Details

Autor(en) / Beteiligte

• Nadi, Ali
• Sharma, Salil
• Snelder, Maaike
• Bakri, Taoufik
• van Lint, Hans
• Tavasszy, Lóránt

Titel

Short-term prediction of outbound truck traffic from the exchange of information in logistics hubs: A case study for the port of Rotterdam

Ist Teil von

• Transportation research. Part C, Emerging technologies, 2021-06, Vol.127, p.103111, Article 103111

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2021

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •We develop a short term prediction model for outbound truck flows around major container seaports.•The model links container pickup schedules and observed outbound truck flows with high accuracy.•We apply the model to assess the impact of sudden changes in container throughput on traffic.•The model captures the time lag and non-proportionality of resulting changes in truck traffic. Short-term traffic prediction is an important component of traffic management systems. Around logistics hubs such as seaports, truck flows can have a major impact on the surrounding motorways. Hence, their prediction is important to help manage traffic operations. However, The link between short-term dynamics of logistics activities and the generation of truck traffic has not yet been properly explored. This paper aims to develop a model that predicts short-term changes in truck volumes, generated from major container terminals in maritime ports. We develop, test, and demonstrate the model for the port of Rotterdam. Our input data are derived from exchanges of operational logistics messages between terminal operators, carriers and shippers, via the local Port Community System. We propose a feed-forward neural network to predict the next one hour of outbound truck traffic. To extract hidden features from the input data and select a model with appropriate features, we employ an evolutionary algorithm in accordance with the neural network model. Our model predicts outbound truck volumes with high accuracy. We formulate 2 scenarios to evaluate the forecasting abilities of the model. The model predicts lag and non-proportional responses of truck flows to changes in container turnover at terminals. The findings are relevant for traffic management agencies to help improve the efficiency and reliability of transport networks, in particular around major freight hubs.