Vehicle speed is one of the main factors that influence the occurrence and severity of the consequences of road traffic accidents. Operating speed can be defined, among other things, as the actual speed at which the largest number of road users drive in conditions of free traffic flow. It can be measured on existing roads, however, on newly designed roads it can only be predicted. For this reason, many researchers have examined the correlation between the elements of the road as well as its surroundings and operating speed. By determining the correlation, models for predicting operating speed were created. As part of this paper, the most significant models for predicting operating speed were analysed. Of course, the largest number of models are stochastic, but in recent years, models based on artificial intelligence, more precisely on deep learning, have also been created. Accordingly, the goal of this paper is to review the model for predicting the operating speed of vehicles while identifying opportunities for further research and improvement in this area.


    Access

    Download


    Export, share and cite



    Title :

    Operating Vehicles’ Speed Prediction Models




    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Operating Speed Prediction of Vehicles at Combined Curves Using Mixed Effect Modeling Approach

    Joseph, Neena M. / Harikrishna, M. / Anjaneyulu, M. V. L. R. et al. | Springer Verlag | 2022



    A Review of Prediction Models on Operating Speed for Highways

    Chen, T. / Wei, L. / American Society of Civil Engineers | British Library Conference Proceedings | 2010


    Operating Speed models for heavy vehicles on tangents of Spanish two-lane rural roads

    González, B. / Llopis-Castelló, D. / García, A | BASE | 2019

    Free access

    Operating Speed Prediction Models for Horizontal Curves on Rural Four-Lane Highways

    Gong, Huafeng / Stamatiadis, Nikiforos | Transportation Research Record | 2008