Reducing congestion and emissions via roadside unit deployment under mixed traffic flow
Yuhao Liu1
Zhibin Chen2
Siyuan Gong3
Han Liu4
1.Shanghai Key Laboratory of Urban Design and Urban Science,NYU Shanghai,Shanghai,China2.Shanghai Key Laboratory of Urban Design and Urban Science,NYU Shanghai,Shanghai,China;Division of Engineering and Computer Science,NYU Shanghai,Shanghai,China3.The Joint Laboratory for Internet of Vehicles,Ministry of Education-China Mobile Communications Corporation,Xi'an,Shaanxi,China;School of Information Engineering,Chang'an University,Xi'an,Shaanxi,China4.Central Research Institute,China Coal Technology and Engineering Group(CCTEG),Beijing,China
摘要：It is expected that for a long time the future road traffic will be composed of both regular vehicles(RVs)and connected autonomous vehicles(CAVs).As a vehicle-to-infrastructure technology dedicated to facilitating CAV under the mixed traffic flow,roadside units(RSUs)can also improve the quality of information received by CAVs,thereby influencing the routing behavior of CAV users.This paper explores the possibility of leveraging the RSU deployment to affect the route choices of both CAVs and RVs and the adoption rate of CAVs so as to reduce the network congestion and emissions.To this end,we first establish a logit-based stochastic user equilibrium model to capture drivers'route choice and vehicle type choice behaviors provided the RSU deployment plan is given.Particularly,CAV users'perception error can be reduced by higher CAV penetration and denser RSUs deployed on the road due to the improved information quality.With the established equi-librium model,the RSU deployment problem is then formulated as a mathematical program with equilibrium constraints.An active-set algorithm is presented to solve the deployment problem efficiently.Numerical results suggest that an optimal RSU deployment plan can effectively drive the system towards one with lower network delay and emissions.
机标关键词：congestiontrafficmixedflowdeploymentemissionsreducingroadside
论文发表日期：2023-02-28
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：15( 197-211 )
英文信息