Browse Topic: Congestion

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Traffic Flow Prediction System Based on Deep Learning2026-99-05497/10/2026
Traffic flow prediction is of great significance for improving the operation efficiency of the transportation system, optimizing travel experience and reducing traffic congestion. Traditional traffic flow prediction methods are difficult to capture the spatio-temporal nonlinear characteristics of traffic flow due to its simple model and insufficient feature extraction ability. Therefore, an intelligent traffic flow prediction system based on deep learning is proposed, constructs a deep learning model based on graph convolution and fusion of attention mechanism LSTM. Based on this, a traffic flow prediction system is implemented. Experiments show that, on the PeMSD4 and PeMSD4 datasets, the error of the model in RMSE and Mae indicators is significantly reduced compared with the traditional methods, which provides an efficient solution for traffic flow prediction and congestion analysis, and has both theoretical innovation and engineering practical value.
Tang, Zhan, Lu, Xiaoyu, Yang, Nian, Xiang, Xiaohong, Hou, Xiang, Peng, Xiaoli
Research on Urban Bus Timetable Optimization Based on Deep Reinforcement Learning2026-99-05387/10/2026
Public transportation serves as a crucial component of urban mobility, contributing to the alleviation of urban congestion, reduction of travel expenses, and mitigation of air pollution. Nonetheless, the dynamic passenger demand and the complex traffic conditions render traditional bus timetables inadequate, leading to ineffective allocation of public transportation resources. Consequently, it is essential to create bus timetables that are responsive to actual traffic scenarios and fluctuating passenger demand. This study regards the bus timetable planning problem as a Markov decision-making process within a discrete time framework, proposing a deep reinforcement learning-based optimization model for bus timetables. In particular, the model is designed to account for both bus companies and passengers, incorporating a state space and reward calculation method that emphasizes passenger comfort. Then Deep Q-Network (DQN) methodology is employed to issue instructions on whether a bus departure at each time, and bus timetable is generated gradually over time. Experimental results indicate that the proposed approach significantly reduces bus travel costs and enhances the overall travel experience for passengers in comparison to traditional methods.
Xu, Jie, Xia, Dong, Yang, Jianxi, Wang, Bing
Advances in Research on Emergency Lane Activation Model for Highways2026-99-05427/10/2026
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Qin, Fengcai, Chen, Jianqiu, Che, Guoyan, Lou, Benxiao, Wang, Xiang, Ning, Longtang, Zhou, Shixuan, Zhang, Xiyuan, Bao, Chun, Gu, Guobin
Autonomous Aircraft Docking: A Vision-Based Approach to Aircraft Parking Systems2026-26-07866/1/2026
Automated aircraft parking systems enhance airport ground operations by enabling precise and autonomous docking of aircraft at gates. These systems reduce turnaround time, minimize human error, and optimize apron space through real-time object detection, obstacle avoidance, and dynamic path planning. Unlike fixed guided-path methods, the proposed system adapts to congestion and environmental conditions such as low visibility, ensuring safety and efficient maneuvering. Validation through simulation demonstrates the system’s potential to improve operational resilience and support scalable automation in future airport infrastructure.
Penugonda, Navya Sunaina, Ediga, Venkatadiwakar Goud
Preliminary Requirement Elicitation for Urban Air Mobility in India – A Mission-Based Approach2026-26-07776/1/2026
The present paper reports preliminary requirement elicitation for Urban Air Mobility (UAM) from Indian perspective. A mission based approach has been adopted to identify the stakeholders and their respective requirements during different phases of the mission profile. Non adherence to the requirements emerge as possible risks for the mission and need mitigation planning. Three UAM operations for Bengaluru city viz. cargo delivery, organ delivery and passenger transport using UAM vehicle are elaborated. Stakeholders for these missions are identified and associated requirements are reported. For the cargo delivery mission, a detailed analysis is carried out to emphasis on how the India specific statutory restrictions of abiding by the red zone restrictions levied by DGCA impacts the de-tour factor and flight time. A qualitative assessment of the impact of these mission based requirements on the UAM vehicle design is presented.
DE, Manabendra M., Hebbar, Archana, Henry, Devanandham
Dynamic Modeling and Optimization of Expressway Weaving Areas Based on Genetic Algorithm and Cell Transmission Model2026-99-17325/22/2026
The traffic situation at urban expressway interchanges is really complicated in daily life. Cars change lanes very often, and problems from cars merging together are obvious. Traditional traffic models aren’t accurate enough when they try to predict what happens in these areas. To solve this, we suggest a better cellular transport model (CTM) that’s improved using genetic algorithms. It can describe and improve traffic conditions in a flexible way. We picked the interchange on Hohhot’s North Second Ring Expressway for our study. To get traffic data during rush hours—7 to 9 in the morning and 5 to 7 in the evening—we used a few methods together. There was video monitoring with tools like YOLOv8 and DeepSORT, people counting cars by hand, and also VISSIM simulation. The data we collected had things like how fast cars were going, how many were packed in an area, and how much traffic was moving through. With this info, we could see how traffic changes in different parts of the interchange and at different times. Traditional CTMs have their limits. The cells in them are stuck at the same length, their capacity never shifts, and ramps are updated the same way every time. So we fixed three things to make it better. We made the cell lengths change based on how heavy the traffic is. In areas where cars move freely, the cells are split into bigger chunks. But in busy interchange spots, they’re divided into smaller, more detailed pieces. We built a capacity model that can adjust. It uses something called a “bottleneck coefficient” to figure out how much capacity drops when cars merge and cause issues. By mixing virtual cells with real ones, the capacity of the ramp to handle the traffic is improved. This enabled the model to show how waiting in queues affects the number of cars on the road. Validation results show that compared with the traditional model, whose MAPE is 12.3%, the improved model has a mean absolute percentage error (MAPE) of 5.89%. Its root mean square error (RMSE) is 28.6 vehicles per hour. For the traditional model, this number is 67.2 vehicles per hour. So, the accuracy has improved by 57.4%. When we used this improved model to test a new exit plan, the results showed positive signs. During peak hours, the road’s capacity could go up by 16.75% in the morning and 26.02% in the evening. At the same time, serious traffic problems would drop by 52.51%. This shows that the better model can really help make good decisions when optimizing busy traffic areas where cars cross each other.
Duan, Xiangyu, Hu, Bing, Yan, Wang
Autonomous Navigation Strategy and Docking Mechanism for Intelligent Chassis Module of Split Flying Vehicle2026-99-07435/15/2026
In response to the problems of urban traffic congestion and the limited expansion of infrastructure, this paper conducts two core research focusing on the intelligent chassis system of split-type flying vehicle. Firstly, an autonomous navigation strategy for the intelligent chassis module is proposed based on chassis module Navigation 2 architecture, which fuses LIDAR and IMU positioning to plan paths using the A* global planning algorithm on a global cost map, and update the local cost map in real time with sensor data. It is orchestrated by the BT Navigator using a behavior tree, with failures handled by the Recovery Server, to achieve autonomous driving across multiple waypoints. In simulation and closed-field experiments, the system can stably reach the preset target points. The positioning accuracy and trajectory tracking performance can meet the design requirements. Secondly, a mechanical slide rail-type docking structure adapted to the split flying vehicle architecture is designed. Deformation analysis under the representative working conditions are evaluated through finite element software. The test results show that the maximum deformation of this docking structure under typical load is significantly lower than the docking tolerance and positioning repeatability requirements. The structural stiffness and stability meet the design indicators. The above work indicates that the proposed autonomous navigation strategy and the docking structure for the intelligent chassis can effectively support the modular operation of “air trunk & ground terminal” mode, providing a scientific basis for the functional integration and system reliability research of split-type flying vehicles.
