Heavy-duty commercial vehicles (HDCVs) are the key mobile nodes in intelligent transportation systems (ITS). However, their complex operating conditions and the diversity of data sources (such as road conditions, driver behavior, traffic signals, and on-board sensors) present considerable difficulties for accurately estimating the state and perceiving the environment using a single modality of data. This requires effective multi-modal data fusion to enhance the control and decision-making capabilities of HDCVs. This paper addresses this need by proposing a customized multi-modal intelligent transportation data fusion framework for intelligent HDCVs. This paper presents a solution for establishing a multi-modal intelligent transportation data collection platform, including real-scene collection methods and simulation scene collection methods based on the SUMO-MATLAB joint simulation platform. Through three representative case studies, the application methods of multi-modal traffic data

Chen, Zhengxian, Wang, Shaoqi, Jiang, Huiming, Zhou, Fojin, Wang, Mingqiang, Li, Jun

Research on Urban Traffic Congestion Optimisation Strategies Based on V2X Technology

2025-99-0117

11/11/2025

This study investigates urban traffic congestion optimisation strategies based on V2X technology. V2X technology (Vehicles and Internet of Everything) aims to alleviate urban traffic congestion, improve access efficiency, and reduce tailpipe emissions through real-time collection and fusion of traffic data to optimise traffic signal control and path planning. The efficacy of the optimisation strategies under different V2X penetration rates is evaluated by conducting multi-factor orthogonal experiments in different typical congestion scenarios. The experimental results show that the V2X-based signal optimisation, path induction, and event response combination strategies exhibit significant optimisation effects in all three scenarios: node bottleneck, corridor congestion, and event induction. Under the condition of 100% penetration, the combined strategy reduces delay by 41.9% in the node bottleneck scenario, improves accessibility by 28.1% in the corridor congestion scenario, and

Xi, Chaohu, Li, Jiasheng, Qu, Fengzhen, Liu, Hongjun, Liu, Xiaorui, Wang, Chunpeng

An SVM-Based Approach to Develop a New Control Module for H2-Powered Engine using Driver Behaviour Classification

2025-32-0042

11/03/2025

In the next years, the global hydrogen vehicle market is expected to grow at a very high rate. Consequently, it is necessary for scholars and professionals to study and test specific components in order to rise motor efficiency leveraging the new features of connectivity available in smart roads. In particular, our research is focused on the developement of an engine control module driven by evaluation of usage characteristics (e.g., driving style) and "connected-to-x" scenarios using the standard engine control approach. Moreover, the module proposed enables the implementation of "fast running" models to improve the response of vehicles and make the best possible use of H2-powered engine characteristics. That said, in this paper is proposed a new approach to implement the control module, using Support Vector Machine (SVM) as the machine learning algorithm to detect driving style, and consequently modify the parameters of the engine. We choose SVM because i) it is less prone to

Mastroianni, Michele, Merola, Simona, Irimescu, Adrian, De Santis, Marco, Esposito, Christian, Aversano, Lerina

Solving the Multi-Objective Vehicle Routing Problem with Parcel Lockers

2025-99-0050

10/17/2025

With the development of e-commerce and urbanization, logistics distribution has become a key challenge in improving traffic management and efficiency. The use of parcel lockers can alleviate delivery pressure, enhance user experience, and reduce costs. This paper investigates the Multi-Objective Vehicle Routing Problem with Parcel Lockers (MOVRPPL), aiming to optimize transportation costs, customer satisfaction, and the number of vehicles to improve resource utilization. Based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II), this paper proposes the NSGA-II-NI algorithm, which incorporates the nearest neighbor crossover algorithm and route optimization to approximate the Pareto optimal solution set. Experiments using the Solomon dataset are conducted, and the performance is evaluated using the Inverted Generational Distance (IGD) and Hypervolume (HV), compared with the state-of-the-art algorithm NSGA-II-HI. The experimental results show that our method achieves a better

Liu, Yuxin, Wang, Ying

Balancing Safety, Stability, and Comfort in Semi-Truck Platooning: A Study on Safe and Optimal Headways under Real-World Traffic Conditions

2025-99-0017

10/17/2025

On highways, platoons of semi-trucks are a common phenomenon. By maintaining a small headway, these platoons can effectively reduce air resistance, thereby improving fuel efficiency and reducing carbon emissions. However, this driving mode is also accompanied by many safety and operational risks, such as increased risk of rear-end collisions, reduced driving comfort, and susceptibility to interference from other vehicles outside the platoon. Therefore, behavioral analysis and evaluation of semi-truck platoons naturally formed in real traffic environments are of great significance for improving their driving safety, comfort and stability. This study focuses on the headway characteristics of semi-truck platoons, analyzes their headway distribution, headway gap and braking response behavior, and then proposes a safe headway threshold for emergency braking to effectively reduce the probability of rear-end collisions. In addition, the study also defines an optimal headway range to reduce

