One Weird Trick Transit Agencies Might Use to Make Your Ride Faster

Hey, transportation planners, want to improve the speed and reliability of your bus or light rail network?

If so, consider the noble termite.

I don’t mean mimic termites’ rapacious appetite for historic bungalow basements, or their ability to build an England-sized network of mounds visible from space, even though those are both compelling potential transit improvements that could be considered down the road. No, what I’m talking about is a concept called “stigmergy,” by which eusocial creatures (termites, ants, etc.) communicate with each other via modifications to the physical environment in a self-organizing system of stimulation and response.

“Huh?” you’re saying to your computer monitor. “What does this have to do with transit?”

Well, it has to do with the increasing interest around automated vehicles (AVs), and how these technologies can be applied to improving transit systems plagued by interruption and delay. A 2017 paper by a team of researchers from the National Autonomous University of Mexico looked at a novel way to improve transit reliability by incorporating stigmergic principles into a hypothetical AV transit system (Carreón, 2017). In their model, trains (and this model would only work on trains or BRT with a fully protected right of way) would essentially only communicate with the following and preceding trains, telling each other when they left a station and how soon they think they’ll arrive at the next one.

Imagine that you’re on one of these hypothetical trains. As you leave the station, your train leaves behind a marker of sorts (the paper uses the very evocative term “anti-pheromone”) telling the train following yours what time it left the station, and it also radios to the train ahead of it how far behind you are. When your train pulls into its next stop, it receives a message from the train ahead saying, for example, “I left this station at 100 seconds ago.”

The model uses this intercommunication between trains to create a decentralized rule set that, using a vast simplification, dictates that trains must leave the station when the “time gap” between them and the train ahead is greater than the “time gap” between them and the train behind. Various extensions to the algorithm allow the trains to account for sudden stoppages along the line, speeding up or slowing down if necessary, but always endeavoring to maintain an even spacing between vehicles - like ants marching along a branch.

This strict, insectoid-inspired devotion to maintaining spacing between vehicles (often called headways) is increasingly coming into conflict with the way transit systems have traditionally been run: with a stop-based timetable letting riders know exactly* when a bus is scheduled to leave a certain stop. In a headway system, the guiding logic is that riders don’t really care what exact time a transit vehicle leaves a stop, as long as it’s frequent enough that they won’t wait long for the next one, and that the time to their destination is reliable (Walker, 2010). Taking that into account, the payoffs of switching to a stigmergic self-organizing model could be huge, with the end result of the research team’s method being an “improvement in average journey time of approximately 20% and a decrease of 25% in mean headway values.” In a rapidly urbanizing world, transit agencies are uniquely suited to offer more sustainable and efficient transportation solutions than single-occupancy vehicles, but are also put in a position where they must offer competitive service with dwindling funding (Buehler, 2018). Automation could be an essential key to creating more competitive transit, but questions still linger as to whether riders would be willing to use a system that has the cool, unfeeling logic of a termite.

Buehler, R. (2018). Can Public Transportation Compete with Automated and Connected Cars? Journal of Public Transportation, 21(1), 2. 

Carreón G, Gershenson C, Pineda LA (2017) Improving public transportation systems with self-organization: A headway-based model and regulation of passenger alighting and boarding. PLoS ONE 12(12): e0190100. https://doi.org/10.1371/journal.pone.0190100 

Walker, Jarrett (2010, October 25). Beyond “On-Time Performance.” Human Transit. Accessed November 29, 2018, https://humantransit.org/2010/10/beyond-on-time-performance.html


-Edited by Eavan Moore

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