01558200

Component

https://doi.org/10.3141/2489-09

Parking occupancy information is central to the management of parking and traffic demand. This study proposed efficient unsupervised learning algorithms to predict parking occupancy rates. Two types of predictions were studied: (a) an offline prediction, in which next-day occupancy was predicted by using historical data along with various features (day of week, weather, seasonality), and (b) an online prediction, in which occupancy of future hours of the current day was predicted with both historical and real-time data. The two models can be applied to both off-street and on-street parking. Two data sources were used: parking payment kiosks for a visitors’ parking garage and newly deployed real-time spot-by-spot parking sensors for a commuter garage. It was found that, with a proper set of features, the offline method could successfully distinguish different flow patterns, congested or underused, with intensive or mild arrival and departure rates. The offline procedure significantly outperformed both the historical and the previous day’s average. The online method provided generally more accurate predictions than the offline method because it learned from the real-time occupancy data. As time progressed, the mean and maximum error rates of the online prediction decreased to a level well below both the historical average and the offline prediction error. A sharp decline of the prediction error could be obtained when sufficient real-time occupancy data were collected and the type of flow pattern was identified (around 9:00 a.m. in a case study).

01595098

15-4992

English

pp 77–85

2015

Transportation Research Record: Journal of the Transportation Research Board

9780309369572

Print

Figures (6) ; References (15) ; Tables (1)

Algorithms; Forecasting; Highway traffic control; Parking demand; Parking lots; Traffic control

Highways; Operations and Traffic Management

TRIS, TRB, ATRI

Dec 30 2014 1:40PM

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• Deployment of Intelligent Transportation Systems in South East Europe: Current Status and Future Prospects
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• Development of California Regulations to Govern Testing and Operation of Automated Driving Systems
• Empirical Longitudinal Driving Behavior in Authority Transitions Between Adaptive Cruise Control and Manual Driving
• Evaluation and Sign-Off Methodology for Automated Vehicle Systems Based on Relevant Driving Situations
• Freeway Short-Term Travel Time Prediction Based on Dynamic Tensor Completion
• In-Car Advice to Reduce Negative Effects of Phantom Traffic Jams
• Investigation into the Role of Rational Ethics in Crashes of Automated Vehicles
• Localization and Driving Behavior Classification with Smartphone Sensors in Direct Absence of Global Navigation Satellite Systems
• Novel Algorithm for Variable Speed Limits and Advisories for a Freeway Corridor with Multiple Bottlenecks
• Observe-Driver-and-Learn Platform for Relevance Estimation in Safety Warning Applications from Vehicular Ad Hoc Network
• Platoon-Based Speed Control Algorithm for Ecodriving at Signalized Intersection
• Robust and Efficient Broadcast Scheme of Event-Driven Warning Messages for Vehicular Environments
• Safe Integration of Fully Automated Road Transport Systems in Urban Environments: Basis for Missing Legal Framework