder workshops have identified key concerns such as privacy, bias, and safety when introducing AI-driven
technologies (Stahl et al., 2023). The ethical and privacy implications of pervasive VRU sensing are highlighted,
especially the need for robust anonymization and informed consent when aggregating individual movement data (Fadhel
et al., 2024). GeoAI frameworks further emphasize values-driven co-design, ensuring that digital twin interventions
align with community values and equitable access goals (Mortaheb and Jankowski, 2023).

7

Scaling from Localized Intersections to Citywide Traffic Networks

In the previous sections, we established the key components of an ideal digital twins for intelligent intersection. It is
worth noting, however, that many existing digital twin implementations have primarily focused on localized or single
intersections (Kampourakis et al., 2023; Wang et al., 2024). Extending these applications to encompass entire citywide
traffic networks remains a significant challenge (Kušić et al., 2023) and requires tackling issues related to computational
efficiency, distributed control, and secure data communication. This section presents a synthesis of 35 papers on digital
twin applications in smart infrastructure, with a focus on scaling from localized intersections to citywide traffic networks.
The discussion is organized into three subsections: Digital Twin Applications Across Smart and Urban Infrastructure,
which examines foundational concepts from CPS, Industry 4.0, and urban computing, as well as the integration of
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Digital Twins for Intelligent Intersections: A Literature Review

AI, IoT, and spatial data models; Control and Communication Frameworks for Scalability, which explores distributed
control architectures, semantic interoperability, and secure communication protocols such as V2X and blockchain; and
Challenges in Scaling Digital Twin Applications, which highlights key computational, organizational, and technological
hurdles and identifies promising strategies such as lightweight models, parallel intelligence, and secure wireless systems
for enabling large-scale deployment.
7.1

Digital Twin Applications Across Smart and Urban Infrastructure

Example smart infrastructure digital twin use cases, from traffic control to smart mobility planning are presented
in Table D.1 in the appendices. Related literature also provides a comprehensive overview of digital twin concepts
within CPS and Industry 4.0 environments. For instance, a systematic review of wireless security testbeds in CPS
establishes the groundwork for understanding the convergence of physical and digital realms (Kampourakis et al., 2023).
Complementary investigations into urban computing and building information modeling for transportation infrastructure
emphasize the integration of digital models with physical structures, a critical prerequisite for scalable digital twin
applications (Alshuwaikhat et al., 2022; Pirdavani et al., 2023). Research on digital twin applications in Industry 4.0
and intelligent highway infrastructure further demonstrates the potential benefits of digital replication in complex
urban environments (Wang et al., 2024; Javaid et al., 2023). Moreover, studies on blockchain, IoT, and AI in logistics
emphasize the importance of secure, distributed data management (Idrissi et al., 2024). Foundational paradigms for
digital twins adoption in the construction sector underscore its transformative potential beyond transportation (Moshood
et al., 2024). Localized digital twin implementations aimed at urban inclusivity have also emerged, showcasing
modular approaches for age-friendly environments (Villanueva-Merino et al., 2024). A systematic review highlights the
integration of AI within digital twin systems across various domains (Kreuzer et al., 2024).
Additional studies expand upon urban digital twin implementations by focusing on efficient traffic data collection
and computational processing. Research on traffic organization and safety highlights the necessity for effective data
collection (Pugachev et al., 2020). Advances in generative spatial AI for sustainable smart cities and pioneering
large flow models for urban digital twin frameworks (Huang et al., 2025) offer promising methods for processing
and simulating complex urban flows. Analyses of Geo CPS with a focus on spatial challenges further illustrate
opportunities and constraints in geospatial data integration (Yan and Sakairi, 2019). Comprehensive methodologies for
fusing heterogeneous urban data sources have also been reviewed (Fadhel et al., 2024). Investigations into sustainable
integration of digital twin in road infrastructure (Ulrich et al., 2023) and the impact of connected corridor volume
data imputations on digital twin performance (Saroj et al., 2023) directly address challenges related to data imputation
and real-time performance, while studies on wireless network digital twins for smart railways (Guan et al., 2024)
and methods for lightweighting digital twin information models (Jin et al., 2024) provide practical approaches to
reducing computational loads. Dedicated investigations into lightweighting processes for digital twin information
models demonstrate strategies for efficient data handling in smart city services.
7.2

