Browse Topic: Traffic management

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GTSRB-Shield: A Novel Machine Learning Framework for Intrusion Detection in Autonomous Vehicles Leveraging the German Traffic Sign Recognition Benchmark2026-26-0611To be published on 01/16/2026
The escalating dependence of Autonomous Vehicles on Intelligent Transportation Systems (ITS) has highlighted the imperative for comprehensive security protocols to safeguard such vehicles against cyber threats. Intrusion Detection Systems (IDS’s) are pivotal in ensuring the protection of these systems by detecting and alleviating unauthorized access and nefarious activities. The German Traffic Sign Recognition Benchmark (GTSRB) database, which encompasses an extensive compilation of traffic sign imagery, functions as a vital asset for the advancement of machine learning-based IDS. This research elucidates an intrusion detection system (IDS) that employs machine learning algorithms to scrutinize the GTSRB database. The proposed IDS emphasize the preprocessing of the GTSRB dataset to extricate pertinent features that can be employed for the training of machine learning models. Research also focuses on model development with machine learning algorithms to classify traffic signs and
Patil, Kamalesh, Akbar Badusha, A., Jadhav, Savitri, Gunale, Kishanprasad
Integrating V2V and V2I Communications for Enhanced Traffic Safety and Efficiency in Connected Vehicular Networks2026-26-0271To be published on 01/16/2026
This paper presents a new approach to improve road safety and traffic flow by combining vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Our study focuses on a system that connects vehicles with each other and with traffic signals to share real-time data about speed, position, and road conditions. Using both analytical models and experimental simulations built in MATLAB/Simulink, we developed a system that predicts and advises the optimal speed for vehicles approaching an intersection. In our design, a Green Light Optimized Speed Advisory (GLOSA) feature suggests an ideal speed for drivers so that they can pass through green lights without unnecessary stopping. This not only improves traffic flow but also reduces fuel consumption and lowers the risk of accidents. We validated our system through a series of simulations and real-time traffic scenarios. The results show that our approach can adapt dynamically to changes in vehicle behavior and traffic density
Pinto, Colin Aubrey, Shah, Ravindra, Karle, Ujjwala
Design of Intelligent Lighting System for Expressway Tunnel2025-99-023212/23/2025
A smart highway tunnels lighting system based on the technology of cloud platform and Internet of Things(IoTs) has been designed to address the common problems of high energy consumption and low level of intelligence in China's highway tunnel lighting system. The highway tunnel lighting system consists of four layers of architecture: platform management layer, local management layer, middle layer and terminal layer. The system collects real-time brightness, lamp brightness, traffic volume and other data outside the tunnel through various sensors deployed on site, and then uploads the collected data to the main controller through LoRa IoTs. The main controller combines the brightness calculation method of the lighting design rules to control the brightness of the tunnel lighting in real time, achieving real-time adjustment of the brightness of the tunnel LED lights and the brightness outside the tunnel, and realizing a safe and energy-saving lighting effect of "lights on when the car
Wang, Juntao, Liu, Jingyang, Liu, Yong, Feng, Xunwei
DMGF-STAN: Spatio-Temporal Attention Network and Dynamic Multi-Graph Fusion for Traffic Flow Prediction2025-99-023812/23/2025
The development of ITS is vital for decreasing traffic congestion and improving traffic scheduling procedures. Traffic prediction is a fundamental component of the development of ITS. Even though a lot of research has been done on modeling intricate spatiotemporal correlations in order to make accurate predictions, traditional methods primarily use predefined graph structures for feature extraction, which leaves out important correlations in the data and leads to limited prediction accuracy. The objective of the DMGF-STAN that we have recently created is to recognize both explicit and latent connections between time and space in traffic flow data that are subjected to various types of alterations. Our framework introduces a dynamic multi-graph expert selection module (DMGE) that combines a multi-graph information aggregation component with a sparse gating network to effectively model complex spatial dependencies. The Dynamic Multi-graph Gating (DMGG) module subsequently integrates
Cheng, Youcai, Bao, Shumei, Ke, Yuhao, Hu, Yongkang
Trajectory Reconstruction at Signalized Intersections in a Vehicle-to-Infrastructure Environment2025-99-021712/23/2025
