Special issue on public transport systems

Abstract

The Journal of Advanced Transportation has been a leader in the area of public transport for over four decades. This special issue presents an outstanding series of novel papers on public and freight transport systems in the areas of demand estimation, planning, and operations to highlight recent advances. In the following, we paraphrase information from the abstracts of the papers. The first two papers deal with demand estimation issues. In “A method to estimate the historical US air travel demand,” Tao Li, Hojong Baik, and Antonio A. Trani propose a route-based optimization model for estimation of the historical air travel demand in the USA. In the paper “Geographic information system - system dynamics procedure for bus rapid transit ridership estimation” by Luis David Galicia and Ruey Long Cheu, a two-step procedure for estimating the total daily ridership of a new bus rapid transit route is proposed. The two-step procedure has been validated with actual demographic and ridership data from two lines. The proposed procedure offers an approach that complements the ridership estimation methods currently accepted by the US Federal Transit Administration. The third and fourth papers deal with urban railway planning issues. In “A stochastic multi-period investment selection model to optimize strategic railway capacity planning,” Yung-Cheng Lai and Mei-Cheng Shih develop a stochastic multi-period investment selection model to assist railroads to allocate capital investments within the strategic capacity planning process. Their novel optimization framework provides a means to cope with unfulfilled demand and demand uncertainty in a long-term multi-period investment selection problem. Using this decision support tool will minimize the risk in strategic capacity planning, subject to demand uncertainty. In “Development of a transfer-cost-based logit assignment model for the Beijing rail transit network using automated fare collection data,” Bingfeng Si, Ming Zhong, Jianfeng Liu, Ziyou Gao, and Jianjun consider the major factors that influence the passenger flow pattern in the Beijing rail transit network through a logit-based network flow assignment model. A full transfer cost function, including transfer walking time, vehicle waiting time, and a penalty for additional transfers, is proposed to simulate passengers' transfer behavior. They show that the models developed are capable of reproducing transfer and route choices made by passengers. The next three papers deal with issues related to information provided to passengers in urban public transport systems. In “A methodology for schedule-based paths recommendation in multimodal public transportation networks,” David Canca, Alejandro Zarzo, Pedro L. González-R, Eva Barrena, and Encarnación Algaba analyze intermodal itinerary recommendations given to passengers in interurban networks with several public transportation modes. Additionally, time, and capacity constraints, as well as seat booking, are considered. The related optimization problem is modeled for a generic user request and solved with the use of a network graph transformation. In “Users' views on current and future real-time bus information systems,” Md. Matiur Rahman, S.C. Wirasinghe, and Lina Kattan examine the users' views and perceptions towards the possible future availability of real-time bus information systems through a face to face survey. The results showed that 35.5% of the respondents agreed that the current information system discouraged them from using public transport. Of those surveyed, 82% board the first arriving bus regardless of the route, even though it may take a longer in-vehicle time to complete the trip. A majority of the respondents (88%) indicated that real-time transit information would not be necessary if bus headways are less than 10 minutes. Information on the next bus arrival time received the highest priority. In “Railway passenger train delay prediction via neural network model,” Masoud Yaghini, Mohammad M. Khoshraftar, and Masoud Seyedabadi present an artificial neural network model to predict the delay of passenger trains in Iranian Railways. To evaluate the proposed model, they compared the results of three different data input methods as well as three different architectures with each other and with some common prediction methods such as decision tree and multinomial logistic regression. The final paper is on bus transit operations. In “A rule-based model for integrated operation of bus priority signal timings and travelling speed,” Wanjing Ma, Yue Liu, and Baoxin Han study the integrated operation of signal timings and bus speed to provide priority to busses at isolated intersections when real-time adjustment of bus speed is available. A set of integrated operational rules is developed for busses with and without schedule deviation with the objective of minimizing bus schedule deviation, bus fuel consumption, and emissions.