Dispersing Transit Commutes with Financial Incentives and Data
Urban Engines, a startup run by Shiva Shivakumar and Balaji Prabhakar, is using 'crowd-sensing' real-time transit data and financial incentives to draw transit commuters away from peak hour traffic.
According to Eric Jaffe, Urban Engines' use of real-time data allows them to "produce interactive data visualizations that give short-term congestion insights (this platform is overcrowded, trains on this line are bunching) and longer-term traffic trends (on rainy days this station needs more cars). Transit operators can use that information to scheduled and dispatch train supply more efficiently."
Urban Engines' model of financially incentivizing off-peak commutes has already proven successful in three cities (Bangalore, Singapore, and Palo Alto). Per Jaffe, during a six month pilot study in Bangalore, "[roughly] 14,000 locals were given the chance to commute outside peak hours; every time they did, they improved their odds of winning a weekly raffle that paid out prizes ranging from $10 to $240. Over the course of the pilot, commuter traveling pre-rush hour doubled, and the average morning commute time of all bus riders fell from 71 to 54 minutes."
As Jaffe points out, Urban Engines' model has not been applied to drivers nor does it address "the generally entrenched nature of commute habits." And, while Urban Engines' real-time model helps identify system needs, it does not deal with systemic issues within transit agencies such as "limited equipment and personnel" to respond to such needs.