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.     

Uncertainty in Bus Arrival Time Predictions: Treating Heteroscedasticity With a Metamodel Approach, O'Sullivan, Aidan, Pereira Francisco C., Zhao Jinhua, and Koutsopoulos Harilaos N. , 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...

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

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, Scully, Tim, Attanucci John, and Zhao Jinhua , Transportation Research Record: Journal of the Transportation Research Board, (Submitted)

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, Li, Corinna, Zegras Christopher, Zhao Fang, Qin Zhengquan, Shahid Ayesha, Ben-Akiva Moshe, Pereira Francisco C., and Zhao Jinhua , Transportation Research Record: Journal of the Transportation Research Board, (Submitted)

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....

Smartphone-based Mobility Mapping and Perceived Air Quality Evaluation in Beijing, Li, Zelin , Department of Urban Studies and Planning, Volume MCP/MST, Cambridge, MA, (2016)

Recently, the rapid development of smartphone technologies has brought new opportunities for the citizen travel survey. Based on a survey performed using a smartphone app, Moves, in Beijing, China, this thesis discusses the survey design and implementation process as well as the mobility analysis methods. The survey was launched in January 2016. This thesis is based on data from 258 subjects. 

The air quality is monitored through several objective measures. However, citizens’...

Supervised Statistical Learning for Individual Level Trip Detection using Sparse Call Detail Record Data, Zhao, Zhan, Koutsopoulos Haris N., and Zhao Jinhua , 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...

FMS-TQ: Combining Smartphone and iBeacon 4 Technologies in A Transit Quality Survey, Li, Corinna, Zegras P. Christopher, Zhao Fang, Pereira Francisco, Nawarathne Kalan Vishwanath, Qin Zhengquan, Ben-Akiva Moshe, and Zhao Jinhua , 95th Transportation Research Board Annual Meeting, 08/2015, 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...

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

Behavior and Policy: Connection in Transportation

This course examines the behavioral foundation for policy design, using urban transportation as examples. We introduce multiple frameworks of understanding travel behavior, rational or irrational, contrasting the perspectives of classic economic theory with behavioral economics and social psychology, and suggest corresponding policy interventions: a behavior--theory--policy mapping. Then we present a spectrum of ten instruments for positively influencing behavior and improving welfare: from...

Team Members

Tim Scully's picture
MST-ORC Student
Zhan Zhao's picture
PhD Candidate
Corinna Li's picture
MCP-MST Student
Zelin Li's picture
MST/MCP Student
Jinhua Zhao's picture
Edward H. and Joyce Linde Assistant Professor
Gabriel Goulet-Langlois's picture
MST Student