Events Calendar

PhD Defense- Bei (Penny) Pan

PhD Candidate: Bei (Penny) Pan

Title:
Utilizing Real-World Traffic Data to Forecast the Impact by Traffic Incidents

Committee:
Cyrus Shahabi (chair)
Craig Knoblock
Genevieve Giuliano (outside member)

Abstract:

For the first time, real-time high-fidelity spatiotemporal data on transportation networks of major cities have become available. This gold mine of data can be utilized to learn about the behavior of traffic congestion at different times and locations, potentially resulting in major savings in time and fuel, the two important commodities of the 21st century. Therefore, how to mine valuable information from this data to enable next-generation technologies for unprecedented convenience, have become key topics in spatiotemporal data mining. By utilizing real-world transportation related datasets, this thesis focuses on the address the problems related with impact of traffic incidents. Traffic incidents refer to non-recurring issues occurred on the road network, such as traffic accidents, weather hazard, special events and construction zone closures, which contributes to approximately 50% for traffic congestion.
First, this thesis addresses the fundamental problem of traffic prediction in the presence of traffic incidents by utilizing traffic sensor data & incident reports collected in Los Angeles road networks. The proposed prediction overcomes the deficiency of traditional time-series prediction techniques by considering the unique characteristics for traffic speed time series. Then through the same dataset, this thesis proposes a set of methods to predict the dynamic evolvement for the impact of incidents. Through the surrounding traffic data of traffic incidents, this thesis models the propagation behavior of congestions caused by archived incidents, and develops a set of clustering-based techniques for predicting the similar behavior in the future. Thirdly, besides sensor data, this thesis also mines social media and GPS trajectories for better understanding of the cause of traffic incidents. Specifically, by identifying the unusual travelling behaviors and twitter-like posts from data collected in Beijing, this work detects and analyzes the impact of traffic incidents. Finally, this thesis analyzes the causality relationship among freeway traffic and arterial traffic to provide a comprehensive prediction of incidents’ impact on both freeways and arterial streets. As a result, the next-generation navigation applications built based on the approaches discussed in this thesis can help drivers to effectively avoid the impacted area in real-time and thereby save them considerable amount of travel time.

Location: Ronald Tutor Hall of Engineering (RTH) - 306

Audiences: Everyone Is Invited

Contact: Lizsl De Leon