Days: Monday, June 22nd Tuesday, June 23rd Wednesday, June 24th Thursday, June 25th
View this program: with abstractssession overviewtalk overview
View this program: with abstractssession overviewtalk overview
Amer Shalaby, Professor and Bahen/Tanenbaum Chair in Civil Engineering
Director of the Transit Analytics Lab (TAL)
University of Toronto
This session will provide an open conversation with two of the most prominent international researchers and experts on transit data and analytics.
· Brendon Hemily (Moderator), Senior Advisor, Transit Analytics Lab (TAL), University of Toronto
· Nigel Wilson, Emeritus Professor of Civil and Environmental Engineering, MIT (First Director of the MIT Transit Lab)
· Juan Carlos Munoz Abogabir, Professor, Department of Transport Engineering and Logistics, UC Chile, Minister of Transportation and Telecommunications (2022-2026)
| 11:00 | Following the footprints of visitors: Spatiotemporal public transportation profiles using smart card data (abstract) |
| 11:06 | The Impacts of Real-Time Public Transit Information via Smartphones on Commuter Route Choice Patterns (abstract) |
| 11:12 | Regularity or Punctuality? Investigating the influence of reliability on multimodal public transport choices (abstract) |
| 11:18 | Measuring the impacts of a new light rail system on work performance and health outcomes in the Greater Montréal Region (abstract) |
| 11:24 | Inferring Event-Driven Ridership and OD Patterns from APC Data: A Student Travel Case Study in DC (abstract) |
| 11:00 | Network-Wide Transit Passenger Waiting Level of Service Assessment using Historical APC and AVL Data (abstract) |
| 11:06 | Perceived reliability vs. measured reliability: Understand the relationship (abstract) |
| 11:12 | Analysis of Bus Service Reliability Using GTFS Data: A Case Study in Washington, DC (abstract) |
| 11:18 | Turning Data into Action: Improving Schedule Reliability with AVL and Power BI (abstract) |
| 11:24 | Stop-Level Simulation Framework for Measuring and Modelling Transit Line Reliability Using AVL/APC Data (abstract) |
| 11:30 | Beyond the Schedule: A GPS-Based Framework for Measuring Bus Headway Reliability and Bunching in Mixed Traffic (abstract) |
| 11:00 | Leveraging Interlining for Efficient Electric Bus Deployment: A Toronto Case Study (abstract) PRESENTER: Kareem Othman |
| 11:06 | Assessing and Mitigating Natural Hazard Impacts on Battery-Electric Bus Operations: A Garage-Level Resilience Index Approach (abstract) |
| 11:12 | Integrated Multi-Stage Optimization of Shared Electric Bus and Private EV Charging Networks Using Stackelberg Game Theory (abstract) |
| 11:18 | Data-Driven Prediction of Electric Bus Energy Consumption and Real-Time Range Assessment – Use Case at Société de Transport de Laval, Québec (STL) (abstract) |
| 11:24 | A Data-Driven Framework for Optimizing Electric Bus Allocation and Charging Schedules (abstract) |
| 13:45 | Who would pay? Integrating willingness to pay in a public-transit market segmentation (abstract) |
| 13:51 | Clustering Mobile Ticketing Data to Understand Transit User Behavior (abstract) |
| 13:57 | Characterizing travel patterns of various fare cohorts in a multi-modal transit network with enriched smart card validation data (abstract) |
| 13:45 | Roadway and Curbside Management with Data-driven Support of Tramway Operations (abstract) |
| 13:51 | Using historical positioning data to analyse congestion in bus terminals (abstract) |
| 13:57 | Proactive Headway Management: Real-Time GTFS-Based Prediction and Mitigation of Bunching and Gapping (abstract) |
| 14:03 | iROAM+: Short-Term Bus Bunching Prediction from Multi-Source Operational Data (abstract) |
| 13:45 | Predicting Crew Absences for Railway Operations Using an Hybrid CNN–LSTM Model (abstract) |
| 13:51 | Evaluating the Impact of Schedule Changes on Urban Bus Operations: A Bayesian Structural Time-Series Synthetic Control Analysis (abstract) |
| 13:57 | A line-prioritisation framework for bus frequency adjustments in PT networks (abstract) |
| 14:03 | Analysis of Time Point Selection in Bus Routes: A Comparative Study of Four Canadian Cities Using GTFS Data (abstract) |
| 14:09 | Bus Scheduling and Timepoint Selection: Data-Driven Simulations in Calgary and Kyoto (abstract) |
| 15:15 | A Semi-Automated Access and Equity Impact Analysis of Transit Network Transformations using Open-Source Data and Tools (abstract) |
| 15:21 | Understanding the equity impacts of a public strike: A case study of Montréal, Canada (abstract) |
| 15:27 | Evolving Transportation Equity Data Analytics Tools at TransLink (abstract) |
| 15:33 | Transit Equity Score: A Novel Framework Combining Socioeconomic, Accessibility, and Reliability Data (abstract) |
