Estimating hotspots using a Gaussian mixture model from large-scale taxi GPS trace data

The data collected from taxi vehicles using the global positioning system (GPS) traces provides abundant temporal-spatial information, as well as information on the activity of drivers. Using taxi vehicles as mobile sensors in road networks to collect traffic information is an important emerging approach in efforts to relieve congestion. In this paper, we present a hybrid model for estimating driving paths using a density-based spatial clustering of applications with noise (DBSCAN) algorithm and a Gaussian mixture model (GMM). The first step in our approach is to extract the locations from pick-up and drop-off records (PDR) in taxi GPS equipment. Second, the locations are classified into different clusters using DBSCAN. Two parameters (density threshold and radius) are optimized using real trace data recorded from 1100 drivers. A GMM is also utilized to estimate a significant number of locations; the parameters of the GMM are optimized using an expectation-maximum (EM) likelihood algorithm. Finally, applications are used to test the effectiveness of the proposed model. In these applications, locations distributed in two regions (a residential district and a railway station) are clustered and estimated automatically.

[1]  Henry A. Kautz,et al.  Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields , 2007, Int. J. Robotics Res..

[2]  Gyung-Leen Park,et al.  Analysis of the Passenger Pick-Up Pattern for Taxi Location Recommendation , 2008, 2008 Fourth International Conference on Networked Computing and Advanced Information Management.

[3]  Xing Xie,et al.  Learning transportation mode from raw gps data for geographic applications on the web , 2008, WWW.

[4]  Jifu Guo,et al.  Operational Analysis on Beijing Road Network during the Olympic Games , 2008 .

[5]  Wei-Ying Ma,et al.  Understanding mobility based on GPS data , 2008, UbiComp.

[6]  Jane Yung-jen Hsu,et al.  Context-aware taxi demand hotspots prediction , 2010, Int. J. Bus. Intell. Data Min..

[7]  Liang Liu,et al.  Uncovering cabdrivers' behavior patterns from their digital traces , 2010, Comput. Environ. Urban Syst..

[8]  Xing Xie,et al.  Understanding transportation modes based on GPS data for web applications , 2010, TWEB.

[9]  Yu Zheng,et al.  Computing with Spatial Trajectories , 2011, Computing with Spatial Trajectories.

[10]  Lin Sun,et al.  Hunting or waiting? Discovering passenger-finding strategies from a large-scale real-world taxi dataset , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[11]  Xing Xie,et al.  Where to find my next passenger , 2011, UbiComp '11.

[12]  Qiang Yang,et al.  Activity Recognition from Trajectory Data , 2011, Computing with Spatial Trajectories.

[13]  Daqing Zhang,et al.  Measuring social functions of city regions from large-scale taxi behaviors , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[14]  Guangzhong Sun,et al.  Driving with knowledge from the physical world , 2011, KDD.

[15]  Zhaohui Wu,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 1 Land-Use Classification Using Taxi GPS Traces , 2022 .

[16]  Javier Ortigosa Marin,et al.  Assessment of the Taxi Sector Efficiency and Profitability Based on Continuous Monitoring and Methodology to Review Fares , 2014 .

[17]  S. Ukkusuri,et al.  Characterizing Urban Dynamics Using Large Scale Taxicab Data , 2015 .