Short Term Prediction of Parking Area states Using Real Time Data and Machine Learning Techniques

Jesper Provoost, Luc Wismans, Sander van der Drift, Maurice van Keulen, Andreas Kamilaris

Research output: Contribution to conferencePaperpeer-review

163 Downloads (Pure)

Abstract

Public road authorities and private mobility service providers need information derived from the current and predicted traffic states to act upon the daily urban system and its spatial and temporal dynamics. In this research, a real-time parking area state (occupancy, in- and outflux) prediction model (up to 60 minutes ahead) has been developed using publicly available historic and real time data sources. Based on a case study in a real-life scenario in the city of Arnhem, a Neural Network-based approach outperforms a Random Forest-based one on all assessed performance measures, although the differences are small. Both are outperforming a naive seasonal random walk model. Although the performance degrades with increasing prediction horizon, the model shows a performance gain of over 150% at a prediction horizon of 60 minutes compared with the naive model. Furthermore, it is shown that predicting the in- and outflux is a far more difficult task (i.e. performance gains of 30%) which needs more training data, not based exclusively on occupancy rate. However, the performance of predicting in- and outflux is less sensitive to the prediction horizon. In addition, it is shown that real-time information of current occupancy rate is the independent variable with the highest contribution to the performance, although time, traffic flow and weather variables also deliver a significant contribution. During real-time deployment, the model performs three times better than the naive model on average. As a result, it can provide valuable information for proactive traffic management as well as mobility service providers.
Original languageEnglish
Number of pages20
DOIs
Publication statusPublished - 29 Nov 2019
Event99th Transportation Research Board (TRB) Annual Meeting 2020 - Walter E. Washington Convention Center, Washington, United States
Duration: 12 Jan 202016 Jan 2020
Conference number: 99
http://www.trb.org/AnnualMeeting/AnnualMeeting.aspx

Conference

Conference99th Transportation Research Board (TRB) Annual Meeting 2020
Abbreviated titleTRB 2020
Country/TerritoryUnited States
CityWashington
Period12/01/2016/01/20
Internet address

Keywords

  • Smart parking
  • Machine learning
  • Public data
  • Real-time prediction

Fingerprint

Dive into the research topics of 'Short Term Prediction of Parking Area states Using Real Time Data and Machine Learning Techniques'. Together they form a unique fingerprint.

Cite this