Joint Optimization of Zone Area and Headway for Demand Responsive Transit Service under Heterogeneous Environment

Abstract

This paper presents a mathematical model to optimize zonal demand responsive transit (DRT) considering heterogeneous environment (i.e., community boundary, land use, demand distribution, line-haul travel time, etc.) under the advent of Mobility-as-a-Service (MaaS). Since most previous models over-simplified conditions of the DRT service area, we propose a new modeling approach to formulate the operator and user costs. Passengers with varied expectations of vehicle arrival time at a drop-off location are considered. The average cost is minimized through optimizing service zone areas and associated headways subject to practical constraints (i.e., policy headway and vehicle capacity). A real-world region in the City of Calgary, Canada, is applied to demonstrate the applicability of the model. The impact of real-time vehicle arrival information to the optimal solution is assessed. The relationship between system parameters (i.e., line-haul travel time, demand density, vehicle capacity, and passenger composition, etc.) and the optimized solutions (i.e., zone area, headway, and costs) is explored through the sensitivity analysis.

Demand Response Transit Scheduling Research Based on Urban and Rural Transportation Station Optimization

“…(6) The vehicle departs from the site and returns to the site after completing the demand response. (7) To simplify the calculation, the distance travelled by the bus is assumed to be a straight line between two points.…”

“…The effects of the DK-means variableroute DRT relative to the regional flexible DRT and K-means variable-route DRT on (a) operating cost and (b) running time for each frequency are shown in Figure 11. Tables 5 and 6 are the time for the four shifts to pass through fixed stations (1, 2, 3, 4, 5) and alternative stations (6,7,8,9,10,11,12), where "-" means that the vehicle does not pass the station.…”

“…Lu [6] proposed a flexible feeder bus route model that can serve irregularly shaped networks within cities and improve the accessibility of bus systems in urban areas. Wang [7] points out that it may be beneficial for public transport agencies to adapt to changing markets and provide flexible bus services in rural and suburban areas. Das [8] pointed out that rural feeder services between villages and bus stops are largely a missing component, suggesting that planning for feeder services can help bring benefits to rural communities.…”