Author:
Publisher:
ISBN:
Category : Choice of transportation
Languages : en
Pages : 148
Book Description
Vehicle Occupancy
Author: J. Richard Kuzmyak
Publisher:
ISBN:
Category : Automobile driving
Languages : en
Pages : 86
Book Description
Publisher:
ISBN:
Category : Automobile driving
Languages : en
Pages : 86
Book Description
Vehicle Occupancy Determinators
Author:
Publisher:
ISBN:
Category : Choice of transportation
Languages : en
Pages : 148
Book Description
Publisher:
ISBN:
Category : Choice of transportation
Languages : en
Pages : 148
Book Description
Estimating Auto Occupancy: a Review of Methodology
Author: United States. Office of Highway Planning. Urban Planning Division
Publisher:
ISBN:
Category :
Languages : en
Pages : 70
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 70
Book Description
Vehicle Occupancy Data Collection Methods (phase II)
Author:
Publisher:
ISBN:
Category : Automatic data collection systems
Languages : en
Pages : 140
Book Description
Publisher:
ISBN:
Category : Automatic data collection systems
Languages : en
Pages : 140
Book Description
Improved Vehicle Occupancy Data Collection Methods
Author: K. Heidtman
Publisher:
ISBN:
Category : Traffic accident investigation
Languages : en
Pages : 134
Book Description
Publisher:
ISBN:
Category : Traffic accident investigation
Languages : en
Pages : 134
Book Description
Estimating Auto Occupancy
Author: United States. Office of Highway Planning. Urban Planning Division. Public Transportation Branch
Publisher:
ISBN:
Category : Transportation, Automotive
Languages : en
Pages : 64
Book Description
Publisher:
ISBN:
Category : Transportation, Automotive
Languages : en
Pages : 64
Book Description
Development of Guidance for a Vehicle Occupancy Rate Data Collection Program
Author: Yiqing Xu
Publisher:
ISBN:
Category : Data collection platforms
Languages : en
Pages : 0
Book Description
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.
Publisher:
ISBN:
Category : Data collection platforms
Languages : en
Pages : 0
Book Description
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.
High Occupancy Vehicle (HOV) Guidelines for Planning, Design, and Operations
Author: California. Division of Traffic Operations
Publisher:
ISBN:
Category : Government publications
Languages : en
Pages : 168
Book Description
Publisher:
ISBN:
Category : Government publications
Languages : en
Pages : 168
Book Description
Auto Occupancy Monitoring Study
Author: Cy Ulberg
Publisher:
ISBN:
Category : Automobile driving
Languages : en
Pages : 38
Book Description
Publisher:
ISBN:
Category : Automobile driving
Languages : en
Pages : 38
Book Description
Journal of Transportation and Statistics
Author:
Publisher:
ISBN:
Category : Transportation
Languages : en
Pages : 124
Book Description
Publisher:
ISBN:
Category : Transportation
Languages : en
Pages : 124
Book Description