Measuring Impacts on Demands for Transportation Traffic

To assess behavioural responses to technological innovations (e.g., AV/ACV), it is important to understand the underlying individual and household decision-making processes. The best way to investigate such processes is to conduct experimental surveys. Data collected through such surveys can be used to develop travel demand modelling systems for forecasting demands for transportation in a variety of future contexts with and without AV/CAV.

Despite recent advances in activity-based travel demand modelling, there is a need to improve the behavioural realism of model formulations within activity-based modelling systems, in the context of AV. Specifically, activity-based travel demand models should be upgraded to capture the impacts of widespread implementation of AV on perceptions of travel time and time expenditure choices.

Currently, this research stream experiments the design, implementation, and evaluation of a series of SP/SA surveys about daily activity and travel adaptations to assess the impact of transformative technologies and services on subjects’ travel choices. This research also introduces a next-generation activity-based travel demand modelling framework building on the extensive experience of the research team and the review of travel behavioural models in the context of transformative technology induced mobility services. The framework incorporates the effects of transformative technologies on all model components, including activity generation, activity scheduling, destination location choice, mobility service choice, and transportation mode choice.

21st Century Traffic Control and Management for Large Congested Cities

The objective of this research is to develop a suite of integrated traffic control and management systems for smart cities in the 21st century that exploits emerging innovations in vehicular automation and connectivity and shared mobility services. A prime concern is that emerging automation, connectivity, and mobility services will make travel by car more affordable and bearable, hence potentially exacerbating sprawl and traffic congestion. On the other hand, high levels of automation and pervasive connectivity, if properly exploited using new traffic management systems, offer new opportunities to not only boost infrastructure capacity but also enable new smart and adaptive network-wide traffic demand management systems and policies at a micro trip level (trip/vehicle/passenger levels). The new systems and policies will be implementable and enforceable using technology, with minimal public infrastructure and at reasonable costs.

We introduce two parallel streams of research on (1) dynamic network modeling with automation (quantifying transformation), and (2) supply control and management (traffic control) using connectivity and automation (enabling positive transformation).

1) Planning and Management Analytics for Next-Generation Transit

Emerging automated technologies and shared mobility solutions hold tremendous promise for boosting the efficiency and attractiveness of future transit systems. For example, access to rail stations, particularly in outlying areas, could be greatly improved through integration with high-frequency automated bus feeders on fixed routes or on-demand automated fleets offered ubiquitously by the transit agency or by private mobility service providers. As such, the highly challenging first/last-mile problem could be effectively addressed. The frequency and coverage of bus networks, traditionally in conflict with one another in network design, could simultaneously be enhanced by retrofitting medium-demand corridors with fully or partially automated high-frequency bus routes while introducing microtransit services (consisting of on-demand automated minibuses) in low-density areas currently with poor service. Other transit attributes (such as speed, reliability & transfers) and special operations (e.g. shuttle bus services in response to rail disruptions and integrated multi-modal corridor management) could be greatly improved by utilizing new automated technologies and mobility solutions in an integrated and coordinated operating environment. Additionally, given that future automated fleets are most likely powered electrically, tremendous benefits could be achieved in terms of air quality and operating cost.

Nevertheless, conventional analytical tools used currently in service planning, scheduling and operational management have limited ability to enable transit professionals and companies to formulate, analyze and refine innovative service concepts of the future. In response to this need and opportunity, this research aims at developing new tools for planning and managing next-generation transit systems. This research focuses on two applications: smart microtransit services in low demand contexts, and connected bus operations for seamless passenger transfers.

2) Technical Support for the Evaluation of the Automated Transit Shuttle Pilot in Toronto

The University of Toronto Transportation Research Institute (UTTRI) is providing technical support for the City of Toronto’s Automated Transit Shuttle Pilot project evaluation. This project introduces the operation of a new transit route for Toronto using automated shuttle vehicles that carry approximately 8-12 passengers for 6-12 months. The purpose of this project is to address the capabilities of automation technology to fill the existing need for public transit servicing lower-demand routes. The role of our team is to provide advisory services mainly supporting the planning and data collection efforts, and evaluation of the service operation and collected data.

