| Title |
Parking Data Collection, Storage and Mining in Smart City |
| ID_Doc |
36532 |
| Authors |
Zhao, ZL; Kim, JW; Zhang, L |
| Title |
Parking Data Collection, Storage and Mining in Smart City |
| Year |
2018 |
| Published |
|
| DOI |
10.1145/3291801.3291841 |
| Abstract |
With the continuous and fast development of urbanization, traffic congestion has become a major problem in cities. However, new technologies provide us opportunity to tackle the problem in an efficient way. As people know, intelligent traffic is an important part of a Smart City. Besides, intelligent parking is an essential part of Intelligent Transportation. Internet of Things (IoT) provides to everyone new types of services in order to improve everyday life. As a result, an increasing number of parking management and data acquisition systems were developed by IoT technology. This paper aims to introduce a new system through which data acquisition and storage of parking information could fully automatically take part. To analyzing parking information, in this paper, a new traffic model is proposed to forecast the status of urban traffic in order to improve the efficiency of urban transportation. This system is expected to benefit drivers and the government and to improve urban environment simultaneously. |
| Author Keywords |
Big Data; IoT; traffic model; parking model; cloud storage; parking prediction; Smart City; ZigBee |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000461579000019 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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