2009RePEc: Research Papers in EconomicsOpen access

Improving Mobility through Enhanced Transit Services: Transit Taxi Service for Areas with Low Passenger Demand Density

Yuwei Li, Mark A. Miller, Michael J. Cassidy

Open full text 5 citations

Abstract

This research report is the final deliverable for PATH Task Order 6408: “Improving Mobility through Enhanced Transit Services”. The purpose of this task order is to explore alternative methods of providing transit service to areas with low passenger demand density. This report first presents analytical models for determining optimal headway and line spacing for fixed-route, fixed schedule buses, either with fixed stops or allowing buses to stop anywhere along the route. Next, transit taxi services with either fixed or flexible routes that specifically target focused demand patterns are examined. Potential savings of transit taxi services and flexible routes are quantified. Based on the insights from the theoretical analysis and our survey of real-world practices of providing transit service to areas with low passenger demand density, a pilot program is designed for future field testing of an innovative type of transit taxi operation.

Open-access reader

About this research paper

What this paper is about

This research report is the final deliverable for PATH Task Order 6408: “Improving Mobility through Enhanced Transit Services”. The purpose of this task order is to explore alternative methods of providing transit service to areas with low passenger demand density. This report first presents analytical models for determining optimal headway and line spacing for fixed-route, fixed schedule buses, either with fixed stops or allowing buses to stop anywhere along the route. Next, transit taxi services with either fixed or flexible routes that specifically target focused demand patterns are examined. Potential savings of transit taxi services and flexible routes are quantified. Based on the insights from the theoretical analysis and our survey of real-world practices of providing transit service to areas with low passenger demand density, a pilot program is designed for future field testing of an innovative type of transit taxi operation.

Why it matters

OpenAlex reports 5 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

This research report is the final deliverable for PATH Task Order 6408: “Improving Mobility through Enhanced Transit Services”. The purpose of this task order is to explore alternative methods of providing transit service to areas with low passenger demand density. This report first presents analytical models for determining optimal headway and line spacing for fixed-route, fixed schedule buses, either with fixed stops or allowing buses to stop anywhere along the route. Next, transit taxi services with either fixed or flexible routes that specifically target focused demand patterns are examined. Potential savings of transit taxi services and flexible routes are quantified. Based on the insights from the theoretical analysis and our survey of real-world practices of providing transit service to areas with low passenger demand density, a pilot program is designed for future field testing of an innovative type of transit taxi operation.

Key concepts: Headway, Transit (satellite), Transport engineering, Schedule, Service (business), Public transport, Engineering, Computer science

Related papers

Back to paper searchBrowse research topicsOriginal source
Improving Mobility through Enhanced Transit Services: Transit Taxi Service for Areas with Low Passenger Demand Density — Research Paper | ScholarLens