Park-and-Ride Network Design: A Goal Programming Approach

Xin Yuan Chen; Zhi Yuan Liu; Wei Deng

doi:10.4028/www.scientific.net/AMR.1030-1032.2065

Paper Titles

Multi-Agent Based Simulation Model for Rail Transit Evacuation Process
p.2044

Network Design of Park-and-Ride System to Promote Transit Patronage
p.2050

On Study of Driver’s Shoulder Fatigue in Manual and Automatic on Grassland Highway
p.2054

Optimization for Hazardous Materials Transportation Network Based on Toll Policies
p.2061

Park-and-Ride Network Design: A Goal Programming Approach
p.2065

Predict the Volume of Passenger Transport of Railway Based on Grey Markov Chain Model
p.2069

Predictive Compensate Control for Remote Vehicle Steering Assist when the Driver is in Risky Driving States
p.2073

Railway Transportation of LPG: A Risk Assessment of LPG in the Course of Load-and-Unload
p.2078

Real Ship Experimental Research on Ship Sailing Resistance in Inland Restricted Channel
p.2082

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1030-1032Park-and-Ride Network Design: A Goal Programming...

Park-and-Ride Network Design: A Goal Programming Approach

Abstract:

The paper addresses a park and ride network design problem in a bi-model transport network in a multi-objective decision making framework. A goal programming approach is adopted to solve the multi-objective park and ride network design problem. The goal programming approach considers the user-defined goals and priority structure, which are (i) traffic-efficient goal, (ii) total transit usage goal, (iii) spatial equity goal. This problem is formulated as a bi-level programming model. The upper level programming leads to minimize the deviation from stated goals in the context of a given priority ranking. While the lower level programming model is a modal split/traffic assignment model which is used to assess any given park and ride scheme. A heuristic tabu search algorithm is then adopted to solve this model.

You might also be interested in these eBooks

Materials, Transportation and Environmental Engineering II

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 1030-1032)

Pages:

2065-2068

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1030-1032.2065

Citation:

Cite this paper

Online since:

September 2014

Authors:

Xin Yuan Chen, Zhi Yuan Liu, Wei Deng

Keywords:

Bi-Modal Network, Goal Programming, Location Design, Multi-Objective Network Design, Park-and-Ride

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

References

[1] P. Hamer: Analysing the effectiveness of park and ride as a generator of public transport mode shift. Road & Transport Research, 19(1), (2010), p.51–61.

Google Scholar

[2] W. Lam, M. N. Holyoak and H. Lo: How PARK AND RIDE schemes can be successful in Eastern Asia. Journal of Urban Planning and Development, 127(2), (2001), p.63–78.

DOI: 10.1061/(asce)0733-9488(2001)127:2(63)

Google Scholar

[3] Z. Liu and Q. Meng: Bus-based PARK AND RIDE system: a stochastic model on multimodal network with congestion pricing schemes. International Journal of Systems Science, 45(5), (2014), pp.994-1006.

DOI: 10.1080/00207721.2012.743617

Google Scholar

[4] Z. Liu, Q. Meng and S. Wang: Speed-based toll design for cordon-based congestion pricing scheme. Transportation Research Part C, 31, (2013), pp.83-98.

DOI: 10.1016/j.trc.2013.02.012

Google Scholar

[5] Q. Meng and Z. Liu: Impact Analysis of Cordon-based Congestion Pricing Scheme on Mode-Split of Bimodal Transportation Network. Transportation Research Part C, 21(1), (2012), pp.134-147.

DOI: 10.1016/j.trc.2011.06.007

Google Scholar

[6] S. Wang, Q. Meng, and H. Yang: Global optimization methods for the discrete network design problem. Transportation Research Part B, 50, (2013), pp.42-60.

DOI: 10.1016/j.trb.2013.01.006

Google Scholar

[7] Z. Liu and Q. Meng: Distributed Computing Approaches for Large-scale Probit-based Stochastic User Equilibrium Problem, Journal of Advanced Transportation, 47, (2013), pp.553-571.

DOI: 10.1002/atr.177

Google Scholar

[8] Z. Liu, S. Wang and Q. Meng, (in press). Toll pricing framework under logit-based stochastic user equilibrium constraints, Journal of Advanced Transportation. DOI: 10. 1002/atr. 1255.

DOI: 10.1002/atr.1255

Google Scholar

[9] Z. Liu, Q. Meng, S. Wang and Z. Sun: Global Intermodal Liner Shipping Network Design, Transportation Research Part E, 61, (2014), pp.28-39.

DOI: 10.1016/j.tre.2013.10.006

Google Scholar

[10] Z. Liu, Y. Yan, X. Qu and Y. Zhang: Bus stop-skipping scheme with random travel time, Transportation Research Part C, 35, (2013), pp.46-56.

DOI: 10.1016/j.trc.2013.06.004

Google Scholar

[11] A. Chen and X. Xu. Goal programming approach to solving network design problem with multiple objectives and demand uncertainty. Expert Systems with Applications, 39(4), (2012). 4160-4170.

DOI: 10.1016/j.eswa.2011.09.118

Google Scholar

[12] Y. Bie, D. Wang and H. Qi: Prediction Model of Bus Arrival Time at Signalized Intersection Using GPS Data. Journal of Transportation Engineering, 138(1), (2011), pp.12-20.

DOI: 10.1061/(asce)te.1943-5436.0000310

Google Scholar

[13] J. Ortuzar and L. Willumsen: Modelling Transport, 4th Edition, Chichester: Wiley, (2011).

Google Scholar

[14] F. Glover and M. Laguna: Tabu Search. Handbook of Combinatorial Optimization, Kluwer Academic Publishers, 3, (1999), pp.621-757.

Google Scholar