Skip to content
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:

  1. 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).
  2. 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.
  3. 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.
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.
Technical Report

Insights from Driver Parking Decisions in a Truck Simulator to Inform Curb Management Decisions

 
Download PDF  (3.37 MB)
Publication Date: 2023
Summary:

Millions of people who live and work in cities purchase goods online. As ecommerce and urban deliveries spike, there is an increasing demand for curbside loading and unloading space. To better manage city curb spaces for urban freight, city planners and decision makers need to understand commercial vehicle driver behaviors and the factors they consider when parking at the curb.

Urban freight transportation is a diverse phenomenon. Commercial vehicle drivers must overcome several obstacles and adapt to various rules and policies to properly navigate the intricate metropolitan network and make deliveries and pick-ups. However, other road users and occasionally municipal planners generally view them as contributing considerably to urban congestio, responsible for unauthorized parking, double parking, and exceeding their legal parking time.

These realities reflect the need for a thorough comprehension of commercial vehicle operators’ core decision-making procedures and parking habits to inform and adjust curb management policies and procedures. However, more robust corroborated literature on the subject is needed. The information used in these studies is typically obtained from empirical field research, which, while valuable, is limited to certain situations and case scenarios. Therefore, to improve the operation of urban transportation networks, it is necessary to study commercial vehicle drivers’ parking behavior in a controlled environment.

This project used a heavy vehicle driving simulator to examine commercial vehicle drivers’ curbside parking behaviors in various environments in shared urban areas. Also observed were the interactions between commercial vehicle drivers and other road users.

The experiment was successfully completed by 12 participants. Five independent variables were included in this experiment: number of lanes (two-lane and four-lane roads), bike lane existence, passenger vehicle parking space availability, commercial vehicle loading zones (CVLZs) (no CVLZ, occupied CVLZs, and unoccupied CVLZs), and parking time (short-term parking: 3 to 5 minutes and long-term parking: 20 to 60 minutes). The heavy vehicle driving simulator also collected data regarding participants’ driving speed, eye movement, and stress level.

Results from the heavy vehicle driving simulator experiment indicated that the presence of a bike lane had significant effects on commercial vehicle drivers’ parking decisions., but only a slight effect on fixation duration times. The average fixation duration time, representing how long participants looked at a particular object, on the road with a bike lane was 4.81 seconds, whereas it was 5.25 seconds on roads without a bike lane. Results also showed that the frequency of illegal parking (not parking in the CVLZs) was greater during short-term parking activities, occurring 60 times (45 percent of parking maneuvers). Delivery times also had a slight effect on commercial vehicles’ speed while searching for parking (short-term parking was 17.7 mph; long term parking was 17.2 mph) and on drivers’ level of stress (short-term parking was 8.16 peaks/mins; long-term parking was 8.36 peaks/mins). Seven percent of participants chose to park in the travel lane, which suggested that commercial vehicle operators prioritize minimizing their walking distance to the destination over the violation of parking regulations.

The limited sample size demonstrated the value of our experimental approach but limited the strength of the recommendations that can be applied to practice. With that limitation acknowledged, our preliminary recommendations for city planners include infrastructure installation (i.e., convex mirrors installed at the curbside and CVLZ signs) to help drivers more easily identify legal parking spaces, and pavement markings (i.e., CVLZs, buffered bike lanes) to improve safety when parking. Parking time limits and buffers for bike lanes could improve efficient operation and safety for cyclists and other road users.

For future work, larger sample sizes should be collected. Additional factors could be considered, such as increased traffic flow, pedestrian traffic, conflicts among multiple delivery vehicles simultaneously, various curb use type allocations, and different curb policies and enforcement. Including a larger variety of commercial vehicle sizes and loading, zone sizes would also be of value. A combination of field observations and a driving simulator study could also help validate this investigation’s outcomes.

Authors: Dr. Andisheh RanjbariDr. Anne GoodchildDr. Ed McCormackRishi Verma, David S. Hurwitz (Oregon State University), Yujun Liu (Oregon State University), Hisham Jashami (Oregon State University)
Recommended Citation:
Goodchild, A., McCormack, E., Hurwitz, D., Ranjbari, A., Verma, R., Liu, Y., & Jashami, H. (2023). Insights from Driver Parking Decisions in a Truck Simulator to Inform Curb Management Decisions. PacTrans.