Control and Communication Frameworks for Scalability

A significant portion of the literature focuses on the control and communication frameworks essential for digital twin
scalability. Insights into evaluating performance of measurement equipment in automated traffic control systems (Safiullin et al., 2020) and the design of multi-level traffic management systems (Kerimov et al., 2020) contribute valuable
perspectives on distributed control architectures. High performance computing platforms have also been leveraged for
distributed route computations in traffic flow models (Silva et al., 2025). Agent-based digital twin architectures have
been demonstrated in logistics applications to integrate multiple stakeholders and assets (Xu et al., 2025b). Semantic
interoperability frameworks aligning BIM and IoT sensor data models have been proposed to ensure consistent data
exchange across digital twin and BIM systems (Okonta et al., 2024). Further studies on organizational tensions in
Industry 4.0 reveal managerial and structural challenges for integrating digital twin systems (Dieste et al., 2022).
Testing range principles for intelligent transportation technologies (Zhankaziev et al., 2020) and bibliometric analyses
quantifying the evolution of IoT within smart infrastructure (Stan et al., 2024) highlight the need for robust and scalable
data infrastructures. Investigations into roadside sensor network deployments (An et al., 2023) and GeoAI applications
for re-imagining smart cities (Mortaheb and Jankowski, 2023) further stress the importance of distributed sensing
frameworks. Advanced V2X communication and digital twin integration for traffic light systems (Wágner et al., 2023)
illustrate secure, low-latency data exchange, while technological visions for Meta Smart Twin Cities emphasize the
convergence of metaverse and digital twin paradigms (Mogaji, 2023). Exploratory studies have investigated quantum computing to optimize large-scale digital twin simulations and circular economy models, pointing to significant
performance gains for complex urban networks (Jami and Haleem, 2025). Meanwhile, non-fungible token (NFT)
mechanisms have been proposed to provide secure provenance and asset management for distributed digital twin
ecosystems (Davies et al., 2024). NFT is a cryptographically unique, indivisible, irreplaceable and verifiable token
implemented via blockchain smart-contract protocols that enables the creation, ownership, transfer and verification
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Digital Twins for Intelligent Intersections: A Literature Review

of unique digital (or physical) assets (Valeonti et al., 2021). Democratized control approaches involving multiple
stakeholders and learning-based controllers in voting-based signal management have also been proposed (Korecki et al.,
2024). Finally, the synergistic interplay between AI and digital twins in environmental planning (Bibri et al., 2024) and
interdisciplinary approaches such as parallel intelligence (Zhao et al., 2023) suggest integrative strategies to overcome
challenges in both computation and control.
7.3

Challenges in Scaling Digital Twin Applications

Scaling digital twins applications from localized intersections to citywide networks presents several critical challenges.
Efficient processing of vast urban datasets is essential for real-time simulation and control; however, integrating complex
3D models with continuous sensor feeds remains computationally intensive (Pirdavani et al., 2023). Although approaches
such as lightweight digital twin models (Jin et al., 2024) and advanced generative spatial AI frameworks (Huang et al.,
2025) have been proposed to mitigate computational demands, these challenges persist. Moreover, managing digital
twins across extensive geographical areas requires the development of distributed control architectures capable of
coordinating data from numerous intersections and road segments. While multi-level system models (Kerimov et al.,
2020) and performance evaluation methodologies (Safiullin et al., 2020) provide promising strategies, they also highlight
the complexities inherent in resolving managerial and structural issues.
Additionally, for citywide applications, ensuring secure and reliable data communication is paramount. Models based
on blockchain, IoT, and AI in logistics (Idrissi et al., 2024) and advanced V2X communication systems (Wágner et al.,
2023) are essential for maintaining data integrity and protecting against cyber threats across distributed networks.
Organizational challenges in Industry 4.0 implementations further complicate integration efforts, as described in studies
on structural and managerial tensions (Dieste et al., 2022).
The literature indicates that several key technological advancements are necessary to overcome these challenges.
Enhanced computational algorithms and lightweight digital twin models are critical; the development of efficient
algorithms, leveraging approaches such as generative spatial AI (Huang et al., 2025) and parallel intelligence (Zhao
et al., 2023), can significantly improve computational efficiency, while techniques for reducing the data footprint
of digital twin information models (Jin et al., 2024) help maintain essential detail without overwhelming system
resources. Equally important is the need for robust distributed control and hierarchical management frameworks.
Distributed, multi-level control architectures (Kerimov et al., 2020) that integrate localized controllers with central
supervisory systems offer flexibility and resilience, yet the successful implementation of such systems also requires
addressing inherent organizational tensions (Dieste et al., 2022). Furthermore, establishing secure and reliable data
communication protocols is critical. The adoption of next generation wireless technologies, including 5G/6G and V2X
protocols (Wágner et al., 2023; Gallego-Madrid et al., 2023), combined with blockchain-based solutions and robust
encryption methods (Idrissi et al., 2024), is essential to ensure low-latency and secure data exchange across widespread,
distributed networks.

