Mobility Sensing & Prediction

Measuring Regularity of Individual Travel Patterns, Gabriel Goulet-Langlois, Haris N. Koutsopoulos, Zhan Zhao, and Jinhua Zhao , IEEE Transactions on Intelligent Transportation Systems, (2017)

Regularity is an important property of individual travel behavior, and the ability to measure it enables advances in behavior modeling, mobility prediction, and customer analytics. In this paper, we propose a methodology to measure travel behavior regularity based on the order in which trips or activities are organized. We represent individuals’ travel over multiple days as sequences of “travel events”—discrete and repeatable behavior units explicitly defined based on the research question...

Unified Estimator for Excess Journey Time under Heterogenous Passenger Incidence Behavior using Smartcard Data, Jinhua Zhao, Michael Frumin, Nigel Wilson, and Zhan Zhao , 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...

Real time transit demand prediction capturing station interactions and impact of special events, Peyman Noursalehi, Haris N. Koutsopoulos, and Jinhua Zhao , Transportation Research Part B, (Submitted)

 

Demand for public transportation is highly affected by passengers' experience and the level of service provided. Thus, it is vital for transit agencies to deploy adaptive strategies to respond to changes in demand or supply in a timely manner, and prevent unwanted deterioration in service quality. In this paper, a real time prediction methodology, based on univariate and multivariate state-space models, is developed to predict the short-term passenger arrivals at transit stations. A...

Trip Detection using Sparse Call Detail Record Data, Zhan Zhao, Haris N. Koutsopoulos, and Jinhua Zhao , IEEE Transactions on Intelligent Transportation Systems, (Submitted)

 

Despite a large body of literature on trip detection using call detail record (CDR) data, a fundamental understanding of their limitations is lacking. In particular, the sparse nature of CDR data is not well addressed. This study defines a process that allows physical travel patterns (important to the transportation community) to be inferred from telecommunication patterns captured by CDRs. To reduce the reliance of existing CDR-based trip detection methods on heuristics and...

Mobility as A Language: Predicting Individual Mobility In Public Transportation Using N-Gram Models, Zhan Zhao, Koutsopoulos Haris, and Jinhua Zhao , Transportation Research Board 96th Annual Meeting, (2017)

For public transportation agencies, the ability to provide personalized and dynamic passenger information is crucial for improving the efficiency of demand management and enhancing customer experience. This requires understanding and especially predicting individual travel behavior in the public transportation system, which is challenging because of the heterogeneity among passengers and the variability of their behaviors. This paper presents, to the best of our knowledge, the first attempt...

Enabling Transit Service Quality Co-monitoring Through a Smartphone-Based Platform, Corinna Li, Christopher Zegras, Fang Zhao, Zhengquan Qin, Ayesha Shahid, Moshe Ben-Akiva, Francisco Pereira, and Jinhua Zhao , Transportation Research Record: Journal of the Transportation Research Board, (2017)

The growing ubiquity of smartphones offers public transit agencies an opportunity to transform ways to measure, monitor, and manage service performance. We demonstrate the potential in a new tool for actively engaging customers in measuring satisfaction and co-monitoring bus service quality. The pilot initiative adapted a smartphone-based travel survey system, Future Mobility Sensing (FMS), to collect real-time customer feedback and objective operational measurements on specific bus trips....

Incorporating Mobile Activity Tracking Data In A Transit Agency: Collecting, Comparing, And Trip Mode Inference, Tim Scully, John Attanucci, and Jinhua Zhao , Transportation Research Board 96th Annual Meeting, (2017)

The near ubiquity of smartphones has the potential to transform how researchers, companies, and public transit agencies understand travel behavior. This research analyzes how an emerging class of automatically-collected data based on smartphone GPS and sensor information – referred to here as mobile activity-tracking data – can be used in a transit agency to better understand travel behavior. Through a collaboration with Transport for London, multiple weeks of mobile activity-tracking data...

FMS-TQ: Combining Smartphone and iBeacon 4 Technologies in A Transit Quality Survey, Corinna Li, Christopher Zegras, Fang Zhao, Francisco Pereira, Kalan Vishwanath Nawarathne, Zhengquan Qin, Moshe Ben-Akiva, and Jinhua Zhao , 95th Transportation Research Board Annual Meeting, Washington, D.C., (2016)

The Internet of Things (IoT) will offer transit agencies an opportunity to transform ways to measure, monitor, and manage performance. We demonstrate the potential value of two combined technologies, smartphones and iBeacons, for actively engaging customers in measuring satisfaction and co-monitoring bus service quality. Specifically, we adapt our smartphone-based survey system, Future Mobility Sensing (FMS), to connect with iBeacons for an event-driven approach to measure user-reported...

Clustering the Multi-week Activity Sequences of Public Transport Users, Gabriel Goulet Langlois, Koutsopoulos Haris, and Jinhua Zhao , 95th Transportation Research Board Annual Meeting, 08/2015, Washington, D.C., (2016)

The public transport networks of dense cities such as London serve passengers with widely dierent travel patterns. In line with the diverse lives of urban dwellers, activities and journeys are combined within days and across days in diverse sequences. From personalized customer information, to improved travel demand models, understanding this type of heterogeneity among transit users is relevant to an number of applications core to public transport agencies' function. In this study,...

Supervised Statistical Learning for Individual Level Trip Detection using Sparse Call Detail Record Data, Zhan Zhao, Koutsopoulos Haris, and Jinhua Zhao , 95th Transportation Research Board Annual Meeting, 08/2015, Washington, D.C., (2016)

Despite a large body of literature related to trip detection using Call Detail Record (CDR) data, the fundamental understanding of the limitations of the data is lacking and, particularly, its sparse nature is not well addressed in existing work. This paper proposes a conceptual framework to make explicit distinction between telecommunication patterns captured by CDRs and travel patterns that are of interest to the transportation community. A process is proposed to extract trips from CDRs at...

Individual-Level Trip Detection using Sparse Call Detail Record Data based on Supervised Statistical Learning, Zhan Zhao, Jinhua Zhao, and Koutsopoulos Haris , Transportation Research Board 95th Annual Meeting, (2016)

 

Despite a large body of literature related to trip detection using Call Detail Record (CDR) data, the fundamental understanding of the limitations of the data is lacking and, particularly, its sparse nature is not well addressed in existing work. This paper develops a conceptual framework to make explicit distinction between telecommunication patterns captured by CDRs and travel patterns that are of interest to the transportation community. Motivated by the over-reliance of existing...

Uncertainty in Bus Arrival Time Predictions: Treating Heteroscedasticity With a Metamodel Approach, Aidan O'Sullivan, Francisco Pereira, Jinhua Zhao, and Harilaos Koutsopoulos , IEEE Transactions on Intelligent Transportation Systems, Issue 99, p.1–11, (2016)

Arrival time predictions for the next available bus or train are a key component of modern Traveller Information Systems (TIS). A great deal of research has been conducted within the ITS community developing an assortment of different algorithms that seek to increase the accuracy of these predictions. However, the inherent stochastic and non-linear nature of these systems, particularly in the case of bus transport, means that these predictions suffer from variable sources of error, stemming...

Analyzing Passenger Incidence Behavior in Heterogeneous Transit Services Using Smartcard Data and Schedule-Based Assignment, Michael Frumin, and Jinhua Zhao , 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...

Estimating a Rail Passenger Trip Origin-Destination Matrix Using Automatic Data Collection Systems, Jinhua Zhao, Adam Rahbee, and Nigel Wilson , 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...