Bus Bridging Decision-Support Toolkit: Optimization Framework and Policy Analysis – Alaa Itani

When:
July 5, 2019 @ 11:00 am – 12:00 pm
2019-07-05T11:00:00-04:00
2019-07-05T12:00:00-04:00
Where:
Sandford Fleming Building, ITS Lab and Testbed, Room SF3103
10 King's College Road
3rd Floor
Cost:
Free
Contact:
Pat Doherty
416 978 4175

Urban rail systems suffer frequently from unexpected service disruptions which can result in severe user delays and dissatisfaction.

“Bus Bridging” is the strategy most commonly applied in responding to rail service interruptions in North America and Europe, whereby buses from regular routes are pulled and dispatched to serve as shuttles along the disrupted rail segment until regular train service is restored. In determining the required number of buses and source routes, most transit agencies rely on ad-hoc approaches based on operational experience and constraints, which can lead to extensive delays and queue build-ups at affected stations.

This thesis proposes a decision-support toolkit to assist transit agencies with deploying optimal bus bridging strategies. Itani developed an optimization model, using Genetic Algorithms, to determine the optimal number of shuttle buses and route allocation which minimize the overall subway and bus riders delay for any given rail disruption incident. The generated optimal solutions are sensitive to bus bay capacity constraints along the shuttle service corridor, utilizing methods found in the Transit Capacity and Quality of Service Manual. The optimization model is integrated with a previously developed simulation tool which tracks the evolution of system queues and delays throughout the bus bridging process.

The toolkit was used in an analysis of real-world incident data obtained from the Toronto Transit Commission (TTC) and supplemented by other passenger and travel time data. The bus bridging toolkit showed strong potential to produce efficient shuttle response plans that reduce the transit user delays markedly while ensuring minimum queue formation at disrupted stations and maximizing the efficiency of shuttle buses. A set of bus bridging policy guidelines was developed based on further analysis of the optimization model outputs using a Classification and Regression Tree (CART) model.

head shot of Alaa Itani

Alaa Itani

Alaa Itani is a MASc candidate specializing in Public Transit operations and planning under the supervision of Professor Amer Shalaby. Itani received her Bachelor’s degree in Civil and Environmental Engineering from the American University of Beirut and joined the transportation group at the University of Toronto in 2017.  Itani’s research focuses on managing unplanned rail disruptions through optimizing  shuttle buses response plan and supporting transit agencies in taking decisions during such events.