Hu, Xiaoqiang, Cao, Qiang

A Study of the Applicability of Variable Guide Lanes at Signalized Intersections

2025-99-0013

10/17/2025

In the context of China’s rapidly expanding urbanization, there is an increasing trend of car ownership among residents, which has led to a concomitant rise in traffic demand and a worsening of traffic congestion. To address this challenge, Variable Guided Lanes have been proposed as a novel traffic management strategy. This strategy entails the real-time adjustment of lane function, in response to fluctuations in traffic flow, with the objective of enhancing intersection access efficiency. The present study employs the average delay of vehicles in the inlet lane of the intersection as the discriminating index, and the left-turn and straight flow in the inlet lane as the discriminating condition. The study establishes an equal average delay model and delineates a threshold curve to assess the suitability of the lane for the implementation of Variable Guided Lanes. Furthermore, the study investigates whether the characteristics of the variable lanes are altered for the applicability

Zhang, Qinan, Zhang, Yongzhong

A Review of Current Research and Future Developments in Field Modelling for Traffic Risk Analysis

2025-99-0058

10/17/2025

Discovering the trend of risk changes and formulating risk prevention and control measures are important links in achieving proactive risk prevention and control. Constructing and analyzing field models can visualize the distribution and change of risks and formulate effective risk prevention and control measures. Based on the current situation and trend of field model research, this paper discusses its application in risk identification, aiming to improve the accuracy of risk avoidance. Firstly, different types of field models are classified, and their respective characteristics and application scenarios are introduced. Secondly, the shortcomings in the development of field models are summarised. Finally, in the field of autonomous driving and intelligent traffic management, it is proposed that the accuracy of the model can be improved by multi-scene data fusion, the dynamic response enhances the efficiency of risk avoidance, and the aspect of risk classification in complex

Song, Yulian, Yue, Lihong, Wang, Chunxiao

The Morning Commute Problem with Carpool and Dynamic Toll Scheme

2025-99-0022

10/17/2025

This study extends the bottleneck model to analyze dynamic user equilibrium (UE) in carpooling during the morning peak commute. It is assumed that the carpooling platform offers both traditional human-driven vehicles and novel shared autonomous vehicles. First, we analyze the traffic distribution on a two-lane road. We find that traffic distribution is influenced by the additional cost of carpooling behavior. A corresponding functional relationship is established and visualized. Second, we derive the critical fare threshold for carpooling. Carpooling occurs only when the fare is below this threshold. Third, we obtain the user equilibrium (UE) solution under a specified case, including flow distribution, equilibrium cost, and total number of vehicle. Furthermore, a system-optimal dynamic tolling scheme is proposed to minimize total system cost while maintaining commuter UE. By equating the system marginal cost to the equilibrium cost, we derive the analytical expression for the lane

Zheng, XiaoLong, Zhong, RenXin

Road Traffic State Classification Method Based on AutoEncoder and Clustering Algorithm

2025-99-0048

10/17/2025

Urban road traffic state classification is essential for identifying early-stage deterioration and enabling proactive traffic management. This study presents a novel method to accurately assess the traffic state of urban roads while addressing the limitations of existing methods in spatial generalization performance. The approach consists of three key components. First, several indicators are designed to capture the spatial-temporal evolution mechanisms of traffic state, speed freedom, flow saturation, and their variations over time and space. Then, a feature learning module based on an AutoEncoder network is introduced to reduce the dimensionality of the constructed feature set. This enhances feature distinction while mitigating noise effects on classification results. Third, k-means clustering is applied to analyze significant features extracted from the AutoEncoder latent space, categorizing road traffic states into fluent, basic fluent, moderate congested and severe congested

Wang, Xiaocong, Huang, Min, Guo, Xinling, Xie, Jiemin, Zhang, Xiaolan

The Traffic Light Control Strategy at Intersections in Intelligent Transportation Systems

2025-99-0009

10/17/2025

This thesis explores strategies for controlling traffic signals at intersections within the context of ITS., emphasizing the role of DRL in optimizing traffic flow. In recent years, urbanization and the rapid increase in vehicle numbers in China have exacerbated traffic congestion, significantly hindering urban development. This study explores innovative approaches to alleviating traffic congestion, focusing on smart traffic signal systems that adjust according to real-time traffic conditions. The research reviews fundamental concepts in traffic signal control, including traffic flow, signal phases, and signal cycles, and investigates how DRL can dynamically adjust traffic light cycles to optimize intersection performance. The findings suggest that DRL provides an effective method for managing complex and unpredictable traffic environments, as it enables systems to self-learn and continuously refine their strategies based on environmental changes. The adoption of this technology holds

Liu, Junao, Zuo, Tingyou

Communication Network Construction Behaviors for Robotic Convoying

2025-01-0433

09/16/2025

We develop a set of communications-aware behaviors that enable formations of robotic agents to travel through communications-deprived environments while remaining in contact with a central base station. These behaviors enable the agents to operate in environments common in dismounted and search and rescue operations. By operating as a mobile ad-hoc network (MANET), robotic agents can respond to environmental changes and react to the loss of any agent. We demonstrate in simulation and on custom robotic hardware a methodology that constructs a communications network by “peeling-off” individual agents from a formation to act as communication relays. We then present a behavior that reconfigures the team’s network topology to reach different locations within an environment while maintaining communications. Finally, we introduce a recovery behavior that enables agents to reestablish communications if a link in the network is lost. Our hardware trials demonstrate the systems capability to

