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

Research on Passengers’ Willingness to Travel by Rail Transit Based on Objective Factors and Subjective Perceptions

2025-99-0251

12/23/2025

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

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 Paulo

2025-36-0095

12/18/2025

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 Buses

2025-99-0309

12/17/2025

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

Optimization of Real-Time Traffic Flow Detection Methods Based on Sensor Networks

2025-99-0337

12/17/2025

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

The Design and Simulation of Absolute Priority Bus Lane Incorporating Clearance Distance

2025-99-0365

12/17/2025

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

A Cooperative Conflict-Free Algorithm for Autonomous Vehicles at Unsignalized Intersections Based on Incremental Learning Monte Carlo Tree Search

2025-99-0434

12/10/2025

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

Research on Intelligent Control of Entrance Ramps for Networked Vehicles Based on Deep Reinforcement Learning

2025-99-0414

12/10/2025

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

Yang, Liu

A Cosine-Based Dynamic Pushback Control Method Considering Airport Traffic Congestion

2025-99-0431

12/10/2025

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

Wu, Yingzi, Lian, Guan, Luo, Weizhen, Li, Wenyong, Zhao, Yeqi, Zhang, Hao

Real-time Traffic Congestion Prediction: A Novel Online Learning Method with Multi-Head Attention Mechanism and LSTM-Based Integrated Learning

2025-99-0420

12/10/2025

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

4D Trajectory Prediction and Vertical Guidance Algorithm for Fuel-Optimal

2025-99-0404

12/10/2025

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 Modeling Method of Metro Passenger Flow Congestion Propagation with Dynamic Spatiotemporal Graph Convolutional Networks

2025-99-0455

12/10/2025

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

Chen, Beijia, Wang, Junhang, Shao, Jiayu

Research on Urban Traffic Congestion Optimisation Strategies Based on V2X Technology

2025-99-0117

11/11/2025

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

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

Analysis and Optimization of Subsidy Scheme for China Europe Express

2025-99-0123

11/11/2025

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 Performance

2025-28-0434

10/30/2025

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

Meena, Avadhesh Kumar, Agarwal, Roopak, Sharma, Kamal, Kishore, Kamal

Analysis of the Correlation of Traffic Congestion Based on Multiple Factors: Taking the Roads in Beijing as an Example

2025-99-0074

10/17/2025

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

Feng, Jiarui, Han, Xiran

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

2025-99-0013

10/17/2025

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

Zhang, Qinan, Zhang, Yongzhong

The Analysis of Community Entrance Openness on Transportation and Emission: A Comparison of Different Automobiles

2025-99-0081

10/17/2025

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

Li, Mengyuan, Zhuo, Chenxu, Xiong, Simin, Xu, Lihao

The Traffic Light Control Strategy at Intersections in Intelligent Transportation Systems

2025-99-0009

10/17/2025

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

Liu, Junao, Zuo, Tingyou

Reducing Traffic Congestion with Reinforcement Learning-Driven Signal Control Systems

2025-01-0283

07/02/2025

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

Kalra, Vikhyat, Tulpule, Punit, Giuliani, Pio Michele

Variable Speed Limit Control Based on Deep Deterministic Policy Gradient under Mixed Traffic Environment

12-09-01-0004

05/06/2025

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

Ding, Xibin, Zhang, Zhaolei, Liu, Zhizhen, Tang, Feng

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

12-09-01-0002

04/24/2025

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

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

Effect of Aftermarket Modifications on ADAS Functionality – 2021 Ford F-150 Light Vehicle

2025-01-8057

04/01/2025

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

Bastiaan, Jennifer, Muller, Mike, Morales, Luis

Optimizing Electric Vehicle Charging Station Locations Based on Urban Road Network Congestion Tracing

2025-01-7216

03/19/2025

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

Zeng, Wenyi, Jian, Lu, Hu, Xiaojian

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

2025-01-7134

02/21/2025

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

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

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

2025-01-7190

02/21/2025

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

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

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

2025-01-7189

02/21/2025

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

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

Regulatory and Policy Framework for Urban Drone Logistics Systems

2025-01-7144

02/21/2025

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

Ma, Jie, Yang, Junjie, Diao, Weile, Du, Yiling, Chen, Weiqi

Validating Human Factors-Based Classification Models for Safe and Efficient Mixed-Autonomy Interactions at Intersections Using a Virtual Simulator

12-08-02-0019

02/12/2025

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

Ziraldo, Erika, Oliver, Michele

Technical Paper for 11 ^TH SAE India International Mobility Conference Relevance of Autonomous Vehicles in the Indian Context

2024-28-0122

12/05/2024

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

Mishra, Adarsh, Mathur, Gaurav

Design and Development of a Micromobility Smart Electric Scooter to Revolutionize Urban Commuting

