Estimating Time Spent at the Waste Collection Point by A Garbage Truck with A Multiple Regression Model

Abstract

The planning of the garbage trucks’ routes is an essential process in waste collection companies. The main issues in garbage truck routing are determining the optimal routes, minimizing time, decreasing the costs, and reducing the pollution’s emission. In the literature, the time spent at a waste collection point (WCP) is considered as the average time, or it is not included at all. Time spent at a WCP is determined by the processes of picking up, emptying, and putting down the waste containers and the factors specific for different WCPs. Those factors impact the time spent at WCP significantly. Excluding time spent at a WCP or taking the average of that in the planning approach may lead to the inaccurate estimation of total collection time. The aim of this article is to present the multiple regression model for estimating time spent at a WCP. We analyzed the impact of the WCP factors (i.e., building type and number of containers) on the time that a garbage truck spends at it. We initially considered seven chosen factors, five categorical and two numerical. Based on this, we developed the multiple regression model based on linear regression use. Later, the proposed model was validated based on data obtained from the municipal company operating in Wroclaw city, Poland. The study confirmed that the defined factors significantly affect garbage truck’s time spent at a WCP and should be taken into account during waste collection planning processes’ performance.

Analysis of Municipal Solid Waste Collection Methods Focusing on Zero-Waste Management Using an Analytical Hierarchy Process

“…Field studies carried out within this context show that the collection and transportation costs can be significantly reduced by route optimization [10]. Due to the high share of waste collection costs in the integrated solid waste management system, numerous studies have been carried out on the route optimization of the collection process [9]. The capacity and number of vehicles play a decisive role in the optimization of the route, which aims to collect the waste by traveling the shortest distances [86].…”

“…Additionally, in the same studies, solid waste recycling costs were determined to be USD 93.89/t, USD −67/t, and USD 20.12/t for plastic, paper, and glass, respectively. Naturally, most route optimization efforts result in the minimization of both route distance, duration and exhaust emissions [9,10,83]. It is understood that the routes of the solid waste collection vehicles in the study area are very different from the routes created as a result of optimization using GIS tools.…”

“…It is possible to reduce both the quality losses of waste and the exhaust gas emissions through route optimization processes [9][10][11][12][13][14]. Moreover, the use of geographic information systems (GIS) in the planning of waste management processes [10,[15][16][17][18] can contribute to the execution of waste management processes within the scope of ZWM due to their user-friendly nature and updatable flexibility.…”

Waste collection routing: a survey on problems and methods

“…Most often a single value is given, independent of vehicle or bin type. First steps into identifying system characteristics and their influence on service time have been made (Carlos et al 2019;Giel and Dąbrowska 2021) and we advocate for a widespread inclusion of these results in future WaCo routing research with the aim to generate more precise and more practicable solutions.…”

Infectious waste management during a pandemic: A stochastic location-routing problem with chance-constrained time windows

“…First, we use a 99.9% confidence level for the chance-constrained method, and then the variation on this value is studied in Section 5.4.1 . The service time of processing infectious waste at each small nodes, we adapt the multiple regression resulting from Giel and Dabrowska (2021) . The standard deviation of the service time is assumed to be 60 s. The impact of different deviations is further examined in Section 5.4.3 .…”