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Seminar Series: Experience Building Smart Application Platforms

Date: Friday, April 16, 2021 @ 12:00 PM - 1:00 PM ET

Location: Online via BbCollab

https://ca.bbcollab.com/guest/cb0b2b1b7b034985afadcc31170617c3

Abstract: In this talk we will present our experience building the SAVI (Smart Applications on Virtual Infrastructure) multitier cloud testbed to provide an application platform for smart city experiments. We will describe the CVST (Connected Vehicles for Smart Transportation) application platform which was built on top of SAVI and will then discuss challenges we are currently addressing in our lab to develop a platform for smart cities applications.

Bio: UTTRI associated faculty Dr. Alberto Leon-Garcia is Professor in Electrical and Computer Engineering at the University of Toronto. He is a Life Fellow of the Institute of Electronics an Electrical Engineering “for contributions to multiplexing and switching of integrated services traffic.” He authored the textbooks Probability and Random Processes for Electrical Engineering and Communication Networks: Fundamental Concepts and Key Architecture.Dr. Leon-Garcia was Founder and CTO of AcceLight Networks in Ottawa from 1999 to 2002. He was Scientific Director of the NSERC Strategic Network for Smart Applications on Virtual Infrastructures (SAVI), and Principal Investigator of the project on Connected Vehicles and Smart Transportation. SAVI designed and deployed a national testbed in Canada that converges cloud computing and software-defined networking. CVST designed and deployed an application platform for smart transportation. Leon-Garcia is Founder of StreamWorx.ai which offers massive-scale, real-time streaming, analytics, and machine learning software for network operations and cybersecurity applications.

Video: https://youtu.be/qBmFlXCxqmk

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Seminar Series: Real-time Safety and Mobility Optimization of Traffic Signals in a Connected-vehicle Environment

Date: Friday, April 9, 2021 @ 12:00 PM - 1:00 PM ET

Location: Online via BbCollab

https://ca.bbcollab.com/guest/bc7ae3d0ae084aa8960976774ad4b6b9

Speaker: Dr. Mohamed Abdelhay Essa

Abstract: In the era of connected vehicles (CVs), a considerable amount of high-resolution data on vehicle positions and trajectories will be generated in real time. These data can potentially be used to adapt traffic signals in real time to optimize traffic mobility and safety. Existing research has focused on real-time mobility optimization at signalized intersections, and disregarded the real-time safety optimization despite the potential safety benefits of the CVs technology. This is most likely due to the lack of tools to evaluate traffic safety at signalized intersections in real time. This research presents several advances toward the real-time safety and mobility optimization of traffic signals in a connected-vehicle environment. New methods for the real-time safety evaluation of signalized intersections were proposed using real-world traffic data. Then, a novel adaptive signal control algorithm to optimize traffic safety using CVs data was developed using simulation models and artificial intelligence techniques.

Bio: Mohamed Essa is a Safety Engineering Specialist at the Transportation Systems and Road Safety Department, the BC Ministry of Transportation and Infrastructure (BC MoTI). Before joining the BC MoTI, he was working as a research assistant and transportation engineer at the Bureau of Intelligent Transportation Systems and Freight Security, the University of British Columbia (UBC). He participated in many road-safety projects in various cities in Canada, including Vancouver, Surrey, and Edmonton. He co-authored more than 18 publications in top-tier transportation engineering journals. He received his M.Sc. and Ph.D. degrees in civil engineering from UBC in 2015 and 2020. His primary research areas are road safety, collision prediction models, reliability analysis, traffic conflict techniques, surrogate safety measures, connected and autonomous vehicles, traffic simulation, adaptive traffic signal control systems, and real-time traffic management and optimization. He received several awards, including the Dr. Michel Van Aerde Memorial Scholarship from the Canadian Institute of Transportation Engineers, the Esch Foundation Scholarship from the Transportation Association of Canada, and the Four-Year Doctoral Fellowship from UBC.

Video: Session not recorded by request.

