Spatial Variation of Taxi Demand Using GPS Trajectories and POI Data

Xinmin Liu · Lu Sun · Qiuxia Sun · Ge Gao

33 citations0/0 files storedJournal of Advanced Transportation

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Transportation Planning and Optimization Urban Transport and Accessibility Transportation and Mobility Innovations

Abstract

Taxi as a door-to-door, all-weather way of travel is an important part of the urban transportation system. A fundamental understanding of temporal-spatial variation and its related influential factors are essential for taxi regulation and urban planning. In this paper, we explore the correlation between taxi demand and socio-economic, transport system and land use patterns based on taxi GPS trajectory and POI (point of interest) data of Qingdao City. The geographically weighted regression (GWR) model is used to describe the influence factors of spatial heterogeneity of the taxi demand and visualize the spatial distributions of parameter estimations. Results indicate that during the peak hours, there are some differences in taxi demand between workdays and weekends. Residential density and housing prices increase the number of taxi trips. Road density, parking lot density and bus station density are positively associated with the taxi demand. It is also found that the higher of the proportion of commercial area and public service area, the greater of the taxi demand, while the proportion of residential area and the land use mix have a negative impact on taxi demand. This paper provides some references for understanding the internal urban environmental factors generating from the taxi travel demand, and provides insights for reducing the taxi vacancy rate, forecasting taxi temporal-spatial demand and urban public transportation system planning.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 33 citations exist upstream.

References

1970A Computer Movie Simulating Urban Growth in the Detroit Region 1996Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity 2002Built environments and mode choice: toward a normative framework 2015Revealing travel patterns and city structure with taxi trip data 2012Application of geographically weighted regression to the direct forecasting of transit ridership at station-level 2011Transit ridership forecasting at station level: an approach based on distance-decay weighted regression 2017Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns 2015Uncovering urban human mobility from large scale taxi GPS data

Cited by

2023Exploring spatial heterogeneity in the impact of built environment on taxi ridership using multiscale geographically weighted regression 2021Impact of the mixed degree of urban functions on the taxi travel demand 2020Revealing Urban Traffic Demand by Constructing Dynamic Networks With Taxi Trajectory Data 2023Influence of the built environment on taxi travel demand based on the optimal spatial analysis unit

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.