Real-time crash prediction on expressways using deep generative models

Cai, Qing; Abdel-Aty, Mohamed; Yuan, Jinghui; Lee, Jae Young; Wu, Yina

doi:10.1016/j.trc.2020.102697

Cited by 122 publications

(44 citation statements)

References 36 publications

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“…Typical statistical methods found in related studies mainly include matched case-control logistic regression [4,10,16,18], aggregate log linear model [5], and Bayesian statistics [3,9,14,19]. Algorithms based on neural networks [31,32], fuzzy logic method [20], classification trees [33], machine learning [6,9,34], and deep learning [8,[35][36][37] are encompassed in modern methods. Regarding the intercorrelation problem of traffic variables, statistical approaches usually delete the intercorrelated variables during modelling process [14].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Prediction models using traditional statistical methods such as logistic model [4] and log linear model [5] or machine learning methods such as support vector machine [6] and random forest [7] have been explored. Although statistical methods provide better interpretation for contributing factors, machine learning methods are proved to have higher prediction accuracy [8]. While developing crash prediction model, one critical step is to identify contributing factors related to the occurrence of crash.…”

Section: Introductionmentioning

confidence: 99%

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Crash Prediction on Expressway Incorporating Traffic Flow Continuity Parameters Based on Machine Learning Approach

Tian

Peng

Liu

et al. 2021

Journal of Advanced Transportation

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Real-time crash prediction helps identify and prevent the occurrence of traffic crash. For years, various real-time crash prediction models have been investigated to provide effective information for proactive traffic management. When building real-time crash prediction model, a suitable variable space together with a specific time interval for traffic data aggregation and an appropriate modelling algorithm should be applied. Regarding the intercorrelation problem with variable space, comprehensive real-time crash prediction model considering available traffic data characteristics in applicable circumstances needs to be explored. Taking Xi’an G3001 Expressway as study area, real road traffic and accident data during the period from January 2014 to January 2019 on this expressway are applied for real-time crash prediction. To better capture traffic flow characteristics on expressway and improve the practicality of real-time crash prediction model, two new variables (segment difference coefficient and lane difference coefficient) describing the smoothness and continuity of traffic flow in spatial dimension are developed and incorporated in building the crash prediction model to solve the intercorrelation problem with variable space. Random forest (RF) is then adopted to specify the quantitative relationship between specific variable and crash risk. Real-time crash prediction model based on support vector machine (SVM) using new composed variable space is built. The results show that simplified variable space could contribute to the same classification power in currently used real-time crash prediction models compared with traditional variable space. Moreover, the prediction model based on SVM reaches an accuracy level of 0.9, which performs better than other currently used prediction models.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Crash Prediction on Expressway Incorporating Traffic Flow Continuity Parameters Based on Machine Learning Approach

Tian

Peng

Liu

et al. 2021

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…Traditional traffic crash prediction is usually classified into two types: real-time crash prediction and crash prediction for a given temporal time unit in a selected road segment. Typical real-time crash prediction predicts traffic crashes and examine the influencing factors at 5-minute intervals (11)(12)(14)(15). Other studies tend to predict traffic crash and investigate contributing variables for a longer given temporal time unit, such as hourly and daily (16)(17)(18)(19)(20).…”

Section: Literature Reviewmentioning

confidence: 99%

“…For a given vector of observations y= {𝑦 1 , … , 𝑦 𝑛 } 𝑇 , the conditional quasi log likelihood function, up to a constant, expresses as l(ϑ) = ∑ log p t (y t ; ϑ) = ∑ (y t ln(λ t (ϑ)) − n t=1 n t=1 λ t (ϑ)) (12) where p t (y t ; θ) refers to the probability density function and λ 𝑡 (θ) refers to the conditional mean.…”

Section: Count Data Time Series Modelmentioning

confidence: 99%

Hourly Bridge Freeway Traffic Crash Prediction Using Log Count Data Poisson Time Series INGARCH Model and Its Comparisons With Regression-based Methods