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UT-ITE 2021 Student-Industry Mixer

Date: April 8, 2021

Time: 6:00 PM - 8:00 PM ET

We’re excited to continue our annual seminar series virtually! 

While the regular registration has closed, send us an email at ite@utoronto.ca if you would like to join and we’ll see what we can do!

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Seminar Series: How will economics figure in your engineering career?

Date: Friday, March 26, 2021 @ 12:00 PM - 1:00 PM ET

Location: Online via BbCollab

https://ca.bbcollab.com/guest/0b3d9edc5565423a836e9b3229b0aa8b

Speaker: William Denning

Abstract: The presentation outlines a framework for economics in the transport sector and for transportation planning. It develops microeconomic applications for project or program evaluation. It looks at current practice in transportation economics. It seeks to answer the question, "How will economics figure in your engineering career?"

Bio: William Denning has worked in transportation most of his academic and professional career. He has a BA in Geography and in Economics from the U of Toronto in 1977 and an MA in Regional Science from the U of Pennsylvania in 1980. He has worked for: Ernst & Young, the Canadian Foreign Service, Canadian Pacific, GO Transit, the World Bank, the Ontario Ministry of Finance, and the Ontario Ministry of Transportation. In his later career with the Ontario government he was involved in the creation of Metrolinx, Infrastructure Ontario, the Move Ontario Trust (which funded the provincial contribution to the Spadina Subway extension), and managed MTO's Transportation Economics Office. He draws best practice examples largely from his World Bank experience. He is currently semi-retired and provides selective consulting services to a variety of clients. More detail is available from his LinkedIn profile: https://www.linkedin.com/in/william-denning-6b24a353/c which also includes opinions on contemporary transportation issues.

Video: https://youtu.be/ezKFm_SDc4k

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Seminar Series: Tri-POP: An online platform for smart mobility with prediction, optimization and personalization

Date: Friday, March 19, 2021 @ 12:00 PM - 1:00 PM ET

Location: Online via BbCollab

https://ca.bbcollab.com/guest/32aa0e28d2b14416af95d2dc7477ac43

Speaker: Prof. Moshe Ben-Akiva

Abstract: Tri-POP is an online platform for operations of smart mobility solutions. In its core it combines online analytics for prediction, optimization, and personalization. It uses a bi-level optimization of system and user levels. The system optimization problem is solved periodically (e.g., every 5 minutes) to determine the optimal policy (e.g., pricing, fleet rebalancing, incentive allocation) for attaining system-level objectives (e.g., travel time, welfare, revenue). In our applications the online prediction and the system level optimization are performed by DynaMIT, a simulation-based Dynamic Traffic Assignment (DTA) system that combines historical traffic data and real-time surveillance information through online calibration. The user optimization problem is triggered at every user request to determine a customized menu of options based on individual-level preferences. Online Bayesian inference is employed to update users’ preferences as users make choices. Machine learning algorithms infer the user choices using smartphone sensors. In our applications the user level algorithms are embedded in the Future Mobility Sensing (FMS) platform. Tri-POP is illustrated through applications to flexible mobility on demand, sustainable travel incentives, personalized tolling of managed lanes, and deliveries on demand. Simulation experiments demonstrate the potential benefits of Tri-POP to regulators, operators and users.

Bio: Moshe Ben-Akiva is the Edmund K. Turner Professor of Civil and Environmental Engineering at the Massachusetts Institute of Technology (MIT), Director of the MIT Intelligent Transportation Systems Lab, and Principal Investigator at the Singapore-MIT Alliance for Research and Technology. He holds a PhD degree in Transportation Systems from MIT and was awarded honorary degrees from the University of the Aegean, the Université Lumiére Lyon, the KTH Royal Institute of Technology, and the University of Antwerp. His awards include the Robert Herman Lifetime Achievement Award in Transportation Science from the Institute for Operations Research and the Management Sciences, the Lifetime Achievement Award of the International Association for Travel Behavior Research, the Jules Dupuit prize from the World Conference on Transport Research Society, and the Institute of Electrical and Electronics Engineers ITS Society Outstanding Application Award for DynaMIT, a system for dynamic network management. Ben-Akiva has co-authored two books, including the textbook Discrete Choice Analysis, published by MIT Press, and nearly 400 papers in refereed journals or refereed conferences. He has worked as a consultant in industries such as transportation, energy, telecommunications, financial services and marketing for a number of private and public organizations, including Hague Consulting Group, RAND Europe, and Cambridge Systematics, where he was previously a Senior Principal and member of the Board of Directors. He also was an advisor to Memetrics and ChoiceStream, provided litigation support to Analysis Group and Brattle Group and is the Chief Scientific Advisor to Mobile Market Monitor. He was recently a member of the Future Interstate Highway System Committee of the National Academies of Sciences, Engineering, and Medicine.

