Additional Riderships Estimation under Different Configurations of Bus Rapid Transit System

Mathematical Problems in Engineering, Sep 2015

Different configurations of Bus Rapid Transit (BRT) system may cause different additional riderships. In this paper, in terms of network traffic equilibrium assignment principle, the additional riderships estimation model based on Variational Inequality (VI) model is presented. The bus frequency is related to variables including the travel time, the residence time in terminals, and the dwelling time at the stops. The additional riderships are translated into network additional traffic flow firstly. Given the bus frequency, VI model can be turned into Stochastic User Equilibrium (SUE) model to calculate the other variables. The similarity diagonalization method is used to calculate the elastic bus frequency and finally the network additional traffic flow can be computed. The additional riderships under different configurations of BRT system are compared in the numerical test. The results show that the additional riderships under different configurations have large differences and occupy a high percentage of the total ridership.

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Additional Riderships Estimation under Different Configurations of Bus Rapid Transit System

Additional Riderships Estimation under Different Configurations of Bus Rapid Transit System Wu Lan,1 Chen Xuewu,2 and Lu Tao1 1College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China 2School of Transportation, Southeast University, Nanjing 210096, China Received 14 January 2015; Accepted 16 February 2015 Academic Editor: Wei (David) Fan Copyright © 2015 Wu Lan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Different configurations of Bus Rapid Transit (BRT) system may cause different additional riderships. In this paper, in terms of network traffic equilibrium assignment principle, the additional riderships estimation model based on Variational Inequality (VI) model is presented. The bus frequency is related to variables including the travel time, the residence time in terminals, and the dwelling time at the stops. The additional riderships are translated into network additional traffic flow firstly. Given the bus frequency, VI model can be turned into Stochastic User Equilibrium (SUE) model to calculate the other variables. The similarity diagonalization method is used to calculate the elastic bus frequency and finally the network additional traffic flow can be computed. The additional riderships under different configurations of BRT system are compared in the numerical test. The results show that the additional riderships under different configurations have large differences and occupy a high percentage of the total ridership. 1. Introduction Although the foundation ridership can be generated by the increase of the population and the growth of the economy, additional ridership can be generated by Bus Rapid Transit (BRT) system. Different configurations of BRT system induce the diverse scale of additional riderships. Good configurations of BRT system cause the increase of traffic demand and make additional ridership account for a great percentage of the total ridership. The additional riderships induced by different configurations of BRT system are proposed in this paper, which should be considered in the ridership estimation. The different configurations of BRT system have been attempted by lots of researchers. Laporte et al. [1] reviewed the main optimization methods for the BRT planning. Abdelghany et al. [2] developed a modeling framework for the planning of BRT services in urban transportation network. Aiming at minimizing the total travel time of passengers, Li et al. [3] presented an optimization model for the BRT planning. Schmid [4] proposed a hybrid metaheuristic approach based on large neighborhood search and liner programming to solve the bus rapid transit design problem. In order to capture the effects of BRT services on urban transportation, various methods have been developed, and a majority of those methods are based on simulation. Salem et al. [5] used a CORSIM model to conduct the benefit/cost analysis of the BRT service. Yagi and Mohammadian [6] simulated the BRT development to study the variation of modal split between automobiles and bus rapid transit ridership. Gunawan et al. [7] presented a numerical simulation based on discrete-event approach to identify variables which affects the performance of BRT system. The microscopic simulation techniques are also widely used in the research field of BRT. Yu et al. [8] applied GPS data in the VISSIM-based simulation for BRT system in Beijing. Godavarthi et al. [9] used a microsimulation to find the optimum volume/capacity ratio on BRT routes. Cervero and Kang [10] measured the impacts of implementing BRT on adjacent land usage and land value, by using multilevel models. Based on six characters of BRT system including infrastructure, transport capacity, service level, economic results, safety and emergency management, and energy saving and emission reduction [11], established an evaluation methodology for BRT operation. To study the modal shifts to BRT from other modes like automobiles, normal buses, Wang et al. [12] developed a binary logistic analysis approach. Falbel et al. [13] studied the impacts of implementation of BRT on the traffic improvement over the urban transport network, including the additional ridership to the transit system. Currie and Delbosc [14] used several regression models to explore the relationship between BRT design features and increment of ridership. Sun et al. [15] established a numerical model for the headway optimization as well as scheduling combination of BRT vehicles to increase ridership and improve operation performance of BRT. There are few previous literatures that studied the additional ridership under different configurations of BRT system. This paper developed a numerical test based on Variational Inequality (VI) model for additional ridership estimation under different c (...truncated)


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Wu Lan, Chen Xuewu, Lu Tao. Additional Riderships Estimation under Different Configurations of Bus Rapid Transit System, Mathematical Problems in Engineering, 2015, 2015, DOI: 10.1155/2015/406545