Noren, Charles, Chaudhary, Sahil, Shirose, Burhanuddin, Vundurthy, Bhaskar, Travers, Matthew

Edge Artificial Intelligence in Connected, Cooperative and Automated Mobility

EPR2025009

05/20/2025

With many stakeholders involved, and major investments supporting it, the advancements in automated driving (AD) are undoubtedly there. Generally speaking, the motivation for advancing AD is driver convenience and road safety. Regarding the development of AD, original equipment manufacturers, technology start-ups, and AD systems developers have taken different approaches for automated vehicles (AVs). Some manufacturers are on the path toward stand-alone vehicles, mostly relying on onboard sensors and intelligence. On the other hand, the connected, cooperative, and automated mobility (CCAM) approach relies on additional communication and information exchange to ensure safe and secure operation. CCAM holds great potential to improve traffic management, road safety, equity, and convenience. In both approaches, there are increasingly large amounts of data generated and used for AD functions in perception, situational awareness, path prediction, and decision-making. The use of artificial

Van Schijndel-de Nooij, Margriet, Beiker, Sven

Leveraging Advanced Data Technologies for Smart Traffic Management

EPR2025008

05/20/2025

The transportation industry is transforming with the integration of advanced data technologies, edge devices, and artificial intelligence (AI). Intelligent transportation systems (ITS) are pivotal in optimizing traffic flow and safety. Central to this are transportation management centers, which manage transportation systems, traffic flow, and incident responses. Leveraging Advanced Data Technologies for Smart Traffic Management explores emerging trends in transportation data, focusing on data collection, aggregation, and sharing. Effective data management, AI application, and secure data sharing are crucial for optimizing operations. Integrating edge devices with existing systems presents challenges impacting security, cost, and efficiency. Ultimately, AI in transportation offers significant opportunities to predict and manage traffic conditions. AI-driven tools analyze historical data and current conditions to forecast future events. The importance of multidisciplinary approaches and

Ercisli, Safak

Letter from the Guest Editors

12-08-02-0011

05/06/2025

Letter from the Guest Editors

Liang, Ci, Törngren, Martin

SAE TOMORROW TODAY - Scalable AV Deployment Starts with Fewer Close Calls

1351305/01/2025

The hallmark of exceptional autonomous driving technology isn't just how it reacts in a crisis but how it avoids one altogether. That's the vision behind May Mobility: a world where self-driving cars confidently navigate busy intersections, unexpected detours, and pedestrian-filled crosswalks with the instincts of a seasoned human driver. At the core of May Mobility's technology platform is its patented Multi-Policy Decision Making (MPDM) system. This breakthrough technology uses real-time, in-situ AI to interpret data, continuously learning and adapting to new, complex, and unpredictable driving conditions. By learning on the fly--much like a human driver--May Mobility's AVs can be deployed faster and more cost-effectively than traditional systems. To explore how May Mobility is scaling its AV technology, we spoke with Ed Olson, CEO and Founder, about the company's city-wide AV deployments, strategic partnerships with Toyota and NTT, and its entrance into the rideshare market through

Hineman, Marcie

Traffic Flow Prediction Based on the Optuna–CNN–BiLSTM–GBRT Model

12-09-01-0001

04/24/2025

Traffic flow prediction is very important in traffic-related fields, and increasing prediction accuracy is the primary goal of traffic prediction research. This study proposes a new traffic flow prediction method, which uses the CNN–BiLSTM model to extract features from traffic data, further models these features through GBRT, and uses Optuna to tune important hyperparameters of the overall model. The main contribution of this study is to propose a new combination model with better performance. The model integrates two deep learning models that are widely used in this field and creatively uses GBRT to process the output features of the front-end model. On this basis, the optimal hyperparameters and the robustness of the model are deeply explored, providing an effective and feasible solution to the difficult problems in traffic flow prediction. This model is experimentally studied using three different data transformation methods (original data, wavelet transform, Fourier transform

Ma, Changxi, Jin, Renzhe

A Novel Model for Short-Term Traffic Flow Prediction on Highways Utilizing Multi-Head Attention–BiLSTM–BiGRU

12-09-01-0002

04/24/2025

With the development of intelligent transportation systems and the increasing demand for transportation, traffic congestion on highways has become more prominent. So accurate short-term traffic flow prediction on these highways is exceedingly crucial. However, because of the complexity, nonlinearity, and randomness of highway traffic flows, short-term prediction of its flows can be difficult to achieve the desired accuracy and robustness. This article presents a novel architectural model that harmoniously fuses bidirectional long–short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), and multi-head attention (MHA) components. Bayesian optimization (BO) is also used to determine the optimal set of hyperparameters. Based on the PeMS04 dataset from California, USA, we evaluated the performance of the proposed model across various prediction intervals and found that it performs best within a 5-min prediction interval. In addition, we have conducted comparison and ablation