Zhao, Wenyu, Shi, Qin, Jiang, Cong, He, Zejia
The Impact of Transition Window Duration on Nominal Transitions of Control in Conditional Automation09-14-01-00165/4/2026
The objective of this research was to understand the impact of transition window duration on success and performance during nominal transitions from conditional driving automation (SAE level 3). Because the driver can be disengaged from driving when conditional driving automation is engaged, the central challenge is how to safely transition from automated control to human control. Past research from the literature on Level 3 Automated Driving Systems (L3 ADS) has focused on safety-critical event responses (e.g., responding to a hazard) and on automation that operates at high speeds, which is not representative of the systems currently deployed that operate in lower-speed traffic jam situations [4, 5]. This article presents an analysis of data from several transition-of-control studies with conditional driving automation in a high-fidelity driving simulator. A range of transition window durations were compared, and different transition-of-control behaviors were coded from video data. Transition windows for 4, 6, 8, and 10 s conditions resulted in failures by the drivers to resume control. Success rates by condition were lowest with 4 s transition windows, but also lower with 10 s windows, compared to 6 s, 8 s, or 15 s windows (potential explanations appear in the discussion). Time to first glance back at the forward road and time to first-hand on the steering wheel were predictors of transition of control success across all transition windows. Survival analyses showed that drivers needed to begin the transition process within a few seconds to make successful transitions, even with longer transition windows. The results demonstrate the impact of different transition window durations on transition of control and provide unique insights into the factors influencing transition success in situations representative of those happening on the road now. These results help shape understanding of the requisite time needed for safe transition from automated to manual control and speak to the design recommendations for human–automation interactions.
Gaspar, John, Ahmad, Omar, Schwarz, Chris, Fincannon, Thomas, Jerome, Christian
SAE TOMORROW TODAY - How Kansas is Reshaping Multimodal Transportation135624/7/2026
What does it take to build a multimodal transportation network that actually works for rural communities, growing regions, and everyone in between? In Kansas, that question is critical as the Kansas City Chiefs plan their stadium move and the 2026 FIFA World Cup comes to town. Listen in as we sit down with Matt Messina, Chief of Multimodal Transportation at the Kansas Department of Transportation (KDOT), to explore how Kansas is prioritizing transit solutions for upcoming projects and how community input shapes decisions. It's an insightful journey into the challenges and opportunities of public transit, pedestrian infrastructure, and the future of mobility. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your preferred podcasting platform. Follow SAE on LinkedIn, Instagram, Facebook, X, and YouTube. Follow host Grayson Brulte on LinkedIn, X, and Instagram.
Patterson, Lori
Revolutionizing Urban Mobility in India: The Emergence of the 2-3 Wheel Combi Vehicle and the L2-5 Regulatory Framework2026-26-01871/16/2026
Growing population in Indian cities has led to packed roads. People need a quick option to commute for both personal trips and business needs. The 2-3 Wheel Combination Vehicle is a new, modular solution that switches between a two-wheeler (2W) and a three-wheeler (3W). Hero has designed SURGE S32 to be a sustainable and flexible transportation option. It is world’s first class changing vehicle. The idea is to use a single vehicle for zipping through city traffic, making deliveries, or earning an income. Manufactured to deal with the challenges of modern life, this dual-battery convertible vehicle can easily transform from a two-wheeler to a three-wheeler and vice versa within three minutes. The Surge S32 is a versatile vehicle that replaces the need for multiple specialised vehicles. By lowering the number of vehicles on the road, it decreases road congestion, reduces emissions, and improves livelihoods. It powers by electricity, ensuring sustainability in all aspects. The current Central Motor Vehicle Rules (CMVR) lacked a clause for something this new. A vehicle was either a two-wheeler or a three-wheeler, not both. You might wonder, "How do you even make rules for a vehicle that embodies dual characteristics?” For a new idea to hit the streets, you need new rules. The Ministry of Road Transport & Highways (MoRTH) under the Central Motor Vehicle Rules (CMVR) created a new L2-5 category and established a unique regulatory framework. Developed with our best safety experts and industry minds, this new framework is the foundation that allows these vehicles to exist legally and safely. It shows that India is not just building the vehicles of the future, but the rules for them too. CMVR approval framework for the L2-5 category has made it mandatory for these vehicles to comply with newly formulated standard AIS-177, which was developed carefully with pilot testing and stakeholders inputs. This category balances innovation with practical implementation. It also harmonizes taxation and insurance, while integrating seamlessly with the National Vehicle Registration Portal of India (VAHAN) system at the Regional Transport Office (RTO) level for streamlined registration and dual-configuration. The 2-3W Combi Vehicle establishes a new standard for sustainable urban mobility in India by integrating versatility, safety, and regulatory foresight.
Ali Khan, Feroz, Gupta, Eshan
Establishing a Scalable Virtual Framework for ADAS Validation Traffic Jam Pilot with Real-World Scene Integration2026-26-05061/16/2026
Advanced Driver Assistance Systems (ADAS) are instrumental in improving road safety and minimizing traffic-related incidents. However, their development and validation processes are resource-intensive, requiring substantial time, cost, and domain-specific expertise. Moreover, real-world testing introduces significant safety challenges. To address these issues, virtual simulation platforms offer high-fidelity environments for the secure and efficient testing of ADAS functions. This research presents a virtual validation framework for a Traffic Jam Pilot (TJP) algorithm utilizing such simulators. The framework features detailed models of camera and radar sensors, capturing essential parameters like detection range and field of view, alongside a vehicle plant model and road infrastructure modeling that includes elements such as curvature, slope, banking angles, and varying lane widths. A perception stack is developed using synthetic sensor data and is integrated with the TJP control algorithm to manage the Ego vehicle in dynamic traffic scenarios, including stop-and-go and cut-in maneuvers. The approach enables comprehensive system evaluation in a risk-free environment, significantly reducing development complexity and cost. A key contribution of this work is the generation of virtual test scenarios derived from real-world driving data, allowing for direct, scenario-specific comparisons between simulation outputs and physical-world behavior. The findings underscore the potential of simulation-based validation as a scalable and reliable pathway toward deploying ADAS functions with improved safety and efficiency.
