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.
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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 Student |
Postdoctoral Associate |
MST/MCP Student |
MCP-MST Student |
Assistant Professor at University of Melbourne |
MST-ORC Student |
PhD Candidate |
Postdoctoral Associate |
Post-Doctoral Associate |
Postdoctoral Fellow |
Master of Science in Transportation '19 |
Edward H. and Joyce Linde Associate Professor |