Canadian Consulting Engineer

Computerized Study of Traffic Congestion in Montreal

Consultants MIRO inc. &Groupe conseil Genivar inc.Category: Studies and SoftwareThrough its Miro subsidiary, the Genivar consulting group of Montreal developed a system that measures the position, spe...

October 1, 2000   Canadian Consulting Engineer

Consultants MIRO inc. &

Groupe conseil Genivar inc.

Category: Studies and Software

Through its Miro subsidiary, the Genivar consulting group of Montreal developed a system that measures the position, speed and travel time of vehicles on road networks and highways. The system was developed as part of a continued traffic count system implemented by the Quebec Transport Ministry to measure the evolution of travel time on the road and highway networks of greater Montreal. The program was carried out in two phases beginning in 1998. Eight teams in vehicles crisscrossed the road network to measure actual travel times. The counts were taken during the morning and afternoon rush hours at 20-minute intervals. Twenty-three of the most congested routes were covered every month over a year.

Two computerized survey methods were developed by Miro to determine the position of vehicles. The first method, which is semi automated, uses odometers. The second relies on a geographic positioning system (GPS) installed aboard the monitoring vehicles. The system indicates the exact position of the vehicle at all times, allowing the program to measure speed, travel times, delays and waiting lines.

Various computer applications were created to process the data collected and produce status reports which give a very detailed picture of the evolution of traffic congestion. The results, i.e. the length and duration of waiting lines and travel times, are presented in map form using MapInfo software.

The project is the first systematic traffic congestion evaluation program for an entire urban area in Canada. Before this project, only fragmentary information was available for certain road sections or for a few hours of the year.

The project is also the first large-scale Canadian application of semi-automated techniques and GPS technology in traffic data collection. These new technologies allow analysis of traffic conditions in a microscopic detail that was not previously possible.

The Ministry is very satisfied with the validity of the data and pleased to find it has more applications than were originally intended, such as:

calibration of traffic microsimulation models

production of time-space diagrams to synchronize traffic signals

study of the impacts of construction sites on traffic

analysis of ‘shock waves’ created by traffic jams

travel time comparisons on various itineraries

driver behaviour studies

evaluation of latent demand.

The client is using the system for a similar project in Trois-Rivires and is planning to use it for other applications in Quebec City. The first phase budget was $225,000 of which $30,000 covered the development of the system and software. The second phase budget was $270,000, including $20,000 for improvements to the software. CCE

Project name: Computerized assessment of traffic congestion along Montreal’s road and highway networks using semi- automated methods and GPS technology/Projet d’valuation informatise de la congestion du rseau routier de Montral

Award winner: Consultants MIRO inc. and Groupe conseil Genivar inc., Montral.

Project team: Michel Robitaille, ing., Emmanuel Le Colletter, ing., Michel Simard, Catherine Jolin, ing., Francine Denicourt, Francois Genest

Client/owner: Ministre des Transports du Qubec


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1 Comment » for Computerized Study of Traffic Congestion in Montreal
  1. sanaz shaker says:

    I am a Ph.D. student at Polytechnique university (Montreal-Canada) and for my research project, I need a big historical data set including road congestion information (by considering the parameters that caused congestion in the road like an accident, construction and so on) and network congestion information( by considering parameters that caused congestion in the road like delay, packet loss and so on) with aim of network congestion prediction. If I have one data set including both road and network information, it is perfect and if not, having them into two data sets would be good as well.
    Thanks in advance,
    Sanaz

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