Full Speed Ahead: Using AI to Reimagine Urban Transit

Public transportation use is increasing as workers venture back to offices. The pandemic has changed how riders use the transit systems and what they expect from it. AI-based apps can help cities determine where and how to best address customer needs.

Image credit: Moovit

The Coronavirus pandemic shutdown non-essential workplaces as employees set up work from home offices. With fewer commuters, urban transportation systems took a huge hit, with ridership dropping as much as 90 percent in some cities. As businesses begin to welcome back current teleworkers, transit systems are expecting a gradual uptick in ridership, which has been increasing but has not reached pre-pandemic levels.

Before riders return to public transportation, they want assurances that the buses and trains they ride are less crowded and devoid of germs. Many cities have cleaned up their transit systems, literally, disinfecting vehicles and stations with UV robots and wands and installing new air-filtration systems.

During the past year and a half, transit companies have been reimagining how to do business to meet lower demand and changing passenger needs. High ridership levels, once the benchmark of a good transportation system, is no longer an appropriate measure. With fewer 9-to-5 commuters and more riders taking public transportation throughout the day, the need for bolstered rush hour buses and trains is no longer a necessity.

Using 6 Billion Mobility Data Points a Day

Tech companies are helping transit agencies and smart cities get a handle on rider patterns. Moovit created a transit app that uses artificial intelligence and data analytics to assess ridership needs. According to the Moovit’s Global Public Transport Report 2020, riders want more frequent but less populated vehicles during the day. They want real-time information about arrival and departure times and how crowded the bus or train is. They want that information, along with mobile payment capabilities, at their fingertips. Moovit can provide it.

Moovit, an Intel company, is part of the AI-based Mobility as a Service (MaaS) movement. The company’s smartphone app allows customers to plan and adjust their commutes based on real-time transit information. The data is crowdsourced from other riders, and the app connects to the transit system’s customer-facing information, such as schedules and service disruptions, across bus, light rail, and other forms of transportation.

Moovit boasts 930 million users worldwide and collects 6 billion urban mobility data points per day. Buses, trains, stations, and stops are outfitted with sensors that monitor passengers and send that data to the cloud. Using AI, Moovit aggregates and analyzes the anonymous user data to see how citizens travel throughout the city. Moovit tracks where people incur long waits for transportation, as well as average time spent on a daily commute, wait times, distance traveled, and more.

Predicting Transportation Needs

Transportation agencies can use that data to watch overall trends or dig in on a more granular level to plan and optimize operations as riders’ needs shift. For example, a city could monitor passenger levels based on location or zone, type of vehicle, time of day, and day of the week. Moovit also can integrate data from bike, scooter, and ride-sharing services.

The Moovit solution also allows transportation agencies to conduct impact assessments when they consider changes to the system. For example, transportation companies can use data-driven insights to improve space management, reducing crowds on vehicles, in stations, and at bus stops. An On-Demand feature identifies under-utilized areas and suggests shifting buses to more populated routes. It provides operators with a real-time view of the full fleet, dynamic routing, and automated scheduling and dispatch capabilities.

Big data is critical now. People want more buses, less crowded public transportation, cleaner stations and stops. With AI-based transportation solutions, all that information and the analytics driving those decisions are accessible via cloud-based computing and smart phone apps.