Title: GIS-based tools for optimising municipal solid waste collection and transportation routes

Authors: Samra Fatima; Zulfa Hanan Ash'aari; Mohammad Firuz Ramli; Amir Hamzah Sharaai; Muhammad Nawaz Chaudhry

Addresses: Faculty of Forestry and Environment, Universiti Putra Malaysia, 43400 Serdang, Selangor Darul Ehsan, Malaysia ' Faculty of Forestry and Environment, Universiti Putra Malaysia, 43400 Serdang, Selangor Darul Ehsan, Malaysia ' Faculty of Forestry and Environment, Universiti Putra Malaysia, 43400 Serdang, Selangor Darul Ehsan, Malaysia ' Faculty of Forestry and Environment, Universiti Putra Malaysia, 43400 Serdang, Selangor Darul Ehsan, Malaysia ' Faculty of Basic Sciences, Lahore School of Economics, Pakistan

Abstract: The selection of optimal routes can save time and cost in handling the waste. Thus, the current study was designed to optimise the waste disposal routes with the possible shortest travel time using geographic information system (GIS). The collected data were analysed through statistical tools, including Pearson's correlation and regression analysis, using SPSS, version 20. The explanation from the linear regression analysis indicated that 67.4% of the variables selected for the regression gave reasonable indications for the analysis of waste transportation time in the study area. In addition, the other independent variables such as population and number of stops also have a positive and significant impact on the travel time. The resulted predictive model showed the best suitability that could be implemented to resolve the basic solid waste problems in other cities of Pakistan. Furthermore, it can be used to capture data from different fields to resolve transport problems.

Keywords: waste management; geographic information system; GIS; network analysis; regression analysis; shortest routes.

DOI: 10.1504/IJEWM.2023.134504

International Journal of Environment and Waste Management, 2023 Vol.32 No.4, pp.402 - 417

Received: 18 Mar 2020
Accepted: 14 Apr 2021
Published online: 26 Oct 2023 *

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