Chen, Peng, Wang, Tao, Ma, Changxi, Chen, Jun

Cross-Scene Traffic Target Detection and Recognition Based on Improver YOLOv5s

12-09-01-0003

04/24/2025

Aiming at the problem of insufficient cross-scene detection performance of current traffic target detection and recognition algorithms in automatic driving, we proposed an improved cross-scene traffic target detection and recognition algorithm based on YOLOv5s. First, the loss function CIoU of insufficient penalty term in the YOLOv5s algorithm is adjusted to the more effective EIoU. Then, the context enhancement module (CAM) replaces the original SPPF module to improve feature detection and extraction. Finally, the global attention mechanism GCB is integrated with the traditional C3 module to become a new C3GCB module, which works cooperatively with the CAM module. The improved YOLOv5s algorithm was verified in KITTI, BDD100K, and self-built datasets. The results show that the average accuracy of mAP@0.5 is divided into 95.1%, 72.2%, and 97.5%, respectively, which are 0.6%, 2.1%, and 0.6% higher than that of YOLOv5s. Therefore, it shows that the improved algorithm has better detection

Ning, Qianjia, Zhang, Huanhuan, Cheng, Kehan

Reducing Power Consumption through Effective Data Sharing in Platooning Scenarios

2025-01-8051

04/01/2025

Platooning occurs when vehicles travel closely together to benefit from multi-vehicle movement, increased road capacity, and reduced fuel consumption. This study focused on reducing energy consumption under different driving scenarios and road conditions. To quantify the energy consumption, we first consider dynamic events that can affect driving, such as braking and sudden acceleration. In our experiments, we focused on modeling and analyzing the power consumption of autonomous platoons in a simulated environment, the main goal of which was to develop a clear understanding of the different driving and road factors influencing power consumption and to highlight key parameters. The key elements that influence the energy consumption can be identified by simulating multiple driving scenarios under different road conditions. The initial findings from the simulations suggest that by efficiently utilizing the inter-vehicle distances and keeping the vehicle movements concurrent, the power

Khalid, Muhammad Zaeem, Azim, Akramul, Rahman, Taufiq

Fuel Consumption Estimation Using Spatio-Temporal Modeling and Traffic Flow Predictions: A Comparative Analysis

2025-01-8101

04/01/2025

Effective traffic management and energy-saving techniques are increasingly needed as metropolitan areas grow and traffic volumes rise. This work estimates fuel consumption over three selected routes in an urban context using spatio-temporal modeling essentially building on a previously developed approach in traffic prediction and forecasting. A weighted adjacency matrix for a Graph Neural Network (GNN) is constructed in the original approach which combines graph theory frameworks with travel times obtained from average speeds and distances between traffic count stations. Next, the traffic flow estimate uncertainty is measured using Adaptive Conformal Prediction (ACP) to provide a more reliable forecast. This work predicts fuel consumption under different scenarios by utilizing Monte Carlo simulations based on the expected traffic flows providing insights into energy efficiency and the best routes to take. The study compares passenger vehicles' and heavy-duty trucks' mean fuel

Patil, Mayur, Moon, Joon, Hanif, Athar, Ahmed, Qadeer

Robust V2X Cruise Control for Class 8 Trucks in the Presence of Traffic Lights

2025-01-8033

04/01/2025

An implementation of a robust predictive cruise control method for class 8 trucks utilizing V2X communication with connected traffic lights is presented in this work. This method accounts for traffic signal phases with the goal of reducing energy consumption when possible while respecting safety concerns. Tightened constraints are created using a robust model predictive control (RMPC) framework in which constraints are modified so that the safety critical requirements are satisfied even in the presence of disturbances, while requiring only the expected bounds of the disturbances to be provided. In particular, variation in the actuator performance under different conditions presents a unique challenge for this application, which the approach applied in this work is well-suited to handle. The errors resulting from lower-level control and actuator performance are accounted for by treating them as bounded and additive disturbances on the states of the model used in the higher level MPC

Ellison, Evan, Ward, Jacob, Brown, Lowell, Bevly, David M.

An Investigation of the Inter-Vehicle Distances and Lateral Offsets on Aerodynamic Forces and Wake Structures of Vehicles Platooning

2025-01-8769

04/01/2025

In traffic scenarios, the spacing between vehicles plays a key role, as the actions of one vehicle can significantly impact others, particularly with regards to energy conservation. Accordingly, modern vehicles are equipped with inter-vehicle communication systems to maintain specific distances between vehicles. The aerodynamic forces experienced by both leading vehicles (leaders) and following vehicles (followers) are connected to the flow patterns in the wake region of the leaders. Therefore, improving our understanding of the turbulent characteristics associated with vehicles platooning is important. This paper investigates the effects of inter-vehicle distances on the flow structure of two vehicles: a small SUV as the leader and a larger light commercial van as the follower, using a Delayed Detached Eddy Simulation (DDES) CFD technique. The study focuses on three specific inter-vehicle distances: S = 0.28 L, 0.4L, and 0.5L, where S represents the spacing between the two vehicles