8

Discussion

Our comprehensive literature review reveals a rapidly advancing landscape in the application of digital twins for transportation that supports intelligent intersections. The research landscape, categorized into five key themes, demonstrates
significant progress in developing sophisticated architectures, leveraging advanced data processing and simulation,
integrating AI/ML for adaptive control, safety and protection of VRUs, and scaling localized intersection to networklevel for cities. However, this review also illuminates critical gaps and unchallenged assumptions that temper the
current optimism. This discussion synthesizes these findings, offers a critical perspective on the prevailing research
trajectory, and outlines a path forward for developing digital twins that are not merely efficient, but fundamentally safe
and human-centric.
8.1

The Simulation-Reality Gap: From Virtual Models to True Digital Twins

A recurring theme throughout the reviewed literature is the heavy reliance on simulation. While simulators like SUMO
and co-simulation platforms coupling traffic and vehicle models (e.g., CARLA) are invaluable for initial development
and testing, a significant portion of the reviewed literature treats the “digital twin” as a sophisticated, yet one-way,
simulation. Data flows from the physical world (or a proxy for it) into the model, which then runs scenarios and
proposes optimizations. This falls short of the true digital twin paradigm and may just fall into what is called a “digital
shadow,” which is a virtual representation that automatically mirrors real-time data from a physical system, but lacks
the ability to influence or control it (Kritzinger et al., 2018).
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Digital Twins for Intelligent Intersections: A Literature Review

The essence of a digital twin, as envisioned by Grieves (2014) and NASA (Glaessgen and Stargel, 2012), lies in its
continuous, bidirectional synchronization with its physical counterpart. The digital model should not only reflect the
state of the physical intersection but also be capable of actuating changes whose effects are then measured and fed
back into the model, closing the loop. Most studies, however, stop at the simulation stage, presenting performance
improvements in a virtual environment without grappling with the complexities of real-world deployment and feedback
or separately training AI/ML models for traffic control which later on applied but does not affect traffic in real time.
Without this feedback loop, the digital model risks becoming a “digital shadow,” reflecting a reality that has already
passed and making decisions based on potentially stale or idealized data. This one-way data flow fails to account for
the inherent uncertainty and unpredictability of the physical world, from sensor noise and communication latency to
unexpected human behavior.
To fully realize the transformative potential of digital twins for intelligent intersections, we must move beyond simulation
and passive monitoring toward real-time, closed-loop systems that can actively implement, test, and adapt control
strategies in the physical world. This shift from virtual analysis to actionable intervention is essential to ensure that
digital twins are not just analytical tools but operational systems that improve safety, efficiency, and responsiveness on
the ground.
8.2

The Human Blind Spot: Prioritizing Pedestrians and Vulnerable Road Users

Our review reveals a pronounced vehicular-centric bias in the current body of research. The primary metrics for success
are almost universally tied to traffic flow: reducing vehicle delay, minimizing queue lengths, and increasing throughput.
While these are important goals, they often overshadow the most critical function of an intersection, which is to ensure
the safety of all users, especially the most vulnerable.
VRUs, such as pedestrians, cyclists, and micromobility users, are frequently treated as an afterthought or are modeled
with simplistic assumptions. While vehicles adhere to predictable kinematic models, pedestrians and cyclists exhibit
complex, stochastic behaviors influenced by social cues, perceived risk, and environmental context. The review shows
a dearth of research integrating fine-grained, agent-based models of pedestrian behavior into digital twins. Most
models either ignore pedestrians or use coarse, aggregated flow approximations. This is a critical oversight. An
intersection that is highly efficient for cars but terrifying or dangerous for a child, an elderly person, or a person with a
disability has failed in its fundamental civic duty. Thus, agencies like USDOT have initiatives and funding dedicated
to safer roads, including the Safe Streets and Roads for All (SS4A) competitive grant program (USDOT, 2025) and
the U.S. DOT Research, Development, and Technology Strategic Plan for Fiscal Years 2022–2026, which outlines
the Department’s research priorities, objectives, and strategies for improving transportation safety, equity, climate
resilience, and innovation (USDOT, 2022). We envision that a true digital twin balances traffic flow efficiency with
pedestrian safety, going beyond mere automation to support more holistic and inclusive mobility solutions. Our focus
on safety-first digital twins seeks to rebalance these priorities, ensuring that VRU protection is a primary objective
function, not a secondary constraint.
8.3

The Ethics of Surveillance, Bias, and Accountability in Digital Twins

As digital twins evolve from passive simulations to active, decision-making systems embedded in urban infrastructure,
their ethical implications demand careful scrutiny. Despite increasing sophistication in data-driven control algorithms
and sensor-rich environments, the reviewed literature rarely interrogates the full spectrum of ethical concerns surrounding
their real-world deployment.
First, there are serious privacy concerns. Digital twins for intelligent intersections often rely on high-resolution sensor
data, such as video feeds, LiDAR, and mobile device signals, to capture real-time traffic dynamics. While essential
for accurate modeling and control, such data streams can inadvertently capture personally identifiable information,
especially of pedestrians and cyclists. Without strong safeguards, such as robust anonymization protocols, secure data
handling, and transparent data governance, these systems risk enabling mass surveillance, eroding public trust and
infringing on privacy rights.
Second, algorithmic bias poses a critical risk. Models trained on biased datasets due to under-representation of certain
populations, skewed spatial sampling, or historical inequities, can reinforce and even amplify systemic disparities.
For example, optimization focused primarily on vehicle throughput in affluent areas may deprioritize pedestrian
safety or service levels in underserved neighborhoods. Ensuring fairness requires embedding ethical frameworks and
fairness-aware learning mechanisms into model design and training pipelines from the outset.
Third, reliability and misuse must be addressed. A system as powerful as a digital twin can be exploited, either intentionally or unintentionally, by its human operators, or manipulated through cyber-physical vulnerabilities. Safeguards
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Digital Twins for Intelligent Intersections: A Literature Review

