Traffic Flow Prediction from Loop Counter Sensor Data using Machine Learning Methods

Kažić, B.M.; Mladenić, Dunja; Košmerlj, Aljaž

doi:10.5220/0005495001190127

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…Both RF and ANN are mainly used as data-driven approaches for travel time prediction [31][32][33]. Contributions in literature dealing with the adoption of RF for demand forecasting are harder to find.…”

Section: Calibration and Validation Methodsmentioning

confidence: 99%

Data Driven Approaches for Sustainable Development of E-Mobility in Urban Areas

et al. 2021

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This study focuses on a modeling framework to support mobility planners and energy providers in the sustainable development of electric mobility in urban areas. Specifically, models are provided to simulate measures for the optimal management of energy demand and thoughtful planning of charging infrastructures in order to avoid congestion on the power grid. The measures, and consequently the models, are classified according to short-term initiatives based on multimodality between electric vehicles and public transport (Park and Ride), as well as medium to long-term initiatives based on the development of an energy-oriented land use of the city. All the models are data-driven, and different sets of floating car data available for the city of Rome (Italy) have been exploited for this aim. The models are currently being implemented in an agent-based simulator for electric urban mobility adopted by the National Agency for Energy and Environment in Italy (ENEA).

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Section: Calibration and Validation Methodsmentioning

confidence: 99%

Data Driven Approaches for Sustainable Development of E-Mobility in Urban Areas

et al. 2021

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“…The short-term traffic volume prediction ability of the k-NN, SVR, and time series models has been assessed in two case studies in [22], showing that the territory of interest plays a key role when predicting speed. There are also other studies, such as [23][24][25][26], showing that the prediction accuracy of Neural Networks and SVR is better than that of other machine learning models for short-term traffic speed prediction.…”

Section: Introductionmentioning

confidence: 93%

A Comparison of Machine Learning Methods for the Prediction of Traffic Speed in Urban Places

et al. 2019

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Rising interest in the field of Intelligent Transportation Systems combined with the increased availability of collected data allows the study of different methods for prevention of traffic congestion in cities. A common need in all of these methods is the use of traffic predictions for supporting planning and operation of the traffic lights and traffic management schemes. This paper focuses on comparing the forecasting effectiveness of three machine learning models, namely Random Forests, Support Vector Regression, and Multilayer Perceptron—in addition to Multiple Linear Regression—using probe data collected from the road network of Thessaloniki, Greece. The comparison was conducted with multiple tests clustered in three types of scenarios. The first scenario tests the algorithms on specific randomly selected dates on different randomly selected roads. The second scenario tests the algorithms on randomly selected roads over eight consecutive 15 min intervals; the third scenario tests the algorithms on random roads for the duration of a whole day. The experimental results show that while the Support Vector Regression model performs best at stable conditions with minor variations, the Multilayer Perceptron model adapts better to circumstances with greater variations, in addition to having the most near-zero errors.

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