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http://hdl.handle.net/11375/15956
Title: | GIS-based Episode Reconstruction Using GPS Data for Activity Analysis and Route Choice Modeling |
Other Titles: | GIS-based Episode Reconstruction Using GPS Data |
Authors: | Dalumpines, Ron |
Advisor: | Scott, Darren |
Department: | School of Geography and Geology |
Keywords: | GPS;time use diary;episode extraction;multinomial logit;travel behavior;mode detection;episode reconstruction;GIS;map-matching;route choice;path size logit;potential path area;scale estimation;Python;ArcGIS;activity analysis;trip reconstruction;smartphone;global positioning system;geographic information system;toolkit;work trip;shop trip;potential activity location;land use;activity episode;travel episode;stop episode;transferability;scalability;modularity;scripting;big data;travel survey;respondent burden;preprocessing;multipath error;tracking;purpose detection;segmentation;data filter;data smoothing;fuzzy logic;neural network;decision tree;rule-based algorithm;trajectory;point;road network;network dataset;gateway;shortest path;horizontal dilution of precision;HDOP;commonality factor;mode transfer point;Halifax;Nova Scotia;Space-Time Activity Research;variance inflation factor;shapefile;comma-separated values;traveling salesman problem;data mining;branch-and-bound algorithm;pedestrian network;alternative route;observed route;route efficiency;route attributes;distance;time;heading;bearing;duration;acceleration;latitude;longitude;coordinate;overlay analysis;intersect;shopping;module;data logger;walk;likelihood ratio test;classification table;path generation;kappa statistic;degrees;data collection;transportation research;automate;framework;ArcToolbox;spatio-temporal;navigation;positioning;trace path;spatial data;topology;horizontal accuracy;SPSS;Stata;spatial resolution;temporal resolution;household survey;trip reporting;proximity analysis;DMTI;Desktop Mapping Technologies Inc.;road intersection;route overlap;left turn;right turn;location analysis;time geography;spatial statistics;buffer analysis;cycling;bus transit;public transportation;GEOIDE;AGILE;data need;raw data;urban canyon;endpoint;data cleaning;elevation;satellite;outliers;automatic processing;nearest node;classifier;classification method;short trip;multi-point |
Abstract: | Most transportation problems arise from individual travel decisions. In response, transportation researchers had been studying individual travel behavior – a growing trend that requires activity data at individual level. Global positioning systems (GPS) and geographical information systems (GIS) have been used to capture and process individual activity data, from determining activity locations to mapping routes to these locations. Potential applications of GPS data seem limitless but our tools and methods to make these data usable lags behind. In response to this need, this dissertation presents a GIS-based toolkit to automatically extract activity episodes from GPS data and derive information related to these episodes from additional data (e.g., road network, land use). The major emphasis of this dissertation is the development of a toolkit for extracting information associated with movements of individuals from GPS data. To be effective, the toolkit has been developed around three design principles: transferability, modularity, and scalability. Two substantive chapters focus on selected components of the toolkit (map-matching, mode detection); another for the entire toolkit. Final substantive chapter demonstrates the toolkit’s potential by comparing route choice models of work and shop trips using inputs generated by the toolkit. There are several tools and methods that capitalize on GPS data, developed within different problem domains. This dissertation contributes to that repository of tools and methods by presenting a suite of tools that can extract all possible information that can be derived from GPS data. Unlike existing tools cited in the transportation literature, the toolkit has been designed to be complete (covers preprocessing up to extracting route attributes), and can work with GPS data alone or in combination with additional data. Moreover, this dissertation contributes to our understanding of route choice decisions for work and shop trips by looking into the combined effects of route attributes and individual characteristics. |
URI: | http://hdl.handle.net/11375/15956 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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dalumpines_ron_f_201405_phd.pdf | Dissertation | 2.61 MB | Adobe PDF | View/Open |
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