Development of an accident prediction model for Italian freeways

Torre, Francesca La; Meocci, Monica; Domenichini, Lorenzo; Branzi, Valentina; Tanzi, Niccolo’; Paliotto, Andrea

doi:10.1016/j.aap.2018.12.023

Cited by 46 publications

(28 citation statements)

References 19 publications

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“…e environmental data belong to spatiotemporal data, which are difficult to collect, and the data of annual accident traffic accidents are not easy to quantify, so the weather environment factors are lacking in the model training data. (2) In terms of inflection point prediction, since the inflection point of the trend is unlikely to be discovered by the model in advance, the forecasting ability of the inflection point of the possible future trend is poor.…”

Section: Resultsmentioning

confidence: 99%

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Traffic Accident Prediction Based on LSTM-GBRT Model

Zhang

Chen

Silamu

2020

Journal of Control Science and Engineering

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Road traffic accidents are a concrete manifestation of road traffic safety levels. The current traffic accident prediction has a problem of low accuracy. In order to provide traffic management departments with more accurate forecast data, it can be applied in the traffic management system to help make scientific decisions. This paper establishes a traffic accident prediction model based on LSTM-GBRT (long short-term memory, gradient boosted regression trees) and predicts traffic accident safety level indicators by training traffic accident-related data. Compared with various regression models and neural network models, the experimental results show that the LSTM-GBRT model has a good fitting effect and robustness. The LSTM-GBRT model can accurately predict the safety level of traffic accidents, so that the traffic management department can better grasp the situation of traffic safety levels.

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Section: Resultsmentioning

confidence: 99%

“…Applying traffic accident situation prediction results to traffic planning can improve traffic safety. Many experts and scholars have predicted some indicators of traffic accidents [2,3]. e research methods are mainly divided into three categories, statistical regression method [4], grey prediction [5], and neural network model method.…”

Section: Introductionmentioning

confidence: 99%

Traffic Accident Prediction Based on LSTM-GBRT Model

Zhang

Chen

Silamu

2020

Journal of Control Science and Engineering

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“…In an NB model, the expected number of crashes λ i for observation (or segment in this study) i is generally specified as ( 4, 5, 9, 11, 29 ):…”

Section: Methodsmentioning

confidence: 99%

“…For analytical methods, count-data models have been commonly used for crash frequency analysis since the number of crashes on a roadway entity is a non-negative integer value. Among the count-data models, the negative binomial (NB) model has been widely used because of its ability to address the over-dispersion (variance significantly greater than mean) problem, which is generally presented in crash data (2)(3)(4)(5). However, the conventional NB model still has its limitations and the main flaw of the model is its inability to fully account for unobserved heterogeneity, as discussed below.…”

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confidence: 99%

“…With regard to safety factors, the highway geometries including road cross-sections ( 3, 5, 26, 27 ), horizontal and vertical alignments ( 28, 29 ), and traffic characteristics ( 30 ) have been thoroughly studied elsewhere. Undoubtedly, results from past studies progressively contributed to the development of safety countermeasures and the design of safer highways.…”

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confidence: 99%

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A Correlated Random Parameters Model with Heterogeneity in Means to Account for Unobserved Heterogeneity in Crash Frequency Analysis

Huo

Leng

Hou

et al. 2020

Transportation Research Record: Journal of the Transportation R

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Unobserved heterogeneity induced by omitted variables is a major challenge in developing reliable road safety models. In recent years, the random parameters negative binomial (RPNB) model has been used frequently in crash frequency analysis to account for unobserved heterogeneity. However, the majority of past studies of the RPNB model assumed that there was no correlation between different sources of unobserved heterogeneity, which is not always true given the complex interactions of safety factors. Compared with the RPNB model, a more flexible random parameters model that is the correlated random parameters negative binomial with heterogeneity in means (CRPNBHM) model was proposed in this study. Results indicate that the CRPNBHM model could not only capture the otherwise unobserved heterogeneity, but also track the underlying correlation among different sources of unobserved heterogeneity, thus outperforming the RPNB model. In addition, new insights into the interactions of safety factors (e.g., the joint safety effects of heavy trucks and pavement rutting depth) were obtained from the CRPNBHM model and these are expected to be beneficial in developing effective safety countermeasures. Results from this study demonstrated the CRPNBHM model to be a good alternative for crash frequency analysis, particularly when unobserved heterogeneity was detected.

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