Activity Modelling Using Journey Pairing of Taxi Trajectory Data

Taxi GPS data offers an opportunity to discover behavioural patterns in urban populations. However, raw taxi journey data does not provide a link between outbound and return journeys of individual travellers. Without this information, it is not possible to track individual behaviours. In this study, we propose a novel method for pairing taxi journeys and apply it to taxi trajectory data for the city of Shenzhen, China. Journeys related to three activities are considered: shopping, medical, and work. Results, validated using questionnaire data collected in Shenzhen, quantitatively reveal behavioural patterns and suggest possibilities for applications in urban design.

[1]  Yanchi Liu,et al.  Point-of-Interest Demand Modeling with Human Mobility Patterns , 2017, KDD.

[2]  Shaogang Gong,et al.  Support vector regression and classification based multi-view face detection and recognition , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[3]  Qingquan Li,et al.  Geographical Huff Model Calibration using Taxi Trajectory Data , 2017, IWCTS@SIGSPATIAL.

[4]  Y Liu,et al.  Quantifying the Distance Effect in Spatial Interactions , 2014 .

[5]  Yuan Tian,et al.  Understanding intra-urban trip patterns from taxi trajectory data , 2012, J. Geogr. Syst..

[6]  Qingquan Li,et al.  Spatio-temporal prediction of shopping behaviours using taxi trajectory data , 2018, 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA).

[7]  Bo Hu,et al.  Exploratory calibration of a spatial interaction model using taxi GPS trajectories , 2012, Comput. Environ. Urban Syst..

[8]  A. Fotheringham.  SPATIAL STRUCTURE AND DISTANCE‐DECAY PARAMETERS , 1981, Annals of the Association of American Geographers.

[9]  Yong Gao,et al.  Understanding Urban Traffic-Flow Characteristics: A Rethinking of Betweenness Centrality , 2013 .

[10]  F. Koppelman,et al.  Activity-Based Modeling of Travel Demand , 2003 .

[11]  Qingquan Li,et al.  Automated prediction of shopping behaviours using taxi trajectory data and social media reviews , 2018, 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA).

[12]  Chaogui Kang,et al.  Intra-urban human mobility patterns: An urban morphology perspective , 2012 .

[13]  Lun Wu,et al.  Intra-Urban Human Mobility and Activity Transition: Evidence from Social Media Check-In Data , 2014, PloS one.

[14]  Pengfei Wang,et al.  Human Mobility Synchronization and Trip Purpose Detection with Mixture of Hawkes Processes , 2017, KDD.