Mosavati, Maziar, Guzman, Arturo, Lounsberry, Todd, Fadler, Gregory

Enhanced Secondary Crash Classification through Functional Class-Based Weighting and Hybrid Machine Learning Models

2025-01-8207

04/01/2025

Road safety and traffic management face significant challenges due to secondary crashes, which frequently cause increased traffic, delays, and collisions. Traditional methods for anticipating secondary crashes often overlook the importance of different road types, resulting in suboptimal predictions and response plans. This research presents a novel method that combines a hybrid machine-learning model with a functional class-based weighting strategy to classify secondary crashes. The functional classes in the dataset are categorized as interstates, arterial roads, collector roads, and local roads. The dataset also includes comprehensive crash narratives and various road attributes. Each functional class is assigned a weight reflecting its proportional importance in the likelihood of a subsequent crash, based on historical data and road usage patterns. This weighting technique is integrated into a hybrid model architecture that trains a Random Forest (RF) model on structured data to

Patil, Mayur, Marik PE, Stephanie

Vehicle–Vehicle Collaborative Control Strategy for Dedicated Lanes of Connected Vehicles

12-08-02-0012

03/28/2025

To alleviate the problem of reduced traffic efficiency caused by the mixed flow of heterogeneous vehicles, including autonomous and human-driven vehicles, this article proposes a vehicle-to-vehicle collaborative control strategy for a dedicated lane in a connected and automated vehicle system. First, the dedicated lane’s operating efficiency and formation performance are described. Then, the characteristics of connected vehicle formations are determined, and a control strategy for heterogeneous vehicle formations was developed. Subsequently, an interactive strategy was established for queueing under the coordination of connected human-driven and autonomous vehicles, and the queue formation, merging, and splitting processes are divided according to the cooperative interaction strategy. Finally, the proposed lane management and formation strategies are verified using the SUMO+Veins simulation software. The simulation results show that the dedicated lane for connected vehicles can

Zhang, Xiqiao, Cui, Leqi, Yang, Longhai, Wang, Gang

Freeway Traffic Conflict Forecasting: A Machine Learning Approach with RF-LSTM Integration

2025-01-7138

03/19/2025

This paper aims to forecast and examine traffic conflicts by integrating Random Forest (RF) alongside Long Short-Term Memory Network (LSTM). The paper begins with the Random Forest method, pinpointing essential elements affecting traffic conflicts, revealing that the speed difference between interacting vehicles and their leaders, as well as the average headway and distance have significant effects on the occurrence of traffic conflicts. The forecasted Time to Collision (TTC) metric demonstrates extraordinary accuracy, confirming the creation of a precise traffic conflict forecast model. The model expertly predicts the vehicle's trajectory. This model skillfully anticipates vehicle paths and potential traffic conflict, demonstrating strong alignment with actual traffic patterns and offering support for traffic management by highlighting imminent risks. Merging RF with feature selection and LSTM for temporal dynamics enhances the forecasting capability. Furthermore, it also illuminates

Cui, XinYuan, Shi, Xiaomeng, Shao, Yichang

A Unified-BEV Network for Joint 3D Object Detection and Map Segmentation in Complex Traffic Scenario

2025-01-7202

03/19/2025

Recently, the multi-view image-based Bird’s Eye View (BEV) perception for autonomous driving has gained considerable attention due to its cost-effectiveness and capacity for rich semantic information. However, the majority of existing studies focus primarily on improving the performance of single task, neglect to utilize the dense and robust BEV representation that is beneficial for various downstream tasks such as 3D object detection, semantic map segmentation. These approaches inherently add extra computational burden due to repeated feature extraction and propagation for different tasks. To this end, we develop a network that simultaneously performs 3D object detection and map segmentation in a unified BEV representation space with multi-camera perspective view (PV) image inputs. Firstly, a shared network includes image feature extractor and PV-BEV transformation is employed to generate a unified BEV feature. The BEV feature serves as the input for the decoders of various tasks

Li, Mohan, Song, Tao, Xu, Yanhai, Zhou, Zhisong, Zhou, Guofeng, Liu, Xulei

Research on Digital Twin Model Construction Method for Smart Highway

2025-01-7175

02/21/2025

To facilitate the construction of a robust transport infrastructure, it is essential to implement a digital transformation of the current highway system. The concept of digital twins, which are virtual replicas of physical assets, offers a novel approach to enhancing the operational efficiency and predictive maintenance capabilities of highway networks. The present study begins with an exhaustive examination of the demand for the smart highway digital twin model, underscoring the necessity for a comprehensive framework that addresses the multifaceted aspects of digital transformation. The framework, as proposed, is composed of six integral components: spatiotemporal data acquisition and processing, multidimensional model development, model integration, application layer construction, model iteration, and model governance. Each element is critical in ensuring the fidelity and utility of the digital twin, which must accurately reflect the dynamic nature of highway systems. The

Zhang, Yawen, Cai, Xianhua

Evaluation Method for the Impacts of Truck Platoons Lateral Control on Pavement Performance

