Balancing energy consumption and risk of delay in high speed trains: A three-objective real-time eco-driving algorithm with fuzzy parameters

Abstract Eco-driving is an energy efficient traffic operation measure that may lead to important energy savings in high speed railway lines. When a delay arises in real time, it is necessary to recalculate an optimal driving that must be energy efficient and computationally efficient. In addition, it is important that the algorithm includes the existing uncertainty associated with the manual execution of the driving parameters and with the possible future traffic disturbances that could lead to new delays. This paper proposes a new algorithm to be executed in real time, which models the uncertainty in manual driving by means of fuzzy numbers. It is a multi-objective optimization algorithm that includes the classical objectives in literature, running time and energy consumption, and as well a newly defined objective, the risk of delay in arrival. The risk of delay in arrival measure is based on the evolution of the time margin of the train up to destination. The proposed approach is a dynamic algorithm designed to improve the computational time. The optimal Pareto front is continuously tracked during the train travel, and a new set of driving commands is selected and presented to the driver when a delay is detected. The algorithm evaluates the 3 objectives of each solution using a detailed simulator of high speed trains to ensure that solutions are realistic, accurate and applicable by the driver. The use of this algorithm provides energy savings and, in addition, it permits railway operators to balance energy consumption and risk of delays in arrival. This way, the energy performance of the system is improved without degrading the quality of the service.

[1]  Lei Chen,et al.  An integrated metro operation optimization to minimize energy consumption , 2017 .

[2]  Masafumi Miyatake,et al.  Energy Saving Speed and Charge/discharge Control of a Railway Vehicle with On-board Energy Storage by Means of an Optimization Model , 2009 .

[3]  Pandian Vasant,et al.  Improved Tabu Search Recursive fuzzy method for Crude Oil Industry , 2012, Int. J. Model. Simul. Sci. Comput..

[4]  S. Chanas,et al.  A fuzzy approach to the transportation problem , 1984 .

[5]  Kalyanmoy Deb,et al.  An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach , 2014, IEEE Transactions on Evolutionary Computation.

[6]  H. B. Quek,et al.  Pareto-optimal set based multiobjective tuning of fuzzy automatic train operation for mass transit system , 1999 .

[7]  Didier Dubois,et al.  Ranking fuzzy numbers in the setting of possibility theory , 1983, Inf. Sci..

[8]  Tin Kin Ho,et al.  Coast control for mass rapid transit railways with searching methods , 2004 .

[9]  Phil G. Howlett,et al.  Local energy minimization in optimal train control , 2009, Autom..

[10]  Clive Roberts,et al.  Single-Train Trajectory Optimization , 2013, IEEE Transactions on Intelligent Transportation Systems.

[11]  Chao-Shun Chen,et al.  Design of Optimal Coasting Speed for MRT Systems Using ANN Models , 2009 .

[12]  Xiang Li,et al.  A Survey on Energy-Efficient Train Operation for Urban Rail Transit , 2016, IEEE Transactions on Intelligent Transportation Systems.

[13]  Limin Jia,et al.  Energy-Efficient Speed Profile Approximation: An Optimal Switching Region-Based Approach with Adaptive Resolution , 2016 .

[14]  John E. Dennis,et al.  Normal-Boundary Intersection: A New Method for Generating the Pareto Surface in Nonlinear Multicriteria Optimization Problems , 1998, SIAM J. Optim..

[15]  Richard Bellman,et al.  Decision-making in fuzzy environment , 2012 .

[16]  Antonio Fernández-Cardador,et al.  Real time regulation of efficient driving of high speed trains based on a genetic algorithm and a fuzzy model of manual driving , 2014, Eng. Appl. Artif. Intell..

[17]  Erik Dahlquist,et al.  A driver advisory system with dynamic losses for passenger electric multiple units , 2017 .

[18]  B. Schutter,et al.  Optimal trajectory planning for trains – A pseudospectral method and a mixed integer linear programming approach , 2013 .

[19]  Rob M.P. Goverde,et al.  Multi-train trajectory optimization for energy efficiency and delay recovery on single-track railway lines , 2017 .

[20]  Xiao Ma,et al.  Optimization of Train Operation in Multiple Interstations with Multi-Population Genetic Algorithm , 2015 .

[21]  Tad Gonsalves,et al.  Multi objective particle swarm optimization algorithm for the design of efficient ATO speed profiles in metro lines , 2014, Eng. Appl. Artif. Intell..

[22]  Andries Petrus Engelbrecht,et al.  Population-based metaheuristics for continuous boundary-constrained dynamic multi-objective optimisation problems , 2014, Swarm Evol. Comput..

[23]  Thomas Albrecht,et al.  Applications of real-time speed control in rail-bound public transportation systems , 2013 .

[24]  Piotr Lukaszewicz,et al.  Modeling and optimizing energy‐efficient manual driving on high‐speed lines , 2012 .

[25]  Piotr Lukaszewicz,et al.  Optimal design of metro automatic train operation speed profiles for reducing energy consumption , 2011 .

[26]  Kalyanmoy Deb,et al.  An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints , 2014, IEEE Transactions on Evolutionary Computation.

[27]  Eugene Khmelnitsky,et al.  On an optimal control problem of train operation , 2000, IEEE Trans. Autom. Control..

