CTRF 2025: Full Schedule

Welcome to CTRF’s 60th Annual Conference! Enjoy Ottawa

8:30am EDT

Analyzing the relationship between on-street parking and land-use

Wednesday May 28, 2025 8:30am - 8:50am EDT

On-street parking can have considerable impacts on traffic congestion and road safety within cities. In order to manage on-street parking, an in-depth understanding of on-street parking behaviour is required. This paper examines how land-use impacts two critical components of on-street parking: parking location occupancy and parking duration. This paper uses parking transaction data from paid on-street parking locations throughout Toronto to examine the influence of the following five land-uses: low-density residential, high-density residential, hospitals, offices, and universities. The results show that for on-street parking occupancies, locations adjacent to universities generally have the highest occupancies while locations adjacent to hospitals have the lowest occupancies. Contrary to the differences observed in parking location occupancy, parking duration is similar for all the land-uses during weekdays and Saturdays. Further examination of each land-use's impact is discussed within the paper. In addition, it is observed that on-street parking occupancies are significantly lower during weekdays versus weekends but parking duration is similar throughout the week. The results show the importance of location and time dependent parking pricing.

Speakers

Trevor Jones

BA Consulting Group

Eric Miller

Ladan Berahman

80. Jones, Berahman, Miller FullPaper Paper CTRF2025 pdf

Wednesday May 28, 2025 8:30am - 8:50am EDT
Desmarais 1130 55 Laurier Ave E, Ottawa ON K1N 6N5

Park it

8:50am EDT

A tool for inference and extraction of off-street parking capacity

Wednesday May 28, 2025 8:50am - 9:10am EDT

Desmarais 1160

This paper aims to define a methodology to infer off-street parking at the scale of a municipality in the province of Quebec. Firstly, a database structure is proposed which allows the storage of estimates coming from multiple inference methods for a given lot, at multiple points in time_x000D_
A first estimation method using the parking requirements laid out in municipal zoning codes is proposed. A database structure is put forward to represent the variety of parking requirement formulations (based on square footage, seating capacity, etc.) present in zoning codes. Then an inference method using Quebec's municipal tax assessment database is tested. The results are discussed within the context of Quebec City._x000D_
Additionally, a web interface is presented allowing practitioners to complete the calculations, visualize the results and to override the proposed estimate manually or through other automatic estimation methods._x000D_
Finally, an Application Programming Interface (API) is proposed to access the results as part of other travel behaviour studies. The proposed API would allow the extraction of parking capacity within a geopolitical boundary or within a given isochrone of a point of interest.

Speakers

Paul Charbonneau

Polytechnique Montréal

Catherine Morency

81. Charbonneau Morency Paper CTRF2025 pdf

Wednesday May 28, 2025 8:50am - 9:10am EDT
Desmarais 1160 55 Laurier Ave E, Ottawa ON K1N 6N5

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9:10am EDT

Using data fusion to model parking location choices of trucks in the City of Toronto

Wednesday May 28, 2025 9:10am - 9:30am EDT

Desmarais 1160

Trucks' illegal parking results from parking shortage and policies that do not consider the parking requirements of trucks. Understanding trucks' parking behaviour is needed for the design of adequate infrastructure and the development of effective policies. For truck parking, data is limited, incomplete, and of variable quality. This results in decision models with unreliable predictability and transferability, yet these models are needed to inform policy questions. In this study, we synthesize the attributes of truck activity and parking, including attributes of the tour, parking location, industry sector, and commodity type for the purpose of developing a parking location choice model. This is done through the fusion of four data sources to construct a record of observed parking behaviours of trucks, mainly location choices and corresponding attributes. GPS data are used to construct truck trip diaries across the City of Toronto, including stop location, duration, frequency, and trip time of day and length. Business establishment data are used to infer the industry sectors associated with each tour. Data from the Ontario Commercial Vehicle Survey are used to predict the commodity types associated with each tour. Lastly, parking characteristics are inferred from spatial data representing the features of parking infrastructure.