Video: https://youtu.be/3GcpYDonrTI

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Seminar Series: Modelling Freight Vehicle Type Choice using Machine Learning and Discrete Choice Methods

Date: Friday, March 12, 2021 @ 12:00 PM - 1:00 PM ET

Location: Online via BbCollab

https://ca.bbcollab.com/guest/4f57ee641eb74135a9a05bb805a64cc3

Speaker: Usman Ahmed

Abstract: The choice of vehicle type is one of the important logistics decisions made by firms. The complex nature of the choice process is due to the involvement of multiple agents. This study employs a random forest machine learning algorithm to represent these complex interactions with limited information about shipment transportation. The data are from commercial travel surveys with information about outbound shipment transportation. This study models the choice among four road transport vehicle types: pickup/cube van, single unit truck, tractor trailer, and passenger car. The characteristics of firms and shipments are used as explanatory variables. Permutation-based variable importance is calculated to interpret the importance of each variable which shows that employment and weight are the most important variables in determining the choice of vehicle type. The random forest model is also compared with the multinomial and mixed logit models. The model prediction results on the testing data are compared. The results show that random forest model outperforms both the multinomial and mixed logit model with an overall increase in accuracy of about 8.3% and 11%, respectively.

Bio: Usman Ahmed is a PhD candidate at the Department of Civil and Mineral Engineering at the University of Toronto, under the supervision of Professor Matthew Roorda. He received his masters’ degree in Transportation Systems in 2018 from the Technical University of Munich and bachelors’ degree in Civil Engineering from the National University of Sciences and Technology, Islamabad. During his master’ studies, he also worked as a research assistant at Modelling Spatial Mobility research group. His research interests include transportation modelling, emissions modelling and machine learning applications in transportation.

Video: Not provided by request.

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Seminar Series: From SOP to DRIP to Business Intelligence? Challenges on Using ITS Data for Transit Planning and Management

Date: Friday, March 3, 2021 @ 12:00 PM - 1:00 PM ET

Location: Online via BbCollab

https://ca.bbcollab.com/guest/3f21a93bb34e482bace02a4710fb4b99

Speaker: Dr. Brendon Hemily 

Abstract: Transit technologies such as Intelligent Transportation Systems (ITS) and Advanced Fare Collection (AFC) as well as others, offer tremendous opportunities to support transit planning and management, but progress in the industry to seize on these opportunities has been slow. The presenter has been involved in many projects over the last three decades related to the use of transit data and analytics. In particular, he has prepared a discussion paper for the U.S. Department of Transportation to identify the uses and challenges in effectively using the new resource of data, and was co-organizer of the TransitData 2020 International Symposium on the Use of Public Transit Automated Data, hosted by TAL last summer. This presentation will explore the possible uses of transit data, and provide insights on the many organizational and technical challenges faced by transit agencies in adapting to the new world of Big Data.

Bio: Dr. Brendon Hemily is an independent transit consultant with 35 years of international transit experience, and serves as Senior Advisor to the UTTRI Transit Analytics Lab (TAL). He focuses on best practices and innovation in the transit industry, in the areas of policy, management, planning, and the effective use of advanced technology, Prior to 2000, Brendon worked at the Canadian Urban Transit Association (CUTA), where he was responsible for all of CUTA’s research, statistical, and technical services. Brendon has a PhD in Transportation and a MS in Civil Engineering, both from MIT, and a BA in Economics from Columbia University. He is Chair of the TRB Public Transportation Group, which has 13 committees, and chairs the Advanced Public Transit Systems Technical Committee of ITS Canada.