2025-01-7152

02/21/2025

The introduction of autonomous truck platoons is expected to result in drastic changes in operational characteristics of freight shipments, which may in turn have significant impacts on efficiency, energy consumption, and infrastructure durability. Since the lateral positions of autonomous trucks traveling consecutively within a lane are fixed and similar (channelized traffic), such platooning operations are likely to accelerate damage accumulation within pavement structures. To further advance the application of truck platooning technology in various pavement environments, this study develops a flexible evaluation method to evaluate the impact of lateral arrangement within autonomous truck platoons on asphalt pavement performance. This method simplifies the impact of intermittent axle load applications along the driving direction within a platoon, supporting platoon controllers in directly evaluating pavement damage for different platoon configurations. Specifically, a truck platoon

Wenlu, Yu, Ye, Qin, Chen, Daoxie, Min, Yitong, Chen, Leilei

Application of Key Node Identification Based on Revised Penalized Local Structural Entropy in Traffic Flow Prediction

2025-01-7174

02/21/2025

In intelligent transportation systems (ITS), traffic flow prediction is a necessary tool for effective traffic management. By identifying and extracting key nodes in the network, it is possible to achieve efficient traffic flow prediction of the whole network using “partial” nodes, as the key nodes contain essential information about changes in the state of the traffic network. This paper proposes a key node identification method based on revised penalty local structure entropy (RPLE) for specific traffic networks. This method takes into account the influence of node distance and traffic flow on identifying important nodes within the traffic network. By introducing a modified penalty term and a comprehensive weight, it achieves a certain level of accuracy in traffic flow prediction using data from key nodes in the network. We compared the RPLE method with different key node identification methods and combined it with different prediction models to compare the traffic flow prediction

Shu, Xin, Ran, Bin

Cooperative Control Strategy for Variable Speed Limit and Dynamic Hard Shoulder Running of Highway On-Ramp Merging Area

2025-01-7207

02/21/2025

To meet the traffic control demands of highway merging areas and address the accuracy error of traffic flow prediction models, a cooperative control strategy based on adaptive prediction horizon Model Predictive Control (MPC) has been proposed for variable speed limits (VSL) and dynamic hard shoulder running (HSR). Firstly, the METANET model was improved based on the characteristics of merging areas and the impact of cooperative control strategy. Secondly, to mitigate the negative impact of the METANET prediction errors on control effectiveness, a fuzzy rule-based adaptive prediction horizon controller is designed. Thirdly, a cooperative control strategy for VSL and dynamic HSR is formulated under the MPC framework, aiming to optimize Total Time Spent(TTS)and Total Travel Distance (TTD), using genetic algorithms equipped with sliding time windows for resolution. Finally, using actual traffic flow data from Changtai Highway, simulation experiments are conducted, involving four scenarios

Li, Jiahui, Zhang, Jian, Wang, Bo

A Traffic Conflict Risk Assessment Model for Highway Construction Zones Based on Trajectory Data

2025-01-7217

02/21/2025

Highway construction zones present substantial safety challenges due to their dynamic and unpredictable traffic conditions. With the rising number of highway projects, limited accident data during brief construction phases underscores the need for alternative safety evaluation methods, such as traffic conflict analysis. This study addresses vehicular safety issues within the Kunshan section of the Shanghai-Nanjing Expressway, focusing on conflict risk assessment through a spatio-temporal analysis of a construction zone. Using drone-captured video, vehicle trajectories were extracted to derive key operational indicators, including speed and acceleration, providing a spatio-temporal foundation for analyzing traffic flow and conflict dynamics. A novel **Comprehensive Collision Risk Index (CCRI)** was introduced, integrating Time-to-Distance-to-Collision (TDTC) and Enhanced Time-to-Collision (ETTC) metrics to enable a multidimensional assessment of conflict risk. The CCRI captures both

Zhang, Yuwen, Guo, Xiucheng, Ma, Yuheng

Analysis of Left-Turn Mixed Traffic Flow Dynamics at Y-Shaped Signalized Intersections Using Cellular Automata

2025-01-7204

02/21/2025

Through the method of on-site video observation, this study divides the intersection area into three parts according to the road traffic characteristics of the Y-shaped signalized intersections, and at the same time obtains the relevant parameters. These parameters include the left-turn speed and traffic density of motor vehicles within both the internal and exit areas, the frequency of lane-changing and queuing behaviors of non-motorized vehicles in the internal area, and the left-turn speed and traffic density of non-motorized vehicles in both the internal and exit areas. The data extraction and analysis of the parameters provide strong data support for further analysis of the subsequent mixed traffic flow. A cellular automaton model is developed using the intersection’s exit area as the scenario. The exit area is divided into three lanes based on the queuing patterns of mixed traffic. Corresponding traffic rules are established according to the traffic density of motorized and non

Yuan, Li, Liu, Xiaowei

Investigating the Effect of Traffic Network Information on the Evolution of Traffic Flow in Intelligent Transportation Systems under Herd Effect

