Trajectory data-based traffic flow studies: A revisit

Li, Li; Jiang, Rui; He, Zhengbing; Chen, Xiqun; Zhou, Xuesong

doi:10.1016/j.trc.2020.02.016

Cited by 182 publications

(69 citation statements)

References 218 publications

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“…This indicates, more dynamic data from various data sources has a bright potential to be incorporated in DF of TFA related studies in the future. Recently, Li et al [156] conduct specific research about trajectory data in the latest development for traffic study purposes. IoT helps in integrating data from different methods for various purposes [27].…”

Section: ) Processing Complexity Issuesmentioning

confidence: 99%

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: ) Processing Complexity Issuesmentioning

confidence: 99%

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

et al. 2021

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“…At present, one of the main research work based on traffic Big Data mining is traffic flow prediction [12]. For the free flow forecast of the expressway, Ma et al [13] proposed a forecasting method for daily traffic flow using a contextual convolutional long short-term memory recurrent neural network.…”

Section: Related Workmentioning

confidence: 99%

“…For a triple of a given relationship r, when constructing a negative triple, the probability of replacing the head or tail entity is calculated according to the Bernoulli distribution. If the head entity is replaced, it is selected from the entity subset in the domain of the relationship type, and if the tail entity is replaced, it is selected from the entity subset in the range of the relationship type, as shown in Equation (12).…”

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confidence: 99%

Research on the Construction of a Knowledge Graph and Knowledge Reasoning Model in the Field of Urban Traffic

et al. 2021

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The integration of multi-source transportation data is complex and insufficient in most of the big cities, which made it difficult for researchers to conduct in-depth data mining to improve the policy or the management. In order to solve this problem, a top-down approach is used to construct a knowledge graph of urban traffic system in this paper. First, the model layer of the knowledge graph was used to realize the reuse and sharing of knowledge. Furthermore, the model layer then was stored in the graph database Neo4j. Second, the representation learning based knowledge reasoning model was adopted to implement knowledge completion and improve the knowledge graph. Finally, the proposed method was validated with an urban traffic data set and the results showed that the model could be used to mine the implicit relationship between traffic entities and discover traffic knowledge effectively.

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“…The vehicle trajectory is one of the main variables in traffic flow theory that allows to extract information regarding microscopic traffic flow characteristics [7][8][9]. The ability to extract the position of the vehicles over time along the roadway can provide an understanding of the way vehicles move and a comprehensive dataset for implementation in several applications regarding traffic flow and safety analysis [10,11].…”

Section: Introduction 1microscopic Traffic Data Acquirementmentioning

confidence: 99%

Utilizing UAVs Technology on Microscopic Traffic Naturalistic Data Acquirement

Anagnostopoulos

Kehagia

2021

Infrastructures

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Research into collecting and measuring reliable, accurate, and naturalistic microscopic traffic data is a fundamental aspect in road network planning scientific literature. The vehicle trajectory is one of the main variables in traffic flow theory that allows to extract information regarding microscopic traffic flow characteristics. Several methods and techniques have been applied regarding the acquisition of vehicle trajectory. The forthcoming applications of intelligent transport systems on vehicles and infrastructure require sufficient and innovative tools to calibrate existing models on more complex situations. Unmanned aerial vehicles (UAVs) are one of the most emerging technologies being used recently in the transportation field to monitor and analyze the traffic flow. The aim of this paper is to examine the use of UAVs as a tool for microscopic traffic data collection and analysis. A comprehensive guiding framework for accurate and cost-effective naturalistic traffic surveys and analysis using UAVs is proposed and presented in detail. Field experiments of acquiring vehicle trajectories on two multilane roundabouts were carried out following the proposed framework. Results of the experiment indicate the usefulness of the UAVs technology on various traffic analysis studies. The results of this study provide a practical guide regarding vehicle trajectory acquirement using UAVs.

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Trajectory data-based traffic flow studies: A revisit

Cited by 182 publications

References 218 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

Research on the Construction of a Knowledge Graph and Knowledge Reasoning Model in the Field of Urban Traffic

Utilizing UAVs Technology on Microscopic Traffic Naturalistic Data Acquirement

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