IEEE Spectrum reports on How to Hail a Passenger

But further data mining produced more nuanced insights: The researchers found that hunting in the early morning, before 7 a.m., resulted in more passenger pickups, while waiting in busy areas could result in more pickups during the height of rush hour, from 6 p.m. to 7 p.m. (Important to note, however, is that some of the data the researchers used was hard to parse: An immobile cab at rush hour might be stuck in traffic, rather than deliberately waiting for a customer, especially in the evening hours, the researchers say.) The top 10 performing drivers—those who had the highest number of passenger pickups overall—consistently chose to hunt for passengers or drive back to a busy area after drop-offs. Eight out of 10 of the lowest-performing drivers chose to wait or stay in the location where they dropped off their last passengers. Waiting, then, is associated with fewer pickups on average.

Daqing Zhang, who led the study, says that the novelty of his group’s research lies in “how to guide the taxi drivers to choose the right strategy and optimal route to find the next passengers.” Zhang’s future research will focus on the route the driver takes when the cab is empty. If it’s generally true that hunting and driving back to busy locations results in more passenger pickups, then cabbies would do even better by choosing the best paths while on the hunt or while driving back to a busy location. An application that could suggest such optimized routes might, in theory, make individual taxi drivers richer. But if every driver starts hunting on optimized routes, the distribution of cabs might change in ways that aren’t optimal for the overall balance of a city’s taxi system.

Several years ago, joint research conducted by a group from Kyoto University and the University of California, Davis, showed just that. They developed a model to better understand the behavioral tendencies and economics at play in a taxi driver’s decision either to cruise for passengers or wait in a taxi bay. But in the Kyoto-Davis study, the tendencies of the individual were considered in relation to taxi availability citywide. The examples, derived from taxis in the Nagoya metropolitan area of Japan, showed that while cruising might increase an individual driver’s overall performance, it could have an adverse effect on “socially motivated system optimization.” Taxi drivers have a propensity for cruising—and for good reason—but too many cruising taxis leads to increased competition and inefficient system performance.

(Via Marginal Revolution.)