Assessment of the effects of expressway geometric design features on the frequency of accident crash rates using high-resolution laser scanning data and GIS

Sameen, Maher Ibrahim; Pradhan, Biswajeet

doi:10.1080/19475705.2016.1265012

Cited by 25 publications

(11 citation statements)

References 27 publications

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“…In Malaysia, recent statistics show that there are nearly 24 deaths per 100,000 people for all road users [1]. Expressways are potential sites of fatal highway accidents in Malaysia.…”

Section: Introductionmentioning

confidence: 99%

Severity Prediction of Traffic Accidents with Recurrent Neural Networks

2017

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Abstract:In this paper, a deep learning model using a Recurrent Neural Network (RNN) was developed and employed to predict the injury severity of traffic accidents based on 1130 accident records that have occurred on the North-South Expressway (NSE), Malaysia over a six-year period from 2009 to 2015. Compared to traditional Neural Networks (NNs), the RNN method is more effective for sequential data, and is expected to capture temporal correlations among the traffic accident records. Several network architectures and configurations were tested through a systematic grid search to determine an optimal network for predicting the injury severity of traffic accidents. The selected network architecture comprised of a Long-Short Term Memory (LSTM) layer, two fully-connected (dense) layers and a Softmax layer. Next, to avoid over-fitting, the dropout technique with a probability of 0.3 was applied. Further, the network was trained with a Stochastic Gradient Descent (SGD) algorithm (learning rate = 0.01) in the Tensorflow framework. A sensitivity analysis of the RNN model was further conducted to determine these factors' impact on injury severity outcomes. Also, the proposed RNN model was compared with Multilayer Perceptron (MLP) and Bayesian Logistic Regression (BLR) models to understand its advantages and limitations. The results of the comparative analyses showed that the RNN model outperformed the MLP and BLR models. The validation accuracy of the RNN model was 71.77%, whereas the MLP and BLR models achieved 65.48% and 58.30% respectively. The findings of this study indicate that the RNN model, in deep learning frameworks, can be a promising tool for predicting the injury severity of traffic accidents.

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“…In Malaysia, recent statistics show that there are nearly 24 deaths per 100,000 people for all road users [1]. Expressways are potential sites of fatal highway accidents in Malaysia.…”

Section: Introductionmentioning

confidence: 99%

Severity Prediction of Traffic Accidents with Recurrent Neural Networks

2017

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“…This system provides the most demanding tools required to analyze RTA and road design that can be noteworthy in achieving road safety [41], manages different types of databases [42], includes data analysis methods [43], provides a suitable platform for big data management [10], and has been widely used as the base platform of many road safety research so far [44]. Generally, application of GIS in road safety analysis includes spatial modeling of accident risk [45,46], spatial and spatiotemporal analyzing of accidents [47,48], extraction of accident hotspots [44,49], preparing accidentrisk map [10,50], identifying spatiotemporal patterns of accidents [51], spatiotemporal clustering of road accidents [52], and exploring the relationships between affective factors and accident rates [53,54]. Researchers often combine GIS with other analysis methods.…”

Section: Paper Machine Learning Applicationmentioning

confidence: 99%

Evaluation of Tree-Based Machine Learning Algorithms for Accident Risk Mapping Caused by Driver Lack of Alertness at a National Scale