| 15:15 | A critical assessment of personalized proactive disruption information in public transport (abstract) |
| 15:21 | Private Platforms, Public Data: Navigating Open Data and Customer Platform Strategy (abstract) |
| 15:27 | Does Bikeshare Improve Transit Network Resilience? A Quasi-Experimental Study of Unplanned Subway Disruptions (abstract) |
| 15:33 | Learning to Adapt: Passenger Behavioral Dynamics During Metro Service Disruptions (abstract) |
| 15:39 | The impact of flooding on public transport operations: A causal regression discontinuity design analysis (abstract) |
| 15:15 | Feasibility of On-Demand Transit in Suburban Networks: A Data-Driven Framework and Case Study of Mississauga (abstract) |
| 15:21 | AI-Driven Zoning Optimization for Elderly-Focused Demand-Responsive Transit: A Reinforcement Learning Guided Genetic Algorithm Applied to Winnipeg, Canada (abstract) |
| 15:27 | A Privacy-Preserving Distributed Framework for Integrating Public Transport and Transport On Demand (abstract) |
| 15:33 | Uncovering Travel Patterns of On-Demand Transit: A Case Study of the Town of Innisfil, Canada (abstract) |
| 15:39 | Deciding Between Fixing and Flexing Transit: A Data-Driven Framework for Fixed-Route Transit Redesign and On-Demand Transit Adoption (abstract) |
| 15:45 | Uber as First/Last-Mile Transit Access: Spatiotemporal Patterns and Stop-Level Variation in a Public MaaS Platform (abstract) |
A small but growing number of transit agencies have developed data management frameworks that enable them to take full advantage of automated vehicle location or AFC data to enhance knowledge about their customers and/or to assess the performance of their service delivery. This session will hear from some transit agency leaders about their data management journey and experience.
· Igor Zaslavsky, Manager, Transit Management Systems, York Region Transit
· Kayleigh Campbell, Manager, Ridership Analysis, Washington Metropolitan Transit Authority (WMATA) - Invited
· Meritxell Font, Director, Ridership Analysis and Forecasting, New York Metropolitan Transportation Authority
· Mahsa Bargahi, Data Specialist, New York Metropolitan Transportation Authority
View this program: with abstractssession overviewtalk overview
| 09:00 | Maximizing Passenger Comfort Through Multi-Criteria Optimization of Bus Stop Amenities (abstract) |
| 09:06 | Drivers of trip satisfaction over time: A case study of rapid transit systems at varying maturity levels in Montreal, Canada (abstract) |
| 09:12 | Weighting What Matters: Attribute Importance in Models of Regional Express Rail Satisfaction and Usage (abstract) |
| 09:18 | Understanding Door-to-Door Public Transport Satisfaction Through App-Based Data Collection (abstract) |
| 09:24 | Identifying key factors of user satisfaction with GTX-A using an ordered logit model (abstract) |
| 09:30 | Customer Comments to Actionable Insights, using AI (abstract) |
| 09:00 | Another One Rides the Bus – Using Automatic Vehicle Locator (AVL) Data to Reduce Overloads at Halifax Transit (abstract) |
| 09:06 | How do sporting events impact public transportation crowding and urban mobility networks? (abstract) |
| 09:12 | An Integrated Framework for Optimizing Bus Scheduling and Operations During the Hajj Season (abstract) |
| 09:18 | WMATA “Event Train” analysis and improvement (abstract) |
| 09:24 | Real-time crowding prediction accuracy with automated passenger counting (APC) data (abstract) |
| 09:30 | Offline reinforcement learning to exploit GPS and loading data for bus crowd management (abstract) |
| 09:36 | Optimizing onboard crowding for urban rail operations (abstract) |
| 09:00 | Changes in the Public Transit Market for a New Light Rail System: A Before-and-After Study in Montreal, Canada (abstract) |
| 09:06 | Understanding the Impact of Small-scale OD Trips on Urban Rail Transit Flow Distribution: A Multi-Scenario Spatiotemporal Analysis (abstract) |
| 09:12 | Revealing Latent Structural Changes in Urban Mobility Networks: A Transformer-Based Embedding Framework for Evaluating the GTX-A Opening (abstract) |
| 09:18 | A Multi-Source Framework for Analyzing Wayfinding Efficiency in Metro Stations (abstract) |
| 09:24 | Automated Long-Term Crowdflow & Wayfinding Impact Analysis for New and Modified Station Layouts (abstract) |
| 09:30 | AAAM: An open-source agent-based package for behavioral dynamics and system performances in urban rail transit (abstract) |
What is the point of “Applied Research” if it isn’t applied? This session will provide some examples of collaboration between academics and transit agencies to build the trust and relationship that enables researchers to develop and implement applications that can inform transit agency service delivery.
