This study proposes a multilayered deep neural network (MLDNN) and a congestion index (CI) based on traffic density factor to forecast traffic congestion directly. Data were collected in Delhi city from a selected location using video cameras during peak hours of weekdays from Monday to Sunday to test the proposed model. Collected data were categorized in a matrix format in the intervals of five-minutes. The input matrix was divided into a number of intervals to train, validate, and test the MLDNN and baseline models, including support vector regression, multi-layer perceptron neural network, gated recurrent unit (GRU) neural network, long short-term memory (LSTM) neural network, convolutional neural network (CNN), CNN-GRU neural network, and CNN-LSTM neural network. Results of the study show that the MLDNN and proposed CI can be applied to predict traffic congestion successfully in heterogeneous traffic.
Traffic congestion forecasting using multilayered deep neural network
Transportation Letters ; 16 , 6 ; 516-526
2024-07-02
11 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Forecasting of Traffic Congestion
British Library Conference Proceedings | 2000
|URBAN TRAFFIC CONGESTION FORECASTING METHOD AND SYSTEM
Europäisches Patentamt | 2023
|Traffic Congestion Forecasting Based on Possibility Theory
Springer Verlag | 2014
|Traffic Congestion Detection from Camera Images using Deep Convolution Neural Networks
Transportation Research Record | 2018
|Convolutional neural network for recognizing highway traffic congestion
Taylor & Francis Verlag | 2020
|