Panichpapiboon, Sooksan
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Panichpapiboon, Sooksan
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Email
sooksan.pa@kmitl.ac.th
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Item type:Publication, Effective variables for urban traffic incident detection(2016-01-18); Chaovalit, PimwadeePast studies on automatic traffic incident detection have mainly focused on the incidents on freeways, which are controlled-access roads. There are not many works on urban traffic incident detection. In addition, the traffic data used in detecting an incident are still mostly collected from fixed sensors such as loop detectors. With the advances in mobile sensing and vehicular technology, it is foreseeable that mobile sensors will be used increasingly in the near future. In fact, traffic data will be collected directly by vehicles. Detecting traffic incidents in an urban road network with the traffic data collected by mobile sensors poses several challenges. First, the urban roads are uncontrolled-access roads, which are typically full of flow-disruptive entities such as traffic signals, intersections, crossings, bus stops, etc. These entities can disrupt the traffic flow in a similar way that an incident does, making it more difficult to detect an incident. Second, it is still not clear which traffic variables, collected by mobile sensors, can be used in detecting an incident in an urban environment. In this paper, we investigate and identify the traffic variables that are effective in detecting an incident in an urban road network. Particularly, speed, acceleration, lane-change ratio and travel time are studied. The results show that these four traffic variables are generally effective for traffic incident detection. However, among the four variables, the least effective one is the travel time. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Incidents detection through mobile sensing(2016-09-06); Chaovalit, PimwadeeCurrently, most automatic traffic incident detection systems rely heavily on data collected from fixed sensors such as inductive loop detectors and surveillance cameras. However, fixed sensors are difficult to install and maintain. Moreover, it is quite costly to deploy a large number of fixed sensors to cover a broad area. It is potentially more efficient to use vehicles as mobile sensors to collect traffic data. In this paper, we introduce a new traffic incident detection algorithm which takes advantage of mobile sensors. This algorithm assesses the likelihood of an incident based on the speed data collected by the mobile sensors. Its performance is evaluated thoroughly, in terms of detection rate, false-alarm ratio, and mean time-to-detection, at various combinations of penetration rates and data sampling periods. The results show that the proposed detection algorithm is potentially promising. For a practical scenario with 10% penetration rate and 30-second sampling period, the algorithm is able to achieve around 80% detection rate.