01551196

Component

Traffic congestion plays an important role in shaping the public debate over transportation policies and programs. But although there are many studies of the intensity and causes of traffic congestion, there are opportunities to improve methods and congestion metrics using newly available traffic data. Common regionally-scaled congestion studies simplify the understanding of congestion. Better congestion metrics would both reflect geographic and temporal patterns of congestion and would identify underlying predictors in an effort to better manage gridlock. While such metrics have previously been intractable due to lack of appropriate data, recent improvements in private sector traffic data now make more complete understandings of congestion possible. This study uses a novel data source from Inrix, Inc. to design and test new modeling approaches to characterize and identify the predictors of urban congestion on the arterial network in the Toronto and Hamilton Census Metropolitan Areas (CMAs). Methods are developed which make two novel contributions. First, models explore the geography of congestion at different scales. Second, models incorporate a discrete-continuous modeling framework which better reflects congestion’s non-linear nature. When tested in the two study regions, results suggest that while congestion is largely a regional phenomenon in Toronto, it is highly localized in Hamilton. Different policy responses and roles for smart growth planning may be in order in each of the regions. This methodology can be deployed in other regions to similarly measure congestion and identify more context-appropriate responses.

This paper was sponsored by TRB committee ABJ30 Urban Transportation Data and Information Systems. Alternate title: Method for Studying Traffic Congestion Using New Data: Focusing on Canadian Regions of Toronto and Hamilton.

01550057

15-0589

English

20p

2015

Transportation Research Board 94th Annual Meeting

Digital/other

Maps; References; Tables

Arterial highways; Congestion management systems; Measures of effectiveness; Metrics (Quantitative assessment); Traffic congestion; Traffic data; Traffic measurement; Urban areas

Hamilton (Canada); Toronto (Canada)

Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2015 Paper #15-0589

TRIS, TRB, ATRI

Dec 30 2014 12:18PM