An ensemble machine learning‐based modeling framework for analysis of traffic crash frequency

Zhang, Xiang; Waller, S. Travis; Jiang, Peng

doi:10.1111/mice.12485

Cited by 42 publications

(25 citation statements)

References 86 publications

(112 reference statements)

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“…ML has also been used in the field of structural health monitoring to analyze vibration response of structures and detection of anomalies extracted from sensors (Azimi & Pekcan, 2020; Gulgec, Takáč, & Pakzad, 2020; Ni, Zhang, & Noori, 2019). DL has also been used to help analyze traffic crash data (X. Zhang, Waller, & Jiang, 2020), analyze crack patterns in concrete structures (Cha, Choi, & Büyüköztürk, 2017; Okazaki, Okazaki, Asamoto, & Chun, 2020), and conduct reliability analysis on infrastructure networks (Nabian & Meidani, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data

Dong

Farahmand

et al. 2020

Computer aided Civil Eng

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The objective of this study is to create and test a hybrid deep learning (DL) model, FastGRNN‐FCN (fast, accurate, stable and tiny gated recurrent neural network‐fully convolutional network), for urban flood prediction and situation awareness using channel network sensors data. The study used Harris County, Texas, as the testbed, and obtained channel sensor data from three historical flood events (e.g., 2016 Tax Day Flood, 2016 Memorial Day Flood, and 2017 Hurricane Harvey Flood) for training and validating the hybrid DL model. The flood data are divided into a multivariate time series and used as the model input. Each input comprises nine variables, including information of the studied channel sensor and its predecessor and successor sensors in the channel network. Precision‐recall curve and F‐measure are used to identify the optimal set of model parameters. The optimal model with a weight of 1 and a critical threshold of 0.59 are obtained through 100 iterations based on examining different weights and thresholds. The test accuracy and F‐measure eventually reach 97.8% and 0.792, respectively. The model is then tested in predicting the 2019 Imelda Flood in Houston and the results show an excellent match with the empirical flood. The results show that the model enables accurate prediction of the spatial–temporal flood propagation and recession and provides emergency response officials with a predictive flood warning tool for prioritizing the flood response and resource allocation strategies.

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Section: Literature Reviewmentioning

confidence: 99%

A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data

Dong

Farahmand

et al. 2020

Computer aided Civil Eng

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“…Ensemble methods are a class of powerful machine learning techniques that combine multiple learners for the same task. It is commonly acknowledged that an ensemble is usually significantly more accurate than a single learner (Zhang, Waller, & Jiang, 2020; Z. Zhou, 2012). In this work, we adopt a training procedure called SWA (Izmailov, Podoprikhin, Garipov, Vetrov, & Wilson, 2018), which serves as an implicit model ensemble method, to boost our model performance.…”

Section: Methodsmentioning

confidence: 99%

Section: Model Ensemblementioning

confidence: 99%

Dynamic origin‐destination flow estimation using automatic vehicle identification data: A 3D convolutional neural network approach

Tang

Cao

Chen

et al. 2020

Computer aided Civil Eng

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Dynamic origin-destination (OD) flow estimation is one of the most fundamental problems in traffic engineering. Despite numerous existing studies, the OD flow estimation problem remains challenging, as there is large dimensional difference between the unknown values to be estimated and the known traffic observations. To meet the needs of active traffic management and control, accurate time-dependent OD flows are required to understand time-of-day traffic flow patterns. In this work, we propose a three-dimensional (3D) convolution-based deep neural network, "Res3D," to learn the high-dimensional correlations between local traffic patterns presented by automatic vehicle identification observations and OD flows. In this paper, a practical framework combining simulation-based model training and few-shot transfer learning is introduced to enhance the applicability of the proposed model, as continuously observing OD flows could be expensive. The proposed model is extensively tested based on a realistic road network, and the results show that for significant OD flows, the relative errors are stable around 5%, outperforming several other models, including prevalent neural networks as well as existing estimation models. Meanwhile, corrupted and out-of-distribution samples are generated as real-world samples to validate Res3D's transferability, and the results indicated a 60% improvement with few-shot transfer learning. Therefore, this proposed framework could help to bridge the gaps between traffic simulations and empirical cases.

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“…Such algorithms have as input a set of roadway-, users-, vehicles-, and environment-related features of the network analyzed. Commonly, they aimed at predicting the crash frequency for stretches of road or intersections using different types of algorithms: k-nearest neighbor [5], support vector machine [6], and tree-based models, such as classification and regression tree, M5-tree, RF, extremely randomized trees, and gradient tree boosting [7][8][9]. Moreover, there are studies [10,11] in which the authors suggested the use of neural networks.…”

Section: Machine Learning Algorithms In Road Safety Analysesmentioning

confidence: 99%

Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning Algorithms

Fiorentini

Losa

2020

Infrastructures

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Crash severity is undoubtedly a fundamental aspect of a crash event. Although machine learning algorithms for predicting crash severity have recently gained interest by the academic community, there is a significant trend towards neglecting the fact that crash datasets are acutely imbalanced. Overlooking this fact generally leads to weak classifiers for predicting the minority class (crashes with higher severity). In this paper, in order to handle imbalanced accident datasets and provide a better prediction for the minority class, the random undersampling the majority class (RUMC) technique is used. By employing an imbalanced and a RUMC-based balanced training set, we propose the calibration, validation, and evaluation of four different crash severity predictive models, including random tree, k-nearest neighbor, logistic regression, and random forest. Accuracy, true positive rate (recall), false positive rate, true negative rate, precision, F1-score, and the confusion matrix have been calculated to assess the performance. Outcomes show that RUMC-based models provide an enhancement in the reliability of the classifiers for detecting fatal crashes and those causing injury. Indeed, in imbalanced models, the true positive rate for predicting fatal crashes and those causing injury spans from 0% (logistic regression) to 18.3% (k-nearest neighbor), while for the RUMC-based models, it spans from 52.5% (RUMC-based logistic regression) to 57.2% (RUMC-based k-nearest neighbor). Organizations and decision-makers could make use of RUMC and machine learning algorithms in predicting the severity of a crash occurrence, managing the present, and planning the future of their works.

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An ensemble machine learning‐based modeling framework for analysis of traffic crash frequency

Cited by 42 publications

References 86 publications

A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data

A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data

Dynamic origin‐destination flow estimation using automatic vehicle identification data: A 3D convolutional neural network approach

Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning Algorithms

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