Sensing and Signal Processing for Vehicle Reidentification and Travel Time Estimation

Ndoye, Miguel-Angel Sanchez; Totten, Virgil; Krogmeier, James V.; Bullock, Darcy M.

doi:10.1109/tits.2010.2092769

Cited by 48 publications

(34 citation statements)

References 15 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

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“…The signature depends on the composition of the vehicle and by matching signatures from two detectors travel times can be estimated. Research has shown that, depending on the driving environment and type of detector, matching rates between 60 % and 100 % can be achieved, see Blokpoel (2009), Ndoye et al (2011) andJeng et al (2010). Since acceleration and retardation affects the signature the matching process is more difficult for urban areas with a lot of stop and start movement.…”

Section: Traffic Signal Detectorsmentioning

confidence: 99%

Highway Traffic State Estimation and Short-term Prediction

Allström¹

2016

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Traffic congestion is increasing in almost all large cities, leading to a number of negative effects such as pollution and delays. However, building new roads is not a feasible solution. Instead, the use of the existing road network has to be optimized, together with a shift towards more sustainable transport modes. In order to achieve this there are several challenges that needs to be addressed. One challenge is the ability to provide accurate information about the current and future traffic state. This information is an essential input to the traffic management center and can be used to influence the choices made by the travelers. Accurate information about the traffic state on highways, where the potential to manage and control the traffic in general is very high, would be of great significance for the traffic managers. It would help the traffic managers to take action before the system reaches congestion and limit the effects of it. At the same time, the collection of traffic data is slowly shifting from fixed sensors to more probe based data collection. This requires an adaptation and further development of the traditional traffic models in order for them to handle and take advantage of the characteristics of all types of data, not just data from the traditionally used fixed sensors.The objective of this thesis is to contribute to the development and implementation of a model for estimation and prediction of the current and future traffic state and to facilitate an adaptation of the model to the conditions of the highway in Stockholm. The model used is a version of the Cell Transmission Model (CTM-v) where the velocity is used as the state variable. Thus, together with a n E ns e m bl e K a l ma n F i l t e r ( E nK F ) i t c a n be u s e d t o f u s e d i f f e r e n t t y pe s of poi n t s p e e d measurements. The model is developed to run in real-time for a large network. Furthermore, a two-stage process used to calibrate the model is implemented. The results from the calibration and validation show that once the model is calibrated, the estimated travel times corresponds well with the ground truth travel times collected from Bluetooth sensors.In order to produce accurate short-term predictions for various networks and conditions it is vital to combine different methods. We have implemented and evaluated a hybrid prediction approach that assimilates parametric and non-parametric short-term traffic state prediction. To predict mainline sensor data we use a neural network, while the CTM-v is ran forward in time in order to predict future traffic states. The results show that both the hybrid approach and the CTM-v prediction without the additional predicted mainline sensor data is superior to a naïve prediction method for longer prediction horizons.

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“…One set of methods utilizes loop detector data along with signal-cycle level data to build models that estimate travel times (Zhang, 1999;Skabardonis, 2005;. Vehicle re-identification techniques have also been used to estimate travel times Coifman, 1998;Ndoye, 2008;Oh, 2002;Kwong, 2008). Other techniques for matching toll tags, license plates, cell phones and MAC addresses have also been tested (Dion, 2006;Wunnava and Wasson, 2008;Quayle, 2010;Malinovskiy, 2010;Haghani, 2010).…”

Section: Literature Reviewmentioning

confidence: 99%

Multimodal Data at Signalized Intersections: Strategies for Archiving Existing and New Data Streams to Support Operations and Planning Fusion and Integration of Arterial Performance Data

Tufte¹,

Monsere²,

Kothuri³

et al. 2013

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Type of Report and Period Covered Sponsoring Agency Code Supplementary Notes AbstractThere is a growing interest in arterial system management due to the increasing amount of travel on arterials and a growing emphasis on multimodal transportation. The benefits of archiving arterial-related data are numerous. This research report describes our efforts to assemble and develop a multimodal archive for the Portland-Vancouver region. There is coverage of data sources from all modes in the metropolitan region; however, the preliminary nature of the archiving process means that some of the data are incomplete and samples. The arterial data sources available in the Portland-Vancouver region and that are covered in this report include data for various local agencies (City of Portland, Clark County, WA, TriMet and C-TRAN) covering vehicle, transit, pedestrian, and bicycle modes. We provide detailed descriptions of each data source and a spatial and temporal classification. The report describes the conceptual framework for an archive and the data collection and archival process, including the process for extracting the data from the agency systems and transferring these data to our multimodal database. Data can be made more useful though the use of improved visualization techniques. Thus as part of the project, a number of novel, online visualizations were created and implemented. These graphs and displays are summarized in this report and example visualizations are shown. As with any automated sensor system, data quality and completeness is an important issue and the challenge of automating data quality is large. Preliminary efforts to validate and monitor data quality and automate data quality processing are explored. Finally, the report presents efforts to combine transit and travel time data and signal timing and vehicle count data to generate some sample congestion measures.

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Sensing and Signal Processing for Vehicle Reidentification and Travel Time Estimation

Cited by 48 publications

References 15 publications

Tracking unmodified smartphones using wi-fi monitors

Tracking unmodified smartphones using wi-fi monitors

Highway Traffic State Estimation and Short-term Prediction

Multimodal Data at Signalized Intersections: Strategies for Archiving Existing and New Data Streams to Support Operations and Planning Fusion and Integration of Arterial Performance Data

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