Artificial Neural Network Based Real-Time Urban Road Traffic State Estimation Framework

Habtie, Ayalew Belay; Abraham, Ajith; Midekso, Dida

doi:10.1007/978-3-319-47715-2_4

Cited by 9 publications

(2 citation statements)

References 39 publications

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“…Habtie et al. [ 22 ] presented an approach to estimate the state of road traffic using the existing cellular network as a source of traffic data, and use a model for estimating the state of the neural network. Porikli and Li [ 23 ] trained a set of Hidden Markov Model chains corresponding to five traffic patterns (stop, heavy congestion, open flow, moderate, and empty congestion), and then used a Maximum Likelihood criterion to determine the state of the separated Hidden Markov Models.…”

Section: Related Workmentioning

confidence: 99%

Modeling and Characterization of Traffic Flows in Urban Environments

Zambrano-Martinez

Calafate

Soler

et al. 2018

Sensors

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Currently, one of the main challenges faced in large metropolitan areas is traffic congestion. To address this problem, adequate traffic control could produce many benefits, including reduced pollutant emissions and reduced travel times. If it were possible to characterize the state of traffic by predicting future traffic conditions for optimizing the route of automated vehicles, and if these measures could be taken to preventively mitigate the effects of congestion with its related problems, the overall traffic flow could be improved. This paper performs an experimental study of the traffic distribution in the city of Valencia, Spain, characterizing the different streets of the city in terms of vehicle load with respect to the travel time during rush hour traffic conditions. Experimental results based on realistic vehicular traffic traces from the city of Valencia show that only some street segments fall under the general theory of vehicular flow, offering a good fit using quadratic regression, while a great number of street segments fall under other categories. Although in some cases such discrepancies are related to lack of traffic, injecting additional vehicles shows that significant mismatches still persist. Thus, in this paper we propose an equation to characterize travel times over a segment belonging to the sigmoid family; specifically, we apply logistic regression, being able to significantly improve the curve fitting results for most of the street segments under analysis. Based on our regression results, we performed a clustering analysis of the different street segments, showing that they can be classified into three well-defined categories, which evidences a predictable traffic distribution using the logistic regression throughout the city during rush hours, and allows optimizing the traffic for automated vehicles.

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Section: Related Workmentioning

confidence: 99%

Modeling and Characterization of Traffic Flows in Urban Environments

Zambrano-Martinez

Calafate

Soler

et al. 2018

Sensors

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“…In ITS, various infrastructure-based technologies (such as magnetic induction coils, and video cameras) are applied into vehicles and roads to detect various traffic parameters. The existing evaluation research of traffic state are mostly based on traditional detectors such as magnetic induction coil, video camera, and cellular networks [2]- [9]. Although these traffic detectors are widely used and have achieved good detection results, they have their own advantages and disadvantages: The advantages of magnetic inductive coil detector are that it has high detection accuracy and only needs a small investment in the preliminary stage, and the disadvantages are that once the coil detector broke down, it needs to close the lane and dig the road surface for maintenance, thus seriously reducing the road capacity and greatly increasing the maintenance difficulty of the coil detector; The advantages of video camera detector are that it can acquire visual images and obtain various traffic information such as traffic volume, density, vehicle type and license plate, and the disadvantages are that the detection results are easily affected by light conditions, such as day and night alternation, and ponding reflection, at the same time, the occlusion between vehicles will also affect the detection results, and the image processing is relatively timeconsuming [10].…”

Section: Introductionmentioning

confidence: 99%

Traffic State Evaluation Using Traffic Noise

Zou

2020

IEEE Access

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Traffic state information is widely applied into all aspects of Intelligent Transportation System (ITS), such as the macro-control of government departments, the implementation of traffic managers' plans, the decision-making of residents travel, and so on. At present, Mel Frequency Cepstrum Coefficient (MFCC) is generally used as characteristic of traffic noise to characterize different traffic states, and performs well in simple noise environment, but performs poorly in complex noise environment. Based on the analysis of traffic noise acquired from a roadside-installed acoustic acquisition equipment, the evaluation problem of traffic state in complex noise environment is considered in this paper. Traffic state is divided into three categories according to traffic speed in our work: free flow (40 km/h and above), saturated flow (10-40 km/h), and jammed flow (0-10 km/h). Teager Energy Operator (TEO) is introduced to improve the MFCC characteristic, thus a novel characteristic called T-MFCC is proposed. Principal Component Analysis (PCA) is introduced to reduce dimension of T-MFCC characteristic, thus a novel characteristic called PT-MFCC is proposed. Support Vector Machine (SVM) optimized by Particle Swarm Optimization (PSO) algorithm is applied as classifier to identify traffic state. Characterization capabilities of two modified characteristics and traditional MFCC characteristic for traffic state are compared in this paper. Experimental results demonstrate that the evaluation accuracy of traffic state based on T-MFCC characteristic is 3.685% higher than that based on MFCC characteristic, and the evaluation accuracy of traffic state based on PT-MFCC characteristic is 26.466% lower than that based on MFCC characteristic. Therefore, T-MFCC characteristic is superior to MFCC characteristic, while MFCC characteristic is superior to PT-MFCC characteristic, namely, T-MFCC characteristic can better characterize traffic state than MFCC characteristic, meanwhile, there are no redundancy attributes in T-MFCC characteristic, thus PCA is not needed to reduce the dimension of T-MFCC characteristic. INDEX TERMS traffic state, traffic noise, intelligent transportation systems, mel frequency cepstrum coefficient, teager energy operator, principal component analysis

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Mobile Technologies in Intelligent Transportation Systems

Malygin

Seliverstov

et al. 2020

Convergent Cognitive Information Technologies

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Artificial Neural Network Based Real-Time Urban Road Traffic State Estimation Framework

Cited by 9 publications

References 39 publications

Modeling and Characterization of Traffic Flows in Urban Environments

Modeling and Characterization of Traffic Flows in Urban Environments

Traffic State Evaluation Using Traffic Noise

Mobile Technologies in Intelligent Transportation Systems

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