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This paper investigates factors associated with traffic crash fatalities in 63 provinces of Vietnam during the period from 2012 to 2014. Random effect negative binomial (RENB) and random parameter negative binomial (RPNB) panel data models are adopted to consider spatial heterogeneity across provinces. In addition, a spatiotemporal model with condi...
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This article accounts for multivariate dependence of the variable of policy interest in dynamic panel data models by disentangling the two sources of intertemporal dependence: one from the effect of the policy variable and the other from mean reversion. In a situation where intensity of the policy varies over time, we estimate the unconditional mea...
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... Various variables could influence regional RSIs. Previous studies reported significant associations between RSIs and other variables, such as population and related variables (Ahmadpur & Gokasar, 2021;Besharati et al., 2020;Erdogan, 2009;Osayomi, 2013;Tortum & Atalay, 2015;Truong et al., 2016;Wang et al., 2019), road length-related indices (Besharati et al., 2020;Erdogan, 2009;Osayomi, 2013;Tortum & Atalay, 2015), the number of health care institutions (Ahmadpur & Gokasar, 2021;Besharati et al., 2020;Truong et al., 2016), and the education and income levels of people (Ahmadpur & Gokasar, 2021;Wang et al., 2019). Given the literature, regional socioeconomic and transport-related variables potentially influencing Iran's regional RSIs were collected and evaluated in this study. ...
... Various variables could influence regional RSIs. Previous studies reported significant associations between RSIs and other variables, such as population and related variables (Ahmadpur & Gokasar, 2021;Besharati et al., 2020;Erdogan, 2009;Osayomi, 2013;Tortum & Atalay, 2015;Truong et al., 2016;Wang et al., 2019), road length-related indices (Besharati et al., 2020;Erdogan, 2009;Osayomi, 2013;Tortum & Atalay, 2015), the number of health care institutions (Ahmadpur & Gokasar, 2021;Besharati et al., 2020;Truong et al., 2016), and the education and income levels of people (Ahmadpur & Gokasar, 2021;Wang et al., 2019). Given the literature, regional socioeconomic and transport-related variables potentially influencing Iran's regional RSIs were collected and evaluated in this study. ...
... Jenks natural breaks apply a nonlinear algorithm to group observations to maximise the within-group homogeneity. In previous studies, the number of clusters in choropleth maps was set to 5, 6, or 7 (Adeleke et al., 2021;Ahmadpur & Gokasar, 2021;Iyanda, 2019;Truong et al., 2016). The increase in the number of clusters could reduce data generalisation. ...
Inadequate regional road safety studies have been conducted in developing countries like Iran. Regarding regional road safety indices (RSIs), a significant disparity between Iranian provinces was observed. Thus, it was aimed to evaluate the regional RSIs in Iran and identify their influencing factors and potential hot spots. Data on regional road crashes, fatalities, demographics, transportation, health institutions, economics, education, and fuel consumption rates were collected. The association between the variables was evaluated using correlation analysis. Using Moran’s I and local Moran indices, provinces’ spatial distributions were evaluated. Hot spot analysis was used to identify factors influencing RSIs. Significant correlations between the variables were detected. A vast local cluster in terms of fatality per injury (as a crash severity index) was identified in the country’s southeast. The distribution patterns of provinces in terms of seven RSIs were cluster-like. Variable groups, including road length, demographic, income, education, and geographic, influence RSIs in hot or cold spot regions. Crashes were severe in underdeveloped and remote provinces. Increasing income and education levels make it possible to reduce crash severity indices in this country. A positive Moran’s I index does not guarantee the existence of significant local cluster cores in a country.
... The study elucidate on the effects of digital resources and dynamic capabilities on DT via the moderating role of organizational slack, using a methodology inspired by Hausman et al. (1984) and Truong et al. (2016). The study utilises two important elements of data, namely, the total word frequency of DT factors used by industrial rms as the explained parameter, and repeated observations of the same rms, resulting in a panel of coupled time-series cross-sections. ...