Agrawal, Mridul, Ithape, Avinash, Sharma, Prashant, Trivedi, Abhishek
Dynamic Integration of Bilateral and Adaptive Cruise Control for Intuitive Vehicle Behavior and Congestion Mitigations2026-26-00371/16/2026
Bilateral Cruise Control (BCC) is a new concept that has been shown to reduce traffic congestion and enhance fuel/energy efficiency compared to Adaptive Cruise Control (ACC). BCC considers both lead and trailing vehicles to determine the ego vehicle’s acceleration, effectively damping any disturbance down the vehicle string and reducing possibilities for congestion. Despite the advantages demonstrated with BCC, one major limitation is its non-intuitive behavior, which stems from the fact that the BCC reacts not just to the lead vehicle but also to the trailing vehicle’s movement. This paper identifies key issues with BCC control and proposes solutions that retain the benefits of BCC while maintaining intuitive behavior. Specifically, a novel switching strategy is proposed to switch between ACC and BCC control modes by critically analyzing the driving conditions. The proposed system ensures acceptable driving behavior with predictable braking and acceleration, resulting in an intuitive and smooth traffic flow. Through seamless integration of ACC and BCC, the system can prevent traffic congestion problems while closely aligning with human driving expectations.
A, Arya, A, Aishwarya, D, Vishal Mitaran, M, Senthil Vel, Kumar, Vimal
Revolutionizing Mass Mobility: Sustainable, Safe, and Smart Transport Ecosystems2026-26-03601/16/2026
Mass Mobility Systems are critical for a sustainable and progressive society. As the world confronts the serious challenges of global warming and urban traffic congestion, efficient mass mobility solutions become critical in reducing carbon footprints and enabling equitable access. Advancement in mass mobility is not limited to electric buses alone but also includes innovations across conventional ICE vehicles, autonomous vehicles, trains, and other integrated transport networks. Safety and accessibility for users remain critical to the sustainability of future mass mobility concepts. The COVID-19 pandemic exposed vulnerabilities in public transportation, highlighting the urgent need for safer and more resilient systems. Road safety, passenger well-being, and hygienic standards must be deeply embedded into future mobility solutions. Furthermore, strong last-mile connectivity will be essential to ensure that mass mobility truly meets the needs of all citizens. An effective Mass Mobility System integrates various modes - buses, trains, and feeder services into a seamless travel experience. Cities like Singapore provide excellent examples of how integrated planning, smart scheduling, and multimodal connectivity can achieve this goal. The future of mass mobility will see an increasing adoption of EV technology, offering significant advantages in reducing emissions, noise, and vibration. However, ICE and hybrid solutions will continue to play a supporting role, especially in specific geographies and use cases. Additionally, the emergence of autonomous vehicles promises to reshape the landscape dramatically, creating an altogether new world of transport possibilities. Artificial Intelligence (AI) will become a key differentiator, enabling smarter route planning, personalized user experiences, and real-time adaptability. This paper explores how Mass Mobility Systems can evolve to balance sustainability, safety, technology, and inclusiveness, offering insights for city planners, mobility operators, and policymakers to create future-ready transport ecosystems.
Vasudevan, M, Kumar S, Ashok, Sridevi, M, Kumar, Rajiv, Kumar, Om
Distributed Robust Control of Heterogeneous Vehicular Platoons Based on IDM Following Model under Communication Delay2025-01-735012/31/2025
With the rapid development of automobile industrialization, the traffic environment is becoming increasingly complex, traffic congestion and road accidents are becoming critical, and the importance of Intelligent Transportation System (ITS) is increasingly prominent. In our research, for the problem of cooperative control of heterogeneous intelligent connected vehicle platoons under ITS considering communication delay. The proposed method integrates the nonlinear Intelligent Driver Model (IDM) and a spacing compensation mechanism, aiming to ensure that the platoon maintains structural stability in the presence of communication disturbances, while also enhancing the comfort and safety of following vehicles. Firstly, construct heterogeneous vehicle platoon system based on the third-order vehicle dynamics model, Predecessor-Leader-Following (PLF) communication topology, and the fixed time-distance strategy, while a nonlinear distributed controller integrating the IDM following behavior and the front-vehicle spacing compensation mechanism is designed to enhance the robustness of the system to delay disturbance. Secondly, leveraging the Lyapunov-Krasovskii functional framework in conjunction with the Moon inequality, an LMI-based stability condition is derived to ensure the uniform asymptotic stability of the system. The corresponding maximum admissible communication delay is then determined, followed by a detailed analysis of the system's string stability. Finally, comparative simulations are conducted on the MATLAB/Simulink platform. Simulation results verify that the proposed controller offers enhanced convergence speed, reduced acceleration variability, and improved suppression of spacing errors under communication delay disturbances. Compared to conventional linear controllers, it demonstrates markedly superior control performance and greater practical applicability. This method provides a valuable reference for the robust design and performance optimization of cooperative control systems for heterogeneous vehicle platoons under communication delay conditions.
Ye, Xin, Kang, Zhongping
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 global and local spatial feature extraction units-specifically the Adaptive Global Similarity Graph Convolution (AGS-GConv) module and Local Spatio-Temporal Attention Graph Convolution (LSTA-GConv) module-through integration of their outputs via dynamic gating fusion mechanisms. These processed features are then coupled with GRU-based codecs for comprehensive spatio-temporal feature learning, ultimately enabling future traffic state prediction. Our model outperforms the most advanced benchmark approaches in terms of prediction accuracy, according to comparative experiments conducted on real-world traffic datasets. The proposed framework can provide urban traffic management centers with short-term congestion forecasts and support dynamic signal cycle adjustments to reduce average delay during peak hours.
Cheng, Youcai, Bao, Shumei, Ke, Yuhao, Hu, Yongkang
Research on Passengers’ Willingness to Travel by Rail Transit Based on Objective Factors and Subjective Perceptions2025-99-025112/23/2025
In the context of the accelerating urbanization process, the problem of urban traffic congestion has become more severe. Rail transit, with its advantages of high efficiency, convenience, and environmental friendliness, has become a key force in alleviating urban traffic pressure. An in - depth exploration of passengers’ willingness to travel by rail transit is of great significance for optimizing urban traffic planning, improving the service quality of rail transit, and promoting the sustainable development of cities. This article starts from two dimensions: objective factors and passengers’ subjective perceptions, and comprehensively uses a variety of research methods to conduct an in - depth study on passengers’ willingness to travel by rail transit. In terms of objective factors, this article analyzes the differences in subjective perceptions among different passenger groups from the perspectives of gender, age, education level, and occupation. In terms of subjective perceptions, this article deeply analyzes the impact of passengers’ perceptions of the internal value, external value, and comfort of rail transit on their travel willingness.
