scholarly journals Toward Safe and Smart Mobility: Energy-Aware Deep Learning for Driving Behavior Analysis and Prediction of Connected Vehicles

Author(s):  
Yang Xing ◽  
Chen Lv ◽  
Xiaoyu Mo ◽  
Zhongxu Hu ◽  
Chao Huang ◽  
...  
Author(s):  
Meng Xie ◽  
Michael Winsor ◽  
Tao Ma ◽  
Andreas Rau ◽  
Fritz Busch ◽  
...  

This paper aims to evaluate the sensitivity of the proposed cooperative dynamic bus lane system with microscopic traffic simulation models. The system creates a flexible bus priority lane that is only activated on demand at an appropriate time with advanced information and communication technologies, which can maximize the use of road space. A decentralized multi-lane cooperative algorithm is developed and implemented in a microscopic simulation environment to coordinate lane changing, gap acceptance, and car-following driving behavior for the connected vehicles (CVs) on the bus lane and the adjacent lanes. The key parameters for the sensitivity study include the penetration rate and communication range of CVs, considering the transition period and gradual uptake of CVs. Multiple scenarios are developed and compared to analyze the impact of key parameters on the system’s performance, such as total saved travel time of all passengers and travel time variation among buses and private vehicles. The microscopic simulation models showed that the cooperative dynamic bus lane system is significantly sensitive to the variations of the penetration rate and the communication range in a congested traffic state. With a CV system and a communication range of 150 m, buses obtain maximum benefits with minimal impacts on private vehicles in the study simulation. The safety concerns induced by cooperative driving behavior are also discussed in this paper.


2021 ◽  
Author(s):  
Fan Wang ◽  
Fan Yang ◽  
Wei Yang ◽  
Huachun Tan ◽  
Bin Ran

2019 ◽  
Vol 20 (2) ◽  
pp. 457-475 ◽  
Author(s):  
Zhiguo Zhao ◽  
Liangjie Zhou ◽  
Yugong Luo ◽  
Keqiang Li

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