Cooperative Eco-Driving at Signalized Intersections in a Partially Connected and Automated Vehicle Environment

The emergence of connected and automated vehicle (CAV) technology has the potential to bring a number of benefits to our existing transportation systems. Specifically, when CAVs travel along an arterial corridor with signalized intersections, they can not only be driven automatically using pre-designed control models but can also communicate with other CAVs and the roadside infrastructure. In this paper, we describe a cooperative eco-driving (CED) system targeted for signalized corridors, focusing on how the penetration rate of CAVs affects the energy efficiency of the traffic network. In particular, we propose a role transition protocol for CAVs to switch between a leader and following vehicles in a string. Longitudinal control models are developed for conventional vehicles in the network and for different CAVs based on their roles and distances to intersections. A microscopic traffic simulation evaluation has been conducted using PTV VISSIM with realistic traffic data collected for the City of Riverside, CA, USA. The effects on traffic mobility are evaluated, and the environmental benefits are analyzed by the U.S. Environmental Protection Agency’s MOtor Vehicle Emission Simulator (MOVES) model. The simulation results indicate that the energy consumption and pollutant emissions of the proposed system decrease, as the penetration rate of CAVs increases. Specifically, more than 7% reduction on energy consumption and up to 59% reduction on pollutant emission can be achieved when all vehicles in the proposed system are CAVs.

[1]  S. Ilgin Guler,et al.  Using connected vehicle technology to improve the efficiency of intersections , 2014 .

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

[3]  Rainer Wiedemann,et al.  SIMULATION DES STRASSENVERKEHRSFLUSSES. , 1974 .

[4]  Guoyuan Wu,et al.  GlidePath: Eco-Friendly Automated Approach and Departure at Signalized Intersections , 2017, IEEE Transactions on Intelligent Vehicles.

[5]  Hao Yang,et al.  Eco-Cooperative Adaptive Cruise Control at Signalized Intersections Considering Queue Effects , 2017, IEEE Transactions on Intelligent Transportation Systems.

[6]  Meng Wang,et al.  Eco approaching at an isolated signalized intersection under partially connected and automated vehicles environment , 2017 .

[7]  Zhiheng Li,et al.  Analysis of Cooperative Driving Strategies for Nonsignalized Intersections , 2018, IEEE Transactions on Vehicular Technology.

[8]  Xiao-Mei Zhao,et al.  Heterogeneous Traffic Mixing Regular and Connected Vehicles: Modeling and Stabilization , 2019, IEEE Transactions on Intelligent Transportation Systems.

[9]  Huei Peng,et al.  Connected and Automated Vehicles , 2016 .

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

[11]  Stephan Winter,et al.  A conceptualization of vehicle platoons and platoon operations , 2017 .

[12]  Nathan van de Wouw,et al.  Design and experimental evaluation of cooperative adaptive cruise control , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[13]  Peng Hao,et al.  Cluster-Wise Cooperative Eco-Approach and Departure Application for Connected and Automated Vehicles Along Signalized Arterials , 2018, IEEE Transactions on Intelligent Vehicles.

[14]  Byungkyu Park,et al.  Assessment of mobility, energy, and environment impacts of IntelliDrive-based Cooperative Adaptive Cruise Control and Intelligent Traffic Signal control , 2010, Proceedings of the 2010 IEEE International Symposium on Sustainable Systems and Technology.

[15]  Carlos Canudas de Wit,et al.  Eco-driving in urban traffic networks using traffic signal information , 2013, 52nd IEEE Conference on Decision and Control.

[16]  Yiheng Feng,et al.  A real-time adaptive signal control in a connected vehicle environment , 2015 .

[17]  Guoyuan Wu,et al.  A Review on Cooperative Adaptive Cruise Control (CACC) Systems: Architectures, Controls, and Applications , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[18]  Peng Hao,et al.  Developing a platoon-wide Eco-Cooperative Adaptive Cruise Control (CACC) system , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[19]  Mehrdad Dianati,et al.  Performance study of a Green Light Optimized Speed Advisory (GLOSA) application using an integrated cooperative ITS simulation platform , 2011, 2011 7th International Wireless Communications and Mobile Computing Conference.

[20]  Byungkyu Brian Park,et al.  Development and Evaluation of a Cooperative Vehicle Intersection Control Algorithm Under the Connected Vehicles Environment , 2012, IEEE Transactions on Intelligent Transportation Systems.

[21]  Yadollah Saboohi,et al.  Model for developing an eco-driving strategy of a passenger vehicle based on the least fuel consumption , 2009 .

[22]  Ardalan Vahidi,et al.  Predictive Cruise Control: Utilizing Upcoming Traffic Signal Information for Improving Fuel Economy and Reducing Trip Time , 2011, IEEE Transactions on Control Systems Technology.

[23]  Kanok Boriboonsomsin,et al.  Dynamic Eco-Driving for Signalized Arterial Corridors and Its Indirect Network-Wide Energy/Emissions Benefits , 2013, J. Intell. Transp. Syst..

[24]  Helbing,et al.  Congested traffic states in empirical observations and microscopic simulations , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[25]  Takayoshi Yoshimura,et al.  Intersection Vehicle Cooperative Eco-Driving in the Context of Partially Connected Vehicle Environment , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.