Mobility Sensing & Prediction

Transportation agencies have traditionally been hampered in planning, managing and evaluating their services by having to rely heavily on costly and unreliable manual data collection systems. However, the development of Information and Telecommunication Technology are changing the amount, type, and quality of data available to planners and managers. We utilize multiple automatic data sources, such as smart cards, GPS vehicle locations, cell phone Call Detailed Records, and mobility tracking apps, to estimate and predict travel demand, explore behavioral regularities, quantify service reliabilities and evaluate travel demand management program.     

Identifying Hidden Visits from Sparse Call Detail Record Data, Zhan Zhao, Haris N. Koutsopoulos, and Jinhua Zhao , European Physical Journal: Data Science, (Submitted)

Despite a large body of literature on trip inference using call detail record (CDR) data, a fundamental understanding of their limitations is lacking. In particular, because of the sparse nature of CDR data, users may travel to a location without being revealed in the data, which we refer to as hidden visits. The existence of hidden visits hinders our ability to extract reliable information about human mobility and travel behavior from CDR data. In this study, we propose a data fusion...

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

Bayesian Inference of Passenger Boarding Strategies at Express Stops with Real-time Bus Arrival Information, Nassir, Neema, Jinhua Zhao, John Attanucci, Frederick P. Salvucci, and Nigel Wilson , Transportation Research Part C, (Submitted)

Efficient design of express and local bus services in urban corridors requires accurate understanding of the travel demand and heterogeneities in passengers’ preferences and needs. Public transit Automated Fare Collection (AFC) systems provide a high-coverage source of data that facilitates an unprecedented opportunity for understanding the demand patterns and passenger preferences for more efficient service designs. In this paper, a Bayesian inference method is proposed to analyze the AFC...

Individual mobility prediction using transit smart card data, Zhan Zhao, Haris N. Koutsopoulos, and Jinhua Zhao , Transportation Research Part C, Volume 89, p.19-34, (2018)

For intelligent urban transportation systems, the ability to predict individual mobility is crucial for personalized traveler information, targeted demand management, and dynamic system operations. Whereas existing methods focus on predicting the next location of users, little is known regarding the prediction of the next trip. The paper develops a methodology for predicting daily individual mobility represented as a chain of trips (including the null set, no travel), each defined as a...

Detecting Pattern Changes in Individual Travel Behavior: A Bayesian Approach, Zhan Zhao, Haris N. Koutsopoulos, and Jinhua Zhao , Transportation Research Part B, (2018)

Although stable in the short term, individual travel patterns are subject to changes in the long term. The ability to detect such changes is critical for developing behavior models that are adaptive over time. We define travel pattern change as "abrupt, substantial, and persistent changes in the underlying pattern of travel behavior" and develop a methodology to detect such changes in individual travel patterns. We specify one distribution for each of the three dimensions of travel behavior...

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...

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...

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...

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...

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...

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...

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....

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...

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,...

TEAM MEMBERS

Abhishek Basu's picture
MST Student
Shan Jiang's picture
Postdoctoral Associate
Corinna Li's picture
MCP-MST Student
Zelin Li's picture
MST/MCP Student
Neema Nassir's picture
Senior Postdoctoral Associate
Tim Scully's picture
MST-ORC Student
Zhan Zhao's picture
PhD Candidate
Zhenliang Ma's picture
Postdoctoral Associate
Peyman Noursalehi's picture
Post-Doctoral Associate
Saeid Saidi's picture
Postdoctoral Fellow
Gabriel Wolofsky's picture
Master of Science in Transportation '19
Jinhua Zhao's picture
Edward H. and Joyce Linde Associate Professor