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

Qiangqiang Liu, Quan Li, Chunfeng Tang, Huanbin Lin, Xiaojuan Ma, Tianjian Chen

External link (DOI)
View presentation:2020-10-28T14:50:00ZGMT-0600Change your timezone on the schedule page
2020-10-28T14:50:00Z
Exemplar figure
ShuttleVis includes (A) a dataset loader and data description; (B) overview of car-hailing reimbursement records across different departments and descriptions of the departure and arrival time; (C) directional clustering configuration view to help analysts identify appropriate travel directions; (D) map view to visualize identified directional and regional clustering results, and comparative ranking view that includes (E1) a ranking of shuttle bus stops in terms of (E2) metrics in consecutive regional clusters along one travel direction, (E3) timetables of selected shuttle routes, and (E4) radar chart showing attribute distributions of selected routes.
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Direct link to video on YouTube: https://youtu.be/IBot29zTmJE

Keywords

Human-centered computing-Visualization- Visualization design and evaluation methods

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 facili- tating assessment of actual, varying travel demands and planning of night customized shuttle systems. A preliminary case study verifies the efficacy of our approach.