Traffic congestion forecasting using multilayered deep neural network

Kranti Kumar , Manoj Kumar , Pritikana Das
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Abstract

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.

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基于多层深度神经网络的交通拥堵预测
本研究提出了一种多层深度神经网络(MLDNN)和基于交通密度因子的拥堵指数(CI),以直接预测交通拥堵情况。为了测试所提出的模型,我们在德里市选定了一个地点,利用视频摄像头收集了周一至周日高峰时段的数据。收集到的数据以 5 分钟为间隔,以矩阵的形式进行分类。输入矩阵被分为若干区间,用于训练、验证和测试 MLDNN 和基线模型,包括支持向量回归、多层感知器神经网络、门控递归单元(GRU)神经网络、长短期记忆(LSTM)神经网络、卷积神经网络(CNN)、CNN-GRU 神经网络和 CNN-LSTM 神经网络。研究结果表明,MLDNN 和拟议的 CI 可成功预测异构交通中的交通拥堵情况。
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来源期刊
CiteScore
6.40
自引率
14.30%
发文量
79
审稿时长
>12 weeks
期刊介绍: Transportation Letters: The International Journal of Transportation Research is a quarterly journal that publishes high-quality peer-reviewed and mini-review papers as well as technical notes and book reviews on the state-of-the-art in transportation research. The focus of Transportation Letters is on analytical and empirical findings, methodological papers, and theoretical and conceptual insights across all areas of research. Review resource papers that merge descriptions of the state-of-the-art with innovative and new methodological, theoretical, and conceptual insights spanning all areas of transportation research are invited and of particular interest.
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