Prediction framework for parking search cruising time and emissions in dense urban areas

Runhua Ivan Xiao () and Miguel Jaller ()
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Runhua Ivan Xiao: University of California
Miguel Jaller: Sustainable Freight Research Program, University of California

Transportation, 2025, vol. 52, issue 4, No 4, 1289-1317

Abstract: Abstract The growing need for temporary pickups/drop-offs and commercial deliveries is crowding out the already inadequate on-street parking spaces designated for car trips, deteriorating the phenomenon of parking search. This paper: (1) uses empirical data and conducts descriptive and comparative analysis using a spatial lag model to analyze the factors influencing average cruising time (ACT) related to parking search, and (2) proposes a novel framework to predict grid-based ACT and to estimate average emission metrics (AEM). The study inputs an aggregated GPS dataset in a 6-month period to the framework and uses New York City and Los Angeles as case study cities. The descriptive and comparative analysis results support the spatial spillover effect of parking search and reveal that residential area, retail area, accommodation, and food services (hotels, restaurants, bars, etc.) employees are the most significant influencing factors on ACT and that temporary pickups/drop-offs and commercial delivery are also unneglectable sources of parking search. The prediction results show a concentrated distribution of ACT in New York City due to private vehicles’ spillover of parking searches. Los Angeles exhibits a relatively high degree of overlap between parking hotspots and emission blackspots, particularly in areas with intense truck activity, further substantiating the close relationship between truck activities and elevated emissions. Following the key findings, the paper proposes several policy recommendations. In practice, this prediction framework can ingest short-term data to provide ACT prediction maps to identify parking hotspots and emission blackspots.

Keywords: Parking search; Spatial lag model; Cruising time; Machine learning; Prediction; Emissions (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11116-023-10455-4

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