Wang, Gang, Huang, Lei, Yang, Yihao
Urban Mobility: A Technological Contribution to the Modeling of a Conceptual Framework in a Cyber-Physical Environment for Reducing Traffic Congestion in the Expanded Downtown Area of São Paulo2025-36-009512/18/2025
Urban mobility is one of the major challenges faced by downtown areas in cities worldwide. Understanding how to improve it is essential, as it directly impacts the quality of life of people who live and work in these regions. There is an inconsistency in the fact that vehicles are produced with high efficiency and effectiveness, yet their purpose does not align with the daily commuting needs of large city centers, especially during peak travel times. The tools used in vehicle manufacturing, such as continuous improvement, lean manufacturing, continuous flow, and the theory of constraints, have been applied to balance transportation mode options. The analyzed scenarios aim to promote sustainable development and contribute to enhancing citizens’ quality of life. This study explores the hypothesis that if the conventional unit of measurement for vehicles, typically expressed in terms of vehicle volume or flow (vehicles/hour), were replaced by a metric based on the number of people transported per square meter per hour (persons/m2/h), the resulting analyses and proposed solutions would be more effective. The research yields two main conclusions in the city of São Paulo, Brazil. First, modifying the metric through technology and conceptual modeling within a cyber-physical environment leads to a more balanced decision-making process in traffic engineering management. Second, on São Paulo’s traditional avenues (Plano de Avenidas), the number of people transported is equivalent to reducing 29.5% of cars per hour during peak hours, which in turn lowers gas emissions from motor vehicles by 64.0%, all while maintaining the same transport capacity without altering the mode of transport. This shift results in several benefits: from a social perspective, it encourages vehicle sharing, both private and collective; from an environmental perspective, it reduces pollutant emissions by promoting the use of chartered buses and ultra-compact electric shared vehicles; and from an economic perspective, it decreases travel times while enabling variable and shared pricing models for vehicles using the city’s main roads.
Mello Filho, Luiz Vicente Figueira de, Canteras, Felippe Benavente, Meyer, Yuri Alexandre, Emiliano, William Machado, Júnior, Vitor Eduardo Molina, Gabriel, João Carlos, Iano, Yuzo
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 improvements in traffic flow and congestion measures.
Xiao, Yujie, Chen, Xiufeng, Wang, Meng, Xu, Ying
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 roadways are fitted with the radar sensors to accomplish not only reliable surveillance of traffic congestions but also timeearlies warning of traffic accidents as opposed to relying on the single-sensor network. As reflected in Fig. 17, the heterogeneous sensor network is more robust against sensor errors because of the complementarity effects without relying on individual sensors. The experimental results highlight the potential for hybrid sensing architecture for intelligent transportation systems(ITS) and provides a well-technical basis to alleviate urban traffic jam and improve transportation efficiency.
Wang, Xinhai
The Design and Simulation of Absolute Priority Bus Lane Incorporating Clearance Distance2025-99-036512/17/2025
To alleviate the congestion in general-purpose lanes while exclusive bus lanes remain idle, this paper proposes absolute-priority bus lane design with clearance distance. By establishing specific clearance distances and lane-changing rules, the proposed design method not only enhances overall road utilization efficiency but also ensures unimpaired bus speeds, thereby maintaining bus priority. The simulation is performed based on cellular automaton (CA) model and the results demonstrate that this design is effective when general-purpose lane traffic density ranges between 0-50 vehicles/km/lane, with greater improvements in other non-public vehicle speeds under longer bus dispatch intervals. These results provide a theoretical basis and practical guidance for future bus lane management.
Wei, Liying, Yang, Nan, Gao, Chang
Research on Modeling Method of Metro Passenger Flow Congestion Propagation with Dynamic Spatiotemporal Graph Convolutional Networks2025-99-045512/10/2025
With the rapid development of metro network operation, metro passenger flow congestion propagation occurs frequently. Accurately modeling passenger flow congestion propagation is crucial for alleviating metro passenger flow congestion and formulating corresponding control strategies. Traditional modeling methods struggle to effectively capture the complex spatiotemporal dependency relationships in metro networks. To improve the accuracy of congestion propagation modeling, this paper proposes a Dynamic Spatiotemporal Graph Convolutional Network (DSTGCN). The model integrates node attributes and temporal encoding through a dynamic adjacency matrix generation module, uses multi-head attention mechanisms to adaptively learn the time-varying propagation intensity between nodes, and combines static topology to construct dynamic adjacency matrices. A multi-scale spatiotemporal feature extraction module is designed, employing temporal convolution and spatial attention mechanisms to mine periodic and local correlation features, and aggregating historical states with different time lags through stacked graph convolutions. Experimental results on real metro datasets verify the effectiveness of each module of the model and reveal the inherent laws of passenger flow congestion propagation in metro networks. The research provides theoretical support for congestion early warning and dynamic regulation in metro network operation.
Chen, Beijia, Wang, Junhang, Shao, Jiayu
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
Research on Intelligent Control of Entrance Ramps for Networked Vehicles Based on Deep Reinforcement Learning2025-99-041412/10/2025
With the acceleration of urbanization, freeway traffic congestion is becoming increasingly serious, especially at entrance ramps, where the concentrated inflow of traffic often leads to increased traffic pressure on the mainline, affecting the overall access efficiency. In order to alleviate the ramp congestion problem, this paper proposes a deep reinforcement learning-based intelligent control method for entrance ramps of network-connected vehicles, which adopts Proximal Policy Optimization (PPO) algorithm to optimize the ramp vehicle flow and speed control strategy in real time by constructing a reinforcement learning control framework. In this paper, simulation experiments are conducted in different traffic density scenarios and compared with the traditional reinforcement learning algorithms DQN and A2C. The experimental results show that the PPO algorithm is able to converge quickly in low, medium and high traffic densities, significantly improve the cumulative reward value, and exhibit higher stability and superiority compared with other algorithms. The research in this paper not only provides an intelligent solution for ramp flow optimization, but also provides theoretical support and technical reference for the development of intelligent transportation system.
Yang, Liu
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 consumption, and improved traffic safety, making it particularly valuable for smart city initiatives and future CAV-dominated transportation systems.
Xue, Yongjie, Gao, Feng, Feng, Qiang, Cui, Shaohua
A Cosine-Based Dynamic Pushback Control Method Considering Airport Traffic Congestion2025-99-043112/10/2025
The rapid growth of the civil aviation industry has placed significant pressure on limited airport runway resources, leading to increased taxiing delays and excessive fuel consumption. These challenges are exacerbated by the constant rise in air traffic, which necessitates more efficient management of airport operations. To mitigate these issues, this study proposes a flexible management approach that categorizes busy periods based on airport traffic density, taking into account the fluctuating load demand at different times of the day. This approach ensures that resource allocation aligns with actual traffic conditions, optimizing operational efficiency. Additionally, leveraging the existing dynamic pushback control framework, this research develops a cosine-based dynamic pushback control model, which incorporates parking stand waiting penalties. This model aims to reduce departure costs by dynamically adjusting the pushback rate according to congestion levels. To further optimize the model, a novel genetic algorithm combined with continuous Markov chains is introduced. This algorithm is designed to determine the optimal control thresholds for different congestion levels throughout the day, ensuring that resources are used effectively while minimizing delays. Simulations conducted using actual operational data from Beijing Capital International Airport demonstrate the effectiveness of the proposed approach. Compared to the uncontrolled pushback method, the cosine-based dynamic pushback control method significantly reduces average taxiway waiting times by 42.92%. Furthermore, this method also reduces fuel consumption and emissions associated with taxiing delays, offering a more sustainable solution to managing airport congestion. This research provides a comprehensive strategy for improving airport operations in high-density air traffic environments.
