Estimating Auto Occupancy

Author : Yiqing Xu
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)

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Download or read book Development of Guidance for a Vehicle Occupancy Rate Data Collection Program written by Yiqing Xu and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transportation planning practices increasingly require knowing the number of occupants per vehicle. Except for manual observations, Virginia has two data sources for obtaining occupancy: the American Community Survey and the National Household Travel Survey, neither of which provides corridor-specific values. This study developed an approach for estimating occupancy based on crash records data—now feasible because Virginia routinely collects, for each crash, the total number of occupants regardless of injury status. This occupancy is not widely available because of privacy concerns but can be obtained through a special tabulation performed by VDOT’s Traffic Engineering Division. Having crash data is not a panacea: as the area of interest shrinks from a district to a roadway segment, the likelihood that crashes alone provide a biased estimate of occupancy increases. Accordingly, the recommended approach for detecting occupancy contains two additional steps beyond extracting crash data: (1) at the jurisdiction level, test whether this bias exists by performing an eta-squared test; if appropriate, perform Type 1 bias correction by ensuring all occupancy groups (e.g., three occupants per vehicle) are synthesized in the crash data set; and (2) at the corridor level, perform Type 2 bias correction by building a correction model incorporating field observations. Yet bias is not necessarily a fatal flaw. At the corridor level, the mean average absolute difference between occupancy based on uncorrected crash data and occupancy collected from field observations was 0.06; use of the Type 2 bias correction model showed a difference of 0.05 between field observations and corrected data when the model was used on a set of data not used to build the model. At the jurisdiction level, the difference between uncorrected occupancies and Type 1 bias correction was never above 0.02 as long as at least 200 vehicles are observed in crashes. This method allows Virginia to estimate occupancies by time period, day type, and functional class. Crash data for VDOT’s Hampton Roads District showed statistically significant differences in occupancies ranging from 1.18 to 1.30 (midweek vs. weekend); 1.15 to 1.22 (AM peak vs. off-peak); and 1.16 to 1.26 (variation among seven functional classes). The study recommends that VDOT establish an occupancy data collection program in one district based on two elements: (1) the extraction of occupancies from crash reports, and (2) an adjustment of these occupancies based on the two bias correction methods studied. These two recommendations need not preclude the possibility of using new technologies, some of which were examined in this study, but the approaches highlighted in this report have been successfully tested on a case study basis in Virginia.