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- W2891071748 abstract "Residential waste collection is an expensive but essential service. Since such a large portion of the cost associated with residential waste collection is attributable to transportation it has become the focus of numerous studies on vehicle routing and optimisation. While vehicle routing models have great potential for reducing waste collection costs, a key challenge remains the input data used in these models. Literature focuses predominantly on problem formulation and solution methods as opposed to developing methods to estimate input parameters. Big data, and in particular GPS devices fitted on waste collection vehicles, offer a great opportunity to address this shortcoming. This is demonstrated, in this paper, by analysing GPS records of waste collection vehicles in and around a waste transfer station. Through the analysis we show that the duration of waste drop-offs follow a power-law distribution, whereas vehicle routing models treat it as deterministic. The fitted distribution can be used in future studies to evaluate the operational impact of stochastic drop-off times on routes developed using traditional routing models. We further analyse whether congestion in the transfer station results in an increase in drop-off duration and found that it does not have a major impact." @default.
- W2891071748 created "2018-09-27" @default.
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- W2891071748 date "2018-08-01" @default.
- W2891071748 modified "2023-09-29" @default.
- W2891071748 title "Using Vehicle GPS Data to Infer Offloading Times of Waste Collection Vehicles at Transfer Stations" @default.
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- W2891071748 doi "https://doi.org/10.1109/icabcd.2018.8465472" @default.
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