Multi-models machine learning methods for traffic flow estimation from Floating Car Data

Li, Jinjian; Boonært, Jacques; Doniec, Arnaud; Lozenguez, Guillaume

doi:10.1016/j.trc.2021.103389

Cited by 27 publications

(8 citation statements)

References 26 publications

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“…In [130], the authors proposed reconstructing tra c ows from the expected travel time using an ML method. They examined the capabilities of the Gaussian Process Regressor (GPR) to handle this issue.…”

Section: Techniques For Tra C Flow Predictionmentioning

confidence: 99%

Artificial Intelligence-Based Traffic Flow Prediction: A Comprehensive Review

Sayed

Abdelhamid

Hefny

2022

Preprint

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The expansion of the Internet of Things has resulted in new creative solutions, such as smart cities, that have made our lives more productive, convenient, and intelligent. The core of smart cities is the Intelligent Transportation System (ITS) which has been integrated into several smart city applications that improve transportation and mobility. ITS aims to resolve traffic issues, especially traffic congestion. Recently, new traffic flow prediction models and frameworks have been rapidly developed in tandem with the introduction of Artificial Intelligence (AI) approaches to improve the accuracy of traffic flow prediction. Traffic forecasting is a crucial duty in the transportation industry. It can significantly affect the design of road constructions and projects in addition to its importance for route planning and traffic rules. Furthermore, traffic congestion is a critical issue in urban areas and overcrowded cities. Therefore, it must be accurately evaluated and forecasted. Hence, a reliable and efficient method for predicting traffic is essential. The main objectives of this study are: First, present a comprehensive review of the most popular machine learning and deep learning techniques applied in traffic prediction. Second, identifying inherent obstacles to applying machine learning and deep learning in the domain of traffic prediction.

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Section: Techniques For Tra C Flow Predictionmentioning

confidence: 99%

Artificial Intelligence-Based Traffic Flow Prediction: A Comprehensive Review

Sayed

Abdelhamid

Hefny

2022

Preprint

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“…The scientific literature also proposes several original approaches for the best and most extensive use of each data source. In particular, a study proposes innovative methods to estimate and reconstruct the traffic flow in transport networks: starting from travel time, estimated by the traffic conditions thanks to the crowd sourced data (FCD), the reconstruction of the traffic flows using the method of machine learning has been proposed (Li et al, 2021). Another study instead proposes algorithms of machine learning and deep learning to predict the flow of traffic, for example in a crossing, allowing adaptive control system, both with the remote control of the traffic lights and applying an algorithm that regulates the timing according to the expected flow (Navarro-Espinoza et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Blending of Floating Car Data and Point-Based Sensor Data to Deduce Operating Speeds under Different Traffic Flow Conditions

Serrone¹

2023

European Transport/Trasporti Europei

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Nowadays, smart mobility can rely on innovative tools for the knowledge of road system conditions, like operating speed data extracted from the so-called Floating Car Data (FCD). Probe vehicles in the traffic flow send to operation centres a large amount of travel information, collected through GPS detection systems, especially with regard to geolocation, date and time, direction and speed. As the sample deriving from these vehicles represents a tiny portion of the entire vehicular fleet, in this paper an analysis and a comparison with data obtained by point-based traffic sensors is proposed. Therefore, the study analyses data collected by inductive loop detectors and microwave radar sensors, that provide information on the entire traffic flow in the time domain, in particular with the aim to identify free flow speed time bands. Afterwards, by means of the fusion between the results obtained from the data coming from these point-based control units and the ones coming from the probe vehicles, a comparison of the operating speeds in the two conditions of constrained and unconstrained traffic flow is performed.

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“…The research on tourist travel behavior focuses on the choice of travel mode and the choice of travel path, such as Prato C [1] using MNL, C-Logit, etc. 6 The discrete selection model analyzes path selection and its influencing factors, in which the road network consists of 216 roads, and considers 6 influencing factors such as distance and free-flow travel time. Aaron Gutierrez [2] studies the use of public transport by tourists in Costa Dorada (Catalonia, Spain) based on the smart travel card data of the public transport network, and distinguishes the passenger groups of different modes of travel according to the attributes of the passengers.…”

Section: Introductionmentioning

confidence: 99%

“…Ma [5] and others use long and short-term memory neural networks (LSTM) to model short-term traffic flow history data, and through case studies and classical forecasting models such as time series models, the results show that their accuracy is significantly improved. Based on floating vehicle data, Jinjian Li [6] et al used Gaussian process regression method to estimate and reconstruct the traffic flow in the traffic network, and the results showed that the prediction accuracy was higher than that of a single model.…”

Section: Introductionmentioning

confidence: 99%

Prediction model of passenger transfer volume between scenic spots based on clustering and dynamic Bayesian network

Sun

Chu

et al. 2022

International Conference on Frontiers of Traffic and Transportation Engineering (FTTE 2022)

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In order to reduce the risks caused by congestion to scenic spot management and tourist safety, a dynamic Bayesian network model based on K-means++ clustering is proposed to realize the prediction of tourist transfer volume between scenic spots. Firstly, the K-means++ method is used to cluster the tourist transfer volume between scenic spots, we select the best number of clustering by the elbow rule, and the grade interval is determined by clustering results. Secondly, we consider the passenger transfer volume and tourist flow as the nodes of the dynamic Bayesian network, which can estimate the probability of tourist transfer from the upstream scenic spots to the target scenic spot, and the tourist volume of the target scenic spot is predicted. Finally, the confusion matrix is used to verify the validity of the proposed model. The case study shows: 1.) The prediction accuracy of the model can reach about 96%, which indicates that the model is suitable for tourist flow prediction. 2.) Compared to ARIMA, SVR, K-means + BN, and K-means + DBN, the proposed model has better prediction accuracy. 3.) The Bayesian network model outperforms deep learning models in interpretability.

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Multi-models machine learning methods for traffic flow estimation from Floating Car Data

Cited by 27 publications

References 26 publications

Artificial Intelligence-Based Traffic Flow Prediction: A Comprehensive Review

Artificial Intelligence-Based Traffic Flow Prediction: A Comprehensive Review

Blending of Floating Car Data and Point-Based Sensor Data to Deduce Operating Speeds under Different Traffic Flow Conditions

Prediction model of passenger transfer volume between scenic spots based on clustering and dynamic Bayesian network

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