must be implemented to prevent tampering, adversarial input manipulation, or human misuse of override functions.
Moreover, the digital twin must be resilient against failures in the physical system or unexpected real-world events, and
should be designed to fail safely, with clearly defined human-in-the-loop override protocols. Ensuring reliability is not
just a matter of technical robustness but also of institutional checks and balances.
Finally, questions of transparency and accountability remain unresolved. As digital twins increasingly influence
real-world decisions, such as modifying signal timing or prioritizing specific user flows, mechanisms for oversight
and public communication become essential. Questions arise such as: “Who is responsible if an AI-driven decision
results in harm?”, “How can these decisions be understood and contested by non-experts?”, and “What transparency
guarantees exist to prevent misuse or unchecked authority?”.
These are not purely technical challenges, but socio-technical ones that demand interdisciplinary solutions across
engineering, computer science, ethics, law, and urban governance. Addressing them requires collaboration among
a diverse array of stakeholders, including municipal and regional governments, national regulatory agencies, urban
planners, civil society organizations, academic institutions, private sector technology developers, and the general public,
whose mobility, privacy, and safety are most directly affected. Ethical integrity, reliability, and resistance to misuse
must be foundational components of any digital twin system intended for public deployment.
8.4

The Challenge of Validation and Sparse Historical Data

The review also raises two pragmatic yet profound questions that are largely unaddressed:
1. How can the effectiveness of a new traffic management strategy be rigorously and safely tested before live
deployment?
2. What happens when historical data is sparse or non-existent for a particular intersection or a rare event?
Regarding the first question, a digital twin offers a revolutionary framework for validation that goes beyond pure
simulation. We propose a multi-stage validation process: Simulation-in-the-Loop (SIL): The initial phase, where
control logic is tested in a fully virtual environment. HIL: The control algorithms are run on the actual traffic controller
hardware, which interacts with the simulated digital twin. Shadow Mode Deployment: This is the crucial, and often
missing, step. The digital twin is deployed in the field, receiving live sensor data and making decisions in parallel with
the existing intersection controller. However, its decisions are not actuated. Instead, they are logged and compared
against the real-world outcomes. This allows for the validation of the twin’s performance and safety under real-world
conditions without any risk to the public. Phased Live Deployment: Only after passing rigorous shadow mode validation
can the system be gradually brought online, perhaps during off-peak hours and with human oversight and safety
overrides.
Another point is the absence of training data, digital twins must incorporate methods beyond traditional supervised
learning. For new intersections or for predicting rare but catastrophic events like near-misses or crashes, there is no ‘big
data’ to learn from. Here, the future lies in probabilistic models and combining physics-based models with modern AI.
Generative AI techniques (e.g., GANs, diffusion models) can be used within the digital twin to create vast libraries of
synthetic but realistic “edge case” scenarios to train and harden the control logic. Furthermore, principles of transfer
learning can be applied, where a model trained on a data-rich intersection can be fine-tuned with minimal data for a new
location.
While the field has made remarkable strides in building the components of digital twins for intelligent intersections, our
review suggests that the current trajectory is focused on computational sophistication rather than holistic, real-world
impact. The next frontier is to bridge the simulation-reality gap, shift from a vehicle-centric to a human-centric
paradigm, and develop robust frameworks for validation and learning under uncertainty.
We can see that future research can be positioned to address these gaps, aiming to develop digital twins that are not just
computationally powerful, but are fundamentally architected for safety, human-centricity, and real-world fidelity.

9

Conclusion

This review shows how digital twin technology offers a transformative framework for managing and optimizing
intelligent intersections. By synthesizing findings across five major thematic areas, we capture the state of the art, key
challenges, and emerging directions for research and implementation. The major takeaways are summarized as follows:
1. Digital Twin Architectures and Frameworks: Recent literature highlights the emergence of modular, multilayered digital twin architectures that incorporate edge computing, microservice-based platforms, and
15