Vehicle trajectories encapsulate critical spatial-temporal information essential for traffic state estimation, congestion analysis, and operational parameter optimization. In a Vehicle-to-Infrastructure (V2I) environment, connected automated vehicles (CAVs) not only continuously transmit their own real-time trajectory data but also utilize onboard sensors to perceive and estimate the motion states of surrounding regular vehicles (RVs) within a defined communication range. These multi-source data streams, when integrated with fixed infrastructure-based detectors such as speed cameras at intersections, create a robust foundation for reconstructing full-sample vehicle trajectories, thereby addressing data sparsity issues caused by incomplete CAV penetration. Building upon classical car-following (CF) theory, this study introduces a novel trajectory reconstruction framework that fuses CAV-generated trajectories and infrastructure-based speed detection data. The proposed method specifically
Bai, Wei, Fu, Chengxin, Yao, Zhihong
Study on the Evaluation Indicator System of Intelligent Capacity Optimization on Highways2025-99-025312/23/2025
Intelligent capacity optimization of highways could realize intelligent enhancement of traffic capacity by optimizing traffic management, improving traffic efficiency and enhancing system synergy without significantly increasing physical lanes. However, there was a lack of a unified and perfect index system to scientifically evaluate the effectiveness of such projects. This paper analyzed the basic theory, evaluation indicator structure and system, and puts forward seven key evaluation dimensions, which including traffic efficiency enhancement, traffic safety improvement, economic and cost-benefit, environmental impacts, technology application and innovation, system reliability and resilience, and service experience. This paper screened the specific evaluation indexes of the seven dimensions and proposes the hierarchical structure of the index system and the weight determination method. This paper constructed a comprehensive, multi-dimensional evaluation index system for highway smart
Che, Xiaolin, Li, Weichen, Zhu, Lili, Li, Xin, Wang, Lin
2025 International Conference on Transportation Infrastructure and Engineering (ICTIE 2025)2025-99-030012/17/2025
TOC
Optimization of Real-Time Traffic Flow Detection Methods Based on Sensor Networks2025-99-033712/17/2025
As modern society develops rapidly, people’s requests for traffic convenience and traffic safety become greater and greater, and it is essential to eliminate traffic congestion and traffic accident to sustainable development of urban areas. Therefore, this paper brings forward novel solution based on hybrid sensor networks to observe the status of traffic in road networks in order to alleviate traffic jam and prevent traffic accident. With the collection of precise traffic flow information at the time, it realizes traffic flow control at crossroads, gives warning in advance with the congestion or accident. We carried out a bunch of simulation experiments in succession, the main discoveries are as follows. a. The energy consumption is great reduced under the sensor deployment rate between 1:50–1:60 (sensor : vehicles). b.The sampling rates can keep a very high level of precise and efficiency under the appropriate range between 1:50–1:60 (sensor : vehicles).The critical segments of
Wang, Xinhai
Basic Diagram Model of Mixed Traffic Flow Considering Buses2025-99-030912/17/2025
The emergence of connected and autonomous vehicle (CAV) technologies has ushered in a new era of mixed traffic flow, where CAVs will coexist with human-driven vehicles (HDVs) for the foreseeable future. To investigate the fundamental relationships among flow, density, and speed in this heterogeneous traffic environment, this study develops a comprehensive analytical framework that explicitly accounts for the impact of bus integration in mixed traffic streams. The study initially identifies vehicle classifications and their respective distribution ratios within heterogeneous traffic streams. A fundamental graphical representation of mixed traffic patterns is established, followed by a comprehensive sensitivity evaluation focusing on free-flow velocity parameters within the proposed framework. Subsequently, a micro-level simulation platform is developed utilizing SUMO software. Research outcomes reveal a favorable link between the percentage of integrated self-driving cars and
Xiao, Yujie, Chen, Xiufeng, Wang, Meng, Xu, Ying
A Cooperative Conflict-Free Algorithm for Autonomous Vehicles at Unsignalized Intersections Based on Incremental Learning Monte Carlo Tree Search2025-99-043412/10/2025
