Revealing spatiotemporal travel demand and community structure characteristics with taxi trip data: A case study of New York City

Abstract

Urban traffic demand distribution is dynamic in both space and time. A thorough analysis of individuals’ travel patterns can effectively reflect the dynamics of a city. This study aims to develop an analytical framework to explore the spatiotemporal traffic demand and the characteristics of the community structure shaped by travel, which is analyzed empirically in New York City. It uses spatial statistics and graph-based approaches to quantify travel behaviors and generate previously unobtainable insights. Specifically, people primarily travel for commuting on weekdays and entertainment on weekends. On weekdays, people tend to arrive in the financial and commercial areas in the morning, and the functions of zones arrived in the evening are more diversified. While on weekends, people are more likely to arrive at parks and department stores during the daytime and theaters at night. These hotspots show positive spatial autocorrelation at a significance level of p = 0.001. In addition, the travel flow at different peak times form relatively stable community structures, we find interesting phenomena through the complex network theory: 1) Every community has a very small number of taxi zones (TZs) with a large number of passengers, and the weighted degree of TZs in the community follows power-law distribution; 2) As the importance of TZs increases, their interaction intensity within the community gradually increases, or increases and then decreases. In other words, the formation of a community is determined by the key TZs with numerous traffic demands, but these TZs may have limited connection with the community in which they are located. The proposed analytical framework and results provide practical insights for urban and transportation planning.

Impact of built environment on residential online car-hailing trips: Based on MGWR model

“…The business residence variable passes all the tests of the model and we conduct a cartographic analysis of the spatio-temporal distributions of the coefficients of the business residence variable at different time periods, as shown in Fig 5 . With the exception of the southern part of the central city and some areas on the edge of the Third Ring, most areas have positive model estimates during the study period, indicating that the residential variable is significantly and positively associated with residential DiDi trips, and consistent with previous related studies, residential factors have a significant impact on car-hailing trips [ 47 , 48 ]. Among the morning peak 1, morning peak 2 and evening peak 1 models, the spatial distributions of business residence variables show some similarity due to the obvious commuting characteristics.…”

Releasing Differentially Private Trajectories with Optimized Data Utility

“…Problem to tackle. Suppose that a data curator , e.g., the New York City Taxi & Limousine Commission [2], has collected a dataset of actual trajectories of the individuals, D A = {l u |u ∈ U }. The data curator would like to publish the dataset to some (potentially untrusted) analyzers, e.g., insurance companies and academic institutions, for facilitating data-mining purposes.…”

“…For instance, New York City Taxi and Limousine Commission publicly releases a trajectory dataset of taxi passengers every month. The data analyzers, such as urban planners, can improve the community division with the help of the spatial-temporal regularity of human movement patterns [2]. However, such data release poses a serious threat to individual location privacy, since (potentially) untrusted analyzers may have great interest in deriving personal identity and sensitive locations from the individual trajectories [3].…”

Exploring the Spatiotemporal Impacts of the Built Environment on Taxi Ridership Using Multisource Data

“…In this section, we analyze the variation of the regression coefficients over time by aggregating TAZs in representative districts and average the coefficients in temporal units. Manhattan is the most important borough of NYC with most trips generated from it [42]. A full analysis of Manhattan plays an important role in transportation policy development.…”