Wu, Yingzi, Lian, Guan, Luo, Weizhen, Li, Wenyong, Zhao, Yeqi, Zhang, Hao
4D Trajectory Prediction and Vertical Guidance Algorithm for Fuel-Optimal2025-99-040412/10/2025
It is necessary to save fuel, shorten flight time and reduce cost in order to achieve maximum economic benefits. In this paper, based on the flight performance of aircraft, a database based on the optimal index of fuel saving is established, and the corresponding four dimension (4D) trajectory prediction information and vertical profile are generated on this basis. Finally, the vertical guidance simulation is carried out to verify the effectiveness of the algorithm. The algorithm can reduce air traffic congestion and improve airport operation efficiency while saving fuel.
Hui, Huihui, Li, Zhiyi
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 improves accessibility by 52.0% in the event-induced scenario. In addition, the experiments analysed the correlation between response rate and delay and showed that for every 10% increase in response rate, delay could be reduced by approximately 8.4 seconds. These findings suggest that V2X technology can significantly improve the operational efficiency of urban traffic and provide new solutions for traffic management.
Xi, Chaohu, Li, Jiasheng, Qu, Fengzhen, Liu, Hongjun, Liu, Xiaorui, Wang, Chunpeng
Analysis and Optimization of Subsidy Scheme for China Europe Express2025-99-012311/11/2025
The analysis of the current subsidy scheme for China Europe Express shows that its effectiveness is limited to lines starting from inland cities and lines with unsaturated demand. A bi-level subsidy optimization model was constructed and Tabu Search algorithm was applied to solve the optimization subsidy plan. The evaluation results of the optimization subsidy scheme indicate that it can more effectively increase the market share of CRE, regulate the balance of freight supply and demand to a certain extent, reduce capacity vacancies, and alleviate line congestion.
Mai, Yuanyuan, Tian, Chunlin
Advancing Vehicle-Level AC Thermal Management System Performance2025-28-043410/30/2025
Modern mobility solutions increasingly rely on HVAC systems due to growing transport demands, traffic congestion, and harsh environmental conditions. These systems, comprising a compressor, evaporator, condenser, and thermal expansion valve, require adequate airflow for optimal performance. Insufficient airflow, caused by factors like undersized ducts, improper fan settings, clogged filters, or high static pressures from duct restrictions, significantly hinders cooling capacity. The objective of this study is to develop a predictive model for passenger vehicle AC system performance under controlled environmental conditions. Discrepancies between predicted and desired performance will trigger a structured problem-solving process involving iterative testing, root cause analysis, and the development of corrective measures. The improvements will be focused on the vehicle-level HVAC design, adhering to customer specifications. This research will also establish an experimental validation protocol and offer recommendations for process optimization to reduce prototype/tooling costs in future projects.
Meena, Avadhesh Kumar, Agarwal, Roopak, Sharma, Kamal, Kishore, Kamal
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 study. The simulation experiment, conducted using Vissim, demonstrates that the implementation of Variable Guide Lanes leads to a 24.6% reduction in the total delay of the east entrance lane and enhances intersection capacity. This outcome substantiates the efficacy of the employed modeling method and the viability of establishing Variable Guide Lanes.
Zhang, Qinan, Zhang, Yongzhong
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 the potential to greatly optimize traffic flow, alleviate congestion, and boost the performance of urban transportation systems. The study concludes that signal control strategies based on DRL present a viable approach to tackling the issues associated with growing traffic and urban congestion.
Liu, Junao, Zuo, Tingyou
Analysis of the Correlation of Traffic Congestion Based on Multiple Factors: Taking the Roads in Beijing as an Example2025-99-007410/17/2025
In recent years, traffic issues in China have been emerging continuously, and the traffic congestion problem in Beijing is particularly prominent. We have explored the relationships between factors such as driving duration, road length, weather conditions in Beijing and traffic congestion. By using the Logistic Regression Model to analyze the relationships among driving duration, road length and traffic congestion, we found that both driving duration and road length are negatively correlated with traffic congestion. The model shows high accuracy and recall rate, demonstrating excellent performance. We also employed the Weighted Average Correlation Model to study the relationship between weather conditions and traffic congestion. The results indicate that traffic congestion is more severe in rain, snow, and foggy weather, while it is less serious in sunny and cloudy weather. Subsequently, through the noise level verification, the stability of the model was confirmed. At the same time, we used Shapley value analysis, Bootstrap confidence intervals, and hypothesis testing to examine the impacts of travel time and road length on traffic congestion. Additionally, we employed Cross-validation and Granger causality test to assess the influence of weather conditions on traffic congestion. The results of these analyses all verify the correctness of our conclusions. Finally, based on these results, we put forward suggestions regarding travel arrangements and the setting of traffic facilities. We Suggests guiding the public to rationally choose travel modes based on congestion and weather. Points out that logistic regression and weighted average models have limitations in capturing non-linear relationships and are sensitive to outliers.
Feng, Jiarui, Han, Xiran
The Analysis of Community Entrance Openness on Transportation and Emission: A Comparison of Different Automobiles2025-99-008110/17/2025
With the escalating rate of urbanization in China, the urban construction sector is encountering numerous challenges, including issues such as traffic congestion and environmental pollution. To enhance traffic efficiency and offer planning guidance for urban development, this study focuses on the fully or partial opening of community entrances. VISSIM is utilized to examine the community opening and simulate the internal road network, while also employing the SPSS data analysis tool for supplementary analysis. The objective of this method is to compare and analyze the traffic conditions and environmental impact of the community before and after its opening with different automobiles. Through the establishment of a comprehensive evaluation system, the study calculates and analyzes the average vehicle speed, noise levels, energy consumption, and carbon dioxide emissions before and after the opening of the community. Finally, several recommendations are proposed to enhance community engagement in order to effectively address the challenges posed by urbanization.
Li, Mengyuan, Zhuo, Chenxu, Xiong, Simin, Xu, Lihao
Reducing Traffic Congestion with Reinforcement Learning-Driven Signal Control Systems2025-01-02837/2/2025
The road network is a critical component of modern urban mobility systems, with signalized traffic intersections playing a pivotal role. Traditionally, traffic light phase timings and durations at intersections are designed by transportation engineers using historical traffic data. Some modern intersections employ trigger-based mechanisms to improve traffic flow; however, these systems often lack global awareness of traffic conditions across multiple intersections within a network. With the increasing availability of traffic data and advancements in machine learning, traffic light systems can be enhanced by modeling them as agents operating in an environment. This paper proposes a Reinforcement Learning (RL) based approach for multi-agent traffic light systems within a simulation environment. The simulation is calibrated using real-world traffic data, enabling RL agents to learn effective control strategies based on realistic scenarios. A key advantage of using a calibrated simulation is that RL agents can experiment with different control actions without compromising safety in real-world traffic. The proposed system demonstrates that RL agents can coordinate and learn optimal policies, effectively reducing overall vehicle wait times in heavy urban traffic scenarios. The reward function is carefully designed to minimize traffic congestion by reducing vehicle wait times. A comparative study between static phase timings, currently used by conventional controllers, and the RL-based policy highlights significant reductions in overall wait times. Additionally, this simulation-based approach allows RL agents to be deployed in real-time, continuously learning and adapting to live traffic data. By implementing updated control policies for traffic light phase timings, the system can effectively reduce congestion and improve traffic flow across the network.
