Predicting Future Road Lengths in Rural Areas Across India

The main goal of this research article is to apply the ARIMA (Auto regressive Integrated Moving average) model to anticipate the length of rural roads in India over the next five years. Given that India is in a critical stage of infrastructure development to draw in foreign direct investments, this becomes imperative. The data Used for this research paper is of secondary in nature and is extracted from government website MORTH (Ministry of road transport and highways India).We conduct an arima model using this data set in which we first conduct a time series analysis to find the trend line and find the stationarity of the model if the stationarity is not found we conduct difference of various orders until the model obtain’s stationarity we then Find out the MAPE value (Mean average percentage error) of different models to find the most suitable model And then predict the future road lens for the next five years from the obtained model. This data is crucial for stakeholders for investment in rural India.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-12-04
DOI
https://doi.org/10.5281/zenodo.14275422
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Predicting Future Road Lengths in Rural Areas Across India

Zenodo (CERN European Organization for Nuclear Research)
Traffic Prediction and Management Techniques
article

Predicting Future Road Lengths in Rural Areas Across India

article en

Abstract

The main goal of this research article is to apply the ARIMA (Auto regressive Integrated Moving average) model to anticipate the length of rural roads in India over the next five years. Given that India is in a critical stage of infrastructure development to draw in foreign direct investments, this becomes imperative. The data Used for this research paper is of secondary in nature and is extracted from government website MORTH (Ministry of road transport and highways India).We conduct an arima model using this data set in which we first conduct a time series analysis to find the trend line and find the stationarity of the model if the stationarity is not found we conduct difference of various orders until the model obtain’s stationarity we then Find out the MAPE value (Mean average percentage error) of different models to find the most suitable model And then predict the future road lens for the next five years from the obtained model. This data is crucial for stakeholders for investment in rural India.

Zenodo (CERN European Organization for Nuclear Research)
Industry, innovation and infrastructure
Openalex Percentile: Top 63%
Traffic Prediction and Management Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.