Identifying the factors that influence taxi demand is very important for understanding where and when people use taxis. A large set of GPS data from New York City taxis is used along with demographic, socioeconomic, and employment data to identify the factors that drive taxi demand. A technique was developed to measure and map transit accessibility on the basis of transit access time (TAT) to understand the relationship between taxi use and transit service. The taxi data were categorized by pickups and drop-offs at different times of day. A multiple linear regression model was estimated for each hour of the day to model pickups and another to model drop-offs. Six important explanatory variables that influence taxi trips were identified: population, education, age, income, TAT, and employment. The influence of these factors on taxi pickups and drop-offs changed at different times of the day. The number of jobs in each industry sector was an indication of the types of economic activities occurring at a location, and in some sectors the number of jobs were strongly associated with taxi use. This study demonstrates the temporal and spatial variation of taxi demand and shows how transit accessibility and other factors affect it.

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A novel methodology used taxi global position system data and high-resolution transit schedule information to compare travel times and travel fares of the two main nondriving travel modes for airport ground access: taxi and transit. Five origin–destination pairs between Pennsylvania Station in New York City and three airports in the New York region were used as an example to demonstrate these methods. An analysis of total trip cost considered both travel time and expenditures on fare. A binary logit model was used to model the mode choice of travelers. The results indicate that transit is the more likely choice during most of the day except the midnight period when transit service has longer headways. A sensitivity analysis shows the relationship between the value of time and total trip cost per passenger for different numbers of passengers traveling together and at different times of day. The higher the value of time and the number of passengers in a group, the more likely it is that a taxi is chosen for airport trips. The attractiveness of one mode relative to the other varies spatially and temporally according to the travel time and price. This paper focuses on understanding temporal variation of total cost of each mode and the effect that this variation is likely to have on mode share.

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