2024-28-0021

10/17/2024

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

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 Conditions

2024-24-0025

09/18/2024

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

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, China

13-06-02-0010

08/08/2024

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

Liu, Chenhui, Luo, Qiuju, Wang, Xingyu

Lateral Platooning Control with Precise Path Tracking and Strong Disturbance Suppression Performance

2024-01-5074

07/24/2024

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

Kurishige, Masahiko

Runtime Safety Assurance of Autonomous Last-Mile Delivery Vehicles in Urban-like Environment

2024-01-2991

07/02/2024

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

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 Simulation

12-08-02-0013

07/01/2024

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

Lu, Ting, Liu, Chenghao, Lin, Sitong, Song, Wenjing

Research on Speed Guidance Strategy at Continuous Signal Intersection Based on Vehicle–Road Coordination Technology

12-07-04-0023

04/13/2024

With the rapid growth of automobile ownership, traffic congestion has become a major concern at intersections. In order to alleviate the blockage of intersection traffic flow caused by signals, reduce the length of vehicle congestion and waiting time, and for improving the intersection access efficiency, therefore, this article proposes a vehicle speed guidance strategy based on the intersection signal change by combining the vehicle–road cooperative technology. The randomness of vehicle traveling speed in the road is being considered. According to the vehicle traveling speed, a speed guidance model is established under different conditions. Finally, the effectiveness of the speed guidance strategy in this article is verified through experimental simulation, and the benefits of the intersection with intelligent control and traditional control are compared, and the experimental results show that the intelligent control method in this article can effectively reduce vehicle congestion and

Li, Wenli, Li, An, Ren, Yongpeng, Wang, Kan

A Guidance Strategy for Parking Space of Autonomous Valet Parking with Cooperative Vehicle Infrastructure System

2024-01-2571

04/09/2024

Lots of invalid volume of traffic occurs as the vehicle repeatedly seeking the valid berth location, due to the existence of information islands, which reduces traffic efficiency, increases traffic congestion and the emission of pollutants. Aiming at eliminating the existence of information islands, this paper proposed a guidance strategy for parking space of autonomous valet parking, as taking merits of cooperative vehicle infrastructure system. The guidance strategy consists of an optimal guidance model and an adaptive ant-colony algorithm. Firstly, the optimal guidance model takes the minimum total parking cost as the objective function and the capacity of parking space as the constraints. Both the objective costs of parking and the subjective cost of customer are taken into accounts in the objective function. Secondly, comparing with traditional method, the adaptive ant-colony algorithm taking two improvements, in order to accelerate the convergence of the algorithm and avoid

Zhang, Zhouping, Liu, Weidong, Sun, Zhipeng, Zhu, Zuwei, Hu, Yiming, Zeng, Dequan

Development of Robust Traction Power Inverter Residing in Integrated Power Electronics for Ultium Electric Vehicles

2024-01-2211

04/09/2024

General Motors (GM) is working towards a future world of zero crashes, zero emissions and zero congestion. It’s “Ultium” platform has revolutionized electric vehicle drive units to provide versatile yet thrilling driving experience to the customers. Three variants of traction power inverter modules (TPIMs) including a dual channel inverter configuration are designed in collaboration with LG Magna e-Powertrain (LGM). These TPIMs are integrated with other power electronics components inside Integrated power electronics (IPE) to eliminate redundant high voltage connections and increase power density. The developed power module from LGM has used state-of-the art sintering technology and double-sided cooled structure to achieve industry leading performance and reliability. All the components are engineered with high level of integration skills to utilize across TPIM variants. Each component in the design is rigorously analyzed and tested from component to system levels to ensure high

Nassiri Bavili, Arash, Basher, Korobi, Chung, Sung, Alam, Khorshed, Lee, Jung-Gi, Choi, Hong Goo, Ko, Jin-young, Anwar, Mohammad

AI Model to Streamline Operations in a Robotic Warehouse

TBMG-50368

04/01/2024

Getting warehouse robots to and from their destinations efficiently while keeping them from crashing into each other is no easy task. It is such a complex problem that even the best path-finding algorithms struggle to keep up with the breakneck pace of e-commerce or manufacturing. In a sense, these robots are like cars trying to navigate a crowded city center. So, a group of MIT researchers who use AI to mitigate traffic congestion applied ideas from that domain to tackle this problem.