Digital Twins for Intelligent Intersections: A Literature Review

AI-enhanced components. These architectures typically include data acquisition, communication, processing,
modeling, and visualization layers, all coordinated through cloud native frameworks. Interoperability and
standardization via open APIs, microservices, and semantic models remain critical for integrating diverse
sensor systems and simulation tools. These advancements address the pressing need for scalable and flexible
digital twin systems that can evolve alongside technological innovations.
2. Data Processing and Simulation Techniques: Effective digital twins depend on robust real-time data ingestion
and heterogeneous fusion across multiple sensor modalities (e.g., LiDAR, radar, loop detectors). Studies
increasingly apply probabilistic methods (e.g., Kalman filtering, clustering, and Bayesian learning) and leverage
AI for imputation and anomaly detection. Simulation platforms like SUMO, CARLA, and co-simulation
frameworks are widely used, often augmented by synthetic data generation and AI-enhanced virtual perception
modules. These tools enable high fidelity testing and calibration, supporting predictive and adaptive traffic
management despite data gaps or uncertainty.
3. AI/ML in Traffic Control: AI/ML algorithms, particularly RL, DL, and GNNs, are at the core of adaptive traffic
signal control in digital twins. RL agents can learn optimal signal timings through simulation, while multiagent systems coordinate control across intersections. Deep learning models support traffic prediction, anomaly
detection, and perception fusion. Increasingly, these models operate within edge-to-cloud environments, allowing real-time inference and continuous model refinement. Some implementations even explore participatory or
explainable AI frameworks to support fairness, transparency, and collaborative decision-making.
4. Safety and Protection of Vulnerable Road Users: Emerging approaches integrate explainable AI, social force
models, and agent-based simulations to model pedestrian and cyclist behaviors. VRU detection and protection
are enhanced via multimodal sensing (e.g., LiDAR, mmWave radar), zero calibration vision systems, and
real-time fusion analytics. Simulation platforms increasingly incorporate behavioral diversity and infrastructure
variation to evaluate how changes affect VRU risk. Ethical and privacy concerns, such as data anonymization
and inclusive design, are also being integrated into digital twin development, ensuring human-centric safety
interventions.
5. Scaling to Citywide Traffic Networks: Moving from intersection-level digital twins to citywide implementations
requires distributed architectures, lightweight modeling strategies, and secure, low latency communication
protocols. Key enablers include 5G/6G networks, blockchain-based data verification, and edge-cloud orchestration frameworks. Recent advances demonstrate the feasibility of coordinated control across corridors,
dynamic routing, and integration with smart infrastructure. However, significant challenges persist in terms of
computational efficiency, standardization, and organizational coordination. Addressing these issues will be
essential for realizing the full potential of digital twins in sustainable and intelligent urban mobility systems.
Despite impressive technological advancements, this review reveals a critical disconnect between the growing computational capabilities of digital twins and their deployment as safe, ethical, and human-centered systems. Four foundational
challenges undermine the current trajectory.
First, the persistence of the simulation-reality gap means many so-called digital twins remain as unidirectional
simulations, which is referred to in literature as “digital shadows,” where they are incapable of acting upon or learning
from the physical world. Without real-time, bidirectional feedback loops and closed-loop deployment protocols, these
systems cannot fulfill their promise of dynamic adaptation and robust control.
Second, there remains a pronounced human blind spot. The vast majority of research prioritizes vehicle throughput,
often at the expense of pedestrians, cyclists, and other vulnerable road users. True intelligence in traffic systems requires
not just optimizing flows, but safeguarding all road users, particularly those most at risk. Rebalancing this focus is
essential to align digital twins with broader societal goals such as equity, inclusivity, and safety.
Third, the field has not adequately addressed the ethical and governance challenges posed by real-world deployment.
As digital twins evolve from passive simulations to active decision-making systems, issues such as privacy, algorithmic
bias, and accountability become critical. Without strong safeguards for anonymization, fairness-aware optimization,
and transparent oversight, these systems risk becoming instruments of surveillance or unintended discrimination.
Interdisciplinary collaboration and ethical frameworks must be embedded into both technical design and institutional
deployment.
Fourth, robust mechanisms for validation and learning in data scarce environments are still lacking. Many studies
assume rich historical data and idealized conditions. However, the real-world deployment of digital twins demands
rigorous testing under uncertainty, from shadow mode validation to the integration of synthetic data, transfer learning,
and probabilistic reasoning for edge case prediction.
These lead us to conclude that future research must move decisively from one-way analysis to active implementation.
This includes embedding human-in-the-loop safeguards, developing equitable optimization objectives, and establishing
16

Digital Twins for Intelligent Intersections: A Literature Review

standards for ethical deployment. The next generation of digital twins should not merely reflect traffic conditions but
they must actively shape safer, more inclusive, and responsive urban environments.
In summary, the path forward lies in reimagining digital twins not as isolated technical constructs, but as socio-technical
systems embedded in the lived fabric of our cities. Only by bridging the simulation-reality divide, centering human
values, and institutionalizing ethical rigor can we realize the full potential of digital twins to transform intersections
from bottlenecks into intelligent, inclusive, and adaptive components of future mobility systems.

Declaration of Generative AI and AI-assisted technologies in the writing process
During the preparation of this work, the authors used Gemini 1.5 Pro and 2.0 Pro, generative AI technologies, to
expedite the filtering of relevant articles and sorting them across different themes, synthesizing of literature and refining
the manuscript’s language and structure. ChatGPT 4o and o4-mini-high provided editorial suggestions to enhance
clarity, coherence, and consistency, and these AI-assisted outputs were then critically reviewed and meticulously revised
by the authors who assume full responsibility for the intellectual content and accuracy of the final publication.

Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have
appeared to influence the work reported in this paper.

Acknowledgement
The authors would like to thank the Princeton University School of Engineering and Applied Science (SEAS) Innovation
Grant for funding this work.

Appendix A: Acronyms and Abbreviations
Table A.1 provides the list of acronyms and abbreviations used in this paper.
Acronym
AI/ML
ANN
API
ARIMA
AV
BIM
C -V2X
CNN
CPS
DBSCAN
DL
DT
DTA
DSRC
EM
GAN
GMM
GNN
HIL

Term
Artificial Intelligence/Machine
Learning
Artificial Neural Network
Application Programming Interface
Autoregressive Integrated Moving Average
Autonomous Vehicle
Building Information Modeling
Cellular Vehicle-to-Everything
Convolutional Neural Network
Cyber-Physical Systems
Density-Based Spatial Clustering
of Applications with Noise
Deep Learning
Digital Twin
Dynamic Traffic Assignment
Dedicated Short-Range Communications
Expectation–Maximization
Generative Adversarial Network
Gaussian Mixture Model
Graph Neural Network
Hardware-in-the-Loop

Brief Description
Computational methods that perform tasks requiring human-like
intelligence.
ML model composed of interconnected nodes for pattern learning
and prediction.
Interface enabling software components to communicate.
Time-series model for short-term forecasting.
Vehicle with automated perception, planning, and control.
Digital representation of built assets for visualization/management.
Cellular standard enabling V2X communications.
Deep learning model effective for image/video tasks.
Integrated computational and physical processes.
Unsupervised clustering algorithm robust to noise.
Type of machine learning that uses artificial neural networks with
multiple layers to analyze data and learn complex patterns
Virtual counterpart of a physical system with live data sync.
Traffic modeling framework for route/flow evolution.
Short-range wireless standard for vehicular comms.
Iterative algorithm for latent variable parameter estimation.
Generative deep learning framework (generator vs discriminator).
Probabilistic model representing data as a mixture of Gaussians.
Deep learning on graph-structured data.
Real-time coupling of controllers with simulated environments.

17

Digital Twins for Intelligent Intersections: A Literature Review

IoT
LiDAR
LLM
MARL

Internet of Things
Light Detection and Ranging
Large Language Model
Multi-Agent
Reinforcement
Learning
Millimeter Wave
Message Queuing Telemetry
Transport
Objective Modular Network
Testbed in C++
Operational Design Domain

mmWave
MQTT
OMNeT++
ODD
PCA
PD
PLC
Redis
RL
RSU
SPaT/MAP

Principal Component Analysis
Proportional–Derivative
Programmable Logic Controller
Remote Dictionary Server
Reinforcement Learning
Roadside Unit
Signal Phase and Timing / MAP
as intersection geometry
Spatio-Temporal Graph Attention
Network
Simulation of Urban MObility
Traffic Control Interface
Unmanned Aerial Vehicle
Vehicle-to-Everything
Variational Autoencoder
Vulnerable Road User
Fifth-Generation New Radio

STGAT
SUMO
TraCI
UAV
V2X
VAE
VRU
5G NR

Network of connected sensors/devices.
Active remote-sensing technology for range/3D perception.
Foundation model trained on large corpora for language tasks.
RL with multiple interacting/coordinated agents.
High-frequency band supporting high-throughput links.
Lightweight publish/subscribe messaging protocol.
Discrete-event simulation framework for networks.
Conditions under which an automated system is designed to operate.
Dimensionality-reduction/feature extraction method.
Feedback control law using proportional and derivative terms.
Industrial controller used in real-time control.
In-memory data store for caching/queues/pub-sub.
Learning control via reward-driven interaction.
Fixed V2X communication node at roadside.
V2X messages conveying signal timing and intersection geometry.
GNN variant modeling spatial-temporal dependencies with attention.
Microscopic traffic simulation platform.
API coupling SUMO with external controllers.
Aircraft without onboard human pilot.
Communication among vehicles, infrastructure, devices, etc.
Probabilistic generative model for representation learning.
Non-motorists such as pedestrians and cyclists (and similar users).
5G air-interface standard supporting C-V2X and low-latency links.

Table A.1: Acronyms and Abbreviations Used in the Paper

Appendix B: Comparison of Digital Twins Review Papers with Respect to Intersection Focus
This appendix presents a comparative overview of existing literature reviews on digital twins, highlighting each paper’s
main contributions, the research fields they cover, and the gaps or future work they identify.
Table B.1: Comparison of Digital Twins review papers with respect to intersection focus.
Title of paper
Digital Twins for cities:
Analyzing the gap between
concepts and current
implementations with a
specific focus on data
integration Jeddoub et al.
(2023)

The Hitchhiker’s Guide to
Fused Twins: A Review of
Access to Digital Twins In
Situ in Smart Cities Grübel
et al. (2022)

Year

Contributions

Field(s)

