Optimal Limited-stop Bus Routes Selection Using a Genetic Algorithm and Smart Card Data

In recent years, express bus service has come into the spotlight by overcoming slow bus operating speeds while maintaining its accessibility when it operates with local bus services. This study developed an optimal limited-stop bus routes selection (LSBRS) guideline as a scenario-based analysis and compared it with case study results. Smart card data and a genetic algorithm (GA) were used to develop the model with different scenarios. Then, total travel time savings as a result of implementing limited-stop bus service generated by the GA model were computed. The effectiveness of each factor was verified by multiple regression analysis, and the LSBRS methodology was determined. This methodology was applied to Suwon, Korea, as a case study. As a result, travel time savings were estimated to be 9.0–19.0%. The ranking of the total travel time savings proposed by the LSBRS methodology presented a similar tendency with that of the casestudy analysis.

[1]  Mark Hickman,et al.  Transit Stop-Level Origin–Destination Estimation through Use of Transit Schedule and Automated Data Collection System , 2011 .

[2]  Ricardo Giesen,et al.  Design of limited-stop services for an urban bus corridor with capacity constraints , 2010 .

[3]  Avishai Ceder,et al.  Optimal Modification of Urban Bus Network Routes Using a Genetic Algorithm , 2015 .

[4]  Randy B Machemehl,et al.  Do Transit Users Just Wait for Buses or Wait with Strategies? , 2009 .

[5]  Md. Khaledur Rahman,et al.  Transit network design by genetic algorithm with elitism , 2014 .

[6]  Stacey Schwarcz.  Service design for heavy demand corridors : limited-stop bus service , 2004 .

[7]  Hasan Khaksar,et al.  Bus Network Design by Considering the Location of Depots: A Case Study of Mashhad , 2013 .

[8]  Navid Kalantari,et al.  Bus Network Modification Problem: A New Approach to Bus Network Design , 2014 .

[9]  Chen Jun,et al.  Estimating Smart Card Commuters Origin-Destination Distribution Based on APTS Data , 2013 .

[10]  Keechoo Choi,et al.  Optimizing Skip-Stop Rail Transit Stopping Strategy using a Genetic Algorithm , 2014 .

[11]  J. Corcoran,et al.  Exploring Bus Rapid Transit passenger travel behaviour using big data , 2014 .

[12]  Bruno Agard,et al.  MINING PUBLIC TRANSPORT USER BEHAVIOUR FROM SMART CARD DATA , 2006 .

[13]  Gi-Ju Choe,et al.  An Estimation of Generalized Cost for Transit Assignment , 2007 .

[14]  Fumitaka Kurauchi,et al.  Variability of commuters’ bus line choice: an analysis of oyster card data , 2014, Public Transp..

[15]  Randy B Machemehl,et al.  OPTIMAL TRANSIT ROUTE NETWORK DESIGN PROBLEM: ALGORITHMS, IMPLEMENTATIONS, AND NUMERICAL RESULTS , 2004 .

[16]  Fabian Cevallos,et al.  Minimizing Transfer Times in Public Transit Network with Genetic Algorithm , 2006 .

[17]  David J. Lovell,et al.  Optimal Time Transfer in Bus Transit Route Network Design using a Genetic Algorithm , 2003 .

[18]  Hiroaki Nishiuchi,et al.  Spatial-Temporal Daily Frequent Trip Pattern of Public Transport Passengers Using Smart Card Data , 2013, Int. J. Intell. Transp. Syst. Res..

[19]  Randy B Machemehl,et al.  Optimal Transit Route Network Design Problem with Variable Transit Demand: Genetic Algorithm Approach , 2006 .

[20]  Ahmed M El-Geneidy,et al.  Estimating Bus Run Times for New Limited-Stop Service Using Archived AVL and APC Data , 2009 .

[21]  Jiří Slavík,et al.  Estimation of a route choice model for urban public transport using smart card data , 2014 .

[22]  Bruno Agard,et al.  Measuring transit use variability with smart-card data , 2007 .

[23]  Vukan R Vuchic,et al.  Urban transit systems and technology , 2007 .

[24]  Steve Robinson.  Measuring Bus Stop Dwell Time and Time Lost Serving Stop with London iBus Automatic Vehicle Location Data , 2013 .

[25]  H. L. Ong,et al.  Solving the feeder bus network design problem by genetic algorithms and ant colony optimization , 2006, Adv. Eng. Softw..