An Analytical Model for the Many-to-One Demand Responsive Transit Systems

Abstract

The demand-responsive transit (DRT) service is an emerging and flexible transit mode to enhance the mobility of the urban transit system by providing personalized services. Passengers can make advanced appointments through smartphone applications. In this paper, an analytical model is proposed for the many-to-one DRT system. The agency and user costs are approximated by closed-form expressions. The agency cost, which is also the operation cost, is approximated by the continuum approximation technique. A nearest-neighbor routing strategy is applied, whereby the vehicle always collects the nearest passenger waiting in the system. The Vickrey queueing theory is adopted as the basis for approximating each component of the user cost, which is composed of the out-of-vehicle and in-vehicle waiting times and schedule deviations, which also depend on the service quality of the DRT system. The results of the numerical experiment show that (1) the agency and user costs are influenced significantly by the demand density, and (2) the DRT operator cannot further decrease the operating cost by solely deploying larger vehicles.

A survey on demand-responsive public bus systems

“…The goal of this service is to minimize the total distance traveled by each vehicle, as well as the in-vehicle travel time of the passengers. Huang et al (2020b) study a door-to-door feeder service. The objective function is a weighted sum of operating costs and passenger costs.…”

Fixed-Route vs. Demand-Responsive Transport Feeder Services: An Exploratory Study Using an Agent-Based Model

“…Chandra and Quadrifoglio [28] extended this model [18], using a gravity-based accessibility model to evaluate the accessibility impacts for first-/last-mile transport connectivity in the case of fixed-route transit and DRT, although using the same uniform demand across space. Other issues investigated through analytical models were fleet sizing based on a given quality of service for users [10], route design [20,29], and the choice between different flexible transit strategies to accommodate a variable demand level [30], or estimating how user and operator costs vary according to the demand density, the service area [31], and the fleet size [32].…”

On understanding the impacts of shared public transportation on urban traffic and road safety using an agent-based simulation with heterogeneous fleets: a case study of Casablanca city

“…It has controlled a set of strategies including a combination of speed control and holding strategies based on the result of analytical models. In this respect, an analytical model has been proposed for the many-to-one process of demand-responsive transit service (Huang et al 2019). The agency and user costs have been approximated by closed-form expressions.…”