This research focuses on two parallel work streams: 1) AV Parking demand, supply, policy, and design, and 2) Freight automation, urban goods movements modelling, and urban pickup-delivery. The first stream analyzes parking demand in the context of AVs and MaaS: Parking behaviour for passengers relates to choices including: a) mode of transportation (transit, walking, bicycle, private automobile, MaaS), b) activity duration and location, c) drop-off/pick-up location, d) parking spot, e) responses to pricing and enforcement. Emphasis is be placed on analysis that is relevant to both urban and suburban centres.

1) AV Parking demand, supply, policy, and design

This stream further investigates and optimizes parking facility design. Parking facility design may include on-street drop-off/pick-up zones or dedicated off-street facilities. We have conducted local area simulations on alternative drop-off/pick-up facility designs and their impacts on traffic. We introduce optimized parking garage designs to maximize facilities’ capacities and develop protocols for optimal storage and repositioning of vehicles within facilities using warehouse inventory management techniques. Additionally, the analysis done on parking design and optimization is used to assess potential parking policies including curb-side management, changes to parking pricing, and parking space requirements for new developments.

2) Freight automation, urban goods movements modelling, and urban pickup-delivery

The second research stream develops urban/suburban freight demand/supply models to enable the testing of automated and transformative technologies in the freight sector. This stream also investigates freight trip and tour generation. Specifically, the implications of wide-scale adoption of automated last mile delivery, and mobile distribution centres, etc. on the orientation of freight tours. We will determine how tours' lengths will change because of automated deliveries and whether and how mobile distribution centres will influence urban and suburban delivery patterns. We are also investigating new operating models for the pickup and delivery of goods in urban and suburban settings associated with driverless vehicle options, such delivery robots, automated mobile distribution centres, and crowdsourced delivery options (moving goods with personal vehicles).

To learn more about this research you can visit the following:

• How self-driving cars could shrink parking lots: U of T research
• Impacts of Autonomous Vehicles on Parking and Congestion
• A Vehicle Routing Problem with Movement Synchronization of Drones, Sidewalk Robots, or Foot-Walkers
• An Assessment of the Use of Autonomous Ground Vehicles for Last Mile Parcel Delivery

The transportation sector has the highest share of GHG emissions among all sectors in Ontario, so significant change is imperative. Anticipating this change and preparing for how it will impact society represents a major challenge. The relationships between transportation and sustainability are multi-dimensional. The negative effects associated with motor vehicle traffic include pedestrian, cyclist and car user injuries, adverse effects of traffic-related air pollutants and noise, inequalities in mobility and accessibility, resource and energy consumption, and GHG emissions. In contrast, transportation provides accessibility to employment and services, parks, and green spaces, as well as places and amenities essential for health.

Given that Ontario and other developed urbanized regions are on the brink of major shifts in the transportation sector that will forever alter the movement of people and products, decision makers require a greater capacity to quantify what these shifts mean for society, in terms of health, quality of life and economic competitiveness. However, tools for this purpose are lacking. We are not currently able to confidently estimate the impacts of current trends, such as the increasing electrification of vehicles and transit or the rise in shared mobility, let alone the anticipated penetration of AVs.

To identify policies that best increase development of sustainable practices, this research quantifies the effects of transformative technologies on various dimensions of sustainability, including lifecycle GHG emissions, energy consumption, air pollution, public health, equity, and accessibility. In the context of this research, two parallel streams of inquiries are being investigated: (1) quantifying the lifecycle emission estimates for transformative transportation technologies under uncertainty, and (2) the development of an integrated tool for quantifying the sustainability impacts of future automated and transformative transportation technologies.