Dataset

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

Publication: Harvard Dataverse

Publication Date: 2023

Summary:

Three different data types were obtained from Oregon State Driving and Bicycling Simulator Laboratory for purpose of this report and they are as follow:

Speed data consists of subject number, average speed, minimum speed, and all the independent variables. Speed data were collected based on the truck’s speed while driving through a certain scenario (out of 24). For each scenario, the average and minimum speed (mph) of 12 drivers were recorded along each segment (scenario) from the start of the road to 150 feet before the intersection (traffic signals).
Eye tracking data consists of subject number, total fixation duration (TFD) in milliseconds, area of interest (AOI), and all the independent variables. TFD data were collected while the truck driver maneuvers through a certain scenario (out of 24). For each scenario, the TFD for each AOI was recorded for 11 subjects along each segment (scenario) from the start of the road to 150 feet before the intersection (traffic signals). AOI represent the area of interest that a driver fixates for a certain of time to generate the total fixation duration.
Eye tracking data consists of subject number, GSR in peaks per minute, and all the independent variables. GSR data were collected while the truck driver maneuvers through a certain scenario (1 out of 24). For each scenario, the peaks per minute data was recorded for 11 subjects along each segment (scenario) from the start of the road to 150 feet before the intersection (traffic signals). Peaks per minute represents the emotional arousal (i.e., something is scary, threating, joyful, etc.) that a driver generates when reacting to a particular event. Fourteen participants were recruited, two of them had a simulator sickness so they were excluded from the data and the analysis. While there are no quality or consistency issues with this data set, it should be noted that the sample is on the smaller side and that should be considered when interpreting derived results. The average values were calculated to apply robust statistical analysis for such data (speed and lateral position). As the experiment consists of 2x2x2x3 factorial design, each participant had to driver through 24 scenarios; therefore, 288 scenario observations were obtained and recorded in the excel file.

Authors: Dr. Anne GoodchildDr. Ed McCormackDr. Andisheh Ranjbari, David Hurwitz

Recommended Citation:
Goodchild, Anne; McCormack, Ed; Ranjbari, Andisheh; Hurwitz, David, 2023, "Developing Better Curb Management Strategies through Understanding Commercial Vehicle Driver Parking Behavior in a Simulated Environment", Harvard Dataverse. https://doi.org/10.7910/DVN/HVAUT3.

Blog

The Freight Space Race: Planning Streets for More Efficient & Sustainable Movement of People & Goods

URL: https://www.goodsmovement2030.com/post/freight-space-race

Publication Date: 2023

Summary:

Space is the scarcest resource in cities. How can we best use street space to do more for more street users?

Mention the “space race” and it tends to conjure up the Cold War-era competition between the United States and the then-USSR to “conquer” outer space. But at the winter meeting of the Urban Freight Lab (UFL), members heard about a different race playing out on our streets right under our noses. It’s what Philippe Crist of the International Transportation Forum (ITF) dubs the freight space race.

That race is about managing the competing demands for space in cities. Users of the space are competing to retain and grow space for their needs to improve deliveries, urban function, and residents’ well-being. For urban freight advocates it’s about making deliveries in cities less disruptive and more sustainable by focusing on the street space use of freight activities. It’s a race involving freight carriers, freight receivers, governments, and communities.

The freight space race isn’t new. But it’s been amplified and made more visible in the wake of the intertwined ecommerce boom and the Covid-19 pandemic, as planners in many cities scrambled to create public spaces for people through things like street closures, parks, and pedestrian ways.

Meantime, by and large, considering city space for goods has been an afterthought. And when goods delivery is considered, it tends to be siloed from the work of planning streets for people. So, there’s a freight plan, maybe. (Our research into 58 of the largest, densest, and fastest-growing cities found most do not have freight plans.) A bike plan. A transit plan. A pedestrian plan. But there’s nothing that integrates everything at the street level across all users.

This siloing hasn’t served cities or the freight sector particularly well. The rise of the “complete streets” concept is a rejoinder of sorts. (And, notably, UFL member Seattle Department of Transportation for the first time plans to create a multimodal and integrated 20-year transportation plan, later this year.) Unsurprisingly, given the less-than-stellar siloed approach to planning, UFL members prioritized planning streets for people and goods as a key topic in the Goods Movement 2030 project.

Recommended Citation:
“The Freight Space Race: Planning Streets for More Efficient & Sustainable Movement of People & Goods” Goods Movement 2030 (blog). Urban Freight Lab, April 4, 2023. https://www.goodsmovement2030.com/post/freight-space-race.

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.