... The matrix of Pearson's correlation coe cients between all the variables taken into account (see Table 5). Large correlation coe cient values suggested that the independent variables' multicollinearity was problematic (Truong et al., 2016). According to test results, none of the independent variables in the model had a correlation coe cient greater than 0.7. ...
Implementing digital transformation in industrial enterprises is a challenging task, necessitating a deeper understanding of the variables that can facilitate its successful accomplishment. This study aims to explore the mechanisms through which organizations can constellate and maintain digital resources to build the capabilities required for effective digital transformation, with a particular focus on the moderating effects of organizational slack.
The study provides empirical support for established theoretical perspectives, such as resource-based theory and dynamic capabilities, emphasizing the importance of leveraging digital resources and dynamic capabilities to achieve digital transformation. To investigate these relationships, we adopted an empirical model using a quantitative research approach with secondary data. The random effect negative binomial regression approach was chosen for analysis. Our sample comprised 3,229 industrial enterprises, generating 32,290 observations from the period spanning 2012 to 2021, sourced from the China stock market and accounting research database.
Our findings confirmed our hypothesis, revealing that digital resources and dynamic capabilities are significant predictors of digital transformation accomplishment. Additionally, we discovered that digital resources have a direct impact on dynamic capability, and dynamic capability partially mediates the relationship between digital resources and digital transformation. Moreover, external organizational slack (OS_EXT) emerged as a significant moderator, negatively influencing the impact of digital resources and dynamic capabilities on digital transformation.
These research findings offer actionable knowledge to industrial leaders, practitioners, and policymakers, empowering them to make informed strategic decisions and enhance the potential for successful digital transformation in industrial enterprises. Notably, this research contributes to the study of digital transformation, specifically in the context of the top seven Chinese provinces. Despite the challenges associated with transitioning from an industrial source to an industrial powerhouse, this study sheds light on the combined influence of organizational slack, digital resources, and dynamic capabilities as variables within a unified prediction model.
... This has also been evidenced by Ly et al. (1267) who found that more than 90% of the emissions of volatile organic compounds are contributed by motorbikes. Some other studies also found a significant connection between Vietnamese motorcycle dependence and increased traffic accidents (Pham et al., 2021;Vu Anh Tuan, 2015;Truong et al., 2016;Chou et al., 2022). In addition to causing increased congestion and pollution, motorcycles have fundamentally changed the character of the city for residents. ...
... Delays in operating on trauma patients can lead to increased morbidity and mortality (Lankester et al., 2000). Truong et al. (2016) have identified the same relationship between hospital density and traffic crash fatalities in Vietnam, indicating that provinces with higher hospital densities are more likely to have lower fatalities. Therefore, a good health system can prevent motor vehicle crash deaths and downgrade fatal accidents to nonfatal accidents. ...
The increase in the number of tourists visiting many destinations in Greece could lead to spillover effects on the safety of tourists with road traffic accidents being the major contributor to the morbidity and mortality of travellers worldwide. We employ data from 51 Greek regions (NUTS-3) during the period 2000–2017 to investigate the role of tourism in road accidents. We examine whether road accidents are affected by factors such as tourism, economic, demographic, meteorological, and risk exposure variables. We reveal that tourism affects road accidents in Greece, and that foreign tourists have a significant influence in this regard. Domestic tourists have a significant effect on non-fatal accidents, unlike foreign travellers, who covary more with cases of severe injuries and fatalities. There is a positive relationship between the number of road traffic accidents and tourism; more and longer stays of tourists coincide with increased vehicular collisions. Our findings suggest that domestic and foreign tourists are important to the national road safety policy plan, and that policymakers should be aware of this.
... Models have also been developed to identify contributing factors to enable traffic authories to develop appropriate countermeasures (Abdulhafedh, 2017;Pradhan and Sameen, 2020;Zheng et al., 2019). Many studies have been developed in recent years (Deublein et al., 2015;Foroutaghe et al., 2019;Huang et al., 2016;Ihueze and Onwurah, 2018;Jafari et al., 2015;Mehmandar et al., 2016;Sameen et al., 2019;Shaik and Hossain, 2020;Truong et al., 2016;Zolala et al., 2016;Wang et al., 2016) to thoroughly understand the effect of micro-level factors on the probability of MC-related crashes in a specific context. Similarly, many studies have sought to understand the impact of macroeconomic factors on injury severity (Iwata, 2010;Bougueroua and Carnis, 2016;Nghiem et al., 2016;Li et al., 2018). ...
