Abstract
Parking Guidance and Information (PGI) systems aim at supporting drivers in finding suitable parking spaces, also by predicting the availability at driver’s Estimated Time of Arrival (ETA), leveraging information about the general parking availability situation. To do these predictions, most of the proposals in the literature dealing with on-street parking need to train a model for each road segment, with significant scalability issues when deploying a city-wide PGI. By investigating a real dataset we found that on-street parking dynamics show a high temporal auto-correlation. In this paper we present a new processing pipeline that exploits these recurring trends to improve the scalability. The proposal includes two steps to reduce both the number of required models and training examples. The effectiveness of the proposed pipeline has been empirically assessed on a real dataset of on-street parking availability from San Francisco (USA). Results show that the proposal is able to provide parking predictions whose accuracy is comparable to state-of-the-art solutions based on one model per road segment, while requiring only a fraction of training costs, thus being more likely scalable to city-wide scenarios.
Highlights
Finding a parking space is one of the main concerns of urban mobility, as it is well recognised that a significant fraction of the traffic in crowded urban areas is originated by drivers cruising in search of a parking space [1]
We found that on-street parking dynamics can be very fast: since each road segment has a limited number of parking spaces
For the case of just 1000 training samples per cluster, a very minor fraction of the original dataset, both the tests with the raw and Kalman features are worse than the baselines: Root Mean Square Error (RMSE) is just slightly higher than the reference values, but the entropy values highlight that the error distribution is larger, so that the reliability of the results is reduced for decision-level systems
Summary
Finding a parking space is one of the main concerns of urban mobility, as it is well recognised that a significant fraction of the traffic in crowded urban areas is originated by drivers cruising in search of a parking space [1] The motivation behind this problem lies in that drivers have no knowledge about where there could be a free parking space matching their expectations. Among the various types of Intelligent Transportation Systems, the Parking Guidance and Information (PGI) solutions, integrated within in-vehicle navigation systems or intended as mobile apps, aim at significantly reduce this problem, by guiding drivers directly towards streets (or parking facilities) with current or future higher availability of free spaces. This up-to-date map can be either pushed to the interested PGI users, or used to feed some prediction algorithms, to forecast the Electronics 2020, 9, 838; doi:10.3390/electronics9050838 www.mdpi.com/journal/electronics
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