Stepwise multi‐criteria and multi‐strategy design of public transit shuttles

Abstract

The idea of designing an integrated smart public transport (transit) shuttle service is stemming from the need to overcome the problem of using an excessive number of cars arriving and parking at a train station within the same time span. This problem results in high parking demand around the train station. Moreover some potential train riders will, instead, use their cars and hence become a party to increasing the traffic congestion. The purpose of this work is to examine an innovative transit shuttle system that will comply with (i) users' needs and desires, (ii) intelligent transportation technologies, and (iii) operator's viability. In order to arrive at a design of this innovative system, new stepwise multi-criteria and multi-strategy concepts are developed in this work. In the search for best solution, the minimum fleet size required for the shuttle service is determined followed by a development of an optimization model and a heuristic algorithm for optimal route-design. The findings show that the heuristic algorithm indeed provides good (optimal in the test case) results. The heuristic procedure then is implemented over medium-and large-sized networks, generated randomly, and found to provide good results within a reasonable running time. At the operational level 10 different routing strategies are examined, with all the combinations of fixed/flexible routes, fixed/ flexible schedules, a uni-or bi-directional concept, and short-cut (shortest path) and/or short-turn (turn-around) concepts. These strategies are investigated by employing a simulation model specifically developed and constructed for this purpose. This simulation model is used in a case study of Castro Valley in California in which the shuttle service is coordinated with the BART (the metro of San Francisco).

Detection and improvement of deficiencies and failures in public-transportation networks using agent-enhanced distribution data mining

“…An IMA is able to sense the environment, process the data/information obtained from the environment, and act to execute the most appropriate actions that contribute to the improvement of the PT system. The paper follows previous studies of the authors for detecting failures in PT and scheduling mobile agents (see, for example, 1998;Ceder 2003Ceder ,2006Ceder , 2010Ceder , 2011Ceder 2008a, 2010;Frenkel et al, 2008;;Elalouf et al 2011). Our approach is directed at automating the data collection process concerning failures, weak segments, and bottlenecks in PT networks and operations planning.…”

Optimal Design of Demand-Responsive Feeder Transit Services with Passengers’ Multiple Time Windows and Satisfaction

“…Other relevant studies on DRTs address the coordination between design of the passenger rail service, station spacing, the design of bus routes, and headways using integrated, robust, and bi-level programming optimization [22][23][24][25]. The above varieties of DRTs are addressed from various objectives, including minimizing the operating costs of the fleet [3,11,26], the shortest length or travel time of the route [27,28], the minimal fleet size required for shuttle service [1,22], ride time, and waiting time of passengers [4,15]. Fortunately, these objectives normally do not affect the properties of DRTs and, thus, similar models and algorithms can be adopted to solve the problem with different objectives.…”

“…In this paper, a DRT is proposed that provides services to conveniently transport passengers from demand points to the rail station [1,40,41]. Using a cellphone app and an open geo-information system(GIS)tool, we can obtain the traveling information of some passengers and the traffic network in the study area.…”

“…A feasible solution to the problem is the planning, design, and implementation of efficient feeder transit services [1,2]. Traditionally, transit services have been divided into two broad categories: the fixed route (FRT) and the demand responsive (DRT).…”

Route Design Model of Feeder Bus Service for Urban Rail Transit Stations

“…Chien et al considered constraints of geography, passenger flow, budget, and others and then proposed a method of operating feeder bus services based on transfer centers under a grid-like road network [11][12][13]. Ceder proposed a potential demand indicator for shuttle buses and established a model based on maximization of the indicator [14,15]. Fan and Machemehl analyzed potential characteristics of bus route optimization under the condition of variable demand and then proposed a nonlinear mixed integer programming model based on multiple objectives [16].…”