[28]  Xiang Li,et al.  A Subway Train Timetable Optimization Approach Based on Energy-Efficient Operation Strategy , 2012, IEEE Transactions on Intelligent Transportation Systems.

[29]  Rongfang Rachel Liu,et al.  Energy-efficient operation of rail vehicles , 2003 .

[30]  P. Howlett.  An optimal strategy for the control of a train , 1990, The Journal of the Australian Mathematical Society. Series B. Applied Mathematics.

[31]  Ching-Ter Chang.  An Approximation Approach for Representing S-Shaped Membership Functions , 2010, IEEE Transactions on Fuzzy Systems.

[32]  Mehmet Turan Soylemez,et al.  Coasting point optimisation for mass rail transit lines using artificial neural networks and genetic algorithms , 2008 .

[33]  Chun-Liang Lin,et al.  Optimisation of train energy-efficient operation for mass rapid transit systems , 2012 .

[34]  C. S. Chang,et al.  Differential evolution based tuning of fuzzy automatic train operation for mass rapid transit system , 2000 .

[35]  Phil Howlett,et al.  Energy-efficient train control , 1994 .

[36]  Masafumi Miyatake,et al.  Optimization of Train Speed Profile for Minimum Energy Consumption , 2010 .

[37]  B. Schutter,et al.  Optimal trajectory planning for trains under fixed and moving signaling systems using mixed integer linear programming , 2014 .

[38]  Ziyou Gao,et al.  Research and development of automatic train operation for railway transportation systems: A survey , 2017 .

[39]  Amie R. Albrecht,et al.  Energy-efficient train control: From local convexity to global optimization and uniqueness , 2013, Autom..

[40]  Rob M.P. Goverde,et al.  Multiple-phase train trajectory optimization with signalling and operational constraints , 2016 .

[41]  Xiang Li,et al.  Optimizing trains movement on a railway network , 2012 .

[42]  Nils Brunsson.  My own book review : The Irrational Organization , 2014 .

[43]  Antonio Fernández-Cardador,et al.  Optimal design of energy-efficient ATO CBTC driving for metro lines based on NSGA-II with fuzzy parameters , 2014, Eng. Appl. Artif. Intell..

[44]  Antonio Fernández-Cardador,et al.  Real time eco-driving of high speed trains by simulation-based dynamic multi-objective optimization , 2018, Simul. Model. Pract. Theory.

[45]  Clive Roberts,et al.  A Multiple Train Trajectory Optimization to Minimize Energy Consumption and Delay , 2015, IEEE Transactions on Intelligent Transportation Systems.

[46]  Zhi Xiao,et al.  The trapezoidal fuzzy soft set and its application in MCDM , 2012 .

[47]  Felix Schmid,et al.  Standardised approach to energy consumption calculations for high-speed rail , 2016 .

[48]  Paola Pellegrini,et al.  Energy saving in railway timetabling: A bi-objective evolutionary approach for computing alternative running times , 2013 .

[49]  Clive Roberts,et al.  Optimal driving strategy for traction energy saving on DC suburban railways , 2007 .

[50]  Tae Won Park,et al.  Operating speed pattern optimization of railway vehicles with differential evolution algorithm , 2013 .

[51]  Antonio Fernández-Cardador,et al.  Fuzzy optimal schedule of high speed train operation to minimize energy consumption with uncertain delays and driver's behavioral response , 2012, Eng. Appl. Artif. Intell..

[52]  Hee-Soo Hwang,et al.  Control strategy for optimal compromise between trip time and energy consumption in a high-speed railway , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[53]  J. M. Mera,et al.  Optimizing Electric Rail Energy Consumption Using the Lagrange Multiplier Technique , 2013 .

[54]  Chung-Fu Chang,et al.  Optimising train movements through coast control using genetic algorithms , 1997 .

[55]  Kemal Keskin,et al.  Energy-Efficient Train Operation Using Nature-Inspired Algorithms , 2017 .

[56]  Fernando Jiménez,et al.  A Multi-Objective Evolutionary Approach for Fuzzy Optimization in Production Planning , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[57]  Yongduan Song,et al.  Energy-Efficient Train Operation in Urban Rail Transit Using Real-Time Traffic Information , 2014, IEEE Transactions on Intelligent Transportation Systems.

[58]  Hong Kam Lo,et al.  An energy-efficient scheduling and speed control approach for metro rail operations , 2014 .

[59]  Paul Weston,et al.  System energy optimisation strategies for metros with regeneration , 2017 .

[60]  Baigen Cai,et al.  Online distributed cooperative model predictive control of energy-saving trajectory planning for multiple high-speed train movements , 2016 .

[61]  Tad Gonsalves,et al.  Design of Robust and Energy-Efficient ATO Speed Profiles of Metropolitan Lines Considering Train Load Variations and Delays , 2015, IEEE Transactions on Intelligent Transportation Systems.

[62]  K. Ichikawa.  Application of Optimization Theory for Bounded State Variable Problems to the Operation of Train , 1968 .

[63]  Moshe Givoni,et al.  Environmental Benefits from Mode Substitution: Comparison of the Environmental Impact from Aircraft and High-Speed Train Operations , 2007 .