Video: https://youtu.be/iCyh9Kxb43c

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Seminar Series: Exploring the Impacts of COVID-19 on Modality Styles for Non-Mandatory Trips in the Greater Toronto Area

Date: Friday, February 26, 2021 @ 12:00 PM - 1:00 PM ET

Location: Online via BbCollab

https://ca.bbcollab.com/guest/9ef71e62d944497f89350b0320261d5f

Speaker: Patrick Loa

Abstract: The COVID-19 pandemic has drastically impacted travel behaviour in cities across the world, affecting how people travel and the reasons people leave their homes. In spite of the pandemic, and consequent the stay-at-home orders, the need to participate in non-mandatory activities remains. These activities, which include grocery shopping, social activities, and recreational activities, play a key role in ensuring that physiological and psychological needs are met. This presentation describes the findings of a study that aimed to investigate the impacts of the pandemic on modal preferences for non-mandatory trips among residents of the Greater Toronto Area (GTA). Looking through the lens of modality styles, which are behavioural dispositions that influence all aspects of travel behaviour, the study analyzed changes in modality styles that occurred as a result of the pandemic and policy implications of these changes. 

Bio: Patrick Loa is a PhD student in Civil Engineering at the University of Toronto, under the supervision of Professor Khandker Nurul Habib. His research primarily focuses on travel demand modelling and the study of the impacts of emerging modes of mobility. Patrick earned both his MASc and BASc at the University of Toronto.

Video: https://youtu.be/uLzpDkSuJVg

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Seminar Series: Data-Driven Approaches in Smart City Operations

Date: Friday, February 19, 2021 @ 12:00 PM - 1:00 PM ET

Location: Online via BbCollab

https://ca.bbcollab.com/guest/a6386cd7a2644ba9892ea076e8994bf8

Speaker: Prof. Sheng Liu

Abstract: This talk presents two projects that build data-driven solutions for city operations planning and management. The first part is devoted to the emerging food delivery operations. Working with a major food delivery service provider in China, we develop a data-driven optimization framework to minimize customers’ expected delivery delay. To capture the driver’s routing behaviors, we propose a machine learning approach that predicts travel time with covariate information. Combined with the travel time prediction, our optimization framework is robust and yields significant reductions in delay times.In the second part, we present how the real-world bike trajectory data can be leveraged for bike lane network design. We integrate the two objectives, coverage and continuity, into the bike lane planning model in view of the cyclists’ utility functions. Efficient model formulations and algorithms have been proposed to solve this large-scale planning problem.

Bio: Dr. Sheng Liu is an Assistant Professor of Operations Management and Statistics at the Rotman School of Management. His research interests lie in smart city operations, data analytics, and optimization. His research has been published in Management Science, Operations Research, Manufacturing & Service Operations Management, and INFORMS Journal on Computing. He received a PhD in Operations Research from UC Berkeley in 2019, and a BSc in Industrial Engineering from Tsinghua University in 2014. He has been working for leading tech companies such as Lyft and Amazon (as a Data/Research Scientist intern).

Video: Not included by request.

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Seminar Series: Who shops online and how sustainable is the delivery process?

Date: Friday, February 12, 2021 @ 12:00 PM - 1:00 PM ET

Location: Hosted by the Smart Freight Centre; register at Eventbrite:

https://www.eventbrite.ca/e/who-shops-online-and-how-sustainable-is-the-delivery-process-miguel-jaller-registration-137328915483

Speaker: Dr. Miguel Jaller

Abstract: In recent years, the rapid adoption of omnichannel distribution strategies has dramatically changed how we shop and the way companies design their retail and distribution operations. All these changes have important implications for the sustainability of our urban transportation systems.This presentation discusses the implications for assessing the shopping demand and logistics decisions under this dynamic environment. Specifically, Dr. Jaller will discuss changes in shopping decisions, from where to shop, what to shop, and how much to shop.Using information from public travel surveys (e.g., American Time Use Survey, and the National Household Travel Survey), Dr. Jaller will discuss the factors that affect shopping decisions.Additionally, the presentation will provide an overview of the impacts of and tradeoffs between decisions about facility location, fleet and technology characteristics, delivery time-windows, and the use of private crowdshipping services. Dr. Jaller will cover the impacts from these decisions when thinking about the sustainability of the system with respect to travel activity and emissions.