... Time-series models include autoregressive integrated moving average (Foroutaghe et al., 2019;Huang et al., 2016;Ihueze and Onwurah, 2018;Mehmandar et al., 2016;Zolala et al., 2016). Other techniques include Poisson model, Bayesian network model, negative binomial panel data model, and neural networks model (Deublein et al., 2015;Jafari et al., 2015; Shaik and Hossain, 2020;Truong et al., 2016;Wang et al., 2016). Only a few studies have applied time-series analysis with the curve estimation technique to identify the factors influencing traffic fatality. ...
... This study, however, did not examine the causes of traffic fatalities. As reported by the government, more than 95% of road traffic accidents in Vietnam are due to some form of traffic violation (Truong et al., 2016). Therefore, it is suggested that future research focuses on the tripartite relationship between policy interventions, travel behavior, and road traffic fatality rate. ...
Motorcycle ownership in Vietnam has increased exponentially during the last two decades. As a result, traffic congestion, emissions, and traffic safety have been on the rise. Of particular concern is the significant increase in the number of traffic accidents due to higher traffic volume that is stimulated by economic development and improved households' economic mobility. During this time frame, many programs, such as traffic safety awareness campaign, have been developed and implemented by transportation agencies to reduce traffic fatalities. This study seeks to understand the long-term effect of policy intervention on traffic fatalities. This is accomplished by developing a log-linear regression model which the response variable is the number of traffic fatalities between 1990 and 2019 and the independent variables include Human Development Index (HDI), motorcycle ownership, and policy interventions. The model estimation results indicate that: (1) a higher degree of economic development (HDI) corresponds to lower traffic fatalities, (2) the motorcycle helmet law has lowered the number of annual traffic fatalities since its enactment, and (3) a higher number of motorcycle trips corresponds to a higher number of traffic fatalities. Based on this study's findings, it is recommended that transportation agencies in Vietnam develop and promote an alternative transportation mode.
... Nevertheless, other than temporal stability, spatial variation remains another problem that cannot be underestimated in safety research. Recent research efforts have proved that biased estimation results might also be attributed to regional differences producing potential heterogeneity in the spatial transferability issues in crash analysis [18,26,28,29], incorporating freeway segments [30], urban arterials [31], urbanized areas [32], counties [33], provinces [34], etc. It is worthwhile to pay additional attention to the effects of contributing factors on injury-severities through integrating temporal and spatial dimensions simultaneously. ...
Accounting for the growing numbers of injuries, fatalities, and property damage, rear-end crashes are an urgent and serious topic nowadays. The vehicle number involved in one crash significantly affected the injury severity outcomes of rear-end crashes. To examine the transferability and heterogeneity across crash types (two-vehicle versus multi-vehicle) and spatiotemporal stability of determinants affecting the injury severity of freeway rear-end crashes, this study modeled the data of crashes on the Beijing-Shanghai Freeway and Changchun-Shenzhen Freeway across 2014-2019. Accommodating the heterogeneity in the means and variances, the random parameters logit model was proposed to estimate three potential crash injury severity outcomes (no injury, minor injury, and severe injury) and identify the determinants in terms of the driver, vehicle, roadway, environment, temporal, spatial, traffic, and crash characteristics. The likelihood ratio tests revealed that the effects of factors differed significantly depending on crash type, time, and freeway. Significant variations were observed in the marginal effects of determinants between two-vehicle and multi-vehicle freeway rear-end crashes. Then, spatiotemporal instability was reported in several determinants, including trucks early morning. In addition, the heterogeneity in means and variances of the random parameters revealing the interactions of random parameters and other insignificant variables suggested the higher risk of determinants including speeding indicators, early morning, evening time, and rainy weather conditions. The current finding accounting for spatio-temporal instability could help freeway designers, decision-makers, management strategies to understand the contributing mechanisms of the factors to develop effective management strategies and measurements.
