Trip chain extraction using smartphone-collected trajectory data

ABSTRACT Travel trajectories of individuals, collected by smartphone sensors, carry substantial trip chain information on advanced urban transportation operation. Automatic information extraction plays a critical role in intelligent transportation data collection and analysis, with higher efficiency and accuracy. This research proposes an entire trip chain extraction procedure, including integrated activity/trip segmentation and travel-mode and activity-type recognition components. Specifically, a unique sliding-window Euclidean distance method is proposed to segment trajectories into travel and activity segments. Four alternative black-box pattern recognition (PR) methods, including decision trees (DT), multilayer perceptrons (MLP), radial basis function neural networks (RBFNN), and support vector machines (SVM), are used to classify travel-mode and activity type. An F-Score-based feature-selecting rule is further introduced to improve the performance of the travel-mode/activity-type classifiers. The proposed extraction procedure is demonstrated using trajectory data collected from volunteers. Test results show that the proposed sliding-window Euclidean distance segmentation approach has error rates as low as 2–4%; recognition tests suggest that SVM performs the best in travel-mode recognition and MLP performs the best in activity-type recognition; feature selection tests reveal that the methods with feature selection achieve higher accuracy than those with total features. Overall, the proposed trip chain extraction procedure achieves an average completion rate above 80%.

[1]  Mahdieh Allahviranloo,et al.  Modeling the activity profiles of a population , 2017 .

[2]  Chih-Jen Lin,et al.  Combining SVMs with Various Feature Selection Strategies , 2006, Feature Extraction.

[3]  Andreas Wagner,et al.  Motion pattern analysis enabling accurate travel mode detection from GPS data only , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[4]  Kentaro Toyama,et al.  Project Lachesis: Parsing and Modeling Location Histories , 2004, GIScience.

[5]  Jean Wolf,et al.  Mode and Activity Identification Using GPS and Accelerometer Data , 2006 .

[6]  Kemal Polat,et al.  A new feature selection method on classification of medical datasets: Kernel F-score feature selection , 2009, Expert Syst. Appl..

[7]  Ahmed F. Abdelghany,et al.  Temporal-Spatial Microassignment and Sequencing of Travel Demand with Activity-Trip Chains , 2003 .

[8]  Yee Leung,et al.  Applying mobile phone data to travel behaviour research: A literature review , 2017 .

[9]  Eui-Hwan Chung,et al.  A Trip Reconstruction Tool for GPS-based Personal Travel Surveys , 2005 .

[10]  Baher Abdulhai,et al.  Framework for Automating Travel Activity Inference Using Land Use Data , 2015 .

[11]  Pasi Fränti,et al.  Detecting movement type by route segmentation and classification , 2012, 8th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom).

[12]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[13]  Zhongyi Zuo,et al.  Trip Activity Chain Pattern Recognition and Travel Trajectory Data Mining , 2015 .

[14]  Fei Yang,et al.  Multimode trip information detection using personal trajectory data , 2016, J. Intell. Transp. Syst..

[15]  Fei Su,et al.  A Method of Traffic Travel Status Segmentation Based on Position Trajectories , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[16]  Baher Abdulhai,et al.  Using Smartphones and Sensor Technologies to Automate Collection of Travel Data , 2013 .

[17]  Tae Youn Jang,et al.  CAUSAL RELATIONSHIP AMONG TRAVEL MODE, ACTIVITY, AND TRAVEL PATTERNS , 2003 .

[18]  Christian Schneider,et al.  Spatiotemporal Patterns of Urban Human Mobility , 2012, Journal of Statistical Physics.

[19]  Akshay Vij,et al.  When is big data big enough? Implications of using GPS-based surveys for travel demand analysis , 2015 .

[20]  Sankar K. Pal,et al.  Multilayer perceptron, fuzzy sets, and classification , 1992, IEEE Trans. Neural Networks.

[21]  Henry A. Kautz,et al.  Learning and inferring transportation routines , 2004, Artif. Intell..

[22]  Will Recker,et al.  Activity Pattern Recognition by Using Support Vector Machines with Multiple Classes , 2013 .

[23]  Ta Theo Arentze,et al.  Pattern Recognition in Complex Activity Travel Patterns: Comparison of Euclidean Distance, Signal-Processing Theoretical, and Multidimensional Sequence Alignment Methods , 2001 .

[24]  Hjp Harry Timmermans,et al.  Transportation mode recognition using GPS and accelerometer data , 2013 .

[25]  Amer Shalaby,et al.  Enhanced System for Link and Mode Identification for Personal Travel Surveys Based on Global Positioning Systems , 2006 .

[26]  Tao Cheng,et al.  Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification , 2012, Comput. Environ. Urban Syst..

[27]  Jean Louise Wolf,et al.  Using GPS data loggers to replace travel diaries in the collection of travel data , 2000 .

[28]  Shanjiang Zhu,et al.  Imputing Trip Purpose Based on GPS Travel Survey Data and Machine Learning Methods , 2013 .

[29]  Peter R. Stopher,et al.  A process for trip purpose imputation from Global Positioning System data , 2013 .

[30]  Filip Biljecki,et al.  Transportation mode-based segmentation and classification of movement trajectories , 2013, Int. J. Geogr. Inf. Sci..

[31]  Bin Ran,et al.  Daily O-D Matrix Estimation using Cellular Probe Data , 2010 .

[32]  Jianhe Du,et al.  Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: Automatic trip end identification issues , 2007 .

[33]  Gary M. Weiss,et al.  Activity recognition using cell phone accelerometers , 2011, SKDD.

[34]  Miguel A. Labrador,et al.  Automating mode detection for travel behaviour analysis by using global positioning systemsenabled mobile phones and neural networks , 2010 .

[35]  Joseph Y. J. Chow,et al.  Causal structure learning for travel mode choice using structural restrictions and model averaging algorithm , 2017 .

[36]  Alexandre M. Bayen,et al.  An ensemble Kalman filtering approach to highway traffic estimation using GPS enabled mobile devices , 2008, 2008 47th IEEE Conference on Decision and Control.

[37]  Chi Xie,et al.  WORK TRAVEL MODE CHOICE MODELING USING DATA MINING: DECISION TREES AND NEURAL NETWORKS , 2002 .

[38]  Satish V. Ukkusuri,et al.  Urban activity pattern classification using topic models from online geo-location data , 2014 .

[39]  Yiqiang Chen,et al.  RECOGNIZING TRANSPORTATION MODE ON MOBILE PHONE USING PROBABILITY FUSION OF EXTREME LEARNING MACHINES , 2013 .

[40]  Randall Guensler,et al.  Elimination of the Travel Diary: Experiment to Derive Trip Purpose from Global Positioning System Travel Data , 2001 .

[41]  Tian Lan,et al.  Zooming into individuals to understand the collective: A review of trajectory-based travel behaviour studies , 2014 .

[42]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[43]  Peter R. Stopher,et al.  Search for a global positioning system device to measure person travel , 2008 .

[44]  Kees Maat,et al.  Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands , 2009 .

[45]  Dieter Fox,et al.  Location-Based Activity Recognition , 2005, KI.

[46]  Deborah Estrin,et al.  Using mobile phones to determine transportation modes , 2010, TOSN.