Modelling Optimum Walking Time With Respect To Travel Purpose and Considering Individual and Environmental Variables (Case Study: Gorgan)

Document Type : Original Article

Authors

1 M.Sc.,Grad., Department of Civil Engineering, Malard Branch, Islamic Azad University, Malard, Tehran, Iran

2 Department of Civil Engineering, Malard Branch, Islamic Azad University, Malard, Tehran, Iran

3 M.Sc., Student, Department of Civil Engineering, Malard Branch, Islamic Azad University, Malard, Tehran, Iran

Abstract

In this research, the duration and appropriate time of walking to reach the routine destinations and meet the needs of individuals based on the different criteria of city districts and neighborhoods including individual, social, economic, and physical ones as well as land uses. The results can evaluate challenges and deficiencies based on the affecting indicators in different regions, and finally, they can lead to the adoption of measures and propositions so that people can fulfill their purposes and needs by walking over a proper time. The analysis method performed here was to by using statistical regression modelling. To reach the goal of this study, first, the city of Gorgan, Golestan province, Iran, was introduced, then, the land use map and land use distribution diagram by neighborhood were given, and next, the demography of the respondents to the questionnaire was investigated. Finally, four modelling types were addressed to measure the walking time for different purposes; 1) occupational, 2) educational, 3) shopping, and 4) recreational. By investigating the results of walking time models in different districts of Gorgan and by considering the t-test coefficients and Watson coefficient, being 1.5-2.5 for all the models which is acceptable, the independent variables of the area and concentration of office land uses, age, and car ownership (to measure the walking time of occupational travels), the independent variables of the area of educational land uses, income, and age (to measure the walking time of educational travels), the independent variables of the area and concentration of service land uses, age, and car ownership (to measure the walking time of shopping travels), the area of greenbelts and parks, and different variables of the area of sporting  land uses, car ownership, and age (to measure the walking time of recreational travels) were determined to be significant. At last, tables showing the coefficients of independent variables and the constant of the prediction model for the walking time of each type of travel were presented.

Keywords

References

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Journal of Transportation Research
Volume 17, Issue 4 - Serial Number 65
October 2020
Pages 247-259

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