Network-wide signal timing stochastic simulation optimization with environmental concerns

Abstract This study addresses a network-wide signal timing optimization problem with environmental concerns by a bi-objective stochastic simulation-based optimization (BOSSO) method. In this method, the global samples evaluated by costly simulation are used to build a type of surrogate model named the regressing Kriging model, which are then employed to predict bi-objectives of untested samples or filter noises from the evaluated samples. An adaptive selector is incorporated to determine which samples in the local trust-region are evaluated by costly simulation and which ones by the built regressing Kriging model. This helps to balance computational costs and accuracies of three quadratic regression models, especially when the variable dimension is high. The non-interactive role of a decision maker is taken to generate more non-dominated solutions around the desired bi-objective point. In the field experiments, an urban road network with 15 signalized and five non-signalized intersections in Changsha, China, is modeled as the simulation scenario by VISSIM. Then, the traffic simulation model is firstly calibrated from two aspects by the BOSSO method, which can well reproduce the reality. After that, the network-wide bi-objective signal timing optimization problem is also solved by the BOSSO method. Numerical results show that compared with the real-field traffic states, the total delay and vehicular emissions are reduced by at most 16.90% and 32.22% respectively under the budged number of simulations. Balance analyses also show the existence of a competing relationship between bi-objectives. Finally, the BOSSO method is validated to outperform three other counterparts (NSGA-II, BOTR and BOEGO) from various aspects.

[1]  M. Bierlaire,et al.  Boosters: A Derivative-Free Algorithm Based on Radial Basis Functions , 2009 .

[2]  Andy J. Keane,et al.  Recent advances in surrogate-based optimization , 2009 .

[3]  Lei Zhang,et al.  Optimal Time-Varying Pricing for Toll Roads Under Multiple Objectives: A Simulation-Based Optimization Approach , 2017, Transp. Sci..

[4]  John J. Grefenstette,et al.  Genetic algorithms in noisy environments , 1988, Machine Learning.

[5]  Jacob Søndergaard.  Optimization using surrogate models - by the space mapping technique , 2003 .

[6]  Bin Ran,et al.  A stochastic simulation-based optimization method for equitable and efficient network-wide signal timing under uncertainties , 2019, Transportation Research Part B: Methodological.

[7]  Christine A. Shoemaker,et al.  ORBIT: Optimization by Radial Basis Function Interpolation in Trust-Regions , 2008, SIAM J. Sci. Comput..

[8]  Fred W. Glover,et al.  Simulation optimization: a review, new developments, and applications , 2005, Proceedings of the Winter Simulation Conference, 2005..

[9]  A. J. Booker,et al.  A rigorous framework for optimization of expensive functions by surrogates , 1998 .

[10]  Jack P. C. Kleijnen,et al.  Constrained optimization in expensive simulation: Novel approach , 2010, Eur. J. Oper. Res..

[11]  Michael C. Fu,et al.  Optimization for Simulation: Theory vs. Practice , 2002 .

[12]  Michel Bierlaire,et al.  A Simulation-Based Optimization Framework for Urban Transportation Problems , 2013, Oper. Res..

[13]  Nagui M. Rouphail,et al.  Effect of Arterial Signalization and Level of Service on Measured Vehicle Emissions , 2003 .

[14]  Chenfeng Xiong,et al.  Time-of-day vehicle mileage fees for congestion mitigation and revenue generation: A simulation-based optimization method and its real-world application , 2016 .

[15]  Gary B. Lamont,et al.  Multiobjective evolutionary algorithms: classifications, analyses, and new innovations , 1999 .

[16]  Hesham A Rakha,et al.  Impact of Stops on Vehicle Fuel Consumption and Emissions , 2003 .

[17]  Arnaud Can,et al.  Assessment of the impact of speed limit reduction and traffic signal coordination on vehicle emissions using an integrated approach , 2011 .

[18]  Enrique Alba,et al.  Reducing vehicle emissions and fuel consumption in the city by using particle swarm optimization , 2014, Applied Intelligence.

[19]  Gunnar Flötteröd,et al.  Efficient calibration techniques for large-scale traffic simulators , 2017 .

[20]  Lei Zhang,et al.  Surrogate‐Based Optimization of Expensive‐to‐Evaluate Objective for Optimal Highway Toll Charges in Transportation Network , 2014, Comput. Aided Civ. Infrastructure Eng..