Kalra, Vikhyat, Tulpule, Punit, Giuliani, Pio Michele
Variable Speed Limit Control Based on Deep Deterministic Policy Gradient under Mixed Traffic Environment12-09-01-00045/6/2025
The existing variable speed limit (VSL) control strategies rely on variable message signs, leading to slow response times and sensitivity to driver compliance. These methods struggle to adapt to environments where both connected automated vehicles (CAVs) and manual vehicles coexist. This article proposes a VSL control strategy using the deep deterministic policy gradient (DDPG) algorithm to optimize travel time, reduce collision risks, and minimize energy consumption. The algorithm leverages real-time traffic data and prior speed limits to generate new control actions. A reward function is designed within a DDPG-based actor-critic framework to determine optimal speed limits. The proposed strategy was tested in two scenarios and compared against no-control, rule-based control, and DDQN-based control methods. The simulation results indicate that the proposed control strategy outperforms existing approaches in terms of improving TTS (total time spent), enhancing the throughput efficiency of the bottleneck area, and reducing the spatial and temporal extent of traffic congestion. Compared to the suboptimal DDQN-based VSL control, the proposed strategy improves TTS by 9.3% in Scenario 1 and by 11% in Scenario 2. The sensitivity analysis shows that the proposed control strategy improves performance as the penetration rate of CAVs increases. However, when the penetration rate reaches a certain threshold, the potential for further optimization becomes limited. Furthermore, higher time-to-collision (TTC) values, influenced by the reward function r 2, enhance traffic safety.
Ding, Xibin, Zhang, Zhaolei, Liu, Zhizhen, Tang, Feng
A Novel Model for Short-Term Traffic Flow Prediction on Highways Utilizing Multi-Head Attention–BiLSTM–BiGRU12-09-01-00024/24/2025
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 studies. This not only proves the effectiveness of the BO strategy but also highlights the advantages of the proposed model in improving predictive accuracy. These results indicate that our model can effectively handle the complexity of highway traffic flows and provide more accurate traffic flow predictions, thereby significantly improving the operational efficiency of highway traffic.
Chen, Peng, Wang, Tao, Ma, Changxi, Chen, Jun
Effect of Aftermarket Modifications on ADAS Functionality – 2021 Ford F-150 Light Vehicle2025-01-80574/1/2025
Both automotive aftermarket vehicle modifications and Advanced Driver Assistance Systems (ADAS) are growing. However, there is very little information available in the public domain about the effect of aftermarket modifications on ADAS functionality. To address this deficiency, a research study was previously performed in which a 2022 Chevrolet Silverado 1500 light truck was tested in four different hardware configurations. These included stock as well as three typical aftermarket configurations comprised of increased tire diameters, a suspension level kit, and two different suspension lift kits. Physical tests were carried out to investigate ADAS performance of lane keeping, crash imminent braking, traffic jam assist, blind spot detection, and rear cross traffic alert systems. The results of the Silverado study showed that the ADAS functionality of that vehicle was not significantly altered by aftermarket modifications. To determine if the results of the Silverado study were significant only for that particular light truck, or if they could be generalized to other light vehicles, a similar study was performed on a 2021 Ford F-150 light truck. Aftermarket modifications applied to the F-150 were like those employed on the Silverado, except a suspension lower kit was added to the hardware configurations tested. Physical test procedures were carried over into the F-150 study, apart from a pedestrian interaction test that was added. The results of the F-150 study are analogous to the results of the Silverado study, in that the ADAS performance of the modified F-150 was found to be comparable to stock for all aftermarket hardware configurations tested. However, the average values for certain ADAS performance metrics differed in some F-150 modified configurations compared to stock. In this work, the results of the F-150 ADAS functionality testing is summarized, and a statistical analysis of the test data collected is presented.
Bastiaan, Jennifer, Muller, Mike, Morales, Luis
Optimizing Electric Vehicle Charging Station Locations Based on Urban Road Network Congestion Tracing2025-01-72163/19/2025
Exhaust emissions from congested road segments constitute a significant source of urban air pollution. Resolving traffic congestion throughout the road network presents considerable challenges. However, alleviating tailpipe emissions on congested roads can be achieved by increasing the proportion of electric vehicles (EVs) in the traffic flow. Therefore, we propose a method for optimizing the layout of EV charging stations based on urban road networks congestion tracing. This method traces congestion sources through similarity between road networks, and evaluates the installation potential value of adjacent candidate installation points using the congestion contribution degree of the road segment as an indicator. The analysis is conducted on 100 routes within the Qinhuai district of Nanjing city, using spatiotemporal similarity metrics. The utilization of point-of-interest and traffic data from online mapping sources overcomes the complexity of road network structure and the sparsity of data collection, making it suitable for large-scale road network research. Clustering the routes using the SimpleHerm algorithm reveals three meaningful clusters, indicating that the Qinhuai district predominantly consists of three main longitudinal routes with lateral road segments converging towards them. By using similarity measures to track congestion, four congestion originating routes were identified. Thus determine the installation potential value of candidate charging stations. The study findings indicate that while Qinhuai district currently possesses an adequate number of EV charging stations to meet basic demands, only 20% of these stations effectively guide EV traffic proportions on congested road segments.
Zeng, Wenyi, Jian, Lu, Hu, Xiaojian
Analysis of Crash Precursors under Different Traffic Flow Conditions with Enhanced Interpretability2025-01-71342/21/2025
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) combined with the XGBoost model to select key features, and interpreted the impact of these features on crash occurrence using the SHapley Additive exPlanations (SHAP) value. Finally, we employed a logistic regression model to evaluate the selected important features, reflecting the relationship between key features and crashes from a broad perspective. The results indicate significant differences in the main factors affecting crashes under different traffic conditions. In the free flow state, the relationship between the variability of flow and speed and crash occurrence is more significant. In the transitional state, the differences in vehicle distribution and speed across lanes significantly affect crashes; while in the congested flow state, the standard deviation of speeds among upstream lanes and the average flow of downstream have a greater impact on crashes. This study not only enhances the interpretability of traffic crash analysis methods but also provides a basis for traffic management departments to formulate corresponding traffic safety strategies for different scenarios.
Zhou, Feixiang, Liu, Shaoweihua, Feng, Shi, Zhang, Yujie, Luo, Xi
Regulatory and Policy Framework for Urban Drone Logistics Systems2025-01-71442/21/2025
The swift and relentless progression of drone technology has ushered in novel opportunities within the realm of urban logistics, especially for the potential of drones to modify last-mile delivery and improve customer fulfillment through mobile application integration, offering the potential for delivery systems that are both efficient and environmentally sustainable. This development is not just a technological leap but a transformative shift in how goods are moved within urban spaces, potentially reducing traffic congestion and emissions from traditional vehicles. Nevertheless, the safety issues of drone flights in cities are becoming increasingly serious, and the accountability related to drone accidents is not clear, raising concerns in society regarding the use and safety of drones. Therefore, to fully utilize the potential of drones in urban logistics, the incorporation of drones into the urban airspace environment necessitates the establishment of a strong regulatory and policy framework, one that is capable of addressing a multitude of concerns, including safety, security, environmental impact, and social equity. This particular research paper undertakes a detailed examination of the existing regulatory landscape that pertains to urban drone logistics systems. It aims to pinpoint the primary challenges that are currently faced in this domain, and subsequently proposes a comprehensive policy framework designed to facilitate the effective implementation of urban drone logistics. To achieve a thorough understanding of the subject matter, this study meticulously reviews and analyzes more than 30 scholarly articles, industry reports, and policy documents, synthesizing the insights gained to provide a well-rounded perspective and policy framework suggestions on the topic.
