A Data-Driven Approach to Estimate Double Parking Events Using Machine Learning Techniques

Double parking is a common occurrence in dense urban areas. It routinely causes danger for cyclists, pedestrians and short-term traffic disruptions that impede traffic flow. Using New York City as a case study, this paper introduces a novel data-driven framework for understanding the influential factors and estimating the actual frequency of double parking through utilizing parking violation tickets, 311 service requests, and social media information with surrounding street characteristics. The number of hotel rooms, traffic volume, commercial usage, block length and curbside parking spaces are ranked as the top five important factors contributing to double parking. Three feature selection methods, LASSO, stability selection and Random Forests techniques are applied to identify those contributing factors. Random Forests, as one of the most effective machine learning techniques is also applied to predict double parking performance of 50 locations in Midtown Manhattan, New York, where ground truth data is available. The Random Forests model achieves 85% prediction accuracy. The study demonstrates that the violation tickets and 311 service requests supplemented with additional street characteristics are able to offer a higher level of prediction accuracy for double parking events. This predictive power can be further applied to a macroscopic or microscopic traffic simulation model to evaluate double parking impacts on traffic delay and safety. In addition, this study can provide transportation agencies insights into effective data collection strategies to identify potential double parking hotspots for better policy-making, enforcement, and management.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ABJ70 Standing Committee on Artificial Intelligence and Advanced Computing Applications.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Gao, Jingqin
  • Ozbay, Kaan
• Conference:
  • Transportation Research Board 96th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2017-1-8 to 2017-1-12
• Date: 2017

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Monograph Title: TRB 96th Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Classification; High risk locations; Machine learning; Parking violations; Traffic forecasting
• Geographic Terms: New York (New York)
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01627714
• Record Type: Publication
• Report/Paper Numbers: 17-04075
• Files: TRIS, TRB, ATRI
• Created Date: Feb 27 2017 5:12PM