Collaborative Research Team Projects – Project 25
Statistical Tools for Spatio-temporal Sensor-based Traffic Data
The goal of this project is to study the effects of longer-term disruptions such as road work on a transportation network. The project aims at developing a sophisticated model that will capture adequately the complex correlation patterns.
Why Develop New Tools to Capture Traffic Data?
Better understanding of the effects of longer-term disruptions such as road work on a transportation network would be helpful for planning network maintenance and for predicting the likely effects of disruptions on traffic patterns.
Road work typically takes place during the summer months, leading to multiple simultaneous disruptions occurring at different locations in the network. The problem of long-term effect disruption on traffic has not been well studied from a spatio-temporal point of view.
Areas of Exploration
The general objective of this research proposal is to build an interdisciplinary research team at the interface of statistics and transportation engineering to tackle statistical and methodological challenges related to the analysis of urban-mobility data collected from three sources: GPS and telematics data, traffic counting devices and smart card systems.
We propose to develop state-of-the-art statistical models through six projects evolving around three research themes:
- Using GPS data to build surrogate measures of safety
- Traffic volume prediction and imputation using counting devices
- Travel time prediction of a bus route using smart card systems
People Behind the Project
Aurélie Labbe | HEC Montréal
Lijun Sun | McGill University
Denis Larocque | HEC Montréal
Léo Belzile | HEC Montréal
Alexandra M. Schmidt | McGill University
Luis Miranda-Moreno | McGill University
Pratheepa Jeganathan | McMaster University
Stefan Steiner | University of Waterloo