Parking system : sensors, deployment, and evaluation
Abstract/Contents
- Abstract
- Parking is a headache in urban areas during busy hours. In this thesis, we propose and deploy an intelligent framework, based on wireless sensing networks, to provide both historical and real-time parking information to better help drivers find available parking spots. Utilizing the data collected by wireless sensors, we can further predict the parking occupancy rate to help commuters plan their daily trips to avoid parking congestion. More precisely, we first describe a system that senses the parking and provides parking availability information for users in a cost-effective and efficient manner. The hardware framework, in addition to being highly scalable, is built on advanced wireless sensor networks and cloud service over the Internet. The parking information provided to the users is set in the form of occupancy rates and expected searching time. Both are obtained from our analytical algorithm processing both historical and real-time data, and are thereafter visualized in a color theme. The entire parking system is deployed and extensively evaluated at Stanford University Parking Structure-1. After building the system and collecting data, we then develop an intuitive and accurate way to interpret parking availability by selecting an efficient algorithm to predict parking occupancy through an aggregation approach. The proposed Adaptive Autoregressive model yields better results than other predictors. Additionally, we disclose a simple empirical error curve that describes prediction performance against the aggregation level to understand the trade-off between estimation accuracy and spots spatial granularity. Finally, our parking information system matches well with real scenarios. Our pipeline of deploying sensing hardware, collecting data, and using analytical tools opens new opportunities for drivers to plan trips strategically and for urban planners to design policies (e.g., dynamic pricing or new parking constructions) comprehensively to balance the mobility demand in cities.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Chen, Xiao |
|---|---|
| Degree committee member | Rajagopal, Ram |
| Thesis advisor | Rajagopal, Ram |
| Associated with | Stanford University, Civil & Environmental Engineering Department |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Xiao Chen. |
|---|---|
| Note | Submitted to the Civil & Environmental Engineering Department. |
| Thesis | Thesis Engineering Stanford University 2022. |
| Location | https://purl.stanford.edu/zb573ry1123 |
Access conditions
- Copyright
- © 2022 by Xiao Chen
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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