2025-01-7215

02/21/2025

Intelligent transportation has emerged as a critical paradigm in the transportation sector, underscoring the growing significance of digital information. The extent to which travelers comprehend transportation network information fundamentally influences the dynamics of traffic flow evolution. Traditional random user equilibrium models assume that travelers possess knowledge of segment flow information; however, they fail to account for route flow information. To date, research has yet to investigate how travelers’ decision-making behaviors are altered following the acquisition of route flow information. When endowed with such information, travelers frequently demonstrate behaviors influenced by the bandwagon effect, adjusting their routes to conform to the choices of the majority. This behavioral modification disrupts the existing equilibrium, resulting in a continued evolution of traffic flow until a new stable state is achieved. To examine the implications of transportation network

Zhou, Bojian, Yu, Yaofeng, Li, Shihao, Li, Kangjiao

Analysis of Crash Precursors under Different Traffic Flow Conditions with Enhanced Interpretability

2025-01-7134

02/21/2025

This study investigates the precursors of crashes under varying traffic states through an in-depth analysis of freeway traffic data. This method effectively addresses the limitations associated with using surrogate measures in traffic safety research. We used the k-means clustering method to categorize traffic states into three types: free flow, transitional state, and congested flow. By employing the case-control study experimental approach, we conducted an in-depth analysis of the traffic data. During the feature selection process, we set matching rules to choose control group data that meet the criteria of time, location, and traffic state. Initially, traffic flow feature variables were constructed based on multiple dimensions, including time window width, spatial location, traffic flow parameters, and statistical characteristics. To reduce feature multicollinearity, we used correlation matrices and variance inflation factors (VIF). We then applied Recursive Feature Elimination (RFE

Zhou, Feixiang, Liu, Shaoweihua, Feng, Shi, Zhang, Yujie, Luo, Xi

Research and Simulation Implementation of Self-Driving Vehicle Trajectory Optimization in Networked Intersection Environment

2025-01-7194

02/21/2025

Nowadays, the rapidly developing Connected and Autonomous Vehicle (CAV) provides a new mode of intersection vehicle cooperative control, which can optimize vehicle trajectories and signal phases in real time and reduce intersection delays through the advantages of vehicle-road cooperative information interaction and the high controllability of CAV. In this paper, the intersection of Jintong West Road and Guanghua Road in Beijing is taken as the research object, and the vehicle trajectory constraints, acceleration constraints, speed constraints, safe driving constraints, signal switch constraints and traffic signal control constraints are set up with the minimization of traffic delay as the objective function. The DQN deep reinforcement learning network is constructed based on vehicle states, vehicle actions, reward functions, and update rules, and starts learning and updating to generate the target network. Then, SUMO software is used to simulate and test and compare the trajectory

Xu, Yuting, Zhang, Yong, Wu, Xianyu

Spatio-Temporal Traffic Forecasting with Uncertainty Quantification via Bayesian Graph Convolution

2025-01-7142

02/21/2025

Traffic prediction plays an important role in urban traffic management and signal control optimization. As research in this area advances, traffic prediction has become increasingly accurate. However, the complexity of the traffic system makes the quantification of uncertainty particularly important, as it is influenced by various factors such as weather changes, emergencies and road construction, which lead to the fluctuation and uncertainty of the traffic state. Although some progress has been made in traffic uncertainty quantification, most methods remain primarily focused on individual traffic observation points, with little exploration of the complex spatiotemporal dependencies at the road network level. In light of this situation, this paper proposes a spatiotemporal traffic prediction model based on Bayesian graph convolutional network, which can effectively capture the spatiotemporal dependence in traffic data, facilitating accurate predictions and comprehensive uncertainty

Li, Linfeng, Lin, Limeng

An Integrated Lane Change Control Method for Freeway Segments with Lane Drop in a Connected Mixed Traffic Environment

2025-01-7190

02/21/2025

Segment with lane drops are very important in freeway systems since they are major constrains to traffic flow and safety. The frequency of capacity reductions and higher safety risks is proportional to an increase in lane-changing actions, which worsen traffic congestion, decrease road capacity, and increase the risk of an accident. Traditional traffic management strategies that rely on physical structures and driver’s decision making often fail under such conditions. This paper provides a detailed lane change control strategy specific to freeway segments with lane reduction in the connected and autonomous vehicle (CAV) environment. The strategy combines both centralized and decentralized techniques to improve the vehicle’s lane-changing behavior and density. A cellular transmission model of lane-level is proposed for the centralized control of the linked vehicles based on the ratio of the driver compliance. The model derives the density equation and transforms the lane-changing

Ma, Yuheng, Guo, Xiucheng, Zhang, Yiming, Cao, Jieyu

Study on Variable Speed Limit Control Strategy of Highway under Intelligent Connected Environment

2025-01-7189

02/21/2025

This paper presents a novel variable speed limit control strategy based on an Improved METANET model aimed at addressing traffic congestion in the bottleneck areas of expressways while considering the impact of an intelligent connected environment. Traffic flow simulation software was employed to compare the outcomes of the traditional variable speed limit model with those derived from the proposed strategy. The results indicated that under three scenarios—main road, ramp, and lane closure—with a 100% penetration rate of intelligent connected vehicles, the average delay for vehicles utilizing the new model decreased by 9.37%, 11.11%, and 7.22%, respectively. This study offers an innovative approach to highway variable speed limits under an intelligent connected environment.

