Browsing by Author "Nian, Chia-Sheng"

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Discovering Phase Timing Information of Traffic Light Systems by Stop-Go Shockwaves
(2015-01-01)
Chuang, Yi-Ta
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Yi, Chih-Wei
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Tseng, Yu-Chee
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Nian, Chia-Sheng
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Ching, Chia-Hao
The cycle lengths and signal transition time of Traffic Light Systems (TLS\'s), or known as the Phase Timing Information (PTI), play a key role in modern transportation systems. However, such information is not always available to the public. In this paper, we propose a crowdsourcing approach to solve this problem by exploiting the stop and go events, abbreviated by SG events, of vehicles on roads happening in front of target traffic lights. The PTI discovery problem is formulated by allowing only part of the vehicles participating in the discovery process. The proposed framework starts with discovering SG events, followed by collapsing these events over multiple signal cycles into one and calculating PTI information through a shockwave technique. The crowdsourcing part may be directly implemented on smartphones. The proposed framework was verified via field trials and simulations. Our simulation results showed that, even with a low penetration rate around 3.8 percent, the root mean square errors of the cycle length, green light and red light signal transition time of a TLS are 0. 04, 1.3 and 5.8 seconds, respectively. The achieved accuracy can be helpful in many PTI-enabled applications.
Toward Crowdsourcing-Based Road Pavement Monitoring by Mobile Sensing Technologies
(2015-08-01)
Yi, Chih-Wei
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Chuang, Yi-Ta
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Nian, Chia-Sheng
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資訊工程學系
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Department of Computer Science
In crowdsourcing applications, the quality of the crowdsourced data is decisive to the success of subsequent system-level mining processes. We proposed a smartphone probe car (SPC) system to monitor road pavement. An SPC is essentially an ordinary vehicle with a mounted smartphone that runs sensing programs to objectively assess bumping caused by road anomalies such as potholes and bumps. The proposed system has several features. First, to allow dynamic forming of SPCs, we develop a signal processing heuristic for the extraction of the vertical acceleration components from the accelerometer readings (upon which bumping detection and road surface anomaly assessment rely). By these means, the proposed system provides a driver-friendly environment, requiring neither complicated installation nor driverassisted training processes, and thus is possible to achieve hassle-free mass deployment such that drivers would be willing to participate in crowdsourcing. Second, based on the underdamped oscillation model, we propose a road anomaly indexing heuristic that is representable for road anomalies rather than vehicle conditions. This will later facilitate the system-level data mining processes in the servers. Third, a prototype SPC system was implemented and extensive field tests were undertaken to verify the performance of our system framework. Furthermore, we experimentally adopted a DENCLUE-like algorithm to mine road anomaly information from reported events to demonstrate any potential benefit from future investigation of data mining process at the system level. We believe the research works introduced in this paper consist the first step toward building an "ecosystem" of SPC-based crowdsourcing traffic and road monitoring applications.