Mobility Sensing & Prediction
For a century, transportation agencies have relied on costly and unreliable manual data collection systems. These approaches have hampered the effective planning, management, and evaluation of mobility services, ultimately reducing efficiency and threatening quality customer service.
The development of Information and Communication Technology (ICT), however, has transformed what was once a data-starved arena into a data-rich environment for planners and managers.
At JTL, we utilize automatic data sources, such as smart-card transactions, GPS-based vehicle locations, cell phone records, and mobility apps to estimate and predict travel demand, explore behavioral patterns, quantify service reliability and evaluate transportation system performance as a whole.
Unified Estimator for Excess Journey Time under Heterogenous Passenger Incidence Behavior using Smartcard Data, , Transportation Research Part C, Volume 34, p.70–88, (2013) |
Excess journey time (EJT), the difference between actual passenger journey times and journey times implied by the published timetable, strikes a useful balance between the passenger’s and operator’s perspectives of public transport service quality. Using smartcard data, this paper tried to characterize transit service quality with EJT under heterogeneous incidence behavior (arrival at boarding stations). A rigorous framework was established for analyzing EJT, in particular for reasoning... |
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Analyzing Passenger Incidence Behavior in Heterogeneous Transit Services Using Smartcard Data and Schedule-Based Assignment, , Journal of the Transportation Research Board, Volume 2274, p.52–60, (2012) |
Passenger incidence (station arrival) behavior has been studied primarily to understand how changes to a transit service will affect passenger waiting times. The impact of one intervention (e.g., increasing frequency) could be overestimated when compared with another (e.g., improving reliability), depending on the assumption of incidence behavior. Understanding passenger incidence allows management decisions to be based on realistic behavioral assumptions. Earlier studies on passenger... |
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Estimating a Rail Passenger Trip Origin-Destination Matrix Using Automatic Data Collection Systems, , Computer-Aided Civil and Infrastructure Engineering, Volume 22, Issue 5, p.376–387, (2007) |
Automatic data collection (ADC) systems are becoming increasingly common in transit systems throughout the world. Although these ADC systems are often designed to support specific fairly narrow functions, the resulting data can have wide-ranging application, well beyond their design purpose. This article illustrates the potential that ADC systems can provide transit agencies with new rich data sources at low marginal cost, as well as the critical gap between what ADC systems directly offer... |
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TEAM MEMBERS
MST 2018 |
Postdoctoral Associate |
MST/MCP 2016 |
MCP/MST 2016 |
Postdoctoral Associate |
MST-ORC Student |
PhD 2018 |
Postdoctoral Associate |
Postdoctoral Associate |
Postdoctoral Fellow |
MST 2019 |
Professor of Cities and Transportation |