Deployment of roadside units to overcome connectivity gap in transportation networks with mixed traffic

Abstract In the foreseeable future, the traffic stream will be likely mixed with connected automated vehicles (CAVs) and regular vehicles (RVs). In the mixed traffic environment, when following a RV, due to the lack of vehicle-to-vehicle communications, it may take longer time for a CAV to sense and react than a human driver, which results in longer time headway and the loss of highway throughput. To address such a connectivity gap, this paper investigates an infrastructure-based solution, i.e., the deployment of roadside units to help CAVs in the heterogeneous traffic stream. Specifically, it is envisioned that these roadside units can sense vehicles in their coverage areas and provide the beyond-line-of-sight motion information to CAVs to empower them to react proactively, as they would do when following other CAVs. This paper is devoted to the analysis of the impacts of this type of roadside units at a strategic planning stage. In doing so, we first derive an analytical link performance function to capture their impact on the link capacity and travel time, and then develop a network equilibrium model to gauge their effect on travelers’ route choices and thus the flow distribution of both RVs and CAVs across the whole network. This modeling development will allow us to conduct a cost-benefit analysis for a given deployment plan of roadside units. For fair analyses, we further develop an optimization model to determine the optimal deployment plan for a given budget, while focusing on the worst case of its impact, because the flow distribution resulting from our network equilibrium model is not unique. Such a model provides a conservative estimate of the benefit brought by roadside units. Lastly, we offer case studies to demonstrate the models and unveil the potential of such an infrastructure-based solution.

[1]  Yuchuan Du,et al.  Optimal design of autonomous vehicle zones in transportation networks , 2017 .

[2]  Dongyao Jia,et al.  Multi anticipative bidirectional macroscopic traffic model considering cooperative driving strategy , 2017 .

[3]  Henry X. Liu,et al.  Risk Averse Second Best Toll Pricing , 2009 .

[4]  Meng Wang,et al.  Infrastructure assisted adaptive driving to stabilise heterogeneous vehicle strings , 2018, Transportation Research Part C: Emerging Technologies.

[5]  Yafeng Yin,et al.  Optimal deployment of autonomous vehicle lanes with endogenous market penetration , 2016 .

[6]  Yafeng Yin,et al.  Robust congestion pricing under boundedly rational user equilibrium , 2010 .

[7]  Vicente Milanés Montero,et al.  Cooperative Adaptive Cruise Control in Real Traffic Situations , 2014, IEEE Transactions on Intelligent Transportation Systems.

[8]  Donald W. Hearn,et al.  An MPEC approach to second-best toll pricing , 2004, Math. Program..

[9]  Di Wu,et al.  Pareto-improving congestion pricing on multimodal transportation networks , 2011, Eur. J. Oper. Res..

[10]  Lu Xing,et al.  Integrated Cooperative Adaptive Cruise and Variable Speed Limit Controls for Reducing Rear-End Collision Risks Near Freeway Bottlenecks Based on Micro-Simulations , 2017, IEEE Transactions on Intelligent Transportation Systems.

[11]  E. Aiyoshi,et al.  Necessary conditions for min-max problems and algorithms by a relaxation procedure , 1980 .

[12]  Qiang Meng,et al.  Designing autonomous vehicle incentive program with uncertain vehicle purchase price , 2019, Transportation Research Part C: Emerging Technologies.

[13]  Soyoung Ahn,et al.  Towards vehicle automation: Roadway capacity formulation for traffic mixed with regular and automated vehicles , 2017 .

[14]  Toshiyuki Yamamoto,et al.  Impact of dedicated lanes for connected and autonomous vehicle on traffic flow throughput , 2018, Physica A: Statistical Mechanics and its Applications.

[15]  Oliver Stein,et al.  How to solve a semi-infinite optimization problem , 2012, Eur. J. Oper. Res..

[16]  J. W. C. van Lint,et al.  Will Automated Vehicles Negatively Impact Traffic Flow , 2017 .

[17]  Hai Yang,et al.  Modeling user adoption of advanced traveler information systems: dynamic evolution and stationary equilibrium , 2001 .

[18]  R. Saigal,et al.  Accelerating the Adoption of Automated Vehicles by Subsidies: A Dynamic Games Approach , 2018, Transportation Research Part B: Methodological.

[19]  Amir Ghiasi,et al.  A mixed traffic capacity analysis and lane management model for connected automated vehicles: A Markov chain method , 2017 .

[20]  Yasuo Asakura,et al.  Endogenous market penetration dynamics of automated and connected vehicles: Transport-oriented model and its paradox , 2017 .

[21]  Steven E Shladover,et al.  Impacts of Cooperative Adaptive Cruise Control on Freeway Traffic Flow , 2012 .

[22]  Bart van Arem,et al.  The Impact of Cooperative Adaptive Cruise Control on Traffic-Flow Characteristics , 2006, IEEE Transactions on Intelligent Transportation Systems.

[23]  Dirk Helbing,et al.  Delays, inaccuracies and anticipation in microscopic traffic models , 2006 .

[24]  Steven E Shladover,et al.  Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data , 2014 .