• Reviewed DT
definitions vs.
3DCM, CIM, SDI
2023

2022

• Proposed
three-level
data-integration
maturity model:
schema, database,
application
• Defined “Fused
Twins” combining
DT + Situated
Analytics
• Classified
emerging
prototypes and five
DT components

Urban geospatial;
Smart Cities; AEC

Smart Cities;
Remote Sensing;
AR/VR; Immersive
Analytics

Gaps / Future Work

• No unified DT
definition
• Lack of generic
data integration
framework

• Need for
embodied, in-situ
interfaces
• Evaluate cognitive
benefits in real
deployments
Continued on next page

18

Digital Twins for Intelligent Intersections: A Literature Review

Title of paper

City Digital Twin Potentials:
A Review and Research
Agenda Shahat et al. (2021)

Exploring Digital Twin
Adaptation to the Urban
Environment: Comparison
with CIM to Avoid Silo-based
Approaches Deprêtre et al.
(2022)

Autonomous Driving Test
Method Based on Digital
Twin: A Survey Niaz et al.
(2021)

A comprehensive review of
Digital Twin technologies in
smart cities Huzzat et al.
(2025)

AI-Powered Digital Twins and
Internet of Things for Smart
Cities and Sustainable
Building Environment Alnaser
et al. (2024)

Comprehensive analysis of
digital twins in smart cities: a
4200-paper bibliometric study
El-Agamy et al. (2024)

Year

Table B.1 – continued from previous page
Contributions
Field(s)
• Mapped current/prospective DT
city benefits and
challenges

2021

2022

• Proposed research
agenda: data
efficiency,
socio-economic
integration, twin
coupling
• Compared DT vs.
CIM via literature
review, practitioner
survey and four
case studies
• Highlighted
governance and
technical
heterogeneity

2021

• Surveyed DT
origins, CPS
integration,
industrial practices
• Proposed
V2X-enabled DT
test framework for
connected vehicles

2025

• Surveyed DT
enabling tech (IoT,
ML, CPS,
blockchain)
• Reviewed urban
case studies across
domains and
outlined challenges
• Systematic review
of 125 papers on
AI+IoT-driven DTs
in buildings

2024

2024

• Thematic analysis:
construction, FM,
energy
optimization,
emerging urban
tech
• Bibliometric
mapping of 4 220
DT papers: trends,
authors, clusters
• Detailed look at
datasets, platforms,
performance
metrics

19

Sustainability;
Smart Cities; Urban
management

Urban planning;
City modelling;
CIM/DT
frameworks

Autonomous
driving; CPS; V2X
testing

Smart Cities;
Digital Engineering;
Urban development

Smart Cities;
Sustainable
buildings; AI; IoT

Smart Cities;
Bibliometrics;
Digital Twin
research

Gaps / Future Work
• Early-stage field;
missing
socioeconomic
modules
• Need full
digital-physical
integration
frameworks

• Fuzzy labels;
siloed research
streams
• Call for
interdisciplinary,
task-oriented
typologies

• DT definition
ambiguity in
transport
• Need real-world
validation of
V2X-DT testbeds
• Lack of standard
metrics and
governance models
• Need
cross-domain,
longitudinal
deployments

• Integration of
AI/IoT platforms
• Need
occupant-centric,
resilient
architectures

• Limited deep
case-study insights
• Need
governance/policy
and metric
standardization

Digital Twins for Intelligent Intersections: A Literature Review

Appendix C: Overview of Data Processing/Fusion Techniques and Simulation Platforms in
Selected Digital Twin Studies
Table C.1 provides a comparative overview of selected digital twin studies with respect to their data processing or fusion
techniques and the simulation platforms they employ. This compilation highlights the diversity of methodological
approaches, including traditional statistical models, clustering algorithms, and advanced AI-driven fusion architectures.
It also showcases the range of simulation environments used, such as SUMO, CARLA, and custom digital twin
frameworks. The table emphasizes emerging trends in real-time analytics, co-simulation, and multi-modal data
integration that are shaping the development of intelligent transportation systems.
Table C.1: Overview of Data Processing/Fusion Techniques and Simulation Platforms in Selected Digital Twin Studies
No.
1

Reference
Kušić et al. (2023)

Year
2023

2

Hassani et al. (2024)

2024

3

2024

4

Yilmaz-Niewerth et al.
(2024)
Bamminger et al. (2023)

Data Processing/Fusion Techniques
Real-time Big Data analytics; continuous
fusion of traffic sensor streams; dynamic
calibration via SUMO.
Complementary, redundant, and cooperative fusion; multi-level fusion classifications; revised JDL model.
–

2023

–

5

Katsumbe et al. (2024)

2024

6

Huang et al. (2025)

2025

7

Saroj et al. (2023)

2023

8

Kuraksin et al. (2020)

2020

9

Angelina et al. (2024)

2024

10

Yadav and Agarwal (2025)

2025

11

2024

12

Villanueva-Merino et al.
(2024)
Papadopoulos et al. (2024)

