Panichpapiboon, Sooksan

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Panichpapiboon, Sooksan
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sooksan.pa@kmitl.ac.th

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    Effective variables for urban traffic incident detection
    (2016-01-18)
    Siripanpornchana, Chaiyaphum
    ;
    Panichpapiboon, Sooksan
    ;
    Chaovalit, Pimwadee
    Past 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.
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    Incidents detection through mobile sensing
    (2016-09-06)
    Siripanpornchana, Chaiyaphum
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    Panichpapiboon, Sooksan
    ;
    Chaovalit, Pimwadee
    Currently, 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.
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    Clearance Estimation through Mobile Sensing
    (2018-08-21)
    Leakkaw, Puttipong
    ;
    Panichpapiboon, Sooksan
    Clearance is a spatial gap between two successive vehicles, which can be used to indicate the traffic condition. Obviously, the average clearance in a congested traffic will be smaller than that in a free-flow traffic. In most current infrastructure-based traffic information systems, clearances between vehicles could be estimated from an image or a video captured by fixed sensors such as traffic surveillance cameras. However, using fixed sensors is not effective in terms of cost, coverage, and convenience. A mobile sensing approach, which vehicles act as mobile sensors and collect the traffic data as they travel, is more appealing. In this paper, we explore a possibility of using a built-in camera on a smartphone for clearance estimation. The estimation algorithm and its accuracy will be discussed. Particularly, our experimental results show that the mean absolute errors of the estimates are within 10% of the actual reference values. This is suitable for a traffic sensing application.
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    Speed estimation through mobile sensing
    (2015-01-26)
    Leakkaw, Puttipong
    ;
    Panichpapiboon, Sooksan
    With the advances in wireless communication and mobile computing, a future infrastructureless self-organizing traffic information system, where vehicles can form a network for exchanging traffic information among themselves, will soon be realized. In an infrastructureless traffic information system, vehicles will act as mobile sensors and collect the traffic data as they travel. Smartphones are a great choice for traffic sensing devices as they are now equipped with a variety of sensors such as global positioning system (GPS) receiver, accelerometer, gyroscope, camera, and microphone. These sensors can be exploited to collect traffic data. Although there are many types of sensors available for traffic sensing, past studies have mainly focused on a GPS receiver. However, a GPS receiver consumes a lot of power and hence it can significantly shorten the battery life. In this paper, we explore the possibility of using other types of sensors on a smartphone for traffic sensing. Particularly, we investigate whether it is possible and how accurate it is to estimate vehicle speed from the data sensed by an accelerometer. The accuracy of our proposed estimation method will be presented.
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    Real-Time Lane Change Detection Through Steering Wheel Rotation
    (2018-07-02)
    Leakkaw, Puttipong
    ;
    Panichpapiboon, Sooksan
    Lane change is an important traffic information. An abnormally high number of lane changes on a particular road section typically suggests that some lanes are blocked due to traffic incidents such as accidents and vehicle break downs. As a result, the lane change information is useful for automatic traffic incident detection. Currently, the lane change information of vehicles on an urban road is typically obtained from over-roadway fixed sensors such as surveillance cameras. However, using fixed sensors has limitations in terms of cost and coverage. It would be more effective if the lane change information could be collected directly from each individual vehicle. In this paper, we introduce a new mobile sensing approach to automatic real-time lane change detection. The lane change event is detected through the corresponding pattern of steering wheel rotation. The results show that the detection algorithm performs extremely well. Overall, it is able to achieve higher than 95% both in terms of precision and recall.