Data collection

Variations in Teens' Perception of Risk Factors for Teen Motor Vehicle Collision Injuries

Chen, Katherine L.
Cooper, Jill F.
Grembek, Offer
Henk, Russell
Tisdale, Stacey M.
2014

Teen drivers, especially males, are known to be at greater risk of being involved in a motor vehicle collision than any other age group. While novice teen drivers’ primary risk factors are commonly known, less is known about what teens perceive as risk factors for peers getting hurt or killed in motor vehicle collisions.

Predictors of Nonstandard Helmet Use Among San Francisco Bay–Area Motorcyclists

Tsui, Casey K.
Rice, Thomas M.
Pande, Swati
2013
Objective: The use of helmets that do not comply with safety standards is common in California. The objective of this study was to describe the use of these nonstandard helmets among San Francisco Bay–area (SFBA) motorcyclists and to identify personal and motorcycle characteristics that are associated with the use of nonstandard helmets.
Methods: A survey of 860 SFBA motorcyclists was conducted. Log-binomial regression was used to estimate risk ratios to compare probabilities of nonstandard helmet use.

Error Consideration for Geocoding Police Reported Collision Data in California

Bigham, John M.
Oum, Sang Hyouk
2014

Geographic Information Systems (GIS) are frequently used to analyze collision data. In order to 3 utilize GIS, the data must be geocoded, or assigned a latitude and longitude coordinate by 4 translating a descriptive location onto street network data. However, the ability for accurate 5 spatial analysis can be limited by geocoding errors that may occur due to limitations in data 6 collection technologies, incorrect data entry due to human error, or inaccurate street reference 7 data.

Estimating Pedestrian Accident Exposure: Automated Pedestrian Counting Devices Report

Bu, Fanping
Greene-Roesel, Ryan
Diogenes, Mara Chagas
Ragland, David R.
2007

Automated methods are commonly used to count motorized vehicles, but are not frequently used to count pedestrians. This is because the automated technologies available to count pedestrians are not very developed, and their effectiveness has not been widely researched. Moreover, most automated methods are used primarily for the purpose of detecting, rather than counting, pedestrians (Dharmaraju et al., 2001; Noyce and Dharmaraju, 2002; Noyce et al., 2006).

Estimating Pedestrian Accident Exposure: Protocol Report

Greene-Roesel, Ryan
Diogenes, Mara Chagas
Ragland, David R.
2007

Walking is a healthful, environmentally benign form of travel, and is the most basic form of human mobility. Walking trips account for more than 8 percent of all trips taken in California, making walking the second most commonly used mode of travel after the personal automobile (Caltrans, 2002). In addition, many trips made by vehicle or public transit begin and end with walking.

Integration of Light Rail Transit into City Streets

Korve, Hans W.
Farran, Jose I.
Mansel, Douglas M.
Levinson, Herbert S.
Chira-Chavala, T.
Ragland, David R.
1996

This report addresses the safety and operating experience of light rail transit (LRT) systems operating in shared (on-street or mall) rights-of-way at speeds that do not exceed 35 mph. It is based on agency interviews, field observations, and accident analyses of 10 LRT systems in the United States and Canada. These systems—in Baltimore, Boston, Buffalo, Calgary, Los Angeles, Portland, Sacramento, San Diego, San Francisco, and San Jose—provide a broad range of current LRT operating practices and problems.

Relative Vulnerability Matrix for Evaluating Multimodal Traffic Safety

Grembek, Offer
2015

The multimodal transportation network includes a mix of inherently different modes. In addition to differences in price, range, and comfort of travel, these modes differ in mass and velocity, which correspond to different orders of magnitude in the kinetic energy carried. This discrepancy in kinetic energy affects both the level of protection of each mode, and the level of damage it can inflict on users of other modes. Unfortunately, accounting for both sides of a crash is often overlooked.

Using Variable Speed Limits To Reduce Rear-End Collision Risks Near Recurrent Bottlenecks

Li, Zhibin
Liu, Pan
Bigham, John M.
Ragland, David R.
2013

Rear-end collisions would occur if vehicle speeds decrease abruptly when encountering kinematic waves (KWs) emanating from active bottlenecks. This study aims to develop a control strategy in variable speed limits (VSL) to reduce rear-end collision risks near recurrent bottlenecks. Using the crash prediction model developed for rear-end collisions related to risky KWs, the effectiveness of VSL control strategies were evaluated in the cell transmission model (CTM). Several strategies were tested in sequence to determine the best case for risk reduction.

Acceptance of drinking and driving and alcohol-involved driving crashes in California

MacLeod, Kara E.
Karriker-Jaffe, Katherine J.
Ragland, David R.
Satariano, William A.
Kelley-Baker, Tara
Lacey, John H.
2015

Background: Alcohol-impaired driving accounts for substantial proportion of traffic-related fatalities in the U.S. Risk perceptions for drinking and driving have been associated with various measures of drinking and driving behavior. In an effort to understand how to intervene and to better understand how risk perceptions may be shaped, this study explored whether an objective environmental-level measure (proportion of alcohol-involved driving crashes in one’s residential city) were related to individual-level perceptions and behavior.

Crashes on and Near College Campuses: A Comparative Analysis of Pedestrian and Bicyclist Safety

Loukaitou-Sideris, Anastasia
Medury, Aditya
Fink, Camille
Grembek, Offer
Shafizadeh, Kevan
Wong, Norman
Orrick, Phyllis
2014

Problem, research strategy, and findings: College campuses are multimodal settings with very high levels of walking and biking in conjunction with high levels of vehicular traffic, which increases risks for bicyclists and pedestrians. In this study, we examine crash data (both police reported and self-reported) and urban form data from three U.S. campuses to understand the spatial and temporal distribution of crashes on the campuses and their immediate periphery.