Project Description:
Although parking facilities are one of the main components of transportation infrastructure, little is known about the incidence of parking-related crashes, injuries, and fatalities. This situation is clearly reflected in non-motor traffic crash statistics (i.e., crashes that occur off-public roadways), as most non-traffic motor crashes occur in parking facilities or private roads. With the increase in emerging autonomous vehicle (AV) technologies, such as self-driving and self-parking, the parking experience is expected to improve. The goal of this research is to explore parking facility design and operational change recommendations to improve parking safety in light of the advent of self-parking features. The research team identified potential design changes and self-parking penetration scenarios to improve safety. Expected changes to parking and street design were assessed in terms of the reduced number of conflicts for vehicles and pedestrians and exposure for pedestrians using microsimulation techniques. In order to fully determine the potential safety benefits of AVs, 15 modeling scenarios with different AV market penetration and parking operational changes were identified and simulated VISSIM using the University of Texas at El Paso (UTEP) campus as a testbed. To determine the total number of conflict points for each scenario, the Surrogate Safety Assessment Model (SSAM) was used. Moreover, this study proposes a new methodology called pedestrian exposure analysis for determining the relative pedestrian exposure for a particular parking facility in terms of vehicles. This methodology utilizes a similar approach by using pedestrian walking time within a particular facility, but also incorporates the rate at which vehicles are expected to enter the shared facility. This way, the pedestrian exposure can be directly related to the number of vehicles that are expected to come in contact with pedestrians.
Data Scope:
VISSIM was used to model three different parking scenarios: on-street parking, an open surface parking lot, and a parking garage. Each scenario was modeled to have varying market penetration rates of 0% (no AVs), 25%, and 75% for a total of 15 unique scenarios. To determine the total number of conflict points for each scenario, the Surrogate Safety Assessment Model (SSAM) was used. This application was developed by the Federal Highway Administration to identify and analyze traffic conflicts using vehicle trajectory data output from microscopic traffic simulation models, in this case VISSIM. SSAM was operated via an executable file and its corresponding libraries. Each trajectory file from the 15 unique scenarios was then used as input into the SSAM software and processed individually. SSAM processes the vehicle trajectory files using predefined time to collision (TTC) and post encroachment time (PET) threshold values. The default values of 1.5 seconds for TTC and 5 seconds for PET were used in this analysis. Once these values were defined, SSAM processed each individual trajectory file and identified a conflict point by identifying whether the paths of two vehicles overlapped during the predefined TTC duration. If a conflict was detected, SSAM recorded data about the conflict, such as type (crossing, rear-end, or lane change), conflict angle, conflict location, as well as speed and acceleration of both vehicles before and after the conflict occurred. Pedestrian exposure analysis was tested only for off-street parking facilities and parking garages. Same parking facilities were selected on the UTEP campus with varying levels of AV market penetration rates; (i) off-street parking lot with 402 parking spaces, and (ii) parking garage consisting of 700 parking spaces. Pedestrian walking times from each parking space were collected by measuring the distance to the nearest pedestrian exit of the parking facility. Each walking distance was then converted to walking time. The walking times were then summed for the entire facility to obtain total walking times. All the findings were kept in csv or Microsoft excel files.
Data Specification:
FHWA’s SSAM manual is attached.
