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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
11.12.2019
DOI
https://doi.org/10.7307/ptt.v31i6.3197
ISSUE
2019 (Vol 31), Issue 6
LICENSE
Copyright (c) 2024 , , , , ,
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This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Dynamic Demand Forecast and Assignment Model for Bike-and-Ride System

Authors:







Keywords:Bike-and-Ride, dynamic demand, generalised cost, user equilibrium model, Frank-Wolfe algorithm

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

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    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.

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    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-

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How to Cite
, . (et al.) 2019. Dynamic Demand Forecast and Assignment Model for Bike-and-Ride System. Traffic&Transportation Journal. 31, 6 (Dec. 2019), 621-632. DOI: https://doi.org/10.7307/ptt.v31i6.3197.

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