UC Irvine

Taxi ridesharing (TRS) is the urban transport alternative that matches separate individual rides to a shared ride, with similar spatial and temporal features. TRS provides a variety of benefits, including saving money for customers, reducing operating costs for taxi operators, cutting the emissions of greenhouse gases and of various air pollutants, and decreasing vehicle miles traveled (VMT). After developing a static one-to-one matching algorithm to evaluate the potential for this approach, I analyzed the year 2014 records of a “big data” public dataset of over 160 million records of canary yellow taxi trips in New York City. I found that approximately 48% to 52% of taxi trips could be shared, with relatively small monthly variation. For the whole year 2014, VMT could be reduced by 98.5 million miles, customers could save $400 million, emissions of CO2 could be reduced by 87.8 million lb., and gasoline consumption could be decreased by 4.5 million gallons. To understand the characteristics of the areas where taxi ride sharing is most promising, I also estimated a Tobit model with census tract socio-economic and land use variable. Results suggest that education, age (generation), car ownership, and employment density have a significant impact on taxi ridesharing.