A two-phase optimization model for the demand-responsive customized bus network design

Abstract This paper proposes a new optimization model for the network design problem of the demand-responsive customized bus (CB). The proposed model consists of two phases: inserting passenger requests dynamically in an interactive manner (dynamic phase) and optimizing the service network statically based on the overall demand (static phase). In the dynamic phase, we propose a hierarchical decision-making model to describe the interactive manner between operator and passengers. The CB network design problem is formulated in a mixed-integer program with the objective of maximizing operator’s revenue. The CB passenger’s travel behavior is measured by a discrete choice model given the trip plan provided by the operator. A dynamic insertion method is developed to address the proposed model in the dynamic phase. For the network design problem in the static phase, the service network is re-optimized based on the confirmed passengers with strict time deviation constraints embedded in the static multi-vehicle pickup and delivery problem. An exact solution method is developed based on the branch-and-bound (B&B) algorithm. Numerical examples are conducted to verify the proposed models and solution algorithms.

[1]  Yu Nie,et al.  Connecting e-hailing to mass transit platform: Analysis of relative spatial position , 2017 .

[2]  Stephan Winter,et al.  Simulating Demand-responsive Transportation: A Review of Agent-based Approaches , 2015 .

[3]  Jian Wang,et al.  An Optimization Method of Passenger Assignment for Customized Bus , 2017 .

[4]  Frank Englert,et al.  Improving service quality in public transportation systems using automated customer feedback , 2016 .

[5]  Karen Aardal,et al.  Enhancing flexible transport services with demand-anticipatory insertion heuristics , 2018 .

[6]  Yang Liu,et al.  DeepPF: A deep learning based architecture for metro passenger flow prediction , 2019, Transportation Research Part C: Emerging Technologies.

[7]  Meng Meng,et al.  Analyzing Customized Bus Service on a Multimodal Travel Corridor: An Analytical Modeling Approach , 2017 .

[8]  Rui Song,et al.  Clustering Passenger Trip Data for the Potential Passenger Investigation and Line Design of Customized Commuter Bus , 2019, IEEE Transactions on Intelligent Transportation Systems.

[9]  Tao Liu,et al.  Commuting by Customized Bus: A Comparative Analysis with Private Car and Conventional Public Transport in Two Cities , 2016 .

[10]  Yu Yuan,et al.  Surrogate-based simulation optimization approach for day-to-day dynamics model calibration with real data , 2019, Transportation Research Part C: Emerging Technologies.

[11]  J. Nelson,et al.  Flexible Transport Services: A New Market Opportunity for Public Transport , 2009 .

[12]  Jean-François Cordeau,et al.  A Branch-and-Cut Algorithm for the Dial-a-Ride Problem , 2006, Oper. Res..

[13]  W. Y. Szeto,et al.  A survey of dial-a-ride problems: Literature review and recent developments , 2018 .

[14]  Xiqun Chen,et al.  Understanding ridesplitting behavior of on-demand ride services: An ensemble learning approach , 2017 .

[15]  Paul M. Schonfeld,et al.  Optimization Models for Comparing Conventional and Subscription Bus Feeder Services , 1991, Transp. Sci..

[16]  Tao Liu,et al.  Analysis of a new public-transport-service concept: Customized bus in China , 2015 .

[17]  Daniele Vigo,et al.  Chapter 6 Vehicle Routing , 2007, Transportation.

[18]  Gilbert Laporte,et al.  A branch-and-price algorithm for the Vehicle Routing Problem with Deliveries, Selective Pickups and Time Windows , 2009, Eur. J. Oper. Res..

[19]  Pan Liu,et al.  Optimal transit fare and service frequency of a nonlinear origin-destination based fare structure , 2016 .

[20]  Gilbert Laporte,et al.  Static pickup and delivery problems: a classification scheme and survey , 2007 .

[21]  Tal Grinshpoun,et al.  A reduction approach to the two-campus transport problem , 2014, J. Sched..

[22]  Tao Liu,et al.  Large-Scale Demand Driven Design of a Customized Bus Network: A Methodological Framework and Beijing Case Study , 2017 .

