Analysis of travel route data from a system efficiency perspective

Analysis of travel route data from a system efficiency perspective

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    Final Report for UTC Year 15
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    Traveler route choice behavior is the cornerstone of numerous advanced traffic management technologies. Yet, few datasets of actual travel routes have been collected and analyzed. There are two specific objectives of the analysis work conducted in this NEUTC research project (the data collection was only partially funded by this project). The first was to advance the methodological aspects of using GPS to collect route data, including constructing the spatial model that automatically identifies trip ends in the large-scale continuous GPS data stream. This work makes use of 10-days of in-vehicle travel routes collected from 256 households using Global Positioning System (GPS) receivers from 2002 to 2003 in Lexington, Kentucky (population 250,000). Information for each vehicle, such as speed, latitude, longitude, and heading was recorded every second or every five seconds (depending on the power feature in the particular vehicle and the corresponding settings of GPS devices). A travel habits survey was also conducted with every driver in each household after the ten-day data collection cycle was completed. This dataset was used for the second project objective: to estimate routing efficiency and selection patterns by determining if route choice is influenced by attitudes, travel habits, roadway characteristics, congestion, or a combination of a few or all of these elements. Surveys were collected from 524 drivers in the 256 households. This study has demonstrated the importance of validating GPS trip dividing methods against known trip start and end locations in order to defensibly measure the accuracy of algorithms. The relatively small range of parameters tested here resulted in a significant variation in accuracy and error results, indicating that trip division is highly sensitive to the parameters used. Some recent studies have found GPS dramatically increases the number of trips reported by travelers. However, it is possible that the method or combination of parameters used to divide the GPS data stream into individual trips significantly affected these trip rate estimates. Therefore, caution should be exercised in interpreting GPS travel data. The analysis of the route choice data in the follow-up surveys indicates that a wide variety of data types influence route consistency: travel habit variables, attitudinal variables, route characteristic variables, and demographic variables. Demographic and route characteristic variables have less influence in the some route models suggesting that attitudinal data may be valuable for predicting specific routes.
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