| 1. |
- Castrellon, Juan Pablo, 1989, et al.
(författare)
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Assessing the eco-efficiency benefits of empty container repositioning strategies via dry ports
- 2023
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Ingår i: Transportation Research Part D. - : Elsevier Ltd. - 1361-9209 .- 1879-2340. ; 120
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Tidskriftsartikel (refereegranskat)abstract
- Trade imbalances and global disturbances generate mismatches in the supply and demand of empty containers (ECs) that elevate the need for empty container repositioning (ECR). This research investigated dry ports as a potential means to minimize EC movements, and thus reduce costs and emissions. We assessed the environmental and economic effects of two ECR strategies via dry ports—street turns and extended free temporary storage—considering different scenarios of collaboration between shipping lines with different levels of container substitution. A multi-paradigm simulation combined agent-based and discrete-event modelling to represent flows and estimate kilometers travelled, CO2 emissions, and costs resulting from combinations of ECR strategies and scenarios. Full ownership container substitution combined with extended free temporary storage at the dry port (FTDP) most improved ECR metrics, despite implementation challenges. Our results may be instrumental in increasing shipping lines’ collaboration while reducing environmental impacts in up to 32 % of the inland ECR emissions. © 2023 The Author(s)
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| 2. |
- Castrellon, Juan Pablo, 1989, et al.
(författare)
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Enabling Factors and Durations Data Analytics for Dynamic Freight Parking Limits
- 2023
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Ingår i: Transportation Research Record. - : SAGE Publications. - 0361-1981 .- 2169-4052. ; 2677:2, s. 219-234
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Tidskriftsartikel (refereegranskat)abstract
- Freight parking operations occur amid conflicting conditions of public space scarcity, competition with other users, and the inefficient management of loading zones (LZ) at cities’ curbside. The dynamic nature of freight operations, and the static LZ provision and regulation, accentuate these conflicting conditions at specific peak times. This generates supply–demand mismatches of parking infrastructure. These mismatches have motivated the development of Smart LZ that bring together technology, parking infrastructure, and data analytics to allocate space and define dynamic duration limits based on users’ needs. Although the dynamic duration limits unlock the possibility of a responsive LZ management, there is a narrow understanding of factors and analytical tools that support their definition. Therefore, the aim of this paper is twofold. Firstly, to identify factors for enabling dynamic parking durations policies. Secondly, to assess data analytics tools that estimate freight parking durations and LZ occupation levels based on operational and locational features. Semi-structured interviews and focus group analyses showed that public space use assessment, parking demand estimation, enforcement capabilities, and data sharing strategies are the most relevant factors when defining dynamic parking limits. This paper used quantitative models to assess different analytical tools that study LZ occupation and parking durations using tracked freight parking data from the City of Vic (Spain). CatBoost outperformed other machine learning (ML) algorithms and queuing models in estimating LZ occupation and parking durations. This paper contributes to the freight parking field by understanding how data analytics support dynamic parking limits definition, enabling responsive curbside management.
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| 3. |
- Ludowieg, Andres Regal, et al.
(författare)
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Using Machine Learning to Predict Freight Vehicles' Demand for Loading Zones in Urban Environments
- 2023
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Ingår i: Transportation Research Record. - : SAGE Publications. - 0361-1981 .- 2169-4052. ; 2677:1, s. 829-842
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Tidskriftsartikel (refereegranskat)abstract
- This paper studies demand for public loading zones in urban environments and seeks to develop a machine learning algorithm to predict their demand. Understanding and predicting demand for public loading zones can: (i) support better management of the loading zones and (ii) provide better pre-advice so that transport operators can plan their routes in an optimal way. The methods used are linear regression analysis and neural networks. Six months of parking data from the city of Vic in Spain are used to calibrate and test the models, where the parking data is transformed into a time-series format with forecasting targets. For each loading zone, a different model is calibrated to test which model has the best performance for the loading zone's particular demand pattern. To evaluate each model's performance, both root mean square error and mean absolute error are computed. The results show that, for different loading zone demand patterns, different models are better suited. As the prediction horizon increases, predicting further into the future, the neural network approaches start to give better predictions than linear models.
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