[论文解读] Short Term Prediction of Parking Area states Using Real Time Data and Machine Learning Techniques

Jesper Provoost, Luc Johannes Josephus Wismans+3 more|University of Twente Research Information|Nov 29, 2019

Smart Parking Systems Research参考文献 20被引用 4

一句话总结

本文提出了一种基于荷兰阿纳姆市历史和实时数据的实时机器学习模型，用于预测未来60分钟内停车区域的占用率、进出流量。神经网络模型在60分钟预测时长远超随机森林和朴素季节性模型，性能提升超过150%，其中实时占用率是最具影响力的预测因子。

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.

研究动机与目标

• 开发一种基于可用数据源的实时城市停车区域状态预测模型。
• 评估机器学习模型在预测未来60分钟内停车占用率、进出流量方面的性能。
• 识别对准确预测最具影响力的输入变量。
• 比较神经网络、随机森林和朴素季节性随机游走模型的预测性能。
• 评估预测精度随预测时延增加而变化的敏感性。

提出的方法

• 模型使用公开的历史和实时数据，包括占用率、交通流量、一天中的时间以及天气状况。
• 使用前馈神经网络和随机森林模型，在多个时间预测时延（最多60分钟）下训练以预测停车状态变量。
• 模型训练与评估基于荷兰阿纳姆市的真实世界案例研究，采用时间交叉验证方法。
• 通过基于置换的特征重要性方法评估各输入变量的贡献。
• 使用RMSE和R²等标准指标衡量性能，涵盖不同预测时延。
• 朴素季节性随机游走模型作为基线用于对比。

实验结果

研究问题

• RQ1 机器学习模型在使用实时数据时，能否准确预测短期停车区域占用率及进出流量？
• RQ2 在不同预测时延下，神经网络与随机森林模型在停车状态预测中哪个表现更优？
• RQ3 实时占用率、时间、交通流量和天气对预测性能的相对贡献如何？
• RQ4 当预测时延从5分钟增加到60分钟时，预测精度如何变化？
• RQ5 与仅预测占用率相比，同时预测进出流量能在多大程度上提升性能？

主要发现

• 神经网络模型在所有性能指标上均优于随机森林和朴素季节性随机游走模型，在60分钟预测时延下性能提升达150%。
• 预测进出流量比预测占用率更具挑战性，相比基线性能提升30%，但对预测时延的敏感性较低。
• 实时占用率是最重要的预测因子，其贡献超过时间、交通流量或天气变量。
• 在实时部署期间，该模型平均性能是朴素模型的三倍。
• 随着预测时延增加，性能逐渐下降，但神经网络在60分钟时延下仍保持较强的预测能力。
• 引入交通流量和天气等非占用率变量可提升模型性能，但占用率仍是主导因素。