Integrating data-driven and simulation models to predict traffic state affected by road incidents

Shafiei, Sajjad; Mihăiţă, Adriana-Simona; Nguyên, Hoàng

doi:10.1080/19427867.2021.1916284

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…An approach has been made in the field of healthcare to utilize ML algorithms for the purpose of learning from the outcomes of DES and making predictions [42]. A study has introduced an approach to improve simulation performance in the transportation sector by incorporating destination prediction [43].…”

Section: Figure 3 the Flow Diagram Of Discrete Event Simulation (Des)mentioning

confidence: 99%

A Predictive Discrete Event Simulation for Predicting Operation Times in Container Terminal

Park,

Kim,

Bae

2024

IEEE Access

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Container terminals (CTs) play a crucial role within the global supply chain in the context of the global container transportation business. The primary obligation associated with these terminals involves ensuring the punctual execution of primary vessel operations, while strictly adhering to the Estimated Time of Departure (ETD) for vessels. To accomplish this goal, it is crucial to develop rigorous and exact operating strategies. Nonetheless, the accurate prediction of operation times in CT presents a significant challenge due to the concurrent involvement of various Container Handling Equipment (CHE) and the occurrence of unforeseen situations. To address this issue, this study proposes a novel approach called Predictive Discrete Event Simulation (PDES) that utilizes data collected from CT to predict the operation times. The PDES is an advanced approach that builds upon the widely used Discrete Event Simulation (DES) technique in the field of simulations. It is specifically designed to provide precise predictions of the operation times in CTs, where multiple events take place concurrently. The PDES in this paper aims to overcome the shortcomings of current simulation-based approaches for prediction in CT. These approaches often rely on predefined task sequences and assumed time for job handling time of CHE in their scenarios, which can result in reduced accuracy when predicting operation times. Through the resolution of these issues, the proposed PDES exhibits the capacity to improve predictive performance. To enhance the predictive performance of operation times in CTs through PDES, two approaches are introduced. The first approach entails the application of Support Vector Machine (SVM) algorithms for the purpose of predicting operation times. This approach is further augmented by integrating it with DES to improve the accuracy of predictive performance. The second approach involves predicting by simulating real-world operational scenarios in CTs using algorithms for CHE assignment. The predictive performance of the proposed PDES is assessed through the utilization of data gathered from Busan Port Terminal (BPT) in South Korea, demonstrating superior performance compared to alternative prediction approaches.

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Section: Figure 3 the Flow Diagram Of Discrete Event Simulation (Des)mentioning

confidence: 99%

A Predictive Discrete Event Simulation for Predicting Operation Times in Container Terminal

Park,

Kim,

Bae

2024

IEEE Access

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“…Ulbricht (1994) used multi-recurrent neural network models to predict the number of traffic flows going through a highway checkpoint between the early hours of 5 am and 9 am and compared the results to other regression models. Shafiei et al (2022) introduced a graphical model comprising information from major roads within the local authority, which was the first time Bayesian networks (BN) were used for traffic flow prediction. According to Jomnonkwao et al (2020), the findings revealed that the BN model outperforms other condensed approaches such as the Random Walk (which considers current traffic flow conditions), the AR model, and a fuzzy-neural model.…”

Section: Related Workmentioning

confidence: 99%

Comprehensive analysis of UK AADF traffic dataset set within four geographical regions of England

Chang,

Xu,

Hall

et al. 2023

Expert Systems

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Traffic flow detection plays a significant part in freeway traffic surveillance systems. Currently, effective autonomous traffic analysis is a challenging task due to the complexity of traffic delays, despite the significant investment spent by authorities in monitoring and analysing traffic congestion. This study builds an intelligent analytic method based on machine‐learning algorithms to investigate and predict road traffic flows in four locations in the United Kingdom (London, Yorkshire and the Humber, North East, and North West) with a range of relevant factors. While aiming to conduct the study, the dataset ‘estimated annual average daily flows (AADFs) Data—major and minor roads’ from the UK government was used. Machine‐learning algorithms are used for this research and classification applied consists of Logistic Regression, Decision Trees, Random Forests, K‐Nearest Neighbors, and Gradient Boosting. Each of these algorithms achieves an accuracy of over 93% and the F1 score of over 95%, with Random Forest outperforming the other algorithms. This analytical approach helps to focus attention on critical areas to reduce traffic flows on major and minor roads in the area. In summary, the findings on traffic analysis have been discussed in detail to demonstrate the practical insights of this study.

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“…Reverberating this, Kabit et al (2014) states that incident-induced delay is one of the most important indicators to quantify its impacts on traffic. Therefore, it is essential to eliminate accidents as quickly as possible (Haule et al, 2020) to reduce uncertainties, as predicting traffic conditions in urban networks is a priority for traffic management centers and this becomes very challenging when the network is affected by traffic incidents that vary in time and space (Shafiei, 2021). In addition to the impact on quality of life caused by congestion, it is noteworthy that transport systems play a critical role for daily commuting, logistics and business travel and that a faulty system leads to greater losses in travel time and costs incurred for rescheduled trips (Hsieh and Feng, 2020).…”

Section: Social Scopementioning

confidence: 99%

Logical Structure of Actions to Reduce the Impacts of Traffic Incidents

Baltar¹,

Abreu²,

Santos³

2021

revistageintec

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Traffic incidents (such as broken-down vehicles, accidents, flat tires and other) constitute an important concern in the urban context, impacting the sustainable development. Thus, currently, the proposition of efficient traffic incident management systems has been encouraged to re-establish road safety and restore the network's traffic capacity. Thus, this paper aims to investigate the main impacts of traffic incidents and elaborate a logical structure of actions that should be employed to improve their management. The results show that many impacts can be identified in the three spheres of sustainable development and improvement actions must accelerate responses to emergencies, invest in Intelligent Transportation System (ITS), develop urban planning with a focus on more roads secure and enforce existing laws and regulations.

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Integrating data-driven and simulation models to predict traffic state affected by road incidents

Cited by 8 publications

References 23 publications

A Predictive Discrete Event Simulation for Predicting Operation Times in Container Terminal

A Predictive Discrete Event Simulation for Predicting Operation Times in Container Terminal

Comprehensive analysis of UK AADF traffic dataset set within four geographical regions of England

Logical Structure of Actions to Reduce the Impacts of Traffic Incidents

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