• Saied Saidi, Associate Professor at University of Calgary
• Martin Trepanier, Professor, École Polytechnique de Montréal, Former Director, CIRRELT
• Brendon Hemily, Senior Advisor, Transit Analytics Lab
| 13:20 | Enhancing GTFS Data Reliability: Utilizing the Mobility Database's Visualization and Validation Framework for Quality Assessment in Canadian Transit Feeds (abstract) |
| 13:26 | Leveraging Passive Data for the Redesign of a Mid-sized Bus Network (abstract) |
| 13:32 | Urban Bus Network Co-Design: A Structural Framework for Evaluation and Redesign (abstract) |
| 13:38 | Network-level analysis through GTFS automation: A scalable framework for transit planning (abstract) |
| 13:44 | Data Architecture for AI-Ready Interoperable Public Transportation Ecosystems (abstract) |
| 13:20 | Improving the Resilience of Schedule-based Bus Operations Using Reinforcement Learning-based Control Strategies (abstract) |
| 13:26 | From Unstructured Alerts to Incident Events: A Machine Learning Approach for Detecting Public Transport Incidents (abstract) |
| 13:32 | Automated Dispatching and Operation Planning for Incident Management research (ADOP) (abstract) |
| 13:38 | ELSSA+: Dynamic State Embedding with Proximity-Aware Transformers for Reinforcement Learning-Based Bus Holding Control (abstract) |
| 13:44 | Headway-Based Dynamic Interlining for Improved Service Regularity (abstract) |
| 13:20 | Beyond Single Failures: A Probabilistic Data-driven Framework for Transit System Vulnerability under Multiple Incidents (abstract) |
| 13:26 | An exploratory study for the possibility of using signalling log data for capacity estimates (abstract) |
| 13:32 | Joint assessment of the impact of future railway services and design of feeder systems: towards an integrated approach (abstract) |
| 13:38 | Guidelines for Data Collection and AI Model Training: Unlocking the Potential of Artificial Intelligence in Transit (abstract) |
| 13:44 | Assessing Transit System Resilience Under Disruption: Spatial-Temporal Dynamics of Passenger-Level Impacts (abstract) |
Artificial Intelligence (AI) has taken the world by storm, and seems to be everywhere. This session will focus on AI in the transit world, with three different perspectives on the topic: first, it will provide a brief introduction to AI tools and some use cases of how they are being used in transit analytics research at TAL, followed by a comprehensive AI Strategy effort being undertaken by Metrolinx, the regional transit provider in the greater Toronto region, and then end with research from France that has used AI tools to build a digital twin of mobility in Paris.
· Amer Shalaby, Director of TAL at the University of Toronto
· Nazanin Esmaili, Ph.D., Director, Payments (PRESTO) Data, Analytics & AI, Metrolinx
· Mostafa Ameli, Associate Professor, HDR, Université Gustave Eiffel Paris
TransitData 2026 illustrates the growing interest among both academics and practitioners in using automated transit data to develop ever more sophisticated transit applications. This session will provide an opportunity for participants to compare their experiences with other participants concerning transit data and analytics through the use of interactive tools.
