This thesis describes a research study relating naturalistic Global Positioning System (GPS) driving data with long-term traffic safety performance for two classes of roadways. These two classes are multilane arterial streets and limited access highways. GPS driving data used for this study was collected from 33 volunteer drivers from July 2012 to March 2013. The GPS devices used were custom GPS data loggers capable of recording speed, position, and other attributes at an average rate of 2.5 hertz.
Linear Referencing in ESRI ArcMAP was performed to assign spatial and other roadway attributes to each GPS data point collected. GPS data was filtered to exclude data with high horizontal dilution of precision (HDOP), incorrect heading attributes or other GPS communication errors.
For analysis of arterial roadways, the Two-Fluid model parameters were chosen as the measure for long-term traffic safety analysis. The Two-Fluid model was selected based on previous research which showed correlation between the Two-Fluid model parameters n and Tm and total crash rate along arterial roadways. Linearly referenced GPS data was utilized to obtain the total travel time and stop time for several half-mile long trips along two arterial roadways, Grand Avenue and California Boulevard, in San Luis Obispo. Regression between log transformed values of these variables (total travel time and stop time) were used to derive the parameters n and Tm. To estimate stop time for each trip, a vehicle “stop” was defined when the device was traveling at less than 2 miles per hour. Results showed that Grand Avenue had a higher value for n and a lower value for Tm, which suggests that Grand Avenue may have worse long-term safety performance as characterized by long-term crash rates. However, this was not verified with crash data due to incomplete crash data in the TIMS database. Analysis of arterial roadways concluded by verifying GPS data collected in the California Boulevard study with sample data collected utilizing a traditional “car chase” methodology, which showed that no significant difference in the two data sources existed when trips included noticeable stop times.
For analysis of highways the derived measurement of vehicle jerk, or rate of change of acceleration, was calculated to explore its relationship with long-term traffic safety performance of highway segments. The decision to use jerk comes from previous research which utilized high magnitude jerk events as crash surrogate, or near-crash events. Instead of using jerk for near-crash analysis, the measurement of jerk was utilized to determine the percentage of GPS data observed below a certain negative jerk threshold for several highway segments. These segments were ¼-mile and ½-mile long. The preliminary exploration was conducted with 39 ¼-mile long segments of US Highway 101 within the city limits of San Luis Obispo. First, Pearson’s correlation coefficients were estimated for rate of ‘high’ jerk occurrences on these highway segments (with definitions of ‘high’ depending on varying jerk thresholds) and an estimate of crash rates based on long-term historical crash data. The trends in the correlation coefficients as the thresholds were varied led to conducting further analysis based on a jerk threshold of -2 ft./sec3 for the ¼-mile segment analysis and -1 ft./sec3 for the ¼-mile segment analysis. Through a negative binomial regression model, it was shown that utilizing the derived jerk percentage measure showed a significant correlation with the total number of historical crashes observed along US Highway 101. Analysis also showed that other characteristics of the roadway, including presences of a curve, presence of weaving (indicated by the presence of auxiliary lanes), and average daily traffic (ADT) did not have a significant correlation with observed crashes. Similar analysis was repeated for 19 ½-mile long segments in the same study area, and it was found the percentage of high negative jerk metric was again significant with historical crashes. The ½-mile negative binomial regression for the presence of curve was also a significant variable; however the standard error for this determination was very high due to a low sample size of analysis segments that did not contain curves.
Results of this research show the potential benefit that naturalistic GPS driving data can provide for long-term traffic safety analysis, even if data is unaccompanied with any additional data (such as live video feed) collected with expensive vehicle instrumentation. The methodologies of this study are repeatable with many GPS devices found in certain consumer electronics, including many newer smartphones.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-2151 |
| Date | 01 August 2013 |
| Creators | Loy, James Michael |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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