[21]  Steven Broekx,et al.  Modelling instantaneous traffic emission and the influence of traffic speed limits. , 2006, The Science of the total environment.

[22]  Yafeng Yin,et al.  Robust signal timing optimization with environmental concerns , 2013 .

[23]  Jong-hyun Ryu,et al.  A Derivative-Free Trust-Region Method for Biobjective Optimization , 2014, SIAM J. Optim..

[24]  Jack P. C. Kleijnen,et al.  Constrained Optimization in Simulation: A Novel Approach , 2008 .

[25]  Alexander Skabardonis,et al.  Prediction of Vehicle Activity for Emissions Estimation Under Oversaturated Conditions Along Signalized Arterials , 2013, J. Intell. Transp. Syst..

[26]  LinRen Zhou,et al.  Response Surface Method Based on Radial Basis Functions for Modeling Large-Scale Structures in Model Updating , 2013, Comput. Aided Civ. Infrastructure Eng..

[27]  Christine A. Shoemaker,et al.  Constrained Global Optimization of Expensive Black Box Functions Using Radial Basis Functions , 2005, J. Glob. Optim..

[28]  Hesham Rakha,et al.  Requirements for Evaluating Traffic Signal Control Impacts on Energy and Emissions Based on Instantaneous Speed and Acceleration Measurements , 2000 .

[29]  Sigrún Andradóttir,et al.  A review of simulation optimization techniques , 1998, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).

[30]  Bart De Schutter,et al.  A mesoscopic integrated urban traffic flow-emission model , 2017 .

[31]  Yaochu Jin,et al.  Surrogate-assisted evolutionary computation: Recent advances and future challenges , 2011, Swarm Evol. Comput..

[32]  Aleksandar Stevanovic,et al.  Optimization of traffic signal timings based on surrogate measures of safety , 2013 .

[33]  I. Mayeres,et al.  THE MARGINAL EXTERNAL COSTS OF URBAN TRANSPORT , 1996 .

[34]  Carolina Osorio,et al.  Urban transportation emissions mitigation: Coupling high-resolution vehicular emissions and traffic models for traffic signal optimization , 2015 .

[35]  Carolina Osorio,et al.  Energy-Efficient Urban Traffic Management: A Microscopic Simulation-Based Approach , 2015, Transp. Sci..

[36]  Hans-Martin Gutmann,et al.  A Radial Basis Function Method for Global Optimization , 2001, J. Glob. Optim..

[37]  K. Marti.  Stochastic Optimization Methods , 2005 .

[38]  Kai Zhang,et al.  Optimizing Traffic Control to Reduce Fuel Consumption and Vehicular Emissions , 2009 .

[39]  Jong-hyun Ryu,et al.  A Trust-Region Algorithm for Bi-Objective Stochastic Optimization , 2011, ICCS.

[40]  Qiang Meng,et al.  A Pareto-optimization approach for a fair ramp metering , 2010 .

[41]  Michael C. Fu,et al.  Optimization via simulation: A review , 1994, Ann. Oper. Res..

[42]  Jack P. C. Kleijnen,et al.  Response surface methodology for constrained simulation optimization: An overview , 2008, Simul. Model. Pract. Theory.

[43]  Lina Kattan,et al.  Variable speed limit: A microscopic analysis in a connected vehicle environment , 2015 .

[44]  Juliane Müller,et al.  SOCEMO: Surrogate Optimization of Computationally Expensive Multiobjective Problems , 2017, INFORMS J. Comput..

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

[46]  Raghu Pasupathy,et al.  Simulation Optimization: A Concise Overview and Implementation Guide , 2013 .

[47]  H. Oliver Gao,et al.  Improving estimates of transportation emissions: Modeling hourly truck traffic using period-based car volume data , 2014 .

[48]  Ilsoo Yun,et al.  Stochastic Optimization for Sustainable Traffic Signal Control , 2009 .

[49]  Katya Scheinberg,et al.  Global Convergence of General Derivative-Free Trust-Region Algorithms to First- and Second-Order Critical Points , 2009, SIAM J. Optim..

[50]  Randy B Machemehl,et al.  OPTIMAL SIGNAL STRATEGY FOR FUEL CONSUMPTION AND EMISSIONS CONTROL AT SIGNALISED INTERSECTIONS , 1996 .

[51]  Andreas Hegyi,et al.  Integrated macroscopic traffic flow, emission, and fuel consumption model for control purposes , 2013 .