[23]  Roberto D. Galvão.  Comments on: Static pickup and delivery problems: a classification scheme and survey , 2007 .

[24]  Leena Suhl,et al.  Design of Customer-oriented Dispatching Support for Railways , 2001 .

[25]  Ronald Schroeter,et al.  Opportunities of Public Transport Experience Enhancements with Mobile Services and Urban Screens , 2013, Int. J. Ambient Comput. Intell..

[26]  Mark E. T. Horn,et al.  Fleet scheduling and dispatching for demand-responsive passenger services , 2002 .

[27]  Abhishek Singhal,et al.  Implementation of interactive transit information kiosks at New York City transit facilities: Analysis of user utilization and lessons learned , 2013 .

[28]  Gilbert Laporte,et al.  The dial-a-ride problem: models and algorithms , 2006, Ann. Oper. Res..

[29]  Wenyi Zhang,et al.  Route Design Problem of Customized Buses: Mixed Integer Programming Model and Case Study , 2018, Journal of Transportation Engineering, Part A: Systems.

[30]  Gilbert Laporte,et al.  Models and branch-and-cut algorithms for pickup and delivery problems with time windows , 2007 .

[31]  Alberto Colorni,et al.  An effective and fast heuristic for the Dial-a-Ride problem , 2007, 4OR.

[32]  Xuesong Zhou,et al.  Customized bus service design for jointly optimizing passenger-to-vehicle assignment and vehicle routing , 2017 .

[33]  Gilbert Laporte,et al.  Dynamic pickup and delivery problems , 2010, Eur. J. Oper. Res..

[34]  Matteo Fischetti,et al.  Combinatorial Benders' Cuts for Mixed-Integer Linear Programming , 2006, Oper. Res..

[35]  Olfa Chebbi,et al.  Reducing the wasted transportation capacity of Personal Rapid Transit systems: An integrated model and multi-objective optimization approach , 2016 .

[36]  Maged Dessouky,et al.  A new regret insertion heuristic for solving large-scale dial-a-ride problems with time windows , 2004 .

[37]  Joseph Kee-Yin Ng,et al.  CB-Planner: A bus line planning framework for customized bus systems , 2019, Transportation Research Part C: Emerging Technologies.

[38]  Gopala Krishna Reddy Kakivaya,et al.  INTRACTABILITY OF THE DIAL-A-RIDE PROBLEM AND A MULTIOBJECTIVE SOLUTION USING SIMULATED ANNEALING , 1998 .

[39]  Jaime Cerdá,et al.  A cluster-based optimization approach for the multi-depot heterogeneous fleet vehicle routing problem with time windows , 2007, Eur. J. Oper. Res..

[40]  Emilio Frazzoli,et al.  Adaptive and Distributed Algorithms for Vehicle Routing in a Stochastic and Dynamic Environment , 2009, IEEE Transactions on Automatic Control.

[41]  Zhiyuan Liu,et al.  A tailored machine learning approach for urban transport network flow estimation , 2019, Transportation Research Part C: Emerging Technologies.

[42]  Walter Ukovich,et al.  A two-phase insertion technique of unexpected customers for a dynamic dial-a-ride problem , 2006, Eur. J. Oper. Res..

[43]  Stefan Feuerriegel,et al.  Making the most of fleets: A profit-maximizing multi-vehicle pickup and delivery selection problem , 2017, Eur. J. Oper. Res..

[44]  Qiang Meng,et al.  Remote park-and-ride network equilibrium model and its applications , 2018 .

[45]  Xiaobo Qu,et al.  Dynamic headway control for high‐frequency bus line based on speed guidance and intersection signal adjustment , 2019, Comput. Aided Civ. Infrastructure Eng..

[46]  J. Mageean,et al.  The evaluation of demand responsive transport services in Europe , 2003 .

[47]  Randolph W. Hall,et al.  NETWORK DESIGN FOR A GRID HYBRID TRANSIT SERVICE , 2004 .