Hybrid Statistical and Machine Learning Methods for Road Traffic Prediction: A Review and Tutorial

Alsolami, Bdoor; Mehmood, Rashid; Albeshri, Aiiad

doi:10.1007/978-3-030-13705-2_5

Cited by 26 publications

(20 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traffic density is estimated to evaluate a road's density, such as an urban signalized junction, by integrating flow and travel time state attributes [43]. Traffic flow is the most fundamental criterion of understanding road capacity and traffic congestion, and it is divided into long-term and short-term predictions [42]. It is an essential measurement for travel navigation decisions [40], transportation management [41], smart city planning [42], and others.…”

Section: A Tfa Attributesmentioning

confidence: 99%

“…Traffic flow is the most fundamental criterion of understanding road capacity and traffic congestion, and it is divided into long-term and short-term predictions [42]. It is an essential measurement for travel navigation decisions [40], transportation management [41], smart city planning [42], and others. Most of the research conducted in this specific area aims to propose a better traffic handling mechanism by making full use of the various source of data, such as GPS, the incoming flow of vehicle, the outgoing flow of vehicle, even meteorological data, including weather, temperature, and wind speed [40], [41].…”

Section: A Tfa Attributesmentioning

confidence: 99%

“…Table IV shows some examples of hybrid models in the TFA study. Performing prediction or estimation based on a single method has its own limitation when aggregating linear and non-linear models [42]. Higher accuracy results can be achieved by various sources integration to the combination algorithm model [37].…”

Section: ) Hybrid Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: A Tfa Attributesmentioning

confidence: 99%

Section: A Tfa Attributesmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Nowadays, machine learning and big data are oriented towards analyzing incomplete and large sensed datasets related to many problems of the megalopolis. In [2], hybrid statistical and machine learning methods for road traffic prediction is proposed. The research described that although many approaches use traditional methodologies, recently hybrid models are used to predict road traffic congestion.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, such hybrid methods are run over stand-alone platforms, and the results are concentrated in particular areas, which limits their accuracy. Alsolami et al [2], also presented a method for road traffic prediction that uses the autoregressive integrated moving average (ARIMA), and support vector machine (SVM) algorithms; Jin et al [33] predicted traffic on freeways using the SVM method.…”

Section: Introductionmentioning

confidence: 99%

Geospatial Modeling of Road Traffic Using a Semi-Supervised Regression Algorithm

Saldaña-Pérez¹,

Torres-Ruiz²,

Moreno-Ibarra³

2019

IEEE Access

View full text Add to dashboard Cite

Nowadays, big cities are facing many challenges with respect to traffic congestion, climate change, air and water pollution, among others. Thus, smart cities are intended to improve the life quality of the citizens, tackling such issues with the integration of information and communication technologies to reduce the impact and achieve a well-being state of citizens. In this work, a model to predict the traffic congestion applying a support vector machine method is proposed. In addition, a crowdsourcing approach based on mining the Twitter social networks collecting events associated with the traffic is also proposed. The main contribution of this research is focused on providing a methodology that characterizes the traffic congestion analyzing crowd-sensed data from a geospatial perspective. This approach was implemented over the Mexico City as a case study, in order to forecast possible future traffic events in the city, in which the citizens share their particular situation to discover alternatives routes for avoiding the traffic congestion. Future works are oriented towards designing mobile applications in order to introduce the proposed approach and integrate information from multiple platforms and navigation systems. INDEX TERMS Geospatial modeling, machine learning, regression, road traffic, urban computing, volunteered geographic information.

show abstract

Insights for Urban Road Safety: A New Fusion-3DCNN-PFP Model to Anticipate Future Congestion from Urban Sensing Data

Dao

Kiran

Zettsu

2021

Periodic Pattern Mining

View full text Add to dashboard Cite

Hybrid Statistical and Machine Learning Methods for Road Traffic Prediction: A Review and Tutorial

Cited by 26 publications

References 39 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

Geospatial Modeling of Road Traffic Using a Semi-Supervised Regression Algorithm

Insights for Urban Road Safety: A New Fusion-3DCNN-PFP Model to Anticipate Future Congestion from Urban Sensing Data

Contact Info

Product

Resources

About