Speakers

Matthew Roorda

CIvil & Mineral Engineering, University of Toronto

Farah Ghizzawi

University of Toronto

82. 2025 CTRF Ghizzawi & Roorda (1) pdf

Wednesday May 28, 2025 9:10am - 9:30am EDT
Desmarais 1160 55 Laurier Ave E, Ottawa ON K1N 6N5

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9:30am EDT

Analysis of reservation changes for a carsharing service in Greater Montreal

Wednesday May 28, 2025 9:30am - 9:50am EDT

Desmarais 1160

Enhancing shared mobility can enable carsharing companies to optimise their services, improve user experiences, and increase adoption rates. Research on carsharing also informs urban planning and policy development. The adoption of carsharing services can help alleviate traffic congestion, reduce greenhouse gas emissions, and address the challenges of limited parking in urban areas by providing viable alternatives to personal vehicle use. This research, which primarily focuses on station-based system, aims to understand how people use carsharing reservation systems. These insights are essential for improving carsharing services. Communauto, Canada's largest carsharing company, has users who frequently modify their reservations, occasionally causing logistical challenges. Using the company's reservation database and R software, statistical analyses link social and temporal variables"”such as modification dates, number of modifications, age, gender, day of the week, and time of day"”to observed behaviours. One notable finding is the phenomenon of "fake cancellations," where 41% of cancelled reservations lead to another honoured reservation on the same day. To identify the key variables that determine whether a reservation will be cancelled or honoured, a decision tree has been constructed. Based on these findings, recommendations will be developed to minimise reservation modifications.

Speakers

Martin Trépanier

Full Professor, Polytechnique Montréal and CIRRELT

Martin Trépanier is a civil engineer and professor at the department of mathematics and industrial engineering of École Polytechnique de Montréal, an engineering school affiliated to the Université de Montréal. He is the titular of the Chair in the transformation of transportation... Read More →

Amaury Philippe

Polytechnique Montréal

Linda Chau

Polytechnique Montreal

83. Chau Philippe Trepanier Paper CTRF2025 (1) pdf

Wednesday May 28, 2025 9:30am - 9:50am EDT
Desmarais 1160 55 Laurier Ave E, Ottawa ON K1N 6N5

Park it

9:50am EDT

Enhancing on-street parking occupancy prediction with spatio temporal graph neural networks integrating exogenous factors

Wednesday May 28, 2025 9:50am - 10:10am EDT

Desmarais 1160

Accurate predictions of on-street parking availability are vital for enhancing parking guidance systems, reducing drivers' search times, and alleviating traffic congestion and CO2 emissions. Traditional methods for predicting parking occupancy often fail to account for both the spatial dependencies between parking blocks and the impact of exogenous factors such as weather, nearby amenities, and traffic conditions._x000D_
This paper presents a novel simplified spatio-temporal graph neural network (SST-GNN) model that addresses these limitations by integrating exogenous data and leveraging a graph-based representation to capture spatial relationships between parking locations. The model also incorporates a temporal mechanism to account for the dynamic evolution of parking demand. By combining intrinsic and contextual factors, the proposed approach significantly improves prediction accuracy._x000D_
Experimental results on a real-world dataset demonstrate that the (SST-GNN) model outperforms traditional benchmarks in forecasting on-street parking occupancy, showcasing its potential to revolutionize smart parking systems.

Speakers

Martin Trépanier

Full Professor, Polytechnique Montréal and CIRRELT

Ayman Agoube

Polytechnique Montréal

84. Agoube Ait El Cadi Delot Tr├®panier Paper CTRF2025 pdf

Wednesday May 28, 2025 9:50am - 10:10am EDT
Desmarais 1160 55 Laurier Ave E, Ottawa ON K1N 6N5

Park it

CTRF 2025