Journal
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
Volume 22, Issue 1, Pages 170-179Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2015.2467592
Keywords
Transportation; schedules; kernel density estimation; visual exploration
Categories
Funding
- IBM
- Moore-Sloan Data Science Environment at NYU
- NYU School of Engineering
- NYU Center for Urban Science and Progress
- ATT
- NSF [CNS-1229185]
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [1405927, 1229185] Funding Source: National Science Foundation
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Public transportation schedules are designed by agencies to optimize service quality under multiple constraints. However, real service usually deviates from the plan. Therefore, transportation analysts need to identify, compare and explain both eventual and systemic performance issues that must be addressed so that better timetables can be created. The purely statistical tools commonly used by analysts pose many difficulties due to the large number of attributes at trip- and station-level for planned and real service. Also challenging is the need for models at multiple scales to Search for patterns at different times and stations, since analysts do not know exactly where or when relevant patterns might emerge and need to compute statistical summaries for multiple attributes at different granularities. To aid in this analysis, we worked in close collaboration with a transportation expert to design TR-EX, a visual exploration tool developed to identify, inspect and compare spatio-temporal patterns for planned and real transportation service. TR-EX combines two new visual encodings inspired by Marey's Train Schedule: Trips Explorer for trip-level analysis of frequency, deviation and speed; and Stops Explorer for station-level study of delay, wait time, reliability and performance deficiencies such as bunching. To tackle overplotting and to provide a robust representation for a large numbers of trips and stops at multiple scales, the system supports variable kernel bandwidths to achieve the level of detail required by users for different tasks. We justify our design decisions based on specific analysis needs of transportation analysts. We provide anecdotal evidence of the efficacy of TR-EX through a series of case studies that explore NYC subway service, which illustrate how TR-EX can be used to confirm hypotheses and derive new insights through visual exploration.
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