Design and Application of Electronic Toll Collection Special Situation Processing System

12-07-03-0018

04/01/2024

In 2018, the state explicitly proposed to “promote the cancellation of expressway toll stations at provincial boundaries.” Electronic toll collection (ETC) has become the main toll collection method on expressways. With the construction of ETC toll lanes, the proportion of ETC vehicles in the expressway traffic flow is increasing, and the rapid processing of vehicle special situations is facing challenges. At present, various provinces have adopted various methods to improve the traffic efficiency and transaction success rate of ETC from the issuance link, customer service link, and lane transaction link. According to statistical data, the average transaction success rate of ETC lane is not higher than 99% at present. As of October 2021, the number of ETC users nationwide has reached 256 million, and there are an average of 40 million ETC transactions per day across the network, that is, about 400,000 special cases need to be processed. How to efficiently deal with special vehicles in

Guo, Xiaohui, Guo, Fengbin, Zhang, Mengwei, Zheng, Fengfei, Liu, Chunya

A Smart Transportation System for Existing Vehicles and Roads Infrastructure to Ease Traffic and Toll Problems in India

2024-26-0181

01/16/2024

India is a highly populous country. The traffic problems faced by the people here are not uncommon. The increase in traffic leads to increase in accidents, pollution, inconvenience and frustration. It also comes with costs of additional fuel and time. Though public transport is extensively available in India, still it isn't sufficient for the population of India. Especially in Metro cities, public transport services are often crowded. So, to travel peacefully people are opting for commuting in their own vehicles. And as a result, more vehicles are coming on roads. Other major reasons for increasing traffic are lack of proper implementation of traffic rules and traffic signals out of sync. In addition to city traffic, congestion is also seen on highways, mainly at toll plazas. Although implementation of FASTag has reduced it to some extent, some toll plazas still face traffic congestion issues. This paper provides an idea to ease the traffic problems in the city and on the highways too

Jain, Pritesh

The Scope of Autonomous Vehicle Mobility with an Indian Context

2024-26-0087

01/16/2024

The advent of autonomous vehicles promises to revolutionize the transportation sector globally, and India, as one of the world's fastest-growing economies, stands at the forefront of this transformative technology. This paper presents a brief overview of the current state and potential implications of autonomous vehicles in the Indian context. With its densely populated cities, diverse traffic conditions, and complex road infrastructure, India presents unique challenges and opportunities for the deployment of autonomous vehicles. This technology has the potential to address critical issues such as road safety, congestion, and pollution while transforming the mobility experience for millions of people. However, several hurdles must be overcome to fully harness its benefits. The paper explores key considerations for the implementation of autonomous vehicles in India. These include adapting the technology to navigate chaotic traffic scenarios, addressing infrastructure limitations, and

Shetty, Sharan Harish

Energy Conservation and Protecting Global Health by Automated Corporate Transport Services Application/System

2024-26-0238

01/16/2024

In a world increasingly concerned with environmental sustainability and traffic congestion, the need for innovative solutions to address daily commuting challenges has become paramount. This paper presents an innovative concept for an application/system that seeks to revolutionize the way corporate employees commute to work. By harnessing the power of data and technology, this application aims to reduce pollution, traffic, and fuel consumption while promoting shared transportation solutions among employees. The paper discusses the key features and benefits of this proposed application and its potential to create a greener and more efficient corporate commuting ecosystem.

Thakare, Ashish

A Dynamic Variable Speed Limit Control Method for Dispersal of Queue Effects in Traffic Accidents

2023-01-7089

12/31/2023

To mitigate the repercussions arising from traffic accidents on highways and prevent the cascading effect of queued vehicles, a comprehensive model is devised. This model is built upon the foundation of a traffic accident impact determination framework, which considers the merging capacity at entry lanes, as well as a dynamic and adaptable variable speed limit model to dissipate queuing congestion. The objective is to promptly restore vehicle flow after accidents, thereby eradicating queueing effects in the affected zone. The efficacy of this approach has been validated using data from the Sutong Bridge accident, and its effectiveness in eliminating vehicle queues has been verified through simulation data in the SUMO platform. Analysis of average speeds before and after implementing varying speed limits reveals that the proposed method can significantly enhance overall traffic efficiency by 37%. Moreover, the model’s versatile parameters demonstrate good applicability, providing

Shi, XiaoMin, Wang, Bo, Ke, Guo

Artificial Bee Colony Algorithm for Smart Car Path Planning in Complex Terrain

2023-01-7062

12/20/2023

Smart cars or autonomous vehicles have garnered significant attention in recent years due to their potential to alleviate traffic congestion, enhance road safety, and improve fuel efficiency. However, effectively navigating through complex terrains requires the implementation of an efficient path planning algorithm. Traditional path planning algorithms often face limitations when confronted with intricate terrains. This study focuses on analyzing the path planning problem for intelligent vehicles in complex terrains by utilizing the optimization evaluation function of the artificial bee colony (ABC) algorithm. Additionally, the impact of turning radius at different speeds is considered during the planning process. The findings indicate that the optimal number of control points varies depending on mission requirements and terrain conditions, necessitating a comparison to obtain the optimal value. Generally, reducing the number of control points facilitates smoother paths, while

Li, DaPeng, Gu, Rui, Zheng, Yujun, Zuo, Songchen

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