Predicting the daily traffc volume from hourly traffc data using artificial neural network

Title:

Predicting the daily traffc volume from hourly traffc data using artificial neural network

Creator:

Siddiquee, M. S. A. and Hoque, S.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:329cd83f-755b-46a6-a3c4-ac94034a49d9
uuid:329cd83f-755b-46a6-a3c4-ac94034a49d9
doi:10.14311/NNW.2017.27.015

Subject:

artificial neural network, traffic data prediction, and estimation

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

The prediction of traffic volume over time is very important to control the flow of traffic on a road network. Traffic count is usually averaged over time to predict for the larger time domain. This paper aims at finding the detail variation of a systematic survey of hourly traffic volume data over a time of four years along the North Bengal corridor of Bangladesh (at Jamuna toll collection point) and its equivalent numerical model by using a Artificial Neural Network. The Neural Network is trained with the intermittent data of 13 weeks over four years and the missing data is interpreted with quite reasonable accuracy (12.67% MAE) with this ANN model. The ANN model captured the variety of trends of the traffic data very accurately as has been depicted in the paper

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2017 Volume:27 | Number:3

Harvested from:

CDK

Metadata only:

false