Analysis and Visualization for Hot Spot Based Route Recommendation Using Short-Dated Taxi GPS Traces

Taxi GPS traces, which contain a great deal of valuable information as regards to human mobility and city traffic, can be extracted to improve the quality of our lives. Since the method of visualized analysis is believed to be an effective way to present information vividly, we develop our analysis and visualization method based on a city’s short-dated taxi GPS traces, which can provide recommendation to help cruising taxi drivers to find potential passengers with optimal routes. With our approach, hot spots for loading and unloading passenger(s) are extracted using an improved DBSCAN algorithm after data preprocessing including cleaning and filtering. Then, this paper describes the start-end point-based similar trajectory method to get coarse-level trajectories clusters, together with the density-based e distance trajectory clustering algorithm to identify recommended potential routes. A weighted tree is defined including such factors as driving time, velocity, distance and endpoint attractiveness for optimal route evaluation from vacant to occupied hot spots. An example is presented to show the effectiveness of our visualization method.

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

[2]  Nikos Pelekis,et al.  Visually exploring movement data via similarity-based analysis , 2012, Journal of Intelligent Information Systems.

[3]  Lin Sun,et al.  Understanding Taxi Service Strategies From Taxi GPS Traces , 2015, IEEE Transactions on Intelligent Transportation Systems.

[4]  Daqing Zhang,et al.  From taxi GPS traces to social and community dynamics , 2013, ACM Comput. Surv..

[5]  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 .

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

[7]  Xing Xie,et al.  T-drive: driving directions based on taxi trajectories , 2010, GIS '10.

[8]  Zhaohui Wu,et al.  Prediction of urban human mobility using large-scale taxi traces and its applications , 2012, Frontiers of Computer Science.

[9]  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).

[10]  Wang Qin,et al.  Continuous clustering trajectory stream of moving objects , 2013, China Communications.

[11]  Jae-Gil Lee,et al.  Trajectory clustering: a partition-and-group framework , 2007, SIGMOD '07.

[12]  Stefan Wrobel,et al.  Visual analytics tools for analysis of movement data , 2007, SKDD.

[13]  João Gama,et al.  Predicting Taxi–Passenger Demand Using Streaming Data , 2013, IEEE Transactions on Intelligent Transportation Systems.

[14]  Zhi-Hua Zhou,et al.  B-Planner: Planning Bidirectional Night Bus Routes Using Large-Scale Taxi GPS Traces , 2014, IEEE Transactions on Intelligent Transportation Systems.

[15]  Lei Zhang,et al.  An efficient trajectory-clustering algorithm based on an index tree , 2012 .

[16]  Chen Xiaoyun,et al.  On measuring the privacy of anonymized data in multiparty network data sharing , 2013, China Communications.

[17]  Siyuan Liu,et al.  Towards mobility-based clustering , 2010, KDD.

[18]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[19]  Yu Zheng,et al.  Travel time estimation of a path using sparse trajectories , 2014, KDD.

[20]  Lokesh Kumar Sharma,et al.  Density Based k-Nearest Neighbors Clustering Algorithm for Trajectory Data , 2011 .

[21]  Qingquan Li,et al.  Mining time-dependent attractive areas and movement patterns from taxi trajectory data , 2009, 2009 17th International Conference on Geoinformatics.

[22]  Xing Xie,et al.  Discovering regions of different functions in a city using human mobility and POIs , 2012, KDD.

[23]  Hong Shen,et al.  Clustering Subtrajectories of Moving Objects Based on a Distance Metric with Multi-dimensional Weights , 2014, 2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming.