Multivariate random parameter Tobit modeling of crashes involving aging drivers, passengers, bicyclists, and pedestrians: Spatiotemporal variations

Ulak, Mehmet Baran; Özgüven, Eren Erman; Vanli, O. Arda; Dulebenets, Maxim A.; Spainhour, Lisa K.

doi:10.1016/j.aap.2018.08.031

Cited by 35 publications

(12 citation statements)

References 45 publications

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“…Anastasopoulos [42] developed a random parameter multivariate Tobit model to account for the unobserved heterogeneity of collision injury rates by severity. Other research also developed a random parameter multivariate Tobit model to analyze the crash rate by injury severity and found significant heterogeneous effects of estimates which varied across observations [43,44]. Guo et al [45] modeled the correlation and heterogeneity in crash rates by different collision types by developing a random parameters multivariate Tobit model.…”

Section: Methodsmentioning

confidence: 99%

Using Bayesian Tobit Models to Understand the Impact of Mobile Automated Enforcement on Collision and Crime Rates

Ibrahim¹,

Sayed

2021

Sustainability

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The Data Driven Approaches to Crime and Traffic Safety approach identifies opportunities where a single enforcement deployment can achieve multiple objectives: reduce collision and crime rates. Previous research focused on modeling both events separately despite evidence suggesting a high correlation. Additionally, there is a limited understanding of the impact of Mobile Automated Enforcement (MAE) on crime or the impact of changing a deployment strategy on collision and crime dates. For this reason, this study categorized MAE deployment into three different clusters. A random-parameter multivariate Tobit model was developed under the Bayesian framework to understand the impact of changing the deployment on collision and crime rates in a neighborhood. Firstly, the results of the analysis quantified the high correlation between collision and crime rates (0.86) which suggest that locations with high collision rates also coincide with locations with high crime rates. The results also demonstrated the safety effectiveness (i.e., reduced crime and collision rates) increased for the clusters that are associated with an increased enforcement duration at a neighborhood level. Understanding how changing the deployment strategy at a macro-level affects collision and crime rates provides enforcement agencies with the opportunity to maximize the efficiency of their existing resources.

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Section: Methodsmentioning

confidence: 99%

Using Bayesian Tobit Models to Understand the Impact of Mobile Automated Enforcement on Collision and Crime Rates

Ibrahim¹,

Sayed

2021

Sustainability

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“…The number of accidents that have occurred at individual tram stops are relatively rare. But at the same time, emergencies arise in which people who cross the carriageway to tram stops may suffer [17,18]. Conflict situations between pedestrians walking to a tram stop and vehicles usually arise when pedestrians cross the carriageway.…”

Section: Approach To the Study Of Conflict Situations Between Pedestrians And Vehicle Drivers In The Area Of Tram Stopsmentioning

confidence: 99%

Pedestrian safety assessment in the area of tram stops

Nikolaeva

2021

E3S Web Conf.

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The problems of conflicts between pedestrians and vehicles in the area of tram stops are of great importance. The choice of pedestrian behavior is greatly influenced by the location of transport infrastructure facilities in the city. This paper presents the results of a field study, which made it possible to identify the main disadvantages of design solutions in the area of tram stops and operating conditions. Investigation of the shortcomings of design solutions in the area of tram stops made it possible to identify the causal relationship of pedestrian behavior. The fundamental relationship between the causal relationships of the behavior of pedestrians in the area of tram stops formed the basis of the safety assessment model. To connect the safety of tram stops and the behavior of pedestrians, we propose a model for assessing the level of safety of tram stops. The model of the safety level of tram stops is based on the values of causal factors, the contribution index and the degree of hazard, which can be obtained as a result of field observations. The resulting model assumes the distribution of tram stops by safety level, which will make it possible to prioritize quality improvement.

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“…Since then, this approach has been applied to many research domains, such as medicines [42], social science [43], and especially in the safety analysis domain [44][45][46][47]. The incorporated spatial and temporal effect terms in the Bayesian spatiotemporal models "borrow strength" from neighboring locations and contiguous time periods to better accommodate the data [48,49]. Compared to the trained spatiotemporal features in the machine learning approaches, the Bayesian statistical approaches usually specify a structure for the spatial and/or temporal effect terms, which would greatly increase the interpretability of the results [50].…”

Section: ) Bayesian Spatiotemporal Approachesmentioning

confidence: 99%

Online Bus Speed Prediction With Spatiotemporal Interaction: A Laplace Approximation-Based Bayesian Approach

Cui

Xie

Hu³

et al. 2021

IEEE Access

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This study proposes a novel Bayesian hierarchical approach for online bus speed prediction by explicitly accounting for the spatiotemporal interaction (STI) of speed observations. The use of Laplace approximation can expedite the estimation of Bayesian models and enable the implementation of online prediction. Large numbers of trials are carried out to identify significant predictors and the optimal length of the look-back time window to achieve the highest prediction accuracy. The spatiotemporal interacting patterns are also explored, and results show that the Type IV model assuming the structured spatial effect interacts with the structured temporal effect can best accommodate the bus speed data. Besides, prediction errors of the Type IV model randomly distribute over time and space. The proposed model can achieve high prediction accuracy and computational efficiency without compromising the interpretability of the contributing factors and the unobserved spatiotemporal heterogeneity. The proposed model can be used to assist public transit operation and management, such as bus scheduling, congestion warning, and the development of proactive measures to mitigate bus delays.

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Multivariate random parameter Tobit modeling of crashes involving aging drivers, passengers, bicyclists, and pedestrians: Spatiotemporal variations

Cited by 35 publications

References 45 publications

Using Bayesian Tobit Models to Understand the Impact of Mobile Automated Enforcement on Collision and Crime Rates

Using Bayesian Tobit Models to Understand the Impact of Mobile Automated Enforcement on Collision and Crime Rates

Pedestrian safety assessment in the area of tram stops

Online Bus Speed Prediction With Spatiotemporal Interaction: A Laplace Approximation-Based Bayesian Approach

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