Dynamic Demand Forecast and Assignment Model for Bike-and-Ride System
Abstract
Bike-and-Ride (B&R) has long been considered as an effective way to deal with urbanization-related issues such as traffic congestion, emissions, equality, etc. Although there are some studies focused on the B&R demand forecast, the influencing factors from previous studies have been excluded from those forecasting methods. To fill this gap, this paper proposes a new B&R demand forecast model considering the influencing factors as dynamic rather than fixed ones to reach higher forecasting accuracy. This model is tested in a theoretical network to validate the feasibility and effectiveness and the results show that the generalised cost does have an effect on the demand for the B&R system.
References
Xi Z, Jingchu C, Chutao C. The Bike-and-ride Research Based on Low Carbon Consideration at Urban Rail Transit Station. Advanced Materials Research. 2014 Nov 5; 2408-2413.
Qin H, Gao J, Kluger R, Wu YJ. Effects of perception on public bike-and-ride: A survey under complex, multifactor mode-choice scenarios. Transportation Research Part F: Traffic Psychology and Behavior. 2018 Apr 30;54: 264-75.
Yang L, Wang Y. Bike-and-Ride Behavior Study in Economic and Technological Development Zone in Xi’an. Procedia – Social and Behavioral Sciences. 2014 Jul 14;138: 168-73.
Albalate D, Bel G. Tourism and urban public transport: Holding demand pressure under supply constraints. Tourism Management. 2010 Jun 1;31(3): 425-33.
Purifoye GY. Transporting Urban Inequality Through Public Transit Designs & Systems. City & Community. 2017 Dec;16(4): 364-8.
Zhao L, Shen L. The impacts of rail transit on future urban land use development: A case study in Wuhan, China. Transport Policy. 2019;81: 396-405.
Brons M, Nijkamp P, Pels E, Rietveld P. Efficiency of urban public transit: A meta analysis. Transportation. 2005 Jan 1;32(1): 1-21.
Martens K. The bicycle as a feedering mode: experiences from three European countries. Transportation Research Part D: Transport and Environment. 2004 Jul 1;9(4): 281-94.
Tang G, Keshav S, Golab L, Wu K. Bikeshare Pool Sizing for Bike-and-Ride Multimodal Transit. IEEE Transactions on Intelligent Transportation Systems. 2018 Jul;19(7): 2279-89.
Jingxu C, Xuewu C, Wei W, Baol F. The demand analysis of bike-and-ride in rail transit stations based on revealed and stated preference survey. Procedia – Social and Behavioral Sciences. 2013 Nov 6;96: 1260-8.
Chan H, Zhi-yun Z. People's Intention towards Public Bicycle System in Wuhan. In: 2015 8th International Symposium on Computational Intelligence and Design (ISCID), 12-13 Dec. 2015, Hangzhou, China. IEEE; 2016. p. 148-151.
Willis DP, Manaugh K, El-Geneidy A. Uniquely satisfied: Exploring cyclist satisfaction. Transportation Research Part F: Traffic Psychology and Behavior. 2013 May 1;18: 136-47.
Fishman E, Washington S, Haworth N. Barriers and facilitators to public bicycle scheme use: A qualitative approach. Transportation Research Part F: Traffic Psychology and Behaviour. 2012 Nov 1;15(6): 686-98.
Kager R, Bertolini L, Te Brömmelstroet M. Characterisation of and reflections on the synergy of bicycles and public transport. Transportation Research Part A: Policy and Practice. 2016 Mar 1;85: 208-19.
Puello LL, Geurs K. Modelling observed and unobserved factors in cycling to railway stations: application to transit-oriented-developments in the Netherlands. European Journal of Transport and Infrastructure Research. 2015 Jan 1;15(1).
Krizek KJ, Stonebraker EW. Bicycling and transit: A marriage unrealized. Transportation Research Record. 2010 Jan;2144(1): 161-7.
Chen YA, Jian LU, Wei WA, Qian WA. A study on the influencing factors of bicycle transportation based on individual mode choice. Journal of Transportation Systems Engineering and Information Technology. 2007;4: 131-7.
Pei M, Haiyan W. Research on the Bicycle Transfer Rail Traffic Behavior Mechanism and Model. Journal of Beijing University of Civil Engineering and Architecture. 2011;2: 36-40.
Advani M, Tiwari G. Bicycle–As a feeder mode for bus service. In: Velo Mondial Conference: Third Global Cycling Planning Conference, Cape Town, South Africa. 2006 Mar.
Shaheen SA, Guzman S, Zhang H. Bikesharing in Europe, the Americas, and Asia: Past, present, and future. Transportation Research Record. 2010 Jan;2143(1): 159-67.
Brandenburg C, Matzarakis A, Arnberger A. Weather and cycling—a first approach to the effects of weather conditions on cycling. Meteorological Applications. 2007 Mar 1;14(1): 61-7.
Flynn BS, Dana GS, Sears J, Aultman-Hall L. Weather factor impacts on commuting to work by bicycle. Preventive Medicine. 2012 Feb 1;54(2): 122-4.
Pucher J, Buehler R. Why Canadians cycle more than Americans: a comparative analysis of bicycling trends and policies. Transport Policy. 2006 May 1;13(3): 265-79.
Heinen E, Maat K, Van Wee B. The role of attitudes toward characteristics of bicycle commuting on the choice to cycle to work over various distances. Transportation Research Part D: Transport and Environment. 2011 Mar 1;16(2): 102-9.
Taylor D, Mahmassani H. Analysis of stated preferences for intermodal bicycle-transit interfaces. Transportation Research Record: Journal of the Transportation Research Board. 1996 Jan 1;1556: 86-95.
