Motorists circling or cruising for on-street parking that is free or priced below market equilibrium can contribute to additional congestion, air pollution, time wasted, driver frustration, and a potential loss of economic competitiveness at destinations where parking is hard to find and where alternative access modes are limited. With increased sensitivity to the need for curb management, there is a need to better understand the prevalence of cruising for parking.
Strategies to quantify cruising have evolved from intercept surveys to deployment of wireless technology sensors. Intercept surveys are effective at understanding the proportion of traffic that is looking for parking (which may not imply excess travel),1 while the wireless technology sensor deployment has been successful in distinguishing vehicles within the traffic stream that are circling for parking; both can be applied in very limited geographies.
The methodology and tool presented in this report extends previous work that relied on global positioning system (GPS) breadcrumb data to overcome the physical limitations of other research methods. Big data allows for a more comprehensive assessment of the extent and location of excess parking search.2, 3 The method also quantifies the measure of most direct policy interest—excess travel from cruising, rather than the proportion of drivers searching for parking. The tool developed to generate reliable estimates of cruising (called Cruise Detector within this report) is a computer program that takes location data harvested from smartphones and sorts out which series of data points represent trips and which do not. Identified trips are then matched to a street network and compared against a shortest path that might have been taken. A trip that exceeds an available shortest path by a given threshold is assumed to include excess travel, most likely, due to parking search.
The methodology and tool provide a data-driven way to identify the locations and times of day where cruising is most prevalent. They can be used by municipalities and other interested parties to understand cruising for parking and the effects of policy interventions on parking search behaviors in order to develop appropriate responses. The methodology and tool can be applied by a skilled geographic information system (GIS) analyst with some familiarity with programming languages. Potential computing resources are also described within the report.
The research team applied the tool and completed case analyses for four U.S. cities: Washington, DC; Atlanta, Georgia; Chicago, Illinois; and Seattle, Washington.
The cases illustrate a range of applications, such as identifying cruising hot spots by both time of day and location and assessing policy impacts:
The report provides analyses results for each city, as well as overall observations considering all cities’ analyses. The highest rates of cruising were found in Seattle and Chicago where 7.3 and 6.8 percent of trips, respectively, showed some portion as cruising.
Through the analyses of the cities, the research team concluded the following:
The report documents lessons learned relating to data quality, tool implementation, and the analyses results. For example, both third-party processed data and raw location data have their own advantages and disadvantages. Therefore, users should carefully assess options for data acquisition, considering factors such as budget and the degree of flexibility desired to conduct additional analysis to find new results beyond what was initially sought. Data quality can vary greatly, and it is recommended to obtain samples of data to assess the resolution and quality. Information on how the data are collected may help the analyst assess potential biases.
1 Millard-Ball, Adam; Hampshire, Robert; and Weinberger, Rachel (2019), “The curious lack of cruising for parking.” Land Use Policy, in press. [ Return to Note 1 ]
2 Weinberger, Rachel; Millard-Ball, Adam; and Hampshire, R (2020) “Parking Search Caused Congestion: Where’s all the fuss?” Transportation Research Part C: Emerging Technologies vol 120. [ Return to Note 2 ]
3 Hampshire, R., Jordon, D., Akinbola, O., Richardson, K., Weinberger, R., Millard-Ball, A. & Karlin-Resnick, J., 2016. Analysis of Parking Search Behavior with Video from Naturalistic Driving. Transportation Research Record: Journal of the Transportation Research Board, 2543, pp.152–158. [ Return to Note 3 ]
United States Department of Transportation - Federal Highway Administration