Qi, Tiancheng, Qu, Xinhui, Gu, Haiyan, Sang, Zheming, Ning, Fangyue

A Car-following Model Considering Acceleration of Multiple Vehicles for Mixed Traffic Flow

2025-01-7195

02/21/2025

The practice of vehicle platooning for managing mixed traffic can greatly enhance safety on the roads, augment overall traffic flow, and boost fuel efficiency, garnering considerable focus in transportation. Existing research on vehicle platoon control of mixed traffic has primarily focused on using the state information of the leading or head vehicle as control input for following vehicles without accounting for the driving variability of Human-driven Vehicles (HDVs), which does not conform to the driving conditions of vehicles in reality. Inspired by this, this paper presents a car-following model for Connected and Automated Vehicles (CAVs) that utilizes communication with multiple preceding vehicles in mixed traffic. The study further investigates the impact of parameters such as the speed and acceleration of preceding vehicles on the car-following behavior of CAVs, as well as the overall effect of different CAV penetration rates on mixed traffic flow. Firstly, a mixed-vehicle

Peng, Fuke, Huang, Xin

Identifying High-Risk Situations in Right-Turn Phase of Heavy-Duty Vehicles Using Naturalistic Driving Data

2025-01-7209

02/21/2025

The interaction between heavy-duty vehicles turning right and non-motor vehicles going straight has led to severe traffic crashes. It is essential to evaluate the driving risk of heavy-duty vehicles in the right-turn phase. Increasingly, studies have explored some indicators associated with driving risk. Based on naturalistic driving data of 121 heavy-duty vehicles in Nanjing, this research combined factor analysis and K-means cluster algorithm to assess the driving risk of two scenarios, one without a blind spot warning and another with a blind spot warning during the right-turn phase. The results have concluded the driving characteristics of heavy-duty vehicles under different risk levels. It formed a set of driving risk level assessment methods for heavy-duty vehicles in the right-turn phase. This evaluation method is expected to identify high-risk right-turn behaviors of heavy-duty vehicles and provide some insights to practitioners for traffic management.

Zhang, Hedi, Fu, Yuanhang, Ma, Yongfeng, Chen, Shuyan

Reviews on Traffic Flow Models for Autonomous Driving: The Artificial Intelligence and Cellular Automata Based Models

2025-01-7127

02/21/2025

Cellular Automata (CA) has emerged as a powerful computational model that has been widely applied in the field of traffic flow simulation, effectively capturing the complex dynamic behaviors of traffic systems and variable environmental conditions. With the rapid advancements in autonomous driving technology, traditional CA traffic flow simulation models for human-driving condition are updating, especially adapting to the Artificial Intelligence (AI) integrated driving behavior of autonomous vehicle (AV). This paper conducts an analysis on the existing explorations of CA-based traffic flow modelling for AVs. First, this paper utilizes the knowledge graph analysis tool “VOSviewer” to visually represent the relations among the state of art studies. The keyword clustering helps to reveal current research hotspots and developmental trajectories. Subsequently, the paper classifies how CA models are improved to adapt the AVs, from the view of the car-following, lane-changing, AV platoon, and

Li, Tianyi, He, Shanglu, Chen, Meng, Lu, Chunyi, Cao, Congyong

EMGLight: A Joint Optimization Method for Emergency Signal Priority and Recovery Based on DDPG

2025-01-7124

02/21/2025

The rapid response of emergency vehicles (EVs) is crucial in safeguarding lives and property during emergencies. However, conventional traffic signal control methods for EV priority often disrupt normal traffic flow, leading to significant delays for general vehicles and decreased overall traffic efficiency. This study proposes EMGLight, a novel traffic signal control framework based on Deep Deterministic Policy Gradient (DDPG), to optimize EV priority and signal recovery jointly. By leveraging DDPG's ability to handle continuous action spaces, EMGLight achieves fine-grained control over traffic signals, adapting dynamically to real-time traffic conditions. Additionally, a dynamic reward mechanism is introduced, balancing EV priority with the recovery needs of general traffic. Simulation results demonstrate that EMGLight outperforms traditional fixed-cycle and greedy preemption methods, significantly reducing EV travel time while minimizing the adverse impact on general traffic flow

Jiang, Xin, Zhang, Jian, Qian, Yu

Deep Learning-Based Road Spill Detection Technology

2025-01-7130

02/21/2025

Rapid identification and cleanup of road debris are essential for enhancing traffic safety and ensuring unobstructed road conditions. Traditional detection methods often face challenges in accurately identifying debris in complex environments with varying light and weather conditions. To address these limitations, this study proposes a deep learning-based road debris detection method designed for improved accuracy and robustness. First, road images are processed using a semantic segmentation approach to remove background information, isolating only the drivable areas. This segmented region is then subjected to further object detection to filter out typical non-debris objects, such as vehicles, pedestrians, and non-motorized vehicles, thereby retaining a focused image that only contains potential debris or spill objects. Lastly, the processed image is compared to a baseline image to detect differences and identify road debris with high precision. Through these steps, the proposed method

Gao, Xiaofei

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