13

Helo and Thai (2024)

2024

14

Louati (2025a)

2025

15

Lv et al. (2022a)

2022

16

Aguiar et al. (2022)

2022

17

Lu et al. (2025)

2025

18

Varga et al. (2023)

2023

Word2Vec-based ML text analytics for
unstructured reports and GIS metadata.
Generative spatial AI foundation model
(Large Flow Model) for urban digital
twins.
K-means clustering; temporal-neighbor
interpolation to impute missing
connected-corridor volume data.
Dynamic Traffic Assignment (DTA)
models; ML submodels for traffic performance metrics.
Fine-grained energy consumption variables via Python scripts for eco-driving
assessments.
Least Squares prediction; multi-criterion
asynchronous classifier on acoustic signals.
GIS data integration; AI analytics for agefriendly urban environment planning.
PCA transformation; DBSCAN clustering for lightweight accident detection
from GPS streams.
Real-time IoT tracking and tracing data
integration for logistics visibility.
ARIMA forecasting; ANN predictive
modeling for traffic and safety indices.
Memory-augmented neural networks for
dynamic complex image segmentation.
Multi-objective optimization; IoT sensor data integration; microservice-based
data-processing architecture.
STGAT for heterogeneous spatiotemporal pavement-data fusion.
–

19

Wágner et al. (2023)

2023

2024

V2X SPaT/MAP message generation and
mixed-reality virtualization.

20

Simulation Platform
SUMO
–
Co-simulation coupling CARLA &
SUMO
Multi-body simulation + ML-based
virtual sensor models
–
Urban Digital Twin framework
Real-time connected-corridor simulation
DTALite / NEXTA
SUMO
CupCarbon
Local Digital Twin framework
–
–
–
Digital Twin simulation environment
Microscopic Traffic Simulator
Digital Twin–enabled highway management
Co-simulation coupling SUMO &
Carla
Co-simulation: SUMO, Veins &
OMNeT++
Continued on next page

Digital Twins for Intelligent Intersections: A Literature Review

No.
20

Table C.1 – continued from previous page
Year Data Processing/Fusion Techniques
2025 Real-time error coefficient integration
with PD control for Pure Pursuit path
tracking.

Reference
Cao et al. (2025)

Simulation Platform
CarSim & MATLAB/Simulink cosimulation

Appendix D: Examples of DT applications in smart infrastructure.
Table D.1 presents diverse examples of DT applications in smart infrastructure, ranging from traffic management to
smart mobility planning.
Table D.1: Examples of DT Applications in Smart Infrastructure
Domain
Traffic Management

Smart Mobility Planning
Road Safety

Predictive Maintenance

Energy Optimization

Communication
works

Net-

Example Application
Smart corridor twin for
real-time flow and signal control Safiullin et al.
(2020); Saroj et al. (2023)
Citywide mobility twin
simulating multimodal
transit Pirdavani et al.
(2023); Stan et al. (2024)
Intersection safety twin
for simulating vehiclepedestrian interactions
Pugachev et al. (2020);
Ulrich et al. (2023)
DT for monitoring infrastructure asset health Alshuwaikhat et al. (2022);
Kerimov et al. (2020)
Integration of BIM and
GIS for urban energy
management Jin et al.
(2024); Huang et al.
(2025)
5G network digital twin
for urban connectivity
Guan et al. (2024);
Wágner et al. (2023)

21

Integrated Technologies
IoT sensors; AI (clustering, interpolation); V2X

Reported Benefits
Optimized congestion
management; real-time
metrics

GIS, transport models;
mobile data; AI forecasting

Data-driven planning;
proactive congestion
mitigation

Cameras, LiDAR; AIbased vision; traffic simulation

Identification of hazards; proactive safety
measures

Structural sensors; IoT;
machine learning

Early fault detection; reduced downtime

BIM; IoT (smart meters);
simulation software

Reduced energy consumption; optimal load
balancing

3D GIS; AI-driven ray
tracing; network telemetry

Enhanced signal coverage; robust, low-latency
connectivity

Digital Twins for Intelligent Intersections: A Literature Review

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M.R. Mahendrini Fernando Ariyachandra and Ioannis Brilakis. Leveraging railway topology to automatically generate
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Asel Villanueva-Merino, Silvia Urra-Uriarte, Jose Luis Izkara, Sergio Campos-Cordobes, Andoni Aranguren, and
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Digital Twins for Intelligent Intersections: A Literature Review

Kuldeep Nautiyal, Durgaprasad Gangodkar, and Manoj Diwakar. Analysis on Advancements in Adaptive Traffic Lights
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Ali Louati. Machine learning framework for sustainable traffic management and safety in AlKharj city

[The evaluation harness truncated this reference: showing the first 120000 of 138126 characters.]
</reference>

<statements>
1. *Edge computing* and edge–fog–cloud continua for low-latency onboard inference and sensor fusion; three-tier User–Cloud–Robot architectures (PhenoRob-P).
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.