计算机科学
停车位
停车指引和信息
交通拥挤
斯威夫特
过程(计算)
空格(标点符号)
多样性(控制论)
实时计算
运输工程
人工智能
操作系统
工程类
程序设计语言
作者
Taiwei Ling,Xin Zhu,Xiaolei Zhou,Shuai Wang
标识
DOI:10.1007/978-3-030-86130-8_21
摘要
With the rapid development of urbanization and the swift rising of the number of vehicles in cities, the process of finding a suitable parking space not only wastes a lot of time but also indirectly aggravates the problem of traffic congestion. To assist the decision-making and alleviate the pain of parking, researchers propose a variety of methods to improve the parking efficiency of existing parking lots. Different from existing studies, we address the parking issue from an incremental rather than a stock perspective. In this paper, we propose a LSTM-based prediction model to make full use of contract parking spaces, which are characterized by the periodic departure time and complementary to the idle space during the peak period of the city. In addition, we utilize multi-source data as the input to improve the prediction performance. We evaluate our model on real-world parking data involved with nearly 14 million parking records in Wuhan. The experimental results show that the average accuracy of the ParkLSTM prediction reaches 91.091%, which is 11.19%–19.70% higher than other parking behavior prediction models.
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