Paper

Intersections and Non-Intersections: A Protocol for Identifying Pedestrian Crash Risk Locations in GIS

URL: https://doi.org/10.3390/ijerph16193565

Publication: International Journal of Environmental Research and Public Health

Volume: 16 (19)

Pages: 3565

Publication Date: 2019

Summary:

Intersection and non-intersection locations are commonly used as spatial units of analysis for modeling pedestrian crashes. While both location types have been previously studied, comparing results is difficult given the different data and methods used to identify crash-risk locations. In this study, a systematic and replicable protocol was developed in GIS (Geographic Information System) to create a consistent spatial unit of analysis for use in pedestrian crash modeling. Four publicly accessible datasets were used to identify unique intersection and non-intersection locations: Roadway intersection points, roadway lanes, legal speed limits, and pedestrian crash records. Two algorithms were developed and tested using five search radii (ranging from 20 to 100 m) to assess the protocol reliability. The algorithms, which were designed to identify crash-risk locations at intersection and non-intersection areas detected 87.2% of the pedestrian crash locations (r: 20 m). Agreement rates between algorithm results and the crash data were 94.1% for intersection and 98.0% for non-intersection locations, respectively. The buffer size of 20 m generally showed the highest performance in the analyses. The present protocol offered an efficient and reliable method to create spatial analysis units for pedestrian crash modeling. It provided researchers a cost-effective method to identify unique intersection and non-intersection locations. Additional search radii should be tested in future studies to refine the capture of crash-risk locations.

Authors: Haena Kim, Mingyu Kang, Anne Moudon, Linda Ng Boyle,

Recommended Citation:
Kang, Mingyu, Anne Vernez Moudon, Haena Kim, and Linda Ng Boyle. 2019. Intersections and Non-Intersections: A Protocol for Identifying Pedestrian Crash Risk Locations in GIS. International Journal of Environmental Research and Public Health 16, no. 19: 3565. https://doi.org/10.3390/ijerph16193565

Developing Better Curb Management Strategies through Understanding Commercial Vehicle Driver Parking Behavior in a Simulated Environment

Project Budget: $180,000 (UW amount: $80,000)

Lead Institution:

University of Washington, Urban Freight Lab (UFL)

Partner Institutions:

Oregon State University

Summary:

This study will use a driving simulator to design a simulation experiment to test the behavior of commercial vehicle drivers under various parking and delivery situations and to analyze their reactions. The ability to modify the simulator’s environment will allow the researchers to relatively easily test a range of scenarios that correspond to different delivery and parking situations.

The simulation experience will be designed in a quarter-cab truck simulator at Oregon State University’s Driving and Bicycling Simulator Laboratory. Various simulation environments will be defined by changing road characteristics (such as land use, number of travel lanes, nearby signals, traffic in adjacent lanes), curb allocations (such as paid parking, commercial vehicle loading zones, and passenger load zones, as well as the size of the loading zones and their availability at the time of the vehicle arrival at the blockface), and other road users (passenger cars, ridehailing vehicles, bikes). Drivers from various categories of age, gender, experience level (less experiences vs. seasoned) and goods type (documents, packages, or heavy goods) will be invited to operate the simulator and make a parking decision in a few simulated environments. The simulator can also monitor distraction (through eye tracking) and the stress level of drivers (through galvanic skin response) when making these decisions and interacting with other road users.

Analyzing parking decisions and driver stress levels based on roadway and driver characteristics will provide insights on travel behaviors and the parking decision-making process of commercial vehicle drivers, and will help city planners improve street designs and curb management policies to accommodate safe and efficient operations in a shared urban roadway environment.

The unique needs of delivery trucks and commercial vehicles are not acknowledged in current design practices. This study is intended to fill these gaps and serve as a valuable resource for policy makers, transportation engineers and urban planners.

Article

A Framework to Assess Pedestrian Exposure Using Personal Device Data

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URL: https://doi.org/10.1177/1071181322661319

Publication: Human Factors and Ergonomics Society

Volume: 66 (1)

Pages: 320 - 324

Publication Date: 2022

Summary:

Capturing pedestrian exposure is important to assess the likelihood of a pedestrian-vehicle crash. In this study, we show how data collected on pedestrians using personal electronic devices can provide insights on exposure. This paper presents a framework for capturing exposure using spatial pedestrian movements based on GPS coordinates collected from accelerometers, deﬁned as walking bouts. The process includes extracting and cleaning the walking bouts and then merging other environmental factors. A zero-inﬂated negative binomial model is used to show how the data can be used to predict the likelihood of walking bouts at the intersection level. This information can be used by engineers, designers, and planners in roadway designs to enhance pedestrian safety.

Authors: Haena Kim, Grace Douglas, Linda Ng Boyle, Anne Moudon, Steve Mooney, Brian Saelens, Beth Ebel

Recommended Citation:
Douglas, G., Boyle, L. N., Kim, H., Moudon, A., Mooney, S., Saelens, B., & Ebel, B. (2022). A Framework to Assess Pedestrian Exposure Using Personal Device Data. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. https://doi.org/10.1177/1071181322661319