Cai

2021

Preprint

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Traffic crash prediction is vital for relevant agencies to take precautionary measures to minimize the economic and social losses from traffic accidents. Currently, the popularity of machine learning, deep learning, and traditional regression-based models in crash predictions eclipsed the use of count data time series models. Count data model has many intrinsic advantages over machine learning based methods in crash analysis. It is an extension of conventional time series regression by extending normal distribution to Poisson and Negative binomial. Meanwhile, covariate variables can get properly incorporated and their influence on dependent variable is well interpreted. This study attempts to compare and examine the performances of the count data time series model with the regression-based models in hourly crash prediction, utilizing traffic crash data from the Sutong Yangtze River Bridge in China. Log linear extension of Poisson distribution integer valued generalized autoregressive conditional heteroscedasticity models (INGARCH), as a type of count data model, is adopted and compared with the zero-inflated Poisson model (ZIP), as well as the cumulative link model for ordinal regression (CLM). The performances of ZIP and log linear extension of INGARCH count data model are similar and superior to the performances of CLM. Results showed that previous traffic accidents influence the crash occurrence in the near future and the employment of count data time series model in hourly crash prediction can appropriately capture this influence, with an average model sensitivity rate of 77.5%.

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“…e development of Intelligent Transportation System (ITS) and advanced transportation information systems (ATIS) is helpful for easily collecting traffic data in real time, promoting the effective and accurate assessment on crash risk on highways and expressways by use of RTCPMs [4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Bayesian Network-Based Real-Time Crash Prediction Model for Urban Elevated Expressway

Liu

Lü

Cheng

et al. 2021

Journal of Advanced Transportation

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Traffic crash is a complex phenomenon that involves coupling interdependency among multiple influencing factors. Considering that interdependency is critical for predicting crash risk accurately and contributes to revealing the underlying mechanism of crash occurrence as well, the present study attempts to build a Real-Time Crash Prediction Model (RTCPM) for urban elevated expressway accounting for the dynamicity and coupling interdependency among traffic flow characteristics before crash occurrence and identify the most probable risk propagation path and the most significant contributors to crash risk. In this study, Dynamic Bayesian Network (DBN) was the framework of the RTCPM. Random Forest (RF) method was employed to identify the most important variables, which were used to build DBN-based RTCPMs. The PC algorithm combined with expert experience was further applied to investigate the coupling interdependency among traffic flow characteristics in the DBN model. A comparative analysis among the improved DBN-based RTCPM considering the interdependency, the original DBN-based RTCPM without considering the interdependency, and Multilayer Perceptron (MLP) was conducted. Besides, the sensitivity and strength of influences analyses were utilized to identify the most probable risk propagation path and the most significant contributors to crash risk. The results showed that the improved DBN-based RTCPM had better prediction performance than the original DBN-based RTCPM and the MLP based RTCPM. The most probable risk influencing path was identified as follows: speed on current segment (V) (time slice 2)⟶V (time slice 1)⟶speed on upstream segment (U_V) (time slice 1)⟶Traffic Performance Index (TPI) (time slice 1)⟶crash risk on current segment. The most sensitive contributor to crash risk in this path was V (time slice 2), followed by TPI (time slice 1), V (time slice 1), and U_V (time slice 1). These results indicate that the improved DBN-based RTCPM has the potential to predict crashes in real time for urban elevated expressway. Besides, it contributes to revealing the underlying mechanism of crash and formulating the real-time risk control measures.

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Real-time crash prediction on expressways using deep generative models

Cited by 122 publications

References 36 publications

Crash Prediction on Expressway Incorporating Traffic Flow Continuity Parameters Based on Machine Learning Approach

Crash Prediction on Expressway Incorporating Traffic Flow Continuity Parameters Based on Machine Learning Approach

Hourly Bridge Freeway Traffic Crash Prediction Using Log Count Data Poisson Time Series INGARCH Model and Its Comparisons With Regression-based Methods

A Dynamic Bayesian Network-Based Real-Time Crash Prediction Model for Urban Elevated Expressway

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