Eco-driving in urban traffic networks using traffic signal information

This work addresses the problem of finding energy-optimal velocity profiles for a vehicle in an urban traffic network. Assuming communication between infrastructure and vehicles (I2V) and a complete knowledge of the upcoming traffic lights timings, a preliminary velocity pruning algorithm is proposed in order to identify the feasible region a vehicle may travel along in compliance with city speed limits. Then, a graph discretizing approach is utilized for advanced selection, among the feasible “green windows”, of the optimal ones in terms of energy consumption. Finally, a velocity trajectory is advised, which will be tracked by the driver-in-the-loop in order to pass through the signalized intersections without stopping. The proposed eco-driving assistance algorithm results are compared to the optimal solution provided by the Dynamic Programming, in order to prove not only the effectiveness but also its capability to be employed online due to its low computational load.

[1]  Olle Sundström,et al.  A generic dynamic programming Matlab function , 2009, 2009 IEEE Control Applications, (CCA) & Intelligent Control, (ISIC).

[2]  Ardalan Vahidi,et al.  Reducing idling at red lights based on probabilistic prediction of traffic signal timings , 2012, 2012 American Control Conference (ACC).

[3]  R D Bretherton,et al.  SCOOT-a Traffic Responsive Method of Coordinating Signals , 1981 .

[4]  Kanok Boriboonsomsin,et al.  Traffic Energy and Emission Reductions at Signalized Intersections: A Study of the Benefits of Advanced Driver Information , 2009 .

[5]  Masafumi Miyatake,et al.  Theoretical study on eco-driving technique for an Electric Vehicle considering traffic signals , 2011, 2011 IEEE Ninth International Conference on Power Electronics and Drive Systems.

[6]  Dimitar Filev,et al.  Analytical and numerical solutions for energy minimization of road vehicles with the existence of multiple traffic lights , 2013, 52nd IEEE Conference on Decision and Control.

[7]  R. Z. Norman,et al.  Some properties of line digraphs , 1960 .

[8]  R S Trayford,et al.  FUEL ECONOMY INVESTIGATION OF DYNAMIC ADVISORY SPEEDS FROM AN EXPERIMENT IN ARTERIAL TRAFFIC , 1984 .

[9]  Prashant Kumar,et al.  Characterisation of nanoparticle emissions and exposure at traffic intersections through fast–response mobile and sequential measurements , 2015 .

[10]  Philippe Moulin,et al.  Evaluation of the Energy Efficiency of a Fleet of Electric Vehicle for Eco-Driving Application , 2012 .

[11]  Kanok Boriboonsomsin,et al.  Arterial velocity planning based on traffic signal information under light traffic conditions , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[12]  Χριστίνα Διακάκη,et al.  Integrated control of traffic flow in corridor networks , 1999 .

[13]  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.

[14]  N. Petit,et al.  Optimal drive of electric vehicles using an inversion-based trajectory generation approach , 2011 .

[15]  M J Wooldridge,et al.  Fuel saving and other benefits of dynamic advisory speeds on a multilane arterial road , 1984 .

[16]  D. Schrank,et al.  2012 Urban Mobility Report , 2002 .

[17]  Hesham A. Rakha,et al.  Eco-driving at signalized intersections using V2I communication , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[18]  Margaret Martonosi,et al.  Leveraging Smartphone Cameras for Collaborative Road Advisories , 2012, IEEE Transactions on Mobile Computing.

[19]  Gordon F. Royle,et al.  Algebraic Graph Theory , 2001, Graduate texts in mathematics.

[20]  Hannes Hartenstein,et al.  A tutorial survey on vehicular ad hoc networks , 2008, IEEE Communications Magazine.

[21]  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.

[22]  Philippe Moulin,et al.  Optimal Energy Management Compliant with Online Requirements for an Electric Vehicle in Eco-Driving Applications , 2012 .

[23]  Marcin Seredynski,et al.  Multi-segment Green Light Optimal Speed Advisory , 2013, 2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum.

[24]  Wissam Dib,et al.  Optimal energy management for an electric vehicle in eco-driving applications , 2014 .