Extracting activity patterns from taxi trajectory data: a two-layer framework using spatio-temporal clustering, Bayesian probability and Monte Carlo simulation

Shuhui Gong · John Cartlidge · Ruibin Bai · Yang Yue · Qingquan Li · Guoping Qiu

43 citations0/0 files storedInternational Journal of Geographical Information Systems

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

Human Mobility and Location-Based Analysis Transportation Planning and Optimization Data Management and Algorithms

Abstract

Global positioning system (GPS) data generated from taxi trips is a valuable source of information that offers an insight into travel behaviours of urban populations with high spatio-temporal resolution. However, in its raw form, GPS taxi data does not offer information on the purpose (or intended activity) of travel. In this context, to enhance the utility of taxi GPS data sets, we propose a two-layer framework to identify the related activities of each taxi trip automatically and estimate the return trips and successive activities after the trip, by using geographic point-of-interest (POI) data and a combination of spatio-temporal clustering, Bayesian inference and Monte Carlo simulation. Two million taxi trips in New York, the United States of America, and ten million taxi trips in Shenzhen, China, are used as inputs for the two-layer framework. To validate each layer of the framework, we collect 6,003 trip diaries in New York and 712 questionnaire surveys in Shenzhen. The results show that the first layer of the framework performs better than comparable methods published in the literature, while the second layer has high accuracy when inferring return trips.

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 — 43 citations exist upstream.

References

2003Using GPS to learn significant locations and predict movement across multiple users 2015Inferring trip purposes and uncovering travel patterns from taxi trajectory data 2011Intra-urban human mobility patterns: An urban morphology perspective 2014Intra-Urban Human Mobility and Activity Transition: Evidence from Social Media Check-In Data 2009Mining time-dependent attractive areas and movement patterns from taxi trajectory data 2017To purchase or not? Why consumers make economically (non-)sustainable consumption choices 2011Exploratory calibration of a spatial interaction model using taxi GPS trajectories 2010Activity identification from GPS trajectories using spatial temporal POIs' attractiveness

Cited by

2020Geographical and temporal huff model calibration using taxi trajectory data 2023Agent-based modelling with geographically weighted calibration for intra-urban activities simulation using taxi GPS trajectories 2022Investigating Human Travel Patterns from an Activity Semantic Flow Perspective: A Case Study within the Fifth Ring Road in Beijing Using Taxi Trajectory Data 2022Enhancing the POI data for trip purpose inference using machine learning techniques

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.