ANN based short-term traffic flow forecasting in undivided two lane highway,Journal of Big Data

当前位置： X-MOL 学术 › J. Big Data › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

ANN based short-term traffic flow forecasting in undivided two lane highway
Journal of Big Data ( IF 8.6 ) Pub Date : 2018-12-11 , DOI: 10.1186/s40537-018-0157-0
Bharti Sharma , Sachin Kumar , Prayag Tiwari , Pranay Yadav , Marina I. Nezhurina

Short term traffic forecasting is one of the important fields of study in the transportation domain. Short term traffic forecasting is very useful to develop a more advanced transportation system to control traffic signals and avoid congestions. Several studies have made efforts for short term traffic flow forecasting for divided and undivided highways across the world. However, all these studies relied on the dataset which are greatly varied between countries due to the technology used for transportation data collection. India is a developing country in which efforts are being done to improve the transportation system to avoid congestion and travel time. Two-lane undivided highways with mixed traffic constitute a large portion of Indian road network. This study is an attempt to develop a short term traffic forecasting model using back propagation artificial neural network for two lane undivided highway with mixed traffic conditions in India. The results were compared with random forest, support vector machine, k-nearest neighbor classifier, regression tree and multiple regression models. It was found that back-propagation neural network performs better than other approaches and achieved an R² value 0.9962, which is a good score.

更新日期：2018-12-11

点击分享查看原文

点击收藏

公开下载

阅读更多本刊最新论文