Hierarchical Bayesian modeling to evaluate the impacts of intelligent speed adaptation considering individuals’ usual speeding tendencies: A correlated random parameters approach

Matsuo, Kojiro; Sugihara, Mitsuru; Yamazaki, Motohiro; Mimura, Yasuhiro; Yang, Jia; Kanno, Komei; Sugiki, Nao

doi:10.1016/j.amar.2020.100125

Cited by 11 publications

(3 citation statements)

References 32 publications

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“…By allowing the effects of exogenous explanatory factors to vary across individual crashes (or segments of population), more efficient, precise, and richer insights can be obtained. To account for unobserved heterogeneity, a broad spectrum of studies have successfully used different methodological alternatives including random parameter models (Anastasopoulos and Mannering, 2009;Zhao and Khattak, 2015;Alarifi et al, 2017;Bhat et al, 2017;Khattak et al, 2019;Khattak and Fontaine, 2020), correlated random parameter models (Fountas et al, 2018a;Fountas et al, 2019;Wali et al, 2019a;Matsuo et al, 2020), random parameter models with heterogeneity-in-means (Venkataraman et al, 2014;Behnood and Mannering, 2017b;Wali et al, 2018c;Hamed and Al-Eideh, 2020), random parameter models with heterogeneity-in-means and variances (Behnood and Mannering, 2017a;Seraneeprakarn et al, 2017;Xin et al, 2017;Behnood and Mannering, 2019;Al-Bdairi et al, 2020;Yu et al, 2020), latent-class models (Eluru et al, 2012;Behnood et al, 2014;Shaheed and Gkritza, 2014;Yasmin et al, 2014a;Fountas et al, 2018b), latent class models with random parameters (Xiong and Mannering, 2013), Markov-switching models Khattak and Wali, 2017), Markov-switching models with random parameters (Xiong et al, 2014), and copula based approaches (Eluru et al, 2010;Yasmin et al, 2014b;Wang et al, 2015a;Wali et al, 2018a;Wali et al, 2018e;Wang et al, 2019). For a detailed discussion on the...…”

Section: Systematic (Observed) and Random (Unobserved) Heterogeneitymentioning

confidence: 99%

The relationship between driving volatility in time to collision and crash-injury severity in a naturalistic driving environment

Wali

Khattak

Karnowski

2020

Analytic Methods in Accident Research

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Section: Systematic (Observed) and Random (Unobserved) Heterogeneitymentioning

confidence: 99%

The relationship between driving volatility in time to collision and crash-injury severity in a naturalistic driving environment

Wali

Khattak

Karnowski

2020

Analytic Methods in Accident Research

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“…On the other hand, it suggests that traffic calming is more important for the safety of children traveling in NCSP. Many previous studies showed the safety effect of traffic calming devices on residential roads [ 47 , 48 ].…”

Section: Discussionmentioning

confidence: 99%

A Method for Locational Risk Estimation of Vehicle–Children Accidents Considering Children’s Travel Purposes

Matsuo

Kosuke

Sugiki

2022

IJERPH

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The reduction in locational traffic accident risks through appropriate traffic safety management is important to support, maintain, and improve children’s safe and independent mobility. This study proposes and verifies a method to evaluate the risk of elementary school students-vehicle accidents (ESSVAs) at individual intersections on residential roads in Toyohashi city, Japan, considering the difference in travel purposes (i.e., school commuting purpose; SCP or non-school commuting purpose: NSCP), based on a statistical regression model and Empirical Bayes (EB) estimation. The results showed that the ESSVA risk of children’s travel in SCP is lower than that in NSCP, and not only ESSVAs in SCP but also most ESSVAs in NSCP occurred on or near the designated school routes. Therefore, it would make sense to implement traffic safety management and measures focusing on school routes. It was also found that the locational ESSVA risk structure is different depending on whether the purpose of the children’s travels is SCP or NSCP in the statistical model. Finally, it was suggested that evaluation of locational ESSVA risks based on the EB estimation is useful for efficiently extracting locations where traffic safety measures should be implemented compared to that only based on the number of accidents in the past.

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“…Collecting data is the basis for planning and introducing preventive measures to increase traffic safety. In this context, geographic information system technology enables the implementation of various methods for studying spatial traffic patterns, such as the Bayesian method considering different distribution functions (Poisson, Poisson-gamma, Poisson-lognormal) [20]; hierarchical models [21]; and methods of spatial statistics, such as kernel density estimation [22], local Moran index [23], and the Getis-Ord G* statistics [24]. Moreover, combining machine-learning algorithms and geospatial models can provide applicable solutions to change complex patterns caused by human activity [25,26].…”

Section: Introductionmentioning

confidence: 99%

Revealing the Spatial Pattern of Weather-Related Road Traffic Crashes in Slovenia

et al. 2021

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Despite an improvement in worldwide numbers, road traffic crashes still cause social, psychological, and financial damage and cost most countries 3% of their gross domestic product. However, none of the current commercial or open-source navigation systems contain spatial information about road traffic crash hot spots. By developing an algorithm that can adequately predict such spatial patterns, we can bridge these still existing gaps in road traffic safety. To that end, geographically weighted regression and regression tree models were fitted with five uncorrelated (environmental and socioeconomic) road traffic crash predictor variables. Significant regional differences in adverse weather conditions were identified; Slovenia lies at the conjunction of different climatic zones characterized by differences in weather phenomena, which further modify traffic safety. Thus, more attention to speed limits, safety distance, and other vehicles entering and leaving the system could be expected. In order to further improve road safety and better implement globally sustainable development goals, studies with applicative solutions are urgently needed. Modern vehicle-to-vehicle communication technologies could soon support drivers with real-time traffic data and thus potentially prevent road network crashes.

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Hierarchical Bayesian modeling to evaluate the impacts of intelligent speed adaptation considering individuals’ usual speeding tendencies: A correlated random parameters approach

Cited by 11 publications

References 32 publications

The relationship between driving volatility in time to collision and crash-injury severity in a naturalistic driving environment

The relationship between driving volatility in time to collision and crash-injury severity in a naturalistic driving environment

A Method for Locational Risk Estimation of Vehicle–Children Accidents Considering Children’s Travel Purposes

Revealing the Spatial Pattern of Weather-Related Road Traffic Crashes in Slovenia

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