Spatiotemporal Pattern Analysis of Taxi Trips in New York City

Abstract

A growing number of extensive data sets provides transportation planners with the necessary means to analyze urban travel patterns and gain insight into urban dynamics. This paper explores the spatial and temporal variation of taxi trips in New York City by analyzing 29 million trip records from a freely available data set. The study examined the role of airports in trip generation and attraction, as well as the variation of travel speed during the day. Comparison of hourly trip frequencies between weekdays and weekend days in each district revealed similarities and differences in the functional drivers of taxi trip demand. This paper presents a negative binomial regression model that predicts the number of taxi trips per district from the subway, train, and bus infrastructure, as well as socioeconomic and land use variables, where eigenvector spatial filtering is applied to explicitly model spatial autocorrelation. Independent of the predictor variables, a combination of subway ridership and taxi trip numbers for each district in a mode mix variable allows, through the use of local indicators of spatial association statistics, the identification of districts that exhibit an increased inclination toward taxi use and that are currently poorly served by public transit. This approach could be used as a decision support tool to determine where investments in rapid transit infrastructure and service would be particularly effective to increase transit mode share.

A Machine Learning Framework for Profitability Profiling and Dynamic Price Prediction for the New York City Taxi Trips

“…Negative binomial regression model and Spatial association were implemented to analyze the travel patterns in urban areas for effective investment decisions in rapid transit infrastructure and service ( Hochmair, 2016) and kernal density analysis was used to detect demand fluctuations (Markou, Rodrigues, & Pereira, 2017). The travel time estimation problem was addressed with a big data-driven approach using a simple baseline for Travel Time Estimation (Wang, Kuo, Kifer, & Li, 2014).…”

Exploring the Intermodal Relationship between Taxi and Subway in Beijing, China

“…The variables used in the negative binomial regression models were selected carefully to avoid collinearity, and different models are selected according to the Akaike information criterion (AIC) and log-likelihood values [22,23]. Table 4 shows the results of the negative binomial regression models for estimating the number of subway-competing taxi trips and taxi trips in the buffer zones of subway stations.…”

“…Huang et al [21] concluded that metro development and the design of station-area neighborhoods have the potential to reduce driving, mitigate its impact on environment, and slow the growth of traffic congestion. Besides, the combination of taxi trips and subway ridership data can provide useful information to identify the underserved areas by public transport [22]. Examining the spatial relationship of taxi trips' origins/destinations and subway stations, Wang and Ross [23] categorized taxi trips into three types: transit-competing, transit-complementing, and transit-extending ones, to explore the inner interaction.…”

Understanding the Dynamics of the Pick-Up and Drop-Off Locations of Taxicabs in the Context of a Subsidy War among E-Hailing Apps

“…Third, researchers have used taxicab trajectories to examine land-use types, reflect the spatial structure of urban areas, and examine the interactions between residents and functional zones. Such approaches have been used in, for example, accessibility analysis of urban road networks [27,28], mining alternative space-time path dynamics of travel [29], mining hotspots and points of interest in urban areas [30][31][32], determining the spatiotemporal attractiveness of specific areas [33,34], detection and analysis of functional regions [35][36][37], classification of land-use types [38,39], analysis of the structure of urban regions [40][41][42][43], observing strong links between public transportation terminals [44], evaluating the effectiveness of urban planning after it has been carried out [45], identifying the spatiotemporal patterns of functionally critical locations in urban transportation networks [46], and locating optimal taxi stands on city maps using pick-up and drop-off locations in Singapore [47].…”