Wardman M, Tight M, Page M. Factors influencing the propensity to cycle to work. Transportation Research Part A: Policy and Practice. 2007 May 1;41(4): 339-50.
Fukuda D, Morichi S. Incorporating aggregate behaviour in an individual’s discrete choice: An application to analyzing illegal bicycle parking behavior. Transportation Research Part A: Policy and Practice. 2007 May 1;41(4): 313-25.
Lin JR, Yang TH. Strategic design of public bicycle sharing systems with service level constraints. Transportation Research Part E: Logistics and Transportation Review. 2011 Mar 1;47(2): 284-94.
Vogel P, Greiser T, Mattfeld DC. Understanding bike-sharing systems using data mining: Exploring activity patterns. Procedia – Social and Behavioral Sciences. 2011 Jan 1;20: 514-23.
Yang L, Wang Y. Bike-and-Ride Behavior Study in Economic and Technological Development Zone in Xi’an. Procedia – Social and Behavioral Sciences. 2014 Jul 14;138: 168-73.
Aziz HA, Nagle NN, Morton AM, Hilliard MR, White DA, Stewart RN. Exploring the impact of walk–bike infrastructure, safety perception, and built-environment on active transportation mode choice: a random parameter model using New York City commuter data. Transportation. 2018 Sep 1;45(5): 1207-29.
Chan H, Zhi-yun Z. People's Intention towards Public Bicycle System in Wuhan. In: 2015 8th International Symposium on Computational Intelligence and Design (ISCID), 12-13 Dec. 2015, Hangzhou, China. IEEE; 2016. p. 148-151.
Yang L, Wang Y. Bike-and-Ride Behavior Study in Economic and Technological Development Zone in Xi’an. Procedia – Social and Behavioral Sciences. 2014 Jul 14;138: 168-73.
Pucher J, Komanoff C, Schimek P. Bicycling renaissance in North America?: Recent trends and alternative policies to promote bicycling. Transportation Research Part A: Policy and Practice. 1999 Sep 1;33(7-8): 625-54.
Pucher J, Dill J, Handy S. Infrastructure, programs, and policies to increase bicycling: an international review. Preventive Medicine. 2010 Jan 1;50: S106-25.
Chen C, Anderson JC, Wang H, Wang Y, Vogt R, Hernandez S. How bicycle level of traffic stress correlate with reported cyclist accidents injury severities: A geospatial and mixed logit analysis. Accident Analysis & Prevention. 2017 Nov 1;108: 234-44.
Lumsdon L, Tolley R. The National Cycle Strategy in the UK: to what extent have local authorities adopted its model strategy approach?. Journal of Transport Geography. 2001 Dec 1;9(4): 293-301.
Martens K. Promoting bike-and-ride: The Dutch experience. Transportation Research Part A: Policy and Practice. 2007 May 1;41(4): 326-38.
Pucher J, Buehler R. Why Canadians cycle more than Americans: a comparative analysis of bicycling trends and policies. Transport Policy. 2006 May 1;13(3): 265-79.
Zhang J, Ren F, Liu X, Shi Y. Transfer Analysis of Bike to Public Transit in Beijing. China Journal of Highway and Transport. 1995;8. Chinese.
Caulfield B, Brick E, McCarthy OT. Determining bicycle infrastructure preferences–A case study of Dublin. Transportation Research Part D: Transport and Environment. 2012 Jul 1;17(5): 413-7.
Kang Z, Zuo Y, Huang Z, Zhou F, Chen P. Research on the Forecast of Shared Bicycle Rental Demand Based on Spark Machine Learning Framework. In: 2017 16th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES), 13-16 Oct 2017, AnYang, China. IEEE; 2017. p. 219-222.
Feng Y, Wang S. A forecast for bicycle rental demand based on random forests and multiple linear regression. In: 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), 24-26 May 2017, Wuhan, China. IEEE; 2017. p. 101-105.
Wu Z, Fan Y, Tao J, Zhang J. Key Laboratory of Road and Traffic Engineering in the Ministry of Education, Tongji University; Shanghai Urban Construction Design and Research Institute; Bike-and-Ride Demand Forecasting in Urban Rail Transit Station. Journal of Wuhan University of Technology (Transportation Science & Engineering). 2013;5.
Jingxu C, Xuewu C, Wei W, Baol F. The demand analysis of bike-and-ride in rail transit stations based on revealed and stated preference survey. Procedia – Social and Behavioral Sciences. 2013 Nov 6;96: 1260-8.
Wardman M, Tight M, Page M. Factors influencing the propensity to cycle to work. Transportation Research Part A: Policy and Practice. 2007 May 1;41(4): 339-50.
Mi W. Study on the Bike-and-Ride for Urban Railway System. Sichuan: Southwest Jiaotong University; 2007.
Altman E, Basar T, Jimenez T, Shimkin N. Competitive routing in networks with polynomial costs. IEEE Transactions on Automatic Control. 2002 Jan;47(1): 92-6.
Yang J, Zhuang Y. An improved ant colony optimization algorithm for solving a complex combinatorial optimization problem. Applied Soft Computing. 2010 Mar 1;10(2): 653-60.
Dorigo M, Maniezzo V, Colorni A. Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics. 1996 Feb 26;26(1): 29-41.
Gong YC, Yang RF, Li CL. Urban Parking Allocation Model Based on Minimum Generalized Parking Costs. In: First International Conference on Transportation Engineering; 2007. p. 527-532.
He S, Fan B. Generalized wardrop principle and its application in regional transportation. Transportation Research Record. 2008 Jan;2085(1): 49-56.
Cho HJ, Chen YK. Finding the e-user Equilibrium Solution Using an Augmented Frank-Wolfe Algorithm. Networks and Spatial Economics. 2010 Dec 1;10(4): 473-85.
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