Abstract
This study proposes a multiobjective mixed integer nonlinear programming model for a last-mile shuttle service to improve bus commuters’ travel time reliability. The approach aims to assign the routes that pick up the transit passengers located at the different stops by shuttle service. A bilevel optimization model is established: the upper model of route design considers the tradeoff between time cost and fare cost when some of the passengers take the shuttles, and the lower model assigns the demand of transit passengers. The proposed model effectively captures the reliability of travel time because related parameters are estimated by a statistical fitting test with a large number of real-world bus geographic information system (GPS) data. Moreover, dynamic demand diverting from conventional transit to shuttle service and travel time reliability, including passenger in-vehicle time (IVT) and waiting time (WT), are fully considered in this model. Since the task is a nonlinear programming model, a two-stage algorithm combined with linearization processing is presented to find an optimal solution. Finally, from the case study of Zhongguancun Software Park zone in Beijing, it is indicated that when last-mile shuttle service is provided, bus passengers’ travel time reliability of last-mile trips can be improved by 14%. The study can be an important reference for improving the low reliability widely existing in the current transit commuters’ last-mile problem.
Highlights
In China, traffic congestions and delays are becoming more and more frequent and severe with the rapid increase of automobile ownership
E area for last-mile trip typically covers narrow roads and suffers frequent traffic congestion. erefore, by conventional transit systems with fixed routes and stops, passengers cannot avoid passing through those congested areas, which results in great difficulty to get to a destination on time [3]
Data Processing Based on geographic information system (GPS) Data. e GPS data are used to estimate the parameters of bus running conditions, which are collected from on-vehicle GPS facilities. e original data sets mainly have two problems: data losses and data errors
Summary
In China, traffic congestions and delays are becoming more and more frequent and severe with the rapid increase of automobile ownership. Is work proposes a robust optimization model for shuttle service considering travel time reliability by designing shuttle routes and estimating the corresponding number of users. It can be used to solve the last-mile trip as an effective measure to alleviate peak travel demand in hotspots [21] It has wide applicability in many areas, such as hospital emergency vehicle path planning [22], disabled public transportation service planning [23], and regional community school bus planning [24]. Yao et al [27] presented a robust optimization model considering travel time uncertainty to satisfy the demand of passengers and provide reliable transit service. In big cities like Beijing, during peak hours, the traffic is heavily congested and the amount of boarding and alighting passengers at “hot stop” will rapidly increase, which will cause the transit system to become extremely unreliable. erefore, field data in big cities should be presented to help design more reliable shuttle services
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