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.

  • Induction_bus_and_car

    Unified Estimator for Excess Journey Time under Heterogenous Passenger Incidence Behavior using Smartcard Data

    Transportation Research Part C
    34
    ,
    (
    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 about passenger’ journey time standards as implied by varying incidence behavior. It was found that although the wrong assumption about passenger incidence behavior and journey time standards could result in a biased estimate of EJT for individual passenger journeys, the unified estimator of EJT proposed in this paper is unbiased at the aggregate level regardless of the passenger incidence behavior (random incidence, scheduled incidence, or a mixture of both). A case study based on the London Overground network (with a tap-in-and-tap-out smartcard system) was conducted to demonstrate the applicability of the proposed method. EJT was estimated using the smartcard (Oyster) data at various levels of spatial and temporal aggregation in order to measure and evaluate the service quality. Aggregate EJT was found to vary substantially across the different London Overground lines and across time periods of weekday service.

  • Induction_bus_and_car

    Analyzing Passenger Incidence Behavior in Heterogeneous Transit Services Using Smartcard Data and Schedule-Based Assignment

    Journal of the Transportation Research Board
    2274
    ,
    (
    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 incidence chose their data samples from stations with a single service pattern such that the linking of passengers to services was straightforward. This choice of data samples simplifies the analysis but heavily limits the stations that can be studied. In any moderately complex network, many stations may have more than one service pattern. This limitation prevents the method from being systematically applied to the whole network and constrains its use in practice. This paper considers incidence behavior in stations with heterogeneous services and proposes a method for estimating incidence headway and waiting time by integrating disaggregate smartcard data with published timetables using schedule-based assignment. This method is applied to stations in the entire London Overground to demonstrate its practicality; incidence behavior varies across the network and across times of day and reflects headways and reliability. Incidence is much less timetable-dependent on the North London Line than on the other lines because of shorter headways and poorer reliability. Where incidence is timetable-dependent, passengers reduce their mean scheduled waiting time by more than 3 min compared with random incidence.

  • Induction_bus_and_car

    Estimating a Rail Passenger Trip Origin-Destination Matrix Using Automatic Data Collection Systems

    Computer-Aided Civil and Infrastructure Engineering
    22
    ,
    (
    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 and what is needed in practice in transit agencies. To close this gap requires data processing and analysis methods with support of technologies such as database management systems (DBMS) and geographic information systems (GIS). This research presents a case study of the automatic fare collection (AFC) system of the Chicago Transit Authority (CTA) rail system and develops a method for inferring rail passenger trip origin-destination (OD) matrices from an origin-only AFC system to replace expensive passenger OD surveys. A software tool is developed to facilitate the method implementation and the results of the application in CTA are reported.

TEAM Members