The paper examines how connected automated vehicles (CAVs) can navigate unsignalized intersections—especially those where major roads differ significantly from minor roads. The proposed method uses an improved incremental learning Monte Carlo Tree Search to quickly determine an optimal passing order for vehicles, adjusting in real time based on road conditions and vehicle states. Numerical experiments demonstrate that this approach achieves conflict-free, real-time cooperative, reducing average delays significantly compared to traditional traffic signal control. Compared to fully-actuated signal control, the proposed method achieves average delay reductions of 19.92s, 16.46s, and 15.47s for CAVs across varying demand patterns. The practical application of this research lies in its potential to enhance traffic efficiency in urban areas by replacing traditional signal-based control with intelligent, autonomous intersection management. This could lead to reduced congestion, lower fuel
Xue, Yongjie, Gao, Feng, Feng, Qiang, Cui, Shaohua
Impact of Maximum Platoon Size of Connected and Automated Vehicles on Safety of On-Ramp Mixed Traffic2025-99-045412/10/2025
This study investigates how the maximum platoon size (MaxPS) of Connected and Automated Vehicles (CAVs) influences traffic safety within mixed traffic environment on freeway on-ramps. Built upon the SUMO simulation framework, a mixed traffic flow model involving CAV platoons is developed for on-ramp scenarios. This paper examines traffic conditions under varying on-ramp inflow volumes and evaluates upstream speed fluctuations in the merging area. Safety indicators such as Time Exposed Time-to-Collision (TET) and Time-Integrated time-to-Collision (TIT) are employed to assess overall traffic safety. Additionally, collision types are analyzed. Results indicate that under low on-ramp inflow conditions, a moderate MaxPS with low CAV penetration rates significantly enhances safety, whereas a larger MaxPS is preferable with high penetration rates. Under moderate on-ramp inflow, limiting the CAV MaxPS to 2 reduces conflicts. As on-ramp inflow increases further, a MaxPS of 1 or 2 leads to a
Pan, Gongyu, Huang, Yujie, Xie, Junping
CAV Optimal Scheduling Method for Unsignalized Intersections Based on Rolling Ant Colony Algorithm2025-99-042812/10/2025
With the development of intelligent networking technology and autonomous driving technology, how to efficiently and safely schedule intelligent networked autonomous vehicles at signalless intersections has become a research hotspot in traffic management. Based on this, this article first designs an objective function that considers both intersection traffic efficiency and intersection traffic safety, taking into account constraints such as safe distance, speed, acceleration, etc., and constructs a signal free intersection CAV traffic scheduling model. On this basis, a model solving algorithm based on rolling ant colony algorithm is proposed. Simulation experiments show that compared with typical signal control methods, this method can significantly improve intersection traffic efficiency and reduce the number of conflicts.
Zhao, Yingjie, Liu, Xiaoming, Ma, Zechao, Wang, Yuanrong
Fuel-Efficient Large-Spacing Truck Platooning in Windy Environments Considering Longitudinal and Lateral Motions2025-99-041912/10/2025
Large-spacing truck platooning offers a balance between operational safety and fuel savings. To enhance its performance in windy environments, this study designs a control system integrating both longitudinal and lateral motions. The longitudinal control module regulates the inter-vehicle spacing within a desired range while generating a fuel-optimal torque profile by minimizing unnecessary decelerations and accelerations. The lateral control module ensures lateral stability and maintains alignment between the trucks to achieve the expected fuel savings. A two-truck platoon is simulated with a 3-sec time gap under varying wind conditions, using experimental data from the on-road cooperative truck platooning trials conducted in Canada. The control system effectively remains spacing errors within the preset safety buffer and limits lateral offsets to 0.07 m, ensuring safe and stable platooning in windy environments. Additionally, the smoother speed profiles and reduced lateral offsets
Jiang, Luo, Shahbakhti, Mahdi
Real-time Traffic Congestion Prediction: A Novel Online Learning Method with Multi-Head Attention Mechanism and LSTM-Based Integrated Learning2025-99-042012/10/2025
Real-time traffic congestion prediction is essential for proactive traffic management, as it enhances the responsiveness of traffic systems, including route guidance, control, and enforcement. However, the heavy reliance on extensive historical data presents a significant challenge for real-time model updates. To overcome this limitation, this study proposes an advanced online learning framework that integrates a multi-head attention mechanism with LSTM-based ensemble learning. This approach incorporates traffic congestion factors as input features and employs average delay per kilometer as the predictive output. The experimental findings indicate that: 1) the proposed approach successfully enables real-time traffic congestion forecasting, and 2) it demonstrates strong adaptability in dynamic traffic environments.