et al. 2021

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Drivers’ lack of alertness is one of the main reasons for fatal road traffic accidents (RTA) in Iran. Accident-risk mapping with machine learning algorithms in the geographic information system (GIS) platform is a suitable approach for investigating the occurrence risk of these accidents by analyzing the role of effective factors. This approach helps to identify the high-risk areas even in unnoticed and remote places and prioritizes accident-prone locations. This paper aimed to evaluate tuned machine learning algorithms of bagged decision trees (BDTs), extra trees (ETs), and random forest (RF) in accident-risk mapping caused by drivers’ lack of alertness (due to drowsiness, fatigue, and reduced attention) at a national scale of Iran roads. Accident points and eight effective criteria, namely distance to the city, distance to the gas station, land use/cover, road structure, road type, time of day, traffic direction, and slope, were applied in modeling, using GIS. The time factor was utilized to represent drivers’ varied alertness levels. The accident dataset included 4399 RTA records from March 2017 to March 2019. The performance of all models was cross-validated with five-folds and tree metrics of mean absolute error, mean squared error, and area under the curve of the receiver operating characteristic (ROC-AUC). The results of cross-validation showed that BDT and RF performance with an AUC of 0.846 were slightly more accurate than ET with an AUC of 0.827. The importance of modeling features was assessed by using the Gini index, and the results revealed that the road type, distance to the city, distance to the gas station, slope, and time of day were the most important, while land use/cover, traffic direction, and road structure were the least important. The proposed approach can be improved by applying the traffic volume in modeling and helps decision-makers take necessary actions by identifying important factors on road safety.

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“…Severity is can be broadly categorized as fatal, serious, and slight. Therefore, it can be handled as a pattern recognition problem, where statistical techniques and machine learning algorithms can be used to predict the severity [1], [2]. This type of prediction is considered to be highly non-linear due to the amount of factors involved in the prediction, such as road type and surface, weather, and light conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the advantage of using machine learning methods as opposed to traditional statistical techniques is the ability to handle nonlinear problems as well as obtaining a general solution that works for a wide variety of data. There have been several attempts in the literature to analyze traffic flow, accidents, and predict accident severity using machine learning paradigms and Geographic Information System (GIS) [1], [3]- [11]. The goal of these studies is to integrate GIS with spatial and temporal analysis in order to reduce accident fatalities by as much as possible.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal Analysis and Machine Learning for Traffic Accidents Prediction

Aldogom

Aburaed

Al-Saad

et al. 2019

2019 2nd International Conference on Signal Processing and Information Security (ICSPIS)

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Traffic accidents impose significant problems in our daily life due to the huge social, environmental, and economic expenses associated with them. The rapid development in data science, geographic data collection, and processing methods encourage researchers to evaluate, delineate traffic accident hotspots, and to effectively predict and estimate traffic accidents. In this study, Kaggle traffic accidents dataset that covers United Kingdom for the time period between 2012-2014 will be investigated. Our methodology consists of three main techniques. First, Morans I method of spatial autocorrelation, and Getis-Ord Gi* statistics will be used to examine and relate traffic accidents dataset in terms of spatial and temporal features. Second, weighted features will be used as inputs for Deep Feedforward Neural Network (DFFNN). Finally, the performance of the proposed DFFNN will be evaluated based on its accuracy, misclassification rate, precision, prevalence, histogram of errors, and confusion matrix. These evaluation metrics are then used as a comparison basis against the performance of Support Vector Machine (SVM). The results will focus on using spatial statistics techniques to effectively weight different features according to their contribution to traffic accidents. Consequently, the output of the DFFNN asserts the likelihood of accident occurrence given a certain location. Furthermore, it would be beneficial to investigate whether these accidents exhibit certain timely patterns, such as certain days or months where accidents potentially occur more frequently. The proposed method can be effectively used by different authorities to implement an improved planning and management approaches for traffic accident reduction. Moreover, it can identify and locate road risk segments where immediate action should be considered.

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Assessment of the effects of expressway geometric design features on the frequency of accident crash rates using high-resolution laser scanning data and GIS

Cited by 25 publications

References 27 publications

Severity Prediction of Traffic Accidents with Recurrent Neural Networks

Severity Prediction of Traffic Accidents with Recurrent Neural Networks

Evaluation of Tree-Based Machine Learning Algorithms for Accident Risk Mapping Caused by Driver Lack of Alertness at a National Scale

Spatio-temporal Analysis and Machine Learning for Traffic Accidents Prediction

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