Ma, Jie, Yang, Junjie, Diao, Weile, Du, Yiling, Chen, Weiqi
Study on Variable Speed Limit Control Strategy of Highway under Intelligent Connected Environment2025-01-71892/21/2025
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
An Integrated Lane Change Control Method for Freeway Segments with Lane Drop in a Connected Mixed Traffic Environment2025-01-71902/21/2025
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 problem before the work zone into merging traffic flow problem. The optimization model is developed based on the total trip time, density deviation, and total lane changes, with constraints on the cell reception capacity and lane changing ratios. Control parameters for lane change distribution are identified using genetic algorithms to solve the problem. For the decentralized control, a reinforcement learning solution is introduced which uses deep Q-networks (DQN) to improve lane-changing actions. The reward function takes into account the traffic efficiency and the impact of lane changing, and the continuous action space is discretized for application. The control mechanism is evaluated by the simulation of a work zone scenario that includes two restricted lanes on the Shanghai-Nanjing Expressway. It also shows that there is an improvement of 3% to 6% in traffic flow and velocity as compared to single-strategy approaches. The collaborative control strategy significantly enhances traffic flow and reduces congestion at bottlenecks and offers valuable information for future traffic control in CAV environments.
Ma, Yuheng, Guo, Xiucheng, Zhang, Yiming, Cao, Jieyu
Validating Human Factors-Based Classification Models for Safe and Efficient Mixed-Autonomy Interactions at Intersections Using a Virtual Simulator12-08-02-00192/12/2025
The introduction of autonomous vehicles (AVs) promises significant improvements to road safety and traffic congestion. However, mixed-autonomy traffic remains a major challenge as AVs are ill-suited to cooperate with human drivers in complex scenarios like intersection navigation. Specifically, human drivers use social cooperation and cues to navigate intersections while AVs rely on conservative driving behaviors that can lead to rear-end collisions, frustration from other road users, and inefficient travel. Using a virtual driving simulator, this study investigates the use of a human factors-informed cooperation model to reduce AV reliance on conservative driving behaviors. Four intersection scenarios, each involving a left-turning AV and a human driver proceeding straight, were designed to obfuscate the right-of-way. The classification models were trained to predict the future priority-taking behavior of the human driver. Results indicate that AVs employing the human factors-informed model were able to navigate the mixed-autonomy intersection scenarios significantly more efficiently without affecting safety or rider comfort when compared to a baseline, cautious AV. Overall, this research contributes to improved mixed-autonomy interactions and provides evidence for the importance of cooperation between AVs and human-driven vehicles.
Ziraldo, Erika, Oliver, Michele
Technical Paper for 11 TH SAE India International Mobility Conference Relevance of Autonomous Vehicles in the Indian Context2024-28-012212/5/2024
Autonomous vehicles (AVs) are positioned to revolutionize transportation, by eliminating human intervention through the use of advanced sensors and algorithms, offering improved safety, efficiency, and convenience. In India, where rapid urbanization and traffic congestion present unique challenges, AVs still hold a significant promise. This technical paper discusses the relevance of autonomous vehicles in the Indian context and the challenges that need to be addressed before the widespread adoption of autonomous vehicles in India. These challenges include the lack of infrastructure, concerns regarding road safety, software vulnerabilities, adaptability of change towards autonomous vehicles, and the management of traffic. The paper also highlights the government's initiatives to encourage the development and adoption of autonomous vehicles, ideology behind the legal framework and the required changes in terms of technological advancements, and urban planning. In a brief manner, this paper tends to mark up the factors impacting the economic and environmental aspects. The aim of this technical paper is to discuss about the adaptability of autonomous vehicles on Indian roads. Our vision for India incorporates autonomous vehicles (AVs) within a comprehensive mobility ecosystem. By seamlessly integrating AVs with public transportation and addressing last-mile connectivity, India has the opportunity to revolutionize its urban mobility while prioritizing inclusivity and sustainability. The paper concludes that while the adoption of autonomous vehicles in India faces significant challenges, the country's vast potential for road travel and the growing demand for sustainable transportation solutions make it a promising market for autonomous vehicles.
Mishra, Adarsh, Mathur, Gaurav
Design and Development of a Micromobility Smart Electric Scooter to Revolutionize Urban Commuting2024-28-002110/17/2024
Urban areas around the world are facing an increasing number of issues, such as air pollution, parking shortages, traffic congestion and inadequate transit options, all of which necessitate innovative solutions. Lot of people are becoming interested in micromobility in urban areas as a replacement for quick excursions and round trips to get to or from transportation services (e.g., Offices, Institutions, Hospitals, Tourist spots, etc.). This research examines the critical role that micromobility plays, concentrating on the effectiveness of micromobility smart electric scooters in resolving urgent urban problems. Micromobility, which includes both human and electric-powered vehicles, presents a viable substitute for normal and short-distance urban commuting. This study presents a micromobility smart electric scooter that is portable and easy to operate, with the goal of transforming urban transportation. 3D model was designed using SOLIDWORKS and analyzed using ANSYS. For strength and lighter weight aluminium 6061 T6 alloy was used, the design also showcases collapsible seat integration and foldable handle. MATLAB Simulink was used to size the motor, battery and simulate the powertrain system. This scooter has a 500W hub motor and a 48V 20Ah Li-Ion Battery which makes commuting easy while taking into account issues like economic feasibility and environmental sustainability. The vehicle has a range between 26-30 km and maximum speed of 20 kmph. An MIT App Inventor application with Bluetooth connectivity is used to switch the powertrain using smart phone via connecting the vehicle through Bluetooth. By encouraging the use of these cutting-edge automobiles, communities may lessen traffic problems and create a more sustainable and livable urban environment.
Tappa, Raju, Singh Chowhan, Sri Aanshu, Shaik, Amjad, Maroju, Abhinav, Talluri, Srinivasa Rao
Optimal Eco-Driving with Infrastructure-to-Vehicle Communication for Speed Adaptation Based on Real-Time Dynamic Macroscopic Traffic Conditions2024-24-00259/18/2024
Eco-driving algorithms use the available information about traffic and route conditions to optimize the vehicle speed and achieve enhanced energy consumption while fulfilling a travel time constraint. Depending on what information is available, when it becomes accessible, and the level of automation of the vehicle, different energy savings can be achieved. In their basic formulation, eco-driving algorithms only leverage static information to evaluate the optimal speed, such as posted speed limits and location of stop signs. More advanced algorithms may also consider dynamic information, such as the speed of the preceding vehicle and Signal Phase and Timing of traffic lights, thus achieving higher energy efficiency. The objective of the proposed work is to develop an eco-driving algorithm that can optimize energy consumption by leveraging not only static route information, but also dynamic macroscopic traffic conditions, which are assumed to be available in real-time through Infrastructure-to-Vehicle communication. In this work, modeling and simulation are used to demonstrate the operation of the algorithm, which is implemented in the controller of an electric truck model. The speed optimization is formulated as an optimal control problem and solved as a hierarchical Model Predictive Control using Approximate Dynamic Programming. Macroscopic traffic congestion is modelled as a dynamic process using the Lighthill-Whitham-Richards model, which is a first-order hyperbolic partial differential equation that models the spatial and temporal evolution of traffic density. The results show that for heavy traffic conditions, the speed adaptation based on real-time macroscopic traffic conditions, that is, considering the characteristic macro scales of traffic congestion, can result in reduced energy consumption, while not affecting the total travel time.