View this program: with abstractssession overviewtalk overview
| 09:00 | Modeling the Toronto PATH with GTFS-Pathways for Wayfinding and Pedestrian Flow Analysis (abstract) |
| 09:06 | Joint Analysis of Neighbourhood and Transit Preferences in Immersive Virtual Reality (abstract) |
| 09:12 | Using LiDAR to Support Service Planning Decisions at WMATA (abstract) |
| 09:18 | Determinants of Participants’ Trip-Booking Frequency in a MaaS Trial: A Double Machine Learning Analysis (abstract) |
| 09:24 | Evaluating Behavioral Responses to Mobility Incentives and Uber Integration in a Public MaaS Platform (abstract) |
| 09:00 | Analytical Tools at Société de Transport Montréal: A Comprehensive Business Intelligence Framework for Public Transport Planning (abstract) |
| 09:06 | An Enhanced Data Specification for Next-Generation Transit Planning and Operations (abstract) |
| 09:12 | Storing and Sharing Transit Data at Scale: A Workflow for Big Data Without Big Costs (abstract) |
| 09:18 | AI-Driven Citizen Development: Building Open-Source Transit Tools Faster and Smarter (abstract) |
| 09:24 | When Counters Miscount: Lessons from Quebec RTC’s Data Journey (abstract) |
| 09:00 | Behavioural Effects of Fare Changes on MaaS Usage of Public Transport (abstract) |
| 09:06 | D.C.'s Parking Cash-Out Policy: Employer Compliance Patterns and Transit Usage Implications (abstract) |
| 09:12 | Estimation of Excess and Foregone Revenue to Support Planning for New Fare Products and Structure (abstract) |
One of the biggest challenges for the transit industry is the variety of data formats being used, most of which are proprietary in nature; this makes managing the data, ensuring quality assurance, and sharing applications extremely difficult. TIDES (the Transit Integrated Data Exchange Specification) is an open-source data specification for transit operations data including vehicle locations (CAD/AVL), passenger counts (APC), and fare transactions (AFC). This session will introduce the specification and outline how it can benefit transit practitioners and academics alike.
· John Levin (retired), Director of Strategic Initiatives, Metro Transit, Minneapolis-St Paul, and TIDES Board Coordinator (and guiding force)
· Christopher Yamas, TIDES Program Manager, Jarvus Innovations
| 13:30 | Estimating Network-Level Transit Origin-Destination Matrices from Fragmented Automatic Data Sources (abstract) |
| 13:36 | Where are my passengers going? Reconstructing passenger flows via anonymized mobility data (abstract) |
| 13:42 | Validating the Use of APC Data to Monitor Changes in City-wide Transit Origin-destination Flows Using Socioeconomic Variables Data (abstract) |
| 13:48 | Estimating Subway Origin–Destination Matrices in Entry-Only Fare Systems Using Passive Wi-Fi Traces and Station Gate Counts (abstract) |
| 13:30 | Red light, green light : bus speed profile visualization along congested roads using GTFS-RT data (abstract) |
| 13:36 | Envisioning Transit: Simple Questions, Open Data, and What We Can Learn From Them (abstract) |
| 13:42 | Quantifying Signal-Delay Contributions to Bus Travel Times with Implications for Scheduling (abstract) |
| 13:48 | Person-Centric Reinforcement Learning for Adaptive Traffic Signal Control: Event-Based Passenger Delay at Stops (abstract) |
| 13:30 | Predicting Bus Station Demand : A Crowdsourced Data Approach (abstract) |
| 13:36 | From Urban Activity to Timetables: Integrating Google Popular Times and GTFS for Station-Level Assessment in Luxembourg City (abstract) |
| 13:42 | Innovative Approaches to Comprehensive Ridership Analysis (abstract) |
| 13:48 | Transforming Passively Collected Smart-card Data to Inputs Reliable for Choice Modeling (abstract) |
The Transit Data Challenge is a first-of-its-kind opportunity to tackle real problems. The Transit Data Challenge invited student teams from Canadian universities to tackle real-world challenges in public transit data. Participants developed innovative solutions at the intersection of transportation, data science, and public policy — showcasing how advanced analytics, artificial intelligence, and modern data infrastructure can make transit systems smarter, more equitable, and more responsive to the communities they serve. This session will present the three Finalist Teams and their applications; more information about the three Finalists is available at https://www.transitdata2026.ca/datachallenge.
To wrap up the TransitData Symposium, we will broaden the perspective beyond just transit data, in order to look towards a future of broad geospatial data and integrated mobility.
· Arif Rafiq, Industry Manager, Transportation, Esri Canada, Executive Board Director, ITS Canada
· Jesse Coleman, Manager Transportation Data & Analytics, City of Toronto