Traffic problems continue to deteriorate because of increasing population in urban areas that rely on many modes of transportation, the transportation infrastructure has achieved considerable strides in the last several decades. This has led to an increase in congestion control difficulties, which directly affect citizens through air pollution, fuel consumption, traffic law breaches, noise pollution, accidents, and loss of time. Traffic prediction is an essential aspect of an intelligent transportation system in smart cities because it helps reduce overall traffic congestion. This article aims to design and enforce a traffic prediction scheme that is efficient and accurate in forecasting traffic flow. Available traffic flow prediction methods are still unsuitable for real-world applications. This fact motivated us to work on a traffic flow forecasting issue using Vision Transformers (VTs). In this work, VTs were used in conjunction with Convolutional neural networks (CNN) to predict traffic congestion in urban spaces on a city-wide scale. In our proposed architecture, a traffic image is fed to a CNN, which generates feature maps. These feature maps are then fed to the VT, which employs the dual techniques of tokenization and projection. Tokenization is used to convert features into tokens containing Vision information, which are then sent to projection, where they are transformed into feature maps and ultimately delivered to LSTM. The experimental results demonstrate that the vision transformer prediction method based on Spatio-temporal characteristics is an excellent way of predicting traffic flow, particularly during anomalous traffic situations. The proposed technology surpasses traditional methods in terms of precision, accuracy and recall and aids in energy conservation. Through rerouting, the proposed work will benefit travellers and reduce fuel use.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Vision Transformer Approach for Traffic Congestion Prediction in Urban Areas




    Publication date :

    2023-04-01


    Size :

    3543176 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Management of Traffic Congestion in Urban Areas

    Vilibald Premzl | DOAJ | 2012

    Free access


    Road Traffic Congestion Monitoring in Urban Areas: A Review

    Sadhukhan, Pampa / Banerjee, Sahali / Das, Pradip K. | Springer Verlag | 2021


    Congestion Prediction of Urban Traffic Employing SRBDP

    Jiang, Pengcheng / Liu, Lei / Cui, Lizhen et al. | IEEE | 2017


    A deep-learning-based prediction model for real-time traffic congestion in urban areas

    Liu, Hua / Ren, Xiaoyong / Xu, Haiyan et al. | SPIE | 2025