Villani, Manfredi, Shiledar, Ankur, Block, Brian, Spano, Matteo, Rizzoni, Giorgio
Exploration of a Precise Traffic Restriction Policy on Urban River-Crossing Corridors: A Case Study in Changsha, China13-06-02-00108/8/2024
Many cities are built around rivers in the world, and the river-crossing corridors are often their traffic bottlenecks, leading to severe congestions. Changsha is a city divided into two parts by a river with eight river-crossing corridors in China. Aiming at this issue, take Changsha as an example, this study explores developing a precise traffic restriction policy on those river-crossing corridors. First, an investigation is conducted to collect traffic flow data of those corridors. It is found that those corridors generally have serious congestion at peak hours, but their congestion levels vary greatly by corridor and direction. Then, two Greenberg models are developed for the 4-lane and 6 & 8-lane corridors, respectively, to figure out their traffic flow features. Third, a precise traffic restriction policy that balances traffic flows in different corridors is proposed. It would restrict 10% of motor vehicles on those most congested corridors, and the restricted vehicles are proportionally diverted to the neighboring non-congested corridors by detour distances. Finally, based on the estimated Greenberg models, traffic speeds of those corridors after traffic restrictions are then predicted. It is found that traffic congestions in those congested corridors are greatly alleviated, and the average travel speed of all the corridors increases by 2.8 km/h at the AM peak and 4.5 km/h at the PM peak, respectively.
Liu, Chenhui, Luo, Qiuju, Wang, Xingyu
Lateral Platooning Control with Precise Path Tracking and Strong Disturbance Suppression Performance2024-01-50747/24/2024
There have been numerous studies on stable platooning, but almost all of them have been on the longitudinal stability problem, wherein, without sufficient longitudinal stability, traffic congestion might occur more frequently than in traffic consisting of manually driven vehicles. Failure to solve this problem would reduce the value of autonomous driving. Recently, some researchers have begun to tackle the lateral stability problem, anticipating shortened intervehicle distances in the future. Here, the intervehicle distance in a platoon should be shortened to improve transportation efficiency. However, if an obstacle to be avoided exists, the following vehicles might have difficulty finding it quickly enough if the preceding vehicle occludes it from their sensors. Also, longer platoons improve transportation efficiency because the number of gaps between platoons is reduced. Hence, in this study, the lateral stability of platoons consisting of autonomous vehicles was analyzed for not only determining how to track the preceding vehicle when there are lateral movements but also suppressing unintentional lateral movement caused by disturbances affecting the vehicles in the platoon. The analytical results indicate that it is not realistic to expect that a single gain controller can both track the reference path to avoid an obstacle and suppress the lateral movement caused by a disturbance to long platoons of 10 vehicles or more. On the basis of these results, a new lateral control strategy was developed that has both good tracking performance for avoiding obstacles and a capability of suppressing harmful movements of vehicles following the one affected by the disturbance. This strategy works by varying the gain depending on the estimated disturbance. A simulation was conducted to examine its effect on platoons consisting of 10 vehicles.
Kurishige, Masahiko
Runtime Safety Assurance of Autonomous Last-Mile Delivery Vehicles in Urban-like Environment2024-01-29917/2/2024
The conventional process of last-mile delivery logistics often leads to safety problems for road users and a high level of environmental pollution. Delivery drivers must deal with frequent stops, search for a convenient parking spot and sometimes navigate through the narrow streets causing traffic congestion and possibly safety issues for the ego vehicle as well as for other traffic participants. This process is not only time consuming but also environmentally impactful, especially in low-emission zones where prolonged vehicle idling can lead to air pollution and to high operational costs. To overcome these challenges, a reliable system is required that not only ensures the flexible, safe and smooth delivery of goods but also cuts the costs and meets the delivery target. In the dynamic landscape of last-mile delivery, LogiSmile, an EU project, introduced a solution to urban delivery challenges through an innovative cooperation between an Autonomous Hub Vehicle (AHV) and an Autonomous Delivery Device (ADD). This work addresses not only these challenges but also provides insight into a future where last-mile delivery is safer, more efficient and nature friendly. As a part of this project, an integrated safety system architecture has been developed for the AHV, featuring a dependability cage (DC) for onboard monitoring of a single autonomous vehicle and a remote command control center (CCC) for offboard monitoring of a fleet of autonomous vehicles. Operating at SAE levels 3/4 (SAE L3/4), the AHV incorporates a safety driver and a monitoring system, ensuring compliance with SAE guidelines. The DC enables safe transitions to degraded/ fail-safe driving modes in response to safety violations of the autonomous driving system (ADS), optimizing the vehicle's operational safety. Additionally, the CCC enhances autonomy by redundantly monitoring the fleet of vehicles via real-time sensor streams, also facilitating the communication with the ADD and the reconfiguration of the driving mode depending on the current road scenario. The project results were successfully demonstrated in Hamburg in 2022, showcasing the practical implementation of the developed safety architecture and the insights gained.
Aslam, Iqra, Aniculaesei, Adina, Buragohain, Abhishek, Zhang, Meng, Bamal, Daniel, Rausch, Andreas
Lane-Changing Behavior’s Impact on Platoon Dispersion in Mixed Autonomous Vehicle Traffic: Insights from Simulation12-08-02-00137/1/2024
This research investigates platoon dispersion characteristics in mixed-traffic flow of autonomous and human-driven vehicles. It presents a cellular automata-based platoon dispersion model. The study’s key findings are as follows: platoon dispersion initially increases and then decreases with the rise in autonomous vehicle proportions. When the autonomous vehicle proportion is approaching 100%, platoon dispersion descends rapidly and is completely eliminated while the proportion is 100%. Compared to platoon consisting entirely of human-driven vehicles, the peak value of standard deviation of vehicle speed is 1.71 times and the travel time drops by 38.19% when the proportion is 1. Moreover, the lane-changing behavior enhances platoon speed, acceleration, and space utilization at micro- and macrolevels by optimizing space resource allocation within the platoon. The study employs a two-lane mixed-flow platoon dispersion model that assumes uniform vehicle characteristics and prioritizes maximizing travel efficiency for autonomous vehicles. These findings bear significant implications for transportation planning and management, providing valuable insights for policymakers, transportation engineers, and researchers.
Lu, Ting, Liu, Chenghao, Lin, Sitong, Song, Wenjing
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