Bio: Miguel Jaller is an Associate Professor and Co-Director of the Sustainable Freight Research Center at the University of California, Davis. He received his B.Sc. and M.Sc. in Industrial Engineering from Universidad del Norte, Colombia, and his M.E. in Transportation Engineering, M.Sc. in Applied Mathematics, and Ph.D. in Transportation Engineering from Rensselaer Polytechnic Institute. His research is highly multi-disciplinary and analyzes the societal and private impacts of transport and logistics operations, technology, and policies to develop tools to achieve a sustainable transportation system. His research interests include sustainable transportation systems, freight transportation and logistics, disaster response logistics, and operations research. Dr. Jaller leads important research projects funded by the National Center for Sustainable Transportation, the Center for Transportation, Environment, and Community Health, and the Pacific Southwest Region University Transportation Centers. He also conducts projects for state and federal agencies such as the California Department of Transportation, and the Environmental Protection Agency.

Video: Unavailable as this is hosted by Smart Freight Centre

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Seminar Series: AI in Transportation – Industry Trends and Opportunities

Date: Friday, February 5, 2021 @ 12:00 PM - 1:00 PM ET

Location: Online via BbCollab

https://ca.bbcollab.com/guest/d25f4392740347458e4416e68871f0f0

Speaker: Daniel Olejarz, M.A.Sc.

Abstract: This seminar focuses on the current state of applied artificial intelligence (AI) in the transportation industry. The main focus is on describing the different types of AI applications that currently exist in transportation and the demand for data science skills in the industry. Case studies and personal anecdotes inform thoughts on what the future holds for AI in transportation.

Bio: Daniel Olejarz is a Transportation Planner at IBI Group, a graduate of U of T’s MASc program, and former President of U of T ITE. Daniel’s graduate research focused on last-mile parcel delivery using autonomous ground vehicles. At IBI, he provides consulting services related to emerging mobility technologies, active transportation, and transportation data analytics.

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Seminar Series: Modelling Mobility Services within Agent-Based Travel Demand Model Systems and Implementation of a Ridehailing Case Study

Date: Friday, January 22, 2021 @ 12:00 PM - 1:00 PM ET

Location: Online via BbCollab

https://ca.bbcollab.com/guest/dbf64bafb55847d7a99476542383f568

Speaker: Dr. Francisco Calderón

Abstract: Modelling increasingly dynamic and complex mobility services requires significant extensions to our conventional agent-based microsimulation (ABM) model systems. A first key aspect focuses on explicit modelling of more complex and dynamic service provision processes, involving several operational activities conducted by service providers (e.g., matching, fleet rebalancing, dynamic pricing, etc.). A second key aspect consists of explicit modelling of human-driven fleets (e.g., most ridehailing services in current practice) in terms of the decisions drivers make to participate in the system throughout a workday. A third key aspect consists of establishing information flows and a model system structure that can accommodate a MaaS environment. This first stage of research efforts provides a comprehensive framework to model emerging mobility services at a level of detail appropriate to their complexity. Implementation of such a framework is then conducted at a second stage of comprehensive research efforts to model a human-driven ridehailing mobility service. Despite strong data limitations for modelling, results demonstrate the framework’s capability of replicating key observed patterns of service metrics such as wait times, and fleet availability, as well as driver’s participation patterns in the system. 

