A Visual Analytics Approach to Scheduling Customized Shuttle Buses via Perceiving Passengers' Travel Demands
This addresses the problem of unreliable demand estimation for shuttle bus operators, but it appears incremental as it builds on existing visual analytics methods.
The paper tackles the challenge of planning customized shuttle bus systems under dynamic commute demand by proposing a visual analytics approach to assess travel demands and plan night shuttle systems, with a preliminary case study verifying its efficacy.
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.