scholarly journals How Does Heterogeneity Affect Freeway Safety? A Simulation-Based Exploration Considering Sustainable Intelligent Connected Vehicles

2020 ◽  
Vol 12 (21) ◽  
pp. 8941
Author(s):  
Yuntao Shi ◽  
Ye Li ◽  
Qing Cai ◽  
Hao Zhang ◽  
Dan Wu

Intelligent connected vehicles (ICVs) are recognized as a new sustainable transportation mode, which could be promising for reducing crashes. However, the mixed traffic consisting of manually driven vehicles and ICVs may negatively affect road safety due to individual heterogeneity. This study investigated heterogeneity effects on freeway safety-based simulation experiments. Two types of vehicle dynamic models were employed to depict dynamic behaviors of manually driven vehicles and adaptive cruise control (ACC) vehicles (a simplified version of ICVs), respectively. Real vehicle trajectories were utilized to calibrate model parameters based on genetic algorithms. Surrogate safety measures were applied to establish the relationship between vehicle behaviors and longitudinal collision risks. Simulation results indicate that the heterogeneity has negative effects on longitudinal safety. With the higher degree of heterogeneity, longitudinal collision risks are increased. Compared to traffic flow consisting of human drivers only, mixed traffic flow may be more dangerous when the market penetration rate of ACC is low, since the ACC system can be recognized as a new source of individual heterogeneity. Findings of this study show that necessary countermeasures should be developed to improve safety for mixed traffic flow from the perspective of transportation safety planning in the near future.

Author(s):  
Jiawei Wang ◽  
Yang Zheng ◽  
Chaoyi Chen ◽  
Qing Xu ◽  
Keqiang Li

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Zhenyu Mei ◽  
Jun Chen

The ongoing controversy about in what condition should we set the curb parking has few definitive answers because comprehensive research in this area has been lacking. Our goal is to present a set of heuristic urban street speed functions under mixed traffic flow by taking into account impacts of curb parking. Two impacts have been defined to classify and quantify the phenomena of motor vehicles' speed dynamics in terms of curb parking. The first impact is called Space impact, which is caused by the curb parking types. The other one is the Time impact, which results from the driver maneuvering in or out of parking space. In this paper, based on the empirical data collected from six typical urban streets in Nanjing, China, two models have been proposed to describe these phenomena for one-way traffic and two-way traffic, respectively. An intensive experiment has been conducted in order to calibrate and validate these proposed models, by taking into account the complexity of the model parameters. We also provide guidelines in terms of how to cluster and calculate those models' parameters. Results from these models demonstrated promising performance of modeling motor vehicles' speed for mixed traffic flow under the influence of curb parking.


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