Real-time conflict-based Bayesian Tobit models for safety evaluation of signalized intersections

Guo, Yanyong; Sayed, Tarek; Essa, Mohamed

doi:10.1016/j.aap.2020.105660

Cited by 90 publications

(43 citation statements)

References 41 publications

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“…With the development of information technology, high solution traffic data were easy to access and apply in transportation systems. Recently, many studies applied ambient sensing-based driving data in proactive safety studies [3,[23][24][25]. For example, Zhang et al [23] predicted the pedestrian-vehicle conflicts at the signalized intersections using the trajectories of pedestrians and vehicles.…”

Section: Proactive Safety Studies On Pedestriansmentioning

confidence: 99%

Real Time Safety Model for Pedestrian Red-Light Running at Signalized Intersections in China

Yin

2021

Sustainability

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The traditional way to evaluate pedestrian safety is a reactive approach using the data at an aggregate level. The objective of this study is to develop real-time safety models for pedestrian red-light running using the signal cycle level traffic data. Traffic data for 464 signal cycles during 16 h were collected at eight crosswalks on two intersections in the city of Nanjing, China. Various real-time safety models of pedestrian red-light running were developed based on the different combination of explanatory variables using the Bayesian Poisson-lognormal (PLN) model. The Bayesian estimation approach based on Markov chain Monte Carlo simulation is utilized for the real-time safety models estimates. The models’ comparison results show that the model incorporated exposure, pedestrians’ characteristics and crossing maneuver, and traffic control and crosswalk design outperforms the model incorporated exposure and the model incorporated exposure, pedestrians’ characteristics, and crossing maneuver. The result indicates that including more variables in the real-time safety model could improve the model fit. The model estimation results show that pedestrian volume, ratio of males, ratio of pedestrians on phone talking, pedestrian waiting time, green ratio, signal type, and length of crosswalk are statistically significantly associated with the pedestrians’ red-light running. The findings from this study could be useful in real-time pedestrian safety evaluation as well as in crosswalk design and pedestrian signal optimization.

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Section: Proactive Safety Studies On Pedestriansmentioning

confidence: 99%

Real Time Safety Model for Pedestrian Red-Light Running at Signalized Intersections in China

Yin

2021

Sustainability

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“…Therefore, based on the above previous studies [16,[27][28][29], if using public transit is unavoidable during an epidemic, moving around the vehicle should be avoided to minimize the possibility of infection.…”

Section: The Transmission Of a Virus By Transportmentioning

confidence: 99%

Epidemic spread simulation in an area with a high-density crowd using a SEIR-based model

Zhou

Dong

et al. 2021

PLoS ONE

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Understanding the spread of infectious diseases is an extremely essential step to preventing them. Thus, correct modeling and simulation approaches are critical for elucidating the transmission of infectious diseases and improving the control of epidemics. The primary objective of this study is to simulate the spread of communicable diseases in an urban rail transit station. Data were collected by a field investigation in the city of Ningbo, China. A SEIR-based model was developed to simulate the spread of infectious diseases in Tianyi station, considering four groups of passengers (susceptible, exposed, infected, and recovered) and a 14-day incubation period. Based on the historical data of infectious diseases, the parameters of the SEIR infectious disease model were clarified, and a sensitivity analysis of the parameters was also performed. The results showed that the contact rate (CR), infectivity (I), and average illness duration (AID) were positively correlated with the number of infections. It was also found that the length of the average incubation time (AIT) was positively correlated with the number of exposed individuals and negatively correlated with the number of infectors. These simulation results provide support for the validity and reliability of using the SEIR model in studies of the spread of epidemics and facilitate the development of effective measures to prevent and control an epidemic.

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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 conflict-based Bayesian Tobit models for safety evaluation of signalized intersections

Cited by 90 publications

References 41 publications

Real Time Safety Model for Pedestrian Red-Light Running at Signalized Intersections in China

Real Time Safety Model for Pedestrian Red-Light Running at Signalized Intersections in China

Epidemic spread simulation in an area with a high-density crowd using a SEIR-based model

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

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