Fuzzy modeling of freeway accident duration with rainfall and traffic flow interactions

Dimitriou, Loukas; Vlahogianni, Eleni I.

doi:10.1016/j.amar.2015.04.001

Cited by 22 publications

(6 citation statements)

References 41 publications

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“…Large number of studies have been devoted to the prediction of traffic incident duration. Dimitriou and Vlahogianni (2015) proposed a fuzzy rule-based system to estimate highway traffic incident durations. Lin et al (2016) proposed an improved M5P model by combining a hazard-based duration model to minimize data heterogeneity in traffic incident duration prediction.…”

Section: Introductionmentioning

confidence: 99%

Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP

Zhu¹,

et al. 2021

JICV

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Purpose Efficient traffic incident management is needed to alleviate the negative impact of traffic incidents. Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management. Previous studies have proposed models for traffic incident duration prediction; however, most of these studies focus on the total duration and could not update prediction results in real-time. From a traveler’s perspective, the relevant factor is the residual duration of the impact of the traffic incident. Besides, few (if any) studies have used dynamic traffic flow parameters in the prediction models. This paper aims to propose a framework to fill these gaps. Design/methodology/approach This paper proposes a framework based on the multi-layer perception (MLP) and long short-term memory (LSTM) model. The proposed methodology integrates traffic incident-related factors and real-time traffic flow parameters to predict the residual traffic incident duration. To validate the effectiveness of the framework, traffic incident data and traffic flow data from Shanghai Zhonghuan Expressway are used for modeling training and testing. Findings Results show that the model with 30-min time window and taking both traffic volume and speed as inputs performed best. The area under the curve values exceed 0.85 and the prediction accuracies exceed 0.75. These indicators demonstrated that the model is appropriate for this study context. The model provides new insights into traffic incident duration prediction. Research limitations/implications The incident samples applied by this study might not be enough and the variables are not abundant. The number of injuries and casualties, more detailed description of the incident location and other variables are expected to be used to characterize the traffic incident comprehensively. The framework needs to be further validated through a sufficiently large number of variables and locations. Practical implications The framework can help reduce the impacts of incidents on the safety of efficiency of road traffic once implemented in intelligent transport system and traffic management systems in future practical applications. Originality/value This study uses two artificial neural network methods, MLP and LSTM, to establish a framework aiming at providing accurate and time-efficient information on traffic incident duration in the future for transportation operators and travelers. This study will contribute to the deployment of emergency management and urban traffic navigation planning.

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Section: Introductionmentioning

confidence: 99%

Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP

Zhu¹,

et al. 2021

JICV

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“…2.1b Fuzzy logic: Fuzzy logic can be suitably applied to represent road accident data and human-related factors [19]. Most common real-world data are numeric or alphanumeric values.…”

Section: Overview Of Arm Fuzzy Logic and Fcfgmentioning

confidence: 99%

Road risk assessment using fuzzy Context-free Grammar based Association Rule Miner

2019

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Road traffic accidents are a major social concern as well as a crucial issue for the public in recent days due to the risk factors involved. Analysing and identifying the major risk factors of road accident is still a challenging task. In this paper, a fuzzy Context-free Grammar (FCFG)-based association rule mining (ARM) technique is proposed to categorize a heterogeneous road accident dataset into two categories based on the critical factors such as total number of accidents (TA), persons killed (PK) and persons injured (PI). The role of the fuzzy grammar in this paper is to govern the entire algorithm using the prescribed grammar rules to proceed further. The considered road accident dataset does not have class labels; hence there is a need to assign class labels for the available data instance. The accident data with assigned class labels are given as input to K-nearest neighbour (KNN) machine learning algorithm in order to train the classifier for testing purpose. Further, the collected test data from the user are utilized by the KNN classifier for carrying out the performance analysis of the proposed algorithm. The case study is conducted on the National Highway roads, India, to examine the proposed approach. The experimentations are executed for road accident records using MATLAB software and the analysis is made using the following performance measures: accuracy, recall or sensitivity, precision or specificity and F1 score. A comparative study is accomplished with existing algorithms in order to show that the proposed algorithm works with improved accuracy of more than 83%. The results suggested that the road users are responsible for the acceptance or rejection of safe or unsafe roads, respectively.

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“…-Weather characteristics: sunny, rainy, and snowy -Temporal characteristics: AM peak, PM peak, night, and weekday -Incident characteristics: lanes, property, severity, debris, road repair, and pothole -Involved vehicle characteristics: bus, van, and truck During the past few years, a variety of methods have been applied to develop freeway accident duration estimating/forecasting models. The most representative approaches can be classified into the following categories: multivariate regression (Garib et al, 1997;Smith K. & Smith B., 2001;Valenti et al, 2010), fuzzy logic model (Choi, 1996;Dimitriou & Vlahogianni, 2015), artificial neural network (Wang et al, 2005), and survival (Chung et (Spławińska, 2015;Pamula, 2012). Thus, kNN and ANN were chosen as the key analytical techniques in this study.…”

Section: Literature Review 21 Accident Duration Forecastmentioning

confidence: 99%

Non-parametric machine learning methods for evaluating the effects of traffic accident duration on freeways

Lee

Wei

Chao³

2017

AoT

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Traffic accidents usually cause congestion and increase travel-times. The cost of extra travel time and fuel consumption due to congestion is huge. Traffic operators and drivers expect an accurately forecasted accident duration to reduce uncertainty and to enable the implementation of appropriate strategies. This study demonstrates two non-parametric machine learning methods, namely the k-nearest neighbour method and artificial neural network method, to construct accident duration prediction models. The factors influencing the occurrence of accidents are numerous and complex. To capture this phenomenon and improve the performance of accident duration prediction, the models incorporated various data including accident characteristics, traffic data, illumination, weather conditions, and road geometry characteristics. All raw data are collected from two public agencies and were integrated and cross-checked. Before model development, a correlation analysis was performed to reduce the scale of interrelated features or variables. Based on the performance comparison results, an artificial neural network model can provide good and reasonable prediction for accident duration with mean absolute percentage error values less than 30%, which are better than the prediction results of a k-nearest neighbour model. Based on comparison results for circumstances, the Model which incorporated significant variables and employed the ANN method can provide a more accurate prediction of accident duration when the circumstances involved the day time or drunk driving than those that involved night time and did not involve drunk driving. Empirical evaluation results reveal that significant variables possess a major influence on accident duration prediction.

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Fuzzy modeling of freeway accident duration with rainfall and traffic flow interactions

Cited by 22 publications

References 41 publications

Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP

Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP

Road risk assessment using fuzzy Context-free Grammar based Association Rule Miner

Non-parametric machine learning methods for evaluating the effects of traffic accident duration on freeways

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