Investigating the influence of connected information on driver behaviour: An analysis of pedestrian-vehicle conflicts in the middle section of urban road

被引:6
作者
Wang, Changshuai [1 ,2 ]
Shao, Yongcheng [1 ]
Zhu, Tong [3 ]
Xu, Chengcheng [1 ]
Zheng, Nan [2 ]
机构
[1] Southeast Univ, Sch Transportat, Nanjing 210096, Peoples R China
[2] Monash Univ, Dept Civil Engn, Clayton, Vic 3800, Australia
[3] Changan Univ, Coll Transportat Engn, Xian 710064, Peoples R China
基金
中国国家自然科学基金;
关键词
Pedestrian safety; Driving simulator test; Pedestrian-vehicle conflicts; Connected information; Visual blind area; Avoidance behavior; CROSSING BEHAVIOR; SPEED; WALKING; TIMES; SAFE;
D O I
10.1016/j.trf.2024.09.012
中图分类号
B849 [应用心理学];
学科分类号
040203 ;
摘要
Due to the vision obstruction caused by visually blind obstacles on urban roads, pedestrians suffer a high crash risk in pedestrian-vehicle conflicts. At the same time, the connected information can potentially improve driver behaviour with an earlier warning and driving aids. To ensure safer interactions between pedestrians and motor vehicles in the middle section of urban roads, this simulator-based study aims to investigate drivers' behaviour under the influence of connected information and predict crash risk during their interaction with pedestrians on urban roads, involving six conflict scenarios based on real-world traffic situations. The test employed a mixed experimental design, with connected information as the between-subject variable. A total of 70 participants were divided into a control group and an experimental group to complete the test. Results from linear mixed-effects models indicated that the presence of connected information and crosswalks positively influenced driver braking behaviour, resulting in a shorter reaction time, longer braking duration and distance, smaller maximum deceleration, and a reduced standard deviation of deceleration. Conversely, visual obstacles led to longer reaction times, while parked cars and buses negatively affected driver behaviour. Further, aggressive drivers exhibited poorer braking behaviour compared to neutral drivers. An explainable machine learning model was developed to predict pedestrian-vehicle crash risks during interactions, demonstrating satisfactory predictive accuracy. The presence of connected information and crosswalks was found to have a positive effect on reducing crash risks and improving safety margins. These findings provide valuable insights for implementing connected driving technology and developing measures to enhance pedestrian safety.
引用
收藏
页码:464 / 483
页数:20
相关论文
共 59 条
[31]  
NHTSA, 2023, Report No. DOT HS 813 458
[32]  
npc, 2004, Road Traffic Safety Law of the People's Republic of China
[33]   Predicting and explaining severity of road accident using artificial intelligence techniques, SHAP and feature analysis [J].
Panda, Chakradhara ;
Mishra, Alok Kumar ;
Dash, Aruna Kumar ;
Nawab, Hedaytullah .
INTERNATIONAL JOURNAL OF CRASHWORTHINESS, 2023, 28 (02) :186-201
[34]   Promoting safe walking and cycling to improve public health: Lessons from the Netherlands and Germany [J].
Pucher, J ;
Dijkstra, L .
AMERICAN JOURNAL OF PUBLIC HEALTH, 2003, 93 (09) :1509-1516
[35]  
Shapley L. S., 1953, VALUE N PERSON GAMES, VII, P307, DOI 10.1515/9781400881970-018
[36]   Is an informed driver a better decision maker? A grouped random parameters with heterogeneity-in-means approach to investigate the impact of the connected environment on driving behaviour in safety-critical situations [J].
Sharma, Anshuman ;
Zheng, Zuduo ;
Kim, Jiwon ;
Bhaskar, Ashish ;
Haque, Md Mazharul .
ANALYTIC METHODS IN ACCIDENT RESEARCH, 2020, 27 (27)
[37]   Estimating and Comparing Response Times in Traditional and Connected Environments [J].
Sharma, Anshuman ;
Zheng, Zuduo ;
Kim, Jiwon ;
Bhaskar, Ashish ;
Haque, Md Mazharul .
TRANSPORTATION RESEARCH RECORD, 2019, 2673 (04) :674-684
[38]   Analysis of the occurrence and severity of vehicle-pedestrian conflicts in marked and unmarked crosswalks through naturalistic driving study [J].
Sheykhfard, Abbas ;
Haghighi, Farshidreza ;
Papadimitriou, Eleonora ;
Van Gelder, Pieter .
TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR, 2021, 76 :178-192
[39]   Machine learning applied to road safety modeling: A systematic literature review [J].
Silva, Philippe Barbosa ;
Andrade, Michelle ;
Ferreira, Sara .
JOURNAL OF TRAFFIC AND TRANSPORTATION ENGINEERING-ENGLISH EDITION, 2020, 7 (06) :775-790
[40]   Investigating the motivation for pedestrians' risky crossing behaviour at urban mid-block road sections [J].
Soathong, Ajjima ;
Chowdhury, Subeh ;
Wilson, Douglas ;
Ranjitkar, Prakash .
TRAVEL BEHAVIOUR AND SOCIETY, 2021, 22 :155-165