Real-Time Measurement of Link Vehicle Count and Travel Time in a Road Network

Kwong, Karric; Kavaler, Robert; Rajagopal, Ram; Varaiya, Pravin

doi:10.1109/tits.2010.2050881

Cited by 57 publications

(14 citation statements)

References 10 publications

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“…Most of the traffic monitoring and tracking systems deployed today instrument streets with special purpose sensors such as magnetic loops [16,15,18], cameras [5], and tolltag readers. These methods tend to focus on highways, and are typically very costly to deploy.…”

Section: Introductionmentioning

confidence: 99%

Tracking unmodified smartphones using wi-fi monitors

Musa

Eriksson

2012

Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems

219

176

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Smartphones with Wi-Fi enabled periodically transmit Wi-Fi messages, even when not associated to a network. In one 12-hour trial on a busy road (average daily traffic count 37,000 according to the state DOT), 7,000 unique devices were detected by a single road-side monitoring station, or about 1 device for every 5 vehicles.In this paper, we describe a system for passively tracking unmodified smartphones, based on such Wi-Fi detections. This system uses only common, off-the-shelf access point hardware to both collect and deliver detections. Thus, in addition to high detection rates, it potentially offers very low equipment and installation cost.However, the long range and sparse nature of our opportunistically collected Wi-Fi transmissions presents a significant localization challenge. We propose a trajectory estimation method based on Viterbi's algorithm which takes second-by-second detections of a moving device as input, and produces the most likely spatio-temporal path taken. In addition, we present several methods that prompt passing devices to send additional messages, increasing detection rates an use signal-strength for improved accuracy.Based on our experimental evaluation from one 9-month deployment and several single-day deployments, passive WiFi tracking detects a large fraction of passing smartphones, and produces high-accuracy trajectory estimates.

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Section: Introductionmentioning

confidence: 99%

Tracking unmodified smartphones using wi-fi monitors

Musa

Eriksson

2012

Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems

219

176

View full text Add to dashboard Cite

show abstract

“…Setelah dipisahkan antara foreground dengan background maka dicari tingkat perubahan warna dari background dan forground sehingga didapat tepi dari forground [4]. Setelah tahap ekstraksi fitur selesai, lakukan proses morpologi, yaitu dilasi dan erosi, fungsi dari proses morfologi ini supaya memudahkan dalam melakukan placakan objek, setelah proses morfologi selesai, dilakukan proses cropping lokasi objek bisa dilakukan [5].…”

Section: Tahap Ekstraksi Fiturunclassified

Aplikasi Mendeteksi Gerakan Tangan untuk Bermain Game Pingpong dengan Teknik Pengolahan Citra

Buana¹

2017

Citec Journal

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“…Based on the idea of levenshtein distance [22] and the shortest path [21] algorithms, we proposed a matching scheme for vehicle speed estimation. As shown in Fig.…”

Section: Optimal Matching Schemementioning

confidence: 99%

“…Kwong et al [21] propose a system that measures the vehicle count and travel time the links of a road network. The measurements require matching vehicle signatures recorded by a wireless magnetic sensor network.…”

Section: Introductionmentioning

confidence: 99%

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A Vehicle Speed Estimation Algorithm Based on Wireless AMR Sensors

Zhang

Zhao

Yuan

2015

Big Data Computing and Communications

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Abstract. This paper proposes an algorithm for vehicle speed estimation based on the use of anisotropic magnetoresistive (AMR) sensors. Speed estimation relies on matching vehicle magnetic signatures from wireless sensors. A scheme based on edit-distance is developed to automatically matching signatures for the vehicles. Experimental results are presented to show that the proposed speed estimation is viable. IntroductionReal time acquisition of traffic information plays a key role in intelligent transportation systems (ITSs). Wireless sensor networks have lots of potential toward providing an ideal solution for traffic information acquisition, such as their low power, small size, low cost, and high accuracy. At present, the sensors used in the traffic information acquisition include the following types: inductive loop detector [1], image (camera) sensor [2,3], acoustic sensor [4,5], infrared sensor [6], ultrasonic sensor [7], etc. The image sensor acquires an abundance of information, but it is vulnerable to bad weather and nighttime operation. The acoustic sensor and infrared sensor are vulnerable to noise in deployed environments. Magnetic sensors based on magneto resistors have recently been proposed for vehicle detection [8,11] because they are quite sensitive, small and more immune to environmental factors such as rain, wind, snow or fog than sensing systems based on video cameras, ultrasound or infrared radiation.Many algorithms have been proposed for dynamic traffic information acquisition system based on wireless AMR sensor. The PATH program of the University of California, Berkeley [12,13] had first extensively explored of AMR sensor network based vehicle detection system. S.Y. Cheung et al. [13] had explored the applications for vehicle detection, speed estimation, and classification. M. Kang et al. [14] proposed a vehicle detector with an AMR sensor and addresses experimental study carried out to show the detector's characteristics and performance. Experiment results show that the vehicle detection accuracy rate is more than 99%, and the accuracy rate to estimate

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Real-Time Measurement of Link Vehicle Count and Travel Time in a Road Network

Cited by 57 publications

References 10 publications

Tracking unmodified smartphones using wi-fi monitors

Tracking unmodified smartphones using wi-fi monitors

Aplikasi Mendeteksi Gerakan Tangan untuk Bermain Game Pingpong dengan Teknik Pengolahan Citra

A Vehicle Speed Estimation Algorithm Based on Wireless AMR Sensors

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