Arterial Performance Measurement System with Wireless Magnetic Sensors

Kavaler, Robert; Kwong, Karric; Raman, Akhila; Varaiya, Pravin; Xing, Daniel

doi:10.1061/41177(415)48

Cited by 6 publications

(5 citation statements)

References 5 publications

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“…where the inner product of expanded basis h x ð Þ, h x i ð Þ h i is further defined by the kernel function K x, x i ð Þ. Two popular choices for the K function are implemented in this research, the polynomial kernel function by Equation (17) and the Gaussian kernel function by Equation (18).…”

Section: Nonlinear Support Vector Machinementioning

confidence: 99%

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Vehicle Reidentification in a Connected Vehicle Environment using Machine Learning Algorithms

Miao

Head

Beak

2018

Transportation Research Record: Journal of the Transportation R

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Deployment of Connected Vehicles will become available for most American cities in next to 20 years. The applications (e.g. mobility, safety, environment) are constantly receiving vehicle data. The current ID protection mechanism assumes a vehicle's ID changes every 5 minutes, the topic of re-matching vehicles is of interests in privacy protection and performance measure research. This paper explores the possibility of re-matching connected vehicles' ID using popular machine learning techniques, including Logistic Regression (LR), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Linear and Nonlinear Support Vector Machine (SVM) and Nearest Neighbor algorithms. An experiment is conducted using a microscopic traffic simulation model through a Software-In-the-Loop technique. The best average mis-matching rate is 14%. To assess potential factors' effects on matching accuracy, a Poisson mixed regression model is analyzed under the Bayesian inference framework. Findings are: different matching algorithms vary in matching performance and the Linear SVM, the QDA and the LDA have the best accuracy results; traffic volume and market penetration rate have little impact on matching results; location and number of vehicles to be matched are considered significant. The results make the performance measure of future CV applications feasible and also suggest that more secure mechanisms are in need to protect the public.

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Section: Nonlinear Support Vector Machinementioning

confidence: 99%

“…Some research has used unique vehicle IDs for identification. Examples include matching unique Bluetooth MAC addresses ( 7 – 13 ), GPS device addresses ( 14 ), and magnetic signatures ( 15 – 18 ). However, not all these ID matching facilities are widely deployed in the U.S. Another approach is vehicle trajectory tracking.…”

Section: Literature Reviewmentioning

confidence: 99%

Vehicle Reidentification in a Connected Vehicle Environment using Machine Learning Algorithms

Miao

Head

Beak

2018

Transportation Research Record: Journal of the Transportation R

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“…The simplest "signature" used for vehicle matching is an estimate of the vehicle's length [15]. Other studies have matched vehicles with richer data sets [16], [17] similar to those used in this study. These algorithms can be readily used in highway or free flow situations, but acceleration due to intersections or congestion causes distortion in signatures which decreases the percentage of correct matches [1].…”

Section: Introductionmentioning

confidence: 98%

Non-linear compensation of vehicle signatures captured from electromagnetic sensors with application to vehicle re-identification

Ernst

Krogmeier

Bullock

2010

13th International IEEE Conference on Intelligent Transportation Systems

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The distribution of travel times over a link in a transportation network can be estimated by observing the actual travel times of individual vehicles traversing the link and creating a histogram of the observations. The fundamental building block of travel time estimation is the reidenfication of vehicles. A variety of techniques can be used for re-identification, but this paper focuses on electromagnetic signatures. The travel time distribution can be sampled using time-stamped signatures captured from vehicles along with an algorithm for signature matching. The method works well for free flowing traffic on a limited access highway, but to apply it on arterials it is necessary to compensate for vehicle acceleration over the sensors, which is manifested as a nonlinear scaling of the time axis in the captured signatures. In this paper we show how to estimate the acceleration from signatures captured from a loop trap and how to compensate for the effect of both varying velocities and acceleration. The results are summarized in terms of receiver operating characteristics to demonstrate the performance improvements associated with velocity and acceleration compensation. When applied to signature matching over a relatively long link it is shown that acceleration compensation offers significant improvement in matching performance.

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“…However, new systems for travel time estimation have emerged which may overcome the limitations of loop detectors. Examples include GPS‐equipped probe vehicles (Hofleitner et al., ), toll tag matching (Turner et al., ), license plate re‐identification (Bertini et al., ; Xu et al., ), cellphone location services (Qiu et al., ), vehicle optical image identification (Kuroiwa et al., ), vehicle magnetic signature matching (Kwong et al., ; Kavaler et al., ; Sanchez et al., , b; Charbonnier et al., ), and Bluetooth MAC address matching (Barcelo et al., ; Brennan et al., ; Haghani et al., ; Quayle et al., ; Hainen et al., ; Richardson et al., ; Bakula et al., ).…”

Section: Introductionmentioning

confidence: 99%

A Generic Approach to Estimate Freeway Traffic Time Using Vehicle ID‐Matching Technologies

Souleyrette

2015

Computer aided Civil Eng

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Arterial Performance Measurement System with Wireless Magnetic Sensors

Cited by 6 publications

References 5 publications

Vehicle Reidentification in a Connected Vehicle Environment using Machine Learning Algorithms

Vehicle Reidentification in a Connected Vehicle Environment using Machine Learning Algorithms

Non-linear compensation of vehicle signatures captured from electromagnetic sensors with application to vehicle re-identification

A Generic Approach to Estimate Freeway Traffic Time Using Vehicle ID‐Matching Technologies

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