Bio: Dr. Francisco Calderón earned his Civil Engineering bachelor’s degree from the University of Cuenca, Ecuador and has design and field working experience in the areas of structures, geotechnical, and transportation. By 2017, Francisco obtained his Master of Engineering degree in Cities Engineering and Management, specializing in Transportation Systems, at the University of Toronto. Francisco continued his graduate studies pursuing a PhD in Transportation Modelling at the University of Toronto, under the supervision of Professor Eric Miller. His final thesis defense was held on November 2020. His main research focus consisted of modelling mobility services and Mobility as a Service (MaaS) within conventional agent-based microsimulation travel demand model systems, emphasizing on a supply-side perspective and detailed modelling of service provision processes.

Video: https://youtu.be/9hH13YmjbSQ

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Seminar Series: Prediction of Greenhouse Gas Emission in Downtown Toronto Using Deep Sequence Learning

Date: Friday, January 15, 2021 @ 12:00 PM - 1:00 PM ET

Location: Online via BbCollab

https://ca.bbcollab.com/guest/738124e3cee349338133216d605e7e81

Speaker: Dr. Lama Alfaseeh

Abstract: Mitigating the substantial undesirable impact of transportation systems on the environment is paramount. Thus, predicting Greenhouse Gas (GHG) emissions is one of the profound topics, especially with the emergence of intelligent transportation systems (ITS). We developed a deep learning framework to predict link-level GHG emission rate (ER) (in CO2eq gram/second) based on the most representative predictors, such as speed, density, and GHG ER of previous time steps. In particular, various specifications of the long short-term memory (LSTM) networks with explanatory variables were examined, and were compared with clustering and the autoregressive integrated moving average (ARIMA) model with explanatory variables. The downtown Toronto road network was used as the study area, and highly detailed data were synthesized using a calibrated traffic microsimulation and MOVES. It was found that LSTM specification with speed, density, GHG ER, and in-links speed from three previous minutes performed the best while adopting two hidden layers, and when the hyper-parameters were systematically tuned. Adopting a 30-second updating interval slightly improved the correlation between true (simulated) and predicted GHG ERs (from predictive models), but contributed negatively to the prediction accuracy as reflected in the increased root mean square error (RMSE) value. Efficiently predicting GHG emissions at a higher frequency with lower data requirements will pave the way for various applications, e.g. anticipatory eco-routing in large-scale road networks to alleviate the adverse impact on global warming.

Bio: Dr. Lama Alfaseeh earned her Bachelor’s Degree in Civil Engineering in 2006 and a Masters Degree in Construction Project Management in 2011 from Damascus University. She started her PhD in 2016, joined the Laboratory of Innovations in Transportation (LiTrans) at Ryerson University in 2017, and defended her dissertation in 2020. Lama was supervised by Dr. Bilal Farooq and her research investigated the impact of employing intelligent vehicles in a distributed routing environment. Lama utilized intelligent transportation systems (ITS) to mitigate the undesired effect on the environment and health. She started a postdoc position in November 2020 where she has been developing predictive models for climatic variables to help structural engineers proactively design concrete structures while considering the impact of climate change.

Video: https://youtu.be/10L0T9nkM8k

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Seminar Series: Achieving Unified Mobility

Date: Friday, December 11, 2020 @ 11:00 AM - 12:00 PM ET

Location: Online Via BbCollab

https://ca.bbcollab.com/guest/39e8d902271b4a9eae88a8797e4e5463

Speaker: Dr. Elliot Siemiatycki, RideShark

Abstract: As a leading provider of software solutions for commuter/transportation demand management, RideShark has evolved over 15 years to promote shared, active and public transportation options. In this presentation, I will discuss the challenges and opportunities we are pursuing in what has come to be known as Mobility-as-a-Service (MaaS). I will provide real-world examples of how our platform is being used and the solutions we are building to provide safe, secure, seamless and, most importantly, sustainable mobility options.

Bio: Elliot Siemiatycki is the Vice President of Innovation and Strategic Initiatives with RideShark, one of the leading enterprise commuter management platforms on the market. Elliot is a seasoned and passionate thought-leader in the automotive and mobility sectors with 10+ years of experience as a policymaker, professor, published author and industry consultant. In his previous role, he was the Portfolio Lead for Mobility Innovation within Ontario’s Ministry of Economic Development. Elliot holds a PhD in Economic Geography from the University of British Columbia.

