TransLoc has announced the availability of a new predictive modeling service for rapid simulation of potential new demand-response transit services. Leveraging big data and proprietary, market-tested scheduling algorithms, TransLoc’s new MicroTransit Simulator helps transit agencies determine successful approaches to enhancing fixed-route services with new flexible transit alternatives to better serve their communities.
“The future of public transit hinges on shifting from a supply model to a demand model and embracing emerging modes to better serve, satisfy and grow ridership. Our MicroTransit Simulator helps agencies deploy new alternatives to fixed transportation alternatives without risk. Now, transit has the tools and the information that’s been missing to make informed decisions on how best to reduce the cost of operations while improving passenger experience,” said Rahul Kumar, vice president of growth, TransLoc.
“TransLoc’s MicroTransit Simulator helps mitigate risk and ensures that we’ll know everything about our microtransit service before a vehicle even hits the road. The days of guessing and hoping in transit planning are over. We see microtransit as the future platform for delivery of paratransit and on-demand services in the region,” said Brandon Policicchio, chief customer and business development officer for Greater Dayton RTA.
Given their public mandate to serve the transportation needs of their entire community, municipal transit agencies are ideally positioned to combine both fixed and flexible transit to help to usher in the future of greener and smarter cities.
The MicroTransit Simulator helps transit agencies address pressing questions such as:
- What will the rider experience (such as wait and ride times) be with this service?
- Which locales and how many rides can we serve with a given number of vehicles?
- How can we adjust our microtransit services to accommodate fluctuating demand while keeping rider experience in mind?
- How many vehicles should we use and how do we balance service quality and cost?
By answering these and other questions, agencies can more easily determine how best to design new flexible microtransit services that deliver an optimal rider experience – all while mitigating costly risks before putting a single vehicle on the road.
“One of the biggest challenges transit agencies confront today is a lack of meaningful data when it comes to finding new and creative ways to push forward these new green and smart city initiatives,” said Kumar. “The MicroTransit Simulator was created to make it infinitely easier for these agencies to successfully imagine, pilot and deploy microtransit services running under complex demand and service scenarios. Using the MicroTransit Simulator, agencies can now eliminate the trial-and-error approaches of the past in favor of data-driven methods to easily identify the most practical ways to deploy flexible transit solutions – long before bearing any of the costs.”
How Simulation Works
There are two parts to the simulation process:
- Simulating inputs: agency-customized rider demand and service design parameters
- Simulating service: schedules produced as though the service were actually running
Simulating Inputs - Ride requests are simulated based on the best information available about where and when riders will need transit. The process of generating ride requests can be informed by multiple datasets, including census data, commuting data and origin/destination data the agency may have from existing services. Ride requests can be customized based on information an agency provides, or it can be generated entirely based on publicly available data for a particular region.
In addition to ride requests, service design parameters such as the number and type of vehicles in a fleet; hours of service; and vehicle capacity are also inputs to the service simulation. Rider demand and service parameters can be set to multiple values in a set of simulations to explore the range of possibilities.
Simulating Microtransit Service - The MicroTransit Simulator takes ride requests and service parameters and dynamically assigns rides to vehicles based on an algorithmic optimization – just as though the service were actually running. This harnesses the same TransLoc algorithm that is used in live microtransit services throughout the country. The output of the algorithm is a schedule of pickups and drop-offs for each vehicle and ride, from which performance metrics can be derived, just as they would be for reporting on a live service.
