Behavior of taxi customers in hailing vacant taxis: a nested logit model for policy analysis

This study models and examines the taxi customers' preferences for hailing vacant taxis on streets. A stated preference survey was conducted to randomly select and interview 1242 taxi customers at taxi stands and pedestrians on streets, who had experiences of taking taxis recently, about their choices under different given hypothetical scenarios. In total, 4968 observations were collected and used for developing the discrete choice models for the analysis. To account for the potential correlations among alternatives, two nested logit models are developed, calibrated, and compared with a standard multinomial logit model in the investigation. The results of likelihood ratio test demonstrate that one of the developed nested logit models is better than the standard multinomial logit model to describe the search behavior of taxi customers. The model results also show that the walking time to and the waiting time at the location for hailing taxis, the extra travel time to the destination because of local circulation for finding a way from the pickup location heading to a passenger's destination, as well as the taxi customers' perceptions for walking to and waiting at taxi stands were found as significant factors to influence their decisions. In addition, the results of market segmentation analysis illustrate the variations in taxi‐search strategies of taxi customers in different districts and regions. Some policy implications on introducing more taxi stands and improving the utilization rates of taxi stands are also discussed. We believe that the proposed models, findings, and discussion are useful for developing micro‐simulation models to evaluate the performance of road traffic networks with taxi services and developing simulation‐based optimization models to answer policy questions related to taxi services. Copyright © 2015 John Wiley & Sons, Ltd.     

Taxis in road pricing zone: should they pay the congestion charge?

This study aims at investigating the impact and feasibility of charging taxis with toll fee in the pricing zone when designing congestion pricing scheme. A bi‐level programming model is developed to compare the maximum social welfares before and after the congestion charge is imposed on taxis. The lower level is a combined network equilibrium model formulated as a variational inequality program, which considers the logit‐based mode split, route choice, elastic demand, and vacant taxi distributions. The upper level is to maximize the social welfare when toll rates vary. The bi‐level problem can be solved by the genetic algorithm, whereas the lower level is solved by the block Gauss–Seidel decomposition approach together with the method of successive averages and diagonalization algorithm. An application with numerical examples is conducted to demonstrate the effectiveness of the proposed model and algorithm and to reveal some interesting findings. Copyright © 2014 John Wiley & Sons, Ltd.     

Determining locations of charging stations for electric taxis using taxi operation data

The adequate provision of charging infrastructure is critical for the effective deployment of electric taxis. This study attempts to locate charging stations for electric taxis reflecting real-world taxi travel patterns identified from taxis equipped with digital tachographs. Data for one week are processed in order to estimate their charge demand. The estimated temporal distribution of charge demand indicates that it varies day-by-day and hour-by-hour. The maximum set covering model is applied for determining the locations of charging stations. The results show that the pre-specified service distance and service coverage rate (defined by the proportion of total demand served) can be critical factors for determining the number and location of charging stations. These factors should be carefully specified by considering the tradeoff between operational efficiency of charging facilities and user convenience.     

Optimal scheduling of a taxi fleet with mixed electric and gasoline vehicles to service advance reservations

This study addresses the problem of scheduling a fleet of taxis that are appointed to solely service customers with advance reservations. In contrast to previous studies that have dealt with the planning and operations of a taxi fleet with only electric vehicles (EVs), we consider that most taxi companies may have to operate with fleets comprised of both gasoline vehicles (GVs) and plug-in EVs during the transition from GV to (complete) EV taxi fleets. This paper presents an innovative multi-layer taxi-flow time-space network which effectively describes the movements of the taxis in the dimensions of space and time. An optimization model is then developed based on the time-space network to determine an optimal schedule for the taxi fleet. The objective is to minimize the total operating cost of the fleet, with a set of operating constraints for the EVs and GVs included in the model. Given that the model is formulated as an integer multi-commodity network flow problem, which is characterized as NP-hard, we propose two simple but effective decomposition-based heuristics to efficiently solve the problem with practical sizes. Test instances generated based on the data provided by a Taiwan taxi company are solved to evaluate the solution algorithms. The results show that the gaps between the objective values of the heuristic solutions and those of the optimal solutions are less than 3%, and the heuristics require much less time to obtain the good quality solutions. As a result, it is shown that the model, coupled with the algorithms, can be an effective planning tool to assist the company in routing and scheduling its fleet to service reservation customers.     

区域ITS项目实施的综合排序方法研究

区域ITS项目实施的综合排序主要受到ITS项目实施的需求程度、影响程度、实施效益以及需要实施的ITS项目之间的内在关系的制约。采用DEMATEL方法对ITS项目实施的需求程度进行排序，采用层次分析法对ITS项目实施的影响程度和实施效益分别进行排序，并提出了采用线性综合排序方法对ITS项目的实施进行综合排序，最后分析ITS系统实施的内在发展规律以此对ITS系统实施的综合排序进行调整。此排序方法即考虑了项目实施的外在因素，又兼顾了项目的内在发展关系。同时将该方法用于江苏省公路ITS项目的实施排序中，避免了ITS项目实施的主观臆断性。