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

Nadi, Ali; Sharma, Salil; Snelder, M.; Bakri, Taoufik; Lint, Hans van; Tavasszy, Lóránt

doi:10.1016/j.trc.2021.103111

Cited by 23 publications

(6 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this study, the SHAP results provide the impact of HVV/TV, which can be used to identify critical traffic conditions for HVV/TV on routes in the real world with online traffic volume data. Furthermore, short-term prediction of truck traffic based on logistics activities [98] and bus traffic by employing a passengeroriented traffic control strategy [99] is possible. Such predictions can help anticipate critical conditions related to the prevalence of heavy vehicles in traffic and aid in their management.…”

Section: Discussionmentioning

confidence: 99%

Analyzing Pile-Up Crash Severity: Insights from Real-Time Traffic and Environmental Factors Using Ensemble Machine Learning and Shapley Additive Explanations Method

Samerei,

Aghabayk,

Montella

2024

Safety

View full text Add to dashboard Cite

Pile-up (PU) crashes, which involve multiple collisions between more than two vehicles within a brief timeframe, carry substantial consequences, including fatalities and significant damages. This study aims to investigate the real-time traffic, environmental, and crash characteristics and their interactions in terms of their contributions to severe PU crashes, which have been understudied. This study investigates and interprets the effects of Total Volume/Capacity (TV/C), “Heavy Vehicles Volume/Total Volume” (HVV/TV), and average speed. For this purpose, the PU crash severity was modelled and interpreted using the crash and real-time traffic data of Iran’s freeways over a 5-year period. Among six machine learning methods, the CatBoost model demonstrated superior performance, interpreted via the SHAP method. The results indicate that avg.speed > 90 km/h, TV/C < 0.6, HVV/TV ≥ 0.1, horizontal curves, longitudinal grades, nighttime, and the involvement of heavy vehicles are associated with the risk of severe PU crashes. Additionally, several interactions are associated with severe PU crashes, including the co-occurrence of TV/C ≈ 0.1, HVV/TV ≥ 0.25, and nighttime; the interactions between TV/C ≈ 0.1 or 0.45, HVV/TV ≥ 0.25, and avg.speed > 90 km/h; horizontal curves and high average speeds; horizontal curves; and nighttime. Overall, this research provides essential insights into traffic and environmental factors driving severe PU crashes, supporting informed decision-making for policymakers.

show abstract

Section: Discussionmentioning

confidence: 99%

Analyzing Pile-Up Crash Severity: Insights from Real-Time Traffic and Environmental Factors Using Ensemble Machine Learning and Shapley Additive Explanations Method

Samerei,

Aghabayk,

Montella

2024

Safety

View full text Add to dashboard Cite

show abstract

“…For applying the algorithm, at precise instants in time, it is necessary to predict if a route R ∈ R is available for a container c ∈ C departing from a node N i ∈ N. Such means that for every node N j ∈ N of the route, there is at least one transport t j ∈ T with available capacity and located at the node N j or arriving in a "short" period of time, for the accumulated time T j > 0 obtained from summing the (estimated) container transportation times between intermediate nodes. Such is a difficult task that it is accomplished by a mixed approach, combining data from pre-determined information (e.g., timetables), measures over the statistical distributions of travel times of historical data, and the outputs of a processing-time estimator of the task duration, applying techniques of machine learning, see [55][56][57][58].…”

Section: Predicting When a Route Is Availablementioning

confidence: 99%

A Modular IoT-Based Architecture for Logistics Service Performance Assessment and Real-Time Scheduling towards a Synchromodal Transport System

Brochado,

Rocha,

Costa

2024

Sustainability

View full text Add to dashboard Cite

Logistics is significantly impacted by quality/quantity issues associated with data collection and data sharing restrictions. Nonetheless, public data from national entities and internet-of-things (IoT) solutions enable the development of integrated tools for performance analysis and real-time optimization of logistics networks. This study proposes a three-module data-driven system architecture that covers (a) logistics data collection tools, (b) logistics services performance evaluation, and (c) the transition to synchromodal systems. Module 1 integrates multisource data from national logistics platforms and embedded devices placed within intermodal containers. A multigraph representation of the problem is conceived. Environmental, economic, and operational data are generated and injected into a digital twin. Thus, key performance indicators (KPIs) are computed by simulation or direct transformation of the collected data. Module 2 uses Multi-directional Efficiency Analysis, an optimization algorithm that benchmarks multimodal transportation routes of containers using prior KPIs. Outputs are a technical performance index relevant to logistics clients and improvement measures for logistics service providers. A real case study application of the solution proposed for Module 2 is presented. Module 3 provides real-time scheduling and assignment models using CP-sat solvers, accommodating varying system dynamics and resource availability, minimizing makespan and operational costs.

show abstract

“…Gao, et al [35] use LSTM to predict the daily volumes of containers that will enter their investigated storage yard. Nadi, et al [36] develop a short-term prediction model for outbound truck flows around major container seaports. In the paper, they use scheduled pick-ups to predict the truck flows.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling Categorized Truck Arrivals at Ports: Big Data for Traffic Prediction

Sheng

Wang

et al. 2023

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Accurate truck arrival prediction is complex but critical for container terminals. A deep learning model combining Gated Recurrent Unit (GRU) and Fully Connected Neural Network (FCNN), is proposed to predict daily truck arrivals using fusion technology. The model can efficiently analyze sequence and cross-section data sets. The new feature in the new model lies in that it, for the first time, incorporates the new parameters influencing traffic volumes such as the vessel-related information, arrival weekdays, and weather conditions into the long-time series of truck arrivals. Furthermore, truck arrivals are predicted in three groups based on their movement purposes: pick-up, delivery, and dual. it also contributes to the literature in a sense that the performance of the model is tested using real big data from a world-leading container port in Southern China. The results generate insightful managerial implications for guiding port traffic management in a generic manner. It reveals the relation of export container arrivals with the Container Yard (CY) closing time of a specific vessel. It is demonstrated the proposed model outperforms the currently available methods with an improved accuracy rate of prediction by 23.44% (dual), 32.09% (pick-up), and 26.99% (delivery), respectively. As a result, the model can better reflect reality compared to the existing ones in the literature. It is also evident that the 3-categorized prediction model can significantly help increase prediction accuracy in comparison with the 2-categorized methods used in practice.

show abstract

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

Cited by 23 publications

References 38 publications

Analyzing Pile-Up Crash Severity: Insights from Real-Time Traffic and Environmental Factors Using Ensemble Machine Learning and Shapley Additive Explanations Method

Analyzing Pile-Up Crash Severity: Insights from Real-Time Traffic and Environmental Factors Using Ensemble Machine Learning and Shapley Additive Explanations Method

A Modular IoT-Based Architecture for Logistics Service Performance Assessment and Real-Time Scheduling towards a Synchromodal Transport System

Modeling Categorized Truck Arrivals at Ports: Big Data for Traffic Prediction

Contact Info

Product

Resources

About