Estimation of the Origin-Destination Matrix for Trucks That Use Highways: A Case Study in Chile
Date
2022
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Article
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22 p.
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Acceso abierto
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Abstract
Nowadays, freight transport is crucial in the functioning of cities worldwide. To dig further into the understanding of urban freight transport movements, in this research, we conducted a case study in which we estimated an origin-destination matrix for the trucks traveling on Autopista Central, one of Santiago de Chile’s most important urban highways. To do so, we used full real-world vehicle-by-vehicle information of freight vehicles’ movements along the highway. This data was collected from several toll collection gates equipped with free-flow and automatic vehicle identification technology. However, this data did not include any vehicle information before or after using the highway. To estimate the origins and destinations of these trips, we proposed a multisource methodology that used GPS information provided by SimpliRoute, a Chilean routing company. Nevertheless, this GPS data involved only a small subset of trucks that used the highway.
In order to reduce the bias, we built a decision tree model for estimating the trips’ origin, whose input data was complemented by other public databases. Furthermore, we computed trip destinations
using proportionality factors obtained from SimpliRoute data. Our results showed that most of the estimated origins belonged to outskirt municipalities, while the estimated destinations were mainly
located in the downtown area. Our findings might help improve freight transport comprehension in the city, enabling the implementation of focused transport policies and investments to help mitigate
negative externalities, such as congestion and pollution.
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Citation
Basso, F.; Pezoa, R.; Tapia, N.; Varas, M. Estimation of the Origin-Destination Matrix for Trucks That Use Highways: A Case Study in Chile. Sustainability 2022, 14, 2645. https://doi.org/10.3390/su14052645
Keywords
Freight transportation, Urban highway, OD matrix, Multi-data sources, Decision trees