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It’s a phrase Toronto subway riders hear all too often: “Shuttle buses are on the way.”

Service disruptions are a fact of life for rail transit systems around the world. The most common remedy is “bus bridging,” in which buses are pulled from regular routes and dispatched to serve as shuttles along a disrupted rail segment.

But the transition rarely goes smoothly. If the buses are not dispatched in time, or if there are not enough of them along a given route, the result is overcrowded sidewalks, long delays and an overall operation that’s less efficient. In New York City, for example, major subway disruptions have been estimated to cost US$389 million per year in lost wages and productivity.

Amer Shalaby, a professor in the University of Toronto’s department of civil and mineral engineering in the Faculty of Applied Science & Engineering, and his team are working on solutions. Over the past few years,   that determine successful bus bridging deployment, and have developed tools that transit agencies can use to make better decisions.

“Bus bridging has gained growing attention in recent years due to the dire need for more efficient strategies to counter the effects of unplanned disruptions of rail service, which are happening more frequently,” says Shalaby. “Our approach is unique in terms of the balance it achieves between a theoretically robust procedure and practical application.”

Much of the team’s work has been conducted by analyzing incident reports provided by transit agencies such as the Toronto Transit Commission (TTC). Using machine learning and queuing analyses, the team was able to recognize factors that have a big impact on bus bridging outcomes, but that are not always taken into account by transit agencies.

“A current strategy might focus the number of buses needed based on the length of the disruption – say, 10 buses every 10 minutes,” says Alaa Itani, a PhD candidate in Shalaby’s Transit Analytics Lab. “But it is equally important to consider other factors, such as which routes to pull the buses from and where to start their initial service.”

Itani gives the example of a disruption in Toronto in 2015 that affected eight stations and lasted for an hour. Her analysis suggested that the buses used for bridging in this case were too few and too far away to effectively deal with the disruption. Using more buses from routes closer to the incident could have cut passenger delays and the longest queue at the disrupted stations by 50 per cent, Itani says.

Even in cases where the number of buses is held constant, being more strategic about which buses are used and where they are deployed can make a difference. In the case of the 2015 disruption in Toronto, Itani’s models suggest a more strategic approach could have reduced total user delays by about 23 per cent.

“There is always a compromise between how far [away] the dispatched buses are and how many riders they would otherwise serve,” says Itani. “If we pay more attention to maintaining that balance, we can get better outcomes.”

Even so, Itani says that there are some situations where shuttle buses simply can’t get to the scene fast enough.

“Our analysis showed that while bus bridging can be effective in less congested subway segments, there are places in the downtown core where bus bridging is constrained by the road or curb capacity at the affected subway stations and thus it is not enough,” says Itani.

“In these cases, agencies are recommended to follow supplementary mitigation plans like directing passengers to parallel routes or encouraging passengers to continue their trips using active modes [like walking or using a bike share program].”

The team has developed two decision support tools to help transit agencies deploy bus bridging more effectively. The first, called DASh-Bus Planner, is designed to help transit agencies assess different shuttle bus deployments and scenarios. The second, called DASh-Bus Optimizer, provides transit operators with a near optimal bus bridging plan in the event of an unplanned rail disruption.

Itani says these tools could not only help agencies better manage disruptions, but could provide strategies to reduce crowding due to ordinary surges in ridership.

“Transit agencies are usually risk averse, so we understand that it may be challenging for them to make the kinds of changes recommended by our tools,” says Itani. “However, the recent pandemic has forced the issue. The disruptions they are currently dealing with could provide an opening for them to re-think their traditional approaches.”