Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023)

Large urban centers and emerging city regions, as well as others in the world, face a series of complex challenges such as traffic congestion as they continue to grow at a rapid pace. Many largest cities around the world, including Canadian cities such as Vancouver and Toronto, are known among top worst for traffic congestion. With increasing traffic congestion causing delays and health problems for motorists and producing extra air pollution, reliable traffic information is key for travelers all over the world. This proposed research will develop new traffic event detection methods for providing such real-time information using massive social media data, and also will build extra tools for integrating human mobility patterns into short-term traffic congestion prediction that will help predict traveler’s road traffic conditions in next 15-60 minutes ahead. The results (models, methods and tools) of this research can be of great use by a new generation of urban planners, city traffic managers and researchers.

The short-term objectives of the proposed research will focus on three key research components that will: 1) model human activity patterns at various spatial and temporal scales to study their interactions with recurrent and non-recurrent traffic situations; 2) examine and evaluate different traffic and traffic-related event detection methods to develop a space-time-semantics based detection approach; and 3) assess and explore how social media data together with related models, tools and workflows can support the provision of real-time road traffic information by developing an collaborative service-oriented open platform. The platform will give researchers, traffic managers and decision makers direct and timely access to real-time traffic data, primarily extracted from various social networks, traffic information services, and suitable computational and analytical models and tools for traffic-related studies.

The long-term goal of my Research Program is to develop a real-time road information infrastructure that visualize, publish and push near real time traffic information to target users and provide optimal routing services, but also capable of predicting real-time traffic congestion taking into non-recurrent traffic situations and long-term traffic considering recurrent patterns. Such an infrastructure can be implemented as a web-based system that integrates cloud computing, web services and GIS for traffic management decision making and transportation planning.
The proposed experimental open platform, with implementation of the models, methods and tools as services, will build strong capacity for sharing road traffic information and services and will open up new opportunities for research sharing, HQP training and collaborations with Canadian government agencies and industry.