A Visual Analytics Approach to Scheduling Customized Shuttle Buses via Perceiving Passengers' Travel Demands

TL;DR

This paper presents a visual analytics method to dynamically assess passenger travel demands and optimize customized shuttle bus scheduling, addressing challenges of demand variability with innovative visualization tools.

Contribution

It introduces a novel visual analytics framework for real-time demand assessment and shuttle scheduling, improving upon traditional static modeling methods.

Findings

Effective visualization of dynamic travel demands

Improved shuttle scheduling accuracy

Validated through a preliminary case study

Abstract

Shuttle buses have been a popular means to move commuters sharing similar origins and destinations during periods of high travel demand. However, planning and deploying reasonable, customized service bus systems becomes challenging when the commute demand is rather dynamic. It is difficult, if not impossible to form a reliable, unbiased estimation of user needs in such a case using traditional modeling methods. We propose a visual analytics approach to facilitating assessment of actual, varying travel demands and planning of night customized shuttle systems. A preliminary case study verifies the efficacy of our approach.