Fu, Chuanyun, Liu, Jiaming, Lu, Zhaoyou, Wumaierjiang, Ayinigeer, Liu, Huahua, Bai, Wei
Minimum Requirements to Support Traffic Signal Priority and PreemptionJ2945/B_202511 (Current)11/20/2025
When identifying the content of this report, one of the goals was that it supports a nationally interoperable method for connected vehicles (CVs) to make traffic signal priority and/or preemption (TSPP) requests of connected intersections (CIs) that support priority and/or preemption services. Given that, this report specifies the over-the-air (OTA) interface between CVs and CIs to support TSPP applications using updated revisions of the SAE J2735 Signal Request Message (SRM) and Signal Status Message (SSM) and the use of a Wireless Access in Vehicular Environments (WAVE) Service Advertisement (WSA) to advertise support for TSPP at a CI. Included are a concept of operations, requirements, design, and message structure definitions developed using a detailed systems engineering process.
Connected Transportation Interoperability Committee
Minimum Requirements to Support Traffic Signal Priority and Preemption™ ASN FileJ2945/BA_202511 (Current)11/20/2025
This Abstract Syntax Notation One (ASN.1) file precisely defines the structure of the data used to implement applications conformant to the SAE J2945/B Standard. Using this ASN.1 specification, a compiler tool can be used to produce encodings to enable applications to easily encode and decode the Signal Request Message (SRM) and Signal Status Message (SSM) messages, along with the Wireless Access in Vehicular Environments (WAVE) Service Advertisement (WSA) application data, defined in SAE J2945/B. Included in the ASN.1 file is the complete SAE J2735 ASN.1 along with the updates to the ASN.1 for the SRM and SSM defined in SAE J2945/B.
Connected Transportation Interoperability Committee
Minimum Requirements to Support Traffic Signal Priority and Preemption™ SET FileJ2945/BS_202511 (Current)11/20/2025
Included in this set are the SAE J2945/B Standard which specifies the over-the-air (OTA) interface between connected vehicles (CVs) and connected intersections (CIs) to support traffic signal priority and preemption (TSPP) applications. It specifies the use of updated revisions of the SAE J2735 Signal Request Message (SRM) and Signal Status Message (SSM) and the use of a Wireless Access in Vehicular Environments (WAVE) Service Advertisement (WSA) to advertise support for TSPP at a CI. Included are a concept of operations, requirements, design, and the Abstract Syntax Notation One (ASN.1) message format, data frame, and data element definitions. Also included is the Abstract Syntax Notation One (ASN.1) file precisely defines the structure of the data used to implement applications conformant to the SAE J2945/B Standard. Using this ASN.1 specification, a compiler tool can be used to produce encodings to enable applications to easily encode and decode the Signal Request Message (SRM) and
Connected Transportation Interoperability Committee
Research on Urban Traffic Congestion Optimisation Strategies Based on V2X Technology2025-99-011711/11/2025
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
Multimodal Intelligent Transportation Data Fusion and Application Methods in Heavy-Duty Vehicles2025-99-013911/11/2025
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
An SVM-Based Approach to Develop a New Control Module for H2-Powered Engine using Driver Behaviour Classification2025-32-004211/03/2025
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 Lockers2025-99-005010/17/2025
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 Conditions2025-99-001710/17/2025
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 Intersections2025-99-001310/17/2025
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 Analysis2025-99-005810/17/2025
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 Scheme2025-99-002210/17/2025
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 Algorithm2025-99-004810/17/2025
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 Systems2025-99-000910/17/2025
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 Convoying2025-01-043309/16/2025
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 MobilityEPR202500905/20/2025
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 ManagementEPR202500805/20/2025
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 Editors12-08-02-001105/06/2025
Letter from the Guest Editors
Liang, Ci, Törngren, Martin
SAE TOMORROW TODAY - Scalable AV Deployment Starts with Fewer Close Calls1351305/01/2025
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 Model12-09-01-000104/24/2025
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–BiGRU12-09-01-000204/24/2025
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 YOLOv5s12-09-01-000304/24/2025
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 Scenarios2025-01-805104/01/2025
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 Analysis2025-01-810104/01/2025
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 Lights2025-01-803304/01/2025
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 Platooning2025-01-876904/01/2025
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 Models2025-01-820704/01/2025
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 Vehicles12-08-02-001203/28/2025
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 Integration2025-01-713803/19/2025
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 Scenario2025-01-720203/19/2025
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 Highway2025-01-717502/21/2025
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 Performance2025-01-715202/21/2025
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 Prediction2025-01-717402/21/2025
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 Area2025-01-720702/21/2025
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 Data2025-01-721702/21/2025
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 Automata2025-01-720402/21/2025
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 Effect2025-01-721502/21/2025
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
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