Interactions between the built and socio-economic environment and driver demographics: spatial econometric models of car crashes in the Columbus Metropolitan Area

Lee, Dongkwan; Guldmann, Jean‐Michel; Rabenau, Burkhard von

doi:10.1080/12265934.2017.1369452

Cited by 7 publications

(8 citation statements)

References 66 publications

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“…The number of bus stops positively impacts crashes across most age groups. As suggested by Lee et al [17], bus stops may increase crash opportunities by increasing pedestrian flows and reducing visibility. The road length in the TAZ has a positive and significant impact in all models.…”

Section: Discussionmentioning

confidence: 95%

“…Xu and Huang [39] suggested that the relationship between crash counts and surrounding environments is influenced by location due to intrinsically different relationships across the region, misspecification of reality, omitted relevant variables, and inappropriately represented the functional form. Lee et al [17] suggested that several unobserved factors, such as road congestion, design, and materials of roads that were built in the same period and location, were spatially correlated. Also, Li et al [50] discovered that the compactness of the urban environment is positively associated with traffic congestion.…”

Section: Discussionmentioning

confidence: 99%

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Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers

2019

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As a characteristic of senior drivers aged 65 +, the low-mileage bias has been reported in previous studies. While it is thought to be a well-known phenomenon caused by aging, the characteristics of urban environments create more opportunities for crashes. This calls for investigating the low-mileage bias and scrutinizing whether it has the same impact on other age groups, such as young and middle-aged drivers. We use a crash database from the Ohio Department of Public Safety from 2006 to 2011 and adopt a macro approach using Negative Binomial models and Conditional Autoregressive (CAR) models to deal with a spatial autocorrelation issue. Aside from the low-mileage bias issue, we examine the association between the number of crashes and the built environment and socio-economic and demographic factors. We confirm that the number of crashes is associated with vehicle miles traveled, which suggests that more accumulated driving miles result in a lower likelihood of being involved in a crash. This implies that drivers in the low mileage group are involved in crashes more often, regardless of the driver’s age. The results also confirm that more complex urban environments have a higher number of crashes than rural environments.

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Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers

2019

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“…Alguns autores, por exemplo, mostram a relação das ocorrências no trânsito com o comportamento adaptativo dos motoristas e a percepção deles sobre o risco desses eventos (Wilde, 2014). Já outros consideram que condicionantes econômicos -PIB per capita (González et al, 2020;Suphanchaimat et al, 2019) e desemprego (Vaz et al, 2017) -e sociodemográficos -educação, densidade populacional, taxa de motorização de veículos, entre outros (Goel et al, 2018;Lee et al, 2018; Andrade e Jorge, 2017) -podem explicar o comportamento da morbimortalidade por esse agravo.…”

Section: Introductionunclassified

“…A técnica de análise espacial, por sua vez, é adequada para estudos no contexto da economia das causas externas, pois permite a identificação das áreas com maior concentração desses agravos em um país (González et al, 2020;Lee et al, 2018;Soro et al, 2017;Rhee et al, 2016). Além disso, pode captar a relação entre indicadores de morbimortalidade e fatores associados, no contexto espaço-temporal, bem como o aspecto regionalizado do setor saúde no Brasil (Mendonça et al, 2017).…”

Section: Introductionunclassified

Fatores associados às internações do Sistema Único de Saúde (SUS) por acidentes de transporte terrestre no Paraná: Análise pelo modelo de painel de dados espaciais

Oikawa,

Favoretto

2023

RBERU

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Este estudou analisou os fatores espaciais associados às taxas de internações hospitalares (suavizada) do SUS por acidentes de transporte terrestre entre os municípios do Paraná, no período de 2008 a 2018. A base teórica utilizada foi o modelo Grossman (2000; 1972b; 1972a). Utilizaram-se o método de suavização bayesiano empírico espacial, a Análise Exploratória de Dados Espaciais e modelo de Painel de Dados Espaciais. Os fatores usados foram o PIB per capita, taxa de motorização de veículos, densidade demográfica e prevalência desses acidentes (vitimização). Os resultados mostraram que as taxas de internações foram associadas positivamente com PIB real per capita e taxa de motorização e, negativamente com densidade demográfica. Constataram-se efeitos das variáveis demográficas dos municípios vizinhos sobre as taxas de hospitalizações do município considerado. As evidências deste estudo podem auxiliar gestores públicos em ações coordenadas sobre alocação de recursos, implementação de políticas de segurança no trânsito, de melhoria do capital humano e da qualidade de vida dos vitimados.

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“…For instance, road traffic crashes caused 1.25 million deaths and cost governments around 3% of GDP globally in 2013 [5]. A review of the literature suggests that the majority of road-related crashes are generally attributed to drivers' faults, which could have been prevented [6][7][8]. Hence, in order to analyze and prevent road traffic crashes more efficaciously, noticeable academic efforts to explore and determine significant factors that may affect vehicle crashes have been made in the field of road transport safety.…”

Section: Introductionmentioning

confidence: 99%

Potentialities of Vehicle Trajectory Big Data for Monitoring Potentially Fatigued Drivers and Explaining Vehicle Crashes on Motorway Sections

Chang

Park

2020

Sustainability

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Task-related fatigue, caused by prolonged driving, is a major cause of vehicle crashes. Despite noticeable academic achievements, monitoring drivers’ fatigue on road sections is still an ongoing challenge which must be met to prevent and reduce traffic accidents. Fortunately, individual instances of vehicle trajectory big data collected through advanced vehicle-GPS systems offer a strong opportunity to trace driving durations. We propose a new approach by which to monitor task-related fatigued drivers by directly using the ratio of potentially fatigued drivers (RFD) to all drivers for each road section. The method used to compute the RFD index was developed based on two inputs: the distribution of the driving duration (extracted from vehicle trajectory data), and the boundary condition of the driving duration between fatigued and non-fatigued states. We demonstrate the potentialities of the method using vehicle trajectory big data and real-life traffic accident data. Results showed that the measured RFD has a strong explanatory power with regard to the traffic accident rate, with a statistical correlation of 0.86 at least, for regional motorway sections. Therefore, it is expected that the proposed approach is a feasible means of successfully monitoring fatigued drivers in the present and near future era of smart-mobility big data.

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Interactions between the built and socio-economic environment and driver demographics: spatial econometric models of car crashes in the Columbus Metropolitan Area

Cited by 7 publications

References 66 publications

Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers

Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers

Fatores associados às internações do Sistema Único de Saúde (SUS) por acidentes de transporte terrestre no Paraná: Análise pelo modelo de painel de dados espaciais

Potentialities of Vehicle Trajectory Big Data for Monitoring Potentially Fatigued Drivers and Explaining Vehicle Crashes on Motorway Sections

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