Video: https://youtu.be/R9JRLw5AFgk

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Seminar Series: Everything you wanted to know about report writing but were afraid to ask

Date: Friday, December 4, 2020 @ 11:00 AM - 12:00 PM ET

Location: Online via BbCollab

https://ca.bbcollab.com/guest/1979ce0a32e9436691fd3cd3583de18c

Speaker: Prof. Eric J. Miller

Abstract:

This presentation discusses various elements of report writing as a guide to graduate students preparing term project reports, journal articles, theses, etc. It is intended to address some typical issues that our graduate students encounter in their writing. Topics discussed include: report organization; report style; treatment of tables, figures, exhibits and appendices; grammar and syntax; spelling and word usage; referencing, footnotes and acknowledgements.

Bio:

Eric J. Miller is Professor of Civil Engineering, Research Director of the Data Management and Travel Modelling Groups as well as Director of the University of Toronto Transportation Research Institute. He is Past Chair of the U.S. Transportation Research Board (TRB) Committee on Travel Behavior and Values, Member Emeritus of the TRB Transportation Demand Forecasting Committee and Past Chair of the International Association for Travel Behaviour Research (IATBR). He served on the US National Academy of Sciences Committee for Determination of the State of the Practice in Metropolitan Area Travel Forecasting. He has chaired or been a member of numerous travel demand modelling peer review panels throughout North America. He is the recipient of the 2009 Wilbur S. Smith Distinguished Educator Award from the Institute of Transportation Engineers, the inaugural winner of the University of British Columbia Margolese National Design for Living Award (2012) and recipient of the 2018 IATBR Lifetime Achievement Award. He is the developer of GTAModel, an advanced regional travel demand modeling system used by municipalities in the Greater Toronto Area (GTA) to forecast travel demand that is based on TASHA, a state-of-the-art agent-based microsimulation model of activity and travel, and ILUTE, an integrated land use-transportation model system for the GTA.

Video: https://youtu.be/WFvswfkts5o

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“Seminar” Series: Talking Demand-Responsive Transit in Canada: Effective, or Exaggerated?

Date: Friday, November 27, 2020 @ 11:00 AM - 12:00 PM ET

Location: Online via BbCollab

https://ca.bbcollab.com/guest/9dccb5b4a2ac49d8b6c9f7a7c568bb5a

Speaker: Dr. Willem Klumpenhouwer 

Join Dr. Willem Klumpenhouwer for a mix of learning and roundtable discussion on demand-responsive transit and its place in the transit system. We'll discuss some of the things learned from his research project in spring 2020 and consider whether demand-responsive transit has the potential to help or hinder transit agencies as they face an uncertain future during a global pandemic.

Unlike other seminar sessions, this session will not be Recorded

Abstract:

Demand-responsive transit, on-demand transit, microtransit. Adding technology-powered flexibility to transit service is not a new concept, but with smartphones in (almost) every pocket and the emergence of cloud-based applications, a huge number of new on-demand transit services are being piloted in Canada. Many politicians, planners, and operators have looked to on-demand transit as a potential cost-saving measure, or to deliver more efficient service to sparse suburban or rural regions of a city.

In many of these cases, technology has outpaced planning. The ease of "starting-up" a new service means that there is a temptation to pilot service without the guidelines and planning framework to back it up. As part of a first step in understanding how to plan on-demand service, we interviewed a number of demand-responsive transit stakeholders in Canada: operators, technology vendors, service providers, and regional transit agencies.

Bio:

Dr. Willem Klumpenhouwer is a Postdoctoral Fellow at the University of Toronto's Transit Analytics Lab. He studies passenger rail and urban transit performance analytics and emerging technologies. He holds a PhD in Transportation Engineering from the University of Calgary and a bachelor's degree in Theoretical Physics from the University of Guelph. In addition to his work as a transit researcher and advocate, he enjoys data visualization projects, and performing improvised theatre.