TY  - JOUR
PY  - 2020//
TI  - An asymmetric-anticipation car-following model in the era of autonomous-connected and human-driving vehicles
JO  - Journal of advanced transportation
A1  - Jafaripournimchahi, Ammar
A1  - Hu, Wusheng
A1  - Sun, Lu
A1  - Farah, Haneen
SP  - e8865814
EP  - e8865814
VL  - 2020
IS  - 
N2  - Herein, we explored the impact of anticipation and asymmetric driving behavior on vehicle's position, velocity, acceleration, energy consumption, and exhaust emissions of CO, HC, and NOx in mixed traffic flow. We present an asymmetric-anticipation car-following model (AAFVD) considering the motion information from two direct preceding vehicles (i.e., human-driving (HD) and autonomous and connected (AC) vehicles platoon) via wireless data transmission. The linear stability approach was used to evaluate the properties of the AAFVD model. Our simulations revealed that the drivers' anticipation factor using the motion information from two direct preceding vehicles in connected vehicles environment can effectively improve traffic flow stability. The vehicle's departure and arrival process while passing through a signal lane with a traffic light considering the anticipation and asymmetric driving behavior, and the motion information from two direct preceding vehicles was explored. Our numerical results demonstrated that the AAFVD model can decrease the velocity fluctuations, energy consumption, and exhaust emissions of vehicles in mixed traffic flow system.<p />  <p>Language: en</p>
LA  - en
SN  - 0197-6729
UR  - http://dx.doi.org/10.1155/2020/8865814
ID  - ref1
ER  -