NDLI: Short-term Traffic Flow Prediction Using Group Method Data Handling (GMDH)-based Abductive Networks

Content Provider	Springer Nature Link
Author	Ratrout, Nedal T.
Copyright Year	2013
Abstract	This study investigates a method of predicting traffic flow that employs the use of abductive networks based on the group method data handling (GMDH) for short-term traffic flow prediction. The GMDH algorithm relies on high-order polynomial input variables and it starts by building regression equations of order 2 or 3 for each pair of input variables. The new input variables are used for predicting the output in lieu of the original input variables. Based on pre-specified selection criteria, the best new input variables (polynomials) in terms of predicting the output are retained and used as input variables for the next layer to generate newer input variables of higher order (order of 4 if started with order 2). This process continues until the added value of the predicting power becomes insignificant and/or the model becomes practically complex for predicting purposes. Models for a linear road network were developed first using both spatial and temporal information without differentiating between weekdays and weekends. In the subsequent efforts, different models were built for weekdays and weekends. It was found that day-specific mode performance is not better than the generic model in predicting traffic flow. Models developed for predicting traffic after 15 min had correlation coefficients between 0.97 and 0.98. Models which were developed to predict traffic after 30 min were also robust but with slightly lower values of correlation coefficient. Due to the self-organizing nature of the models and the minimum required interventions, the models can be easily used by practitioners.
Starting Page	631
Ending Page	646
Page Count	16
File Format	PDF
ISSN	13198025
Journal	Arabian Journal for Science and Engineering
Volume Number	39
Issue Number	2
e-ISSN	21914281
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-09-04
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	GMDH Abductive network Traffic count Traffic flow prediction Engineering Science
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Short-term traffic flow prediction of road network based on deep learning

Neural-Network-Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm

Prediction of Permeability Using Group Method of Data Handling (GMDH) Neural Network from Well Log Data

Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks

UTN-Model-Based Traffic Flow Prediction for Parallel-Transportation Management Systems

Selection of Significant On-Road Sensor Data for Short-Term Traffic Flow Forecasting Using the Taguchi Method

Short-term Traffic Flow Prediction Method in Bayesian Networks Based on Quantile Regression

Prediction of Short-Term Traffic Flow Based on Similarity

Development of a Rutting Prediction Model through Accelerated Pavement Testing Using Group Method of Data Handling (GMDH)

Short-term Traffic Flow Prediction Using Group Method Data Handling (GMDH)-based Abductive Networks

Similar Documents

Short-term traffic flow prediction of road network based on deep learning

Neural-Network-Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm

Prediction of Permeability Using Group Method of Data Handling (GMDH) Neural Network from Well Log Data

Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks

UTN-Model-Based Traffic Flow Prediction for Parallel-Transportation Management Systems

Selection of Significant On-Road Sensor Data for Short-Term Traffic Flow Forecasting Using the Taguchi Method

Short-term Traffic Flow Prediction Method in Bayesian Networks Based on Quantile Regression

Prediction of Short-Term Traffic Flow Based on Similarity

Development of a Rutting Prediction Model through Accelerated Pavement Testing Using Group Method of Data Handling (GMDH)

Short-term Traffic Flow Prediction Using Group Method Data Handling (GMDH)-based Abductive Networks