Online Traffic Condition Evaluation Method for Connected Vehicles Based on Multisource Data Fusion

Wang, Pangwei; Yu, Hongwei; Xiao, Lin; Wang, Li

doi:10.1155/2017/7248189

Cited by 19 publications

(7 citation statements)

References 17 publications

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“…The proposed multilayer model obtained the best result with RMSE of 0.3037 and MRE of 0.2045 compared to few other popular algorithms such as ARIMA, DeepST, and CNN. Wang et al [107] employ multilevel fuzzy theory to fuse features from real-time connected vehicle data to perform traffic condition evaluation. Within one hour of data, 92.6% of total packet data are classified as valid.…”

Section: ) Fuzzy Logicmentioning

confidence: 99%

“…DS works best as a classifier at decision level fusion to detect lane change activity [45], detect misbehaved clusters of vehicles [102], and traffic state estimation [70]. FL may serve either one or both feature level and decision level fusion to achieve traffic prediction [106] and estimation [107], [108]. JPDA is mainly implemented at data level fusion and used mostly during the early stage of data processing in traffic data association [110], [149], and motions [111].…”

Section: A Analysismentioning

confidence: 99%

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Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

et al. 2021

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Recently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application challenges. Ongoing studies on Data Fusion (DF) have produced significant improvement in ITS and manifested an enormous impact on its growth. This paper reviews the implementation of DF methods in ITS to facilitate traffic flow analysis (TFA) and solutions that entail the prediction of various traffic variables such as driving behavior, travel time, speed, density, incident, and traffic flow. It attempts to identify and discuss real-time and multi-sensor data sources that are used for various traffic domains, including road/highway management, traffic states estimation, and traffic controller optimization. Moreover, it attempts to associate abstractions of data level fusion, feature level fusion, and decision level fusion on DF methods to better understand the role of DF in TFA and ITS. Consequently, the main objective of this paper is to review DF methods used for real-time and multi-sensor (heterogeneous) TFA studies. The review outcomes are (i) a guideline of constructing DF methods which involve preprocessing, filtering, decision, and evaluation as core steps, (ii) a description of the recent DF algorithms or methods that adopt real-time and multi-sensor sources data and the impact of these data sources on the improvement of TFA, (iii) an examination of the testing and evaluation methodologies and the popular datasets and (iv) an identification of several research gaps, some current challenges, and new research trends.INDEX TERMS Intelligent transportation systems, traffic flow analysis, data fusion; real-time processing, multi-sensor, heterogeneous data, machine learning.

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Section: ) Fuzzy Logicmentioning

confidence: 99%

Section: A Analysismentioning

confidence: 99%

Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

et al. 2021

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“…Average speed, road occupancy rate, traffic flow density and traffic volume were used as evaluation indicators of FCE. Wang et al used the roadside unit to interact with the actual running vehicles on the road to obtain data [20]. They took the parking time and times, average travel time as the basic indicators to characterize the traffic situation with the fuzzy evaluation method.…”

Section: The Characterization Model Of Traffic Situationmentioning

confidence: 99%

Deep Reinforcement Learning-Based LSTM Model for Traffic Flow Forecasting in Internet of Vehicles

Chen

Luo

Wang

et al. 2021

Proceedings of 2021 Chinese Intelligent Automation Conference

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Nowadays many large cities often suffer from road congestion. Then, it is necessary to improve the working efficiency of urban road network with the help of computational intelligence technologies, within the Internet of vehicles (IoV) environment. We are committed to designing traffic flow prediction methods through the use of machine learning models. In this paper, we predict the traffic flow and traffic situation of the selected road segment. Specifically, we propose a deep reinforcement learning (DRL)-based long short-term memory (LSTM) model to predict the traffic flow. On the basis of it, we characterize the traffic situation with fuzzy comprehensive evaluation (FCE)-based model. The experimental results show that the constructed DRL-based LSTM model can accurately predict the traffic flow data. Meanwhile, the traffic situation characterization model can represent the road traffic situation well.

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“…Traffic monitoring systems have been developed in various ways, and traffic information is collected indirectly or directly depending on the characteristics of a specific monitoring system. Indirect methods estimate traffic status such as travel volume and travel time within a road section based on the data samples collected via roadside units (RSU) or global positioning systems (GPS), which are instances of automatic vehicle identification (AVI) technologies [6][7][8]. However, the estimation performance of these methods is highly dependent on the market penetration rate (MPR) of equipped vehicles for vehicle-to-infrastructure (V2I) communication.…”

Section: Introductionmentioning

confidence: 99%

Development of AI-Based Vehicle Detection and Tracking System for C-ITS Application

Tak

Lee

Song³

et al. 2021

Journal of Advanced Transportation

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There are various means of monitoring traffic situations on roads. Due to the rise of artificial intelligence (AI) based image processing technology, there is a growing interest in developing traffic monitoring systems using camera vision data. This study provides a method for deriving traffic information using a camera installed at an intersection to improve the monitoring system for roads. The method uses a deep-learning-based approach (YOLOv4) for image processing for vehicle detection and vehicle type classification. Lane-by-lane vehicle trajectories are estimated by matching the detected vehicle locations with the high-definition map (HD map). Based on the estimated vehicle trajectories, the traffic volumes of each lane-by-lane traveling direction and queue lengths of each lane are estimated. The performance of the proposed method was tested with thousands of samples according to five different evaluation criteria: vehicle detection rate, vehicle type classification, trajectory prediction, traffic volume estimation, and queue length estimation. The results show a 99% vehicle detection performance with less than 20% errors in classifying vehicle types and estimating the lane-by-lane travel volume, which is reasonable. Hence, the method proposed in this study shows the feasibility of collecting detailed traffic information using a camera installed at an intersection. The approach of combining AI and HD map techniques is the main contribution of this study, which shows a high chance of improving current traffic monitoring systems.

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Online Traffic Condition Evaluation Method for Connected Vehicles Based on Multisource Data Fusion

Cited by 19 publications

References 17 publications

Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

Deep Reinforcement Learning-Based LSTM Model for Traffic Flow Forecasting in Internet of Vehicles

Development of AI-Based Vehicle Detection and Tracking System for C-ITS Application

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