TGLD: A trust-aware game-theoretic lane-changing decision framework for automated vehicles in heterogeneous traffic [0.03%]
基于异构交通的自动化车辆信任感知博弈论车道变换决策框架(TGLD)
Jie Pan,Yongjun Shen,Chengyu He et al.
Jie Pan et al.
Automated vehicles (AVs) face a critical need to adopt socially compatible behaviors and cooperate with human-driven vehicles (HVs) in heterogeneous traffic environments. However, existing lane-changing decision frameworks for AVs rarely ac...
A spatially adaptive empirical Bayes framework with dynamic dispersion parameters for enhanced crash frequency prediction across rural highway networks [0.03%]
一种自适应经验贝叶斯框架在农村公路网络中增强碰撞频率预测中的应用
Seyed Ahmadreza Almasi,Jingzhen Yang
Seyed Ahmadreza Almasi
Traffic crashes often exhibit strong spatial dependence that is insufficiently captured by the Empirical Bayes (EB) method recommended in the Highway Safety Manual (HSM). This study proposes a Spatially Adaptive Empirical Bayes (SA-EB) fram...
Avoidance behavior and movement characteristics of pedestrians under mobile phone distraction [0.03%]
移动电话干扰下行人为避让行为及运动特征研究
Shuchao Cao,Yuhang Wang,Guang Zeng et al.
Shuchao Cao et al.
Smartphone use while walking has become increasingly prevalent, which significantly affects pedestrian safety and increases the risk of traffic accidents, especially in avoidance scenarios. Therefore, to reveal the avoidance mechanism and g...
Exploring spatiotemporal heterogeneity and nonlinear effects in electric vehicle crash risk prediction: A hybrid modeling approach [0.03%]
基于混合模型方法的电动汽车碰撞风险预测的空间异质性和非线性效应研究
Jianglin Lu,Chunjiao Dong,Xuedong Yan et al.
Jianglin Lu et al.
Electric vehicle (EV)-related risk and uncertainty pose critical challenges for urban traffic management. Fine-grained crash risk prediction at 1 km × 1 km and hour-of-day resolution remains difficult due to rapidly evolving, strongly spat...
Identifying environmental factors related to motorcyclist crash rates: variable selection using spatial Random Forest with network distance and barriers [0.03%]
基于网络距离和障碍的随机森林变量选择方法在识别与摩托车碰撞率相关的环境因素中的应用
Bimo Harya Tedjo,Pei-Fen Kuo,Febrian Fitryanik Susanta et al.
Bimo Harya Tedjo et al.
Motorcycle crashes remain a major global safety concern, particularly in many Asian countries where motorcycles are the primary mode of transportation. While previous studies have identified factors associated with motorcycle crashes using ...
Real-time highway crash prediction based on SHAP-RFECV and electronic toll collection data: a new feature selection strategy [0.03%]
基于SHAP-RFECV和电子不停车收费数据的高速公路实时碰撞预测：新的特征选择策略
Junda Huang,Pengpeng Xu,Kunhuo Huang et al.
Junda Huang et al.
Real-time crash prediction has emerged as a critical area of research in traffic safety, aiming to improve safety performance through proactive crash anticipation and management strategies. However, the accuracy and reliability of such pred...
Analysis of AV merging behavior in mixed traffic using large-scale AV driving datasets [0.03%]
基于大规模自动驾驶车辆驾驶数据的混行交通环境下自动驾驶车辆合并行为分析
Md Tanvir Ashraf,Kakan Dey
Md Tanvir Ashraf
Merging is one of the key maneuvers where autonomous vehicles (AVs) can perform significantly better in decision-making and execution than human-driven vehicles (HDVs). However, past studies have investigated AV merging in simulation enviro...
Considering spatial heterogeneity in modeling taxi speeding frequency: An advanced geographically weighted negative binomial regression approach [0.03%]
考虑空间异质性在出租车超速频率建模中的影响：一种先进的地理加权负二项回归方法
Lin Qu,Yue Zhou,Haibo Li et al.
Lin Qu et al.
Speeding is a major contributor to traffic crashes, posing significant risks to drivers, passengers, and other road users. This issue is exacerbated among urban taxi drivers due to factors such as time pressure for passenger pickups, potent...
Interaction patterns exploration and risk assessment within lane changing in terms of initial scenarios and evolution process [0.03%]
基于初始场景及演变过程的车道变更交互模式探索与风险评估
Junhua Wang,Bo Yao,Qiangqiang Shangguan et al.
Junhua Wang et al.
The lane-changing (LC) process is a sequential interaction process influenced by multiple factors, including the driver's decision-making, speed, traffic density, road conditions, and the behavior of surrounding vehicles. However, it remain...
Intelligent defensive driving for autonomous vehicles: Framework, strategy and verification [0.03%]
自主车辆的智能防御性驾驶：框架、策略与验证
Ting Zhang,Zixuan Wang,Hong Wang et al.
Ting Zhang et al.
As autonomous driving advances to higher levels, conventional decision-making algorithms for autonomous vehicles (AVs) often inadequately address long-tail issues composed of low-frequency, high-uncertainty, and extreme scenarios, leading t...