... A considerable number of studies addressed the spatial instability by accommodating the spatial correlations over neighboring geographical space, including freeway segments (Chiou and Fu, 2015), street networks (Castro et al., 2013), urban arterials (Xiao et al., 2019), urbanized areas (Liu et al., 2019), counties (Aguero-Valverde and Jovanis, 2006;Liu and Sharma, 2017), provinces (T. Truong et al., 2016), etc. To account for spatial heterogeneity, a variety of statistical approaches have been adopted in crash frequency or prediction analysis, such as the geographically weighted regression (GWR) models (Hadayeghi et al., 2010;Li et al., 2013;Seun et al., 2020), Bayesian spatial approaches (Quddus, 2008;Dong et al., 2015;Lee et al., 2015), and random parameter models (Coruh et al., 2015;Venkataraman et al., 2011;Venkataraman et al., 2013;Xu and Huang, 2015). ...
The truck involvement could potentially increase the crash frequency and resulted injury outcomes and it is of great necessity to understand the similarities and differences in the mechanism of how determinants influence injury severities of truck-involved and non-truck-involved crashes and explore their spatiotemporal stability. Based on the crash data of Beijing-Shanghai Expressway and Changchun-Shenzhen Expressway over the three years (2017-2019), the heterogeneity and spatiotemporal stability of contributing factors affecting truck-involved and non-truck-involved crashes were investigated through random-parameter logit models with unobserved heterogeneity in means and variances. Three injury severity outcomes of severe injury, minor injury, and no injury were examined considering multiple factors including driver, vehicle, roadway, environmental, temporal, spatial, traffic and crash characteristics. Besides, the spatiotemporal stability was investigated based on the likelihood ratio tests. Marginal effects were also calculated to analyze the spatiotemporal stability and potential heterogeneity of the contributing variables from year to year. The findings exhibited remarkable differences between truck-involved and non-truck-involved crashes, and an overall spatiotemporal instability was observed in the current study while several indicators were also reported to show relative spatial or temporal stability such as length of the horizontal curve, AADT, early morning, cloudy weather. This paper provided some suggestions to prevent crashes for truck-involved and non-truck-involved crashes across different highways respectively and develop safety measures accordingly.
... Macroscopic crash prediction models (MCPM) have been developed to explore the relationships between the safety outcomes of a zone and its zonal characteristics. Many zonal levels have been considered, such as traffic analysis zones, counties, and statistical areas [44][45][46]. e numbers of total crashes and fatal/serious injury crashes (severe crashes) have often been used as safety outcomes in MCPMs [47,48]. ...
Walking, cycling, and feeder bus/tram for first- and last-mile (FLM) train access are often considered to have better health benefits, lower cost, and less environmental impacts than driving. However, little is known about the road safety impacts of these FLM access modes, particularly at a network level. This paper aims to investigate the impacts of train commuters’ access modes on road safety in Victoria, Australia. Macroscopic analyses of crash outcomes in each zone (i.e., Statistical Area Level 1) were performed using negative binomial (NB) and spatially lagged X negative binomial (SLXNB), accounting for potential indirect effects of mode shares in adjacent zones. This macroscopic analysis approach enabled the consideration of the safety effects across the network. The results showed that the SLXNB models outperformed the NB models. Commuting by train, either with walking or car as FLM access mode, was negatively associated with both total and severe crashes. In addition, commuting by train with feeder bus/tram access mode was negatively associated with severe crashes. Interestingly, commuting by train with cycling access mode was negatively associated with total crashes, with a larger effect when compared to walking and car access modes. Overall, the results suggested promoting active transport as FLM train access mode would lead to an improvement in road safety.
... Thus, economic losses are estimated at 5 to 12 billion US $. There are many reasons for crashes, such as using the phone to watch movies, listen to music or text, call while driving [1], [2]. Those behaviors are very potential to cause crashes. ...
In this paper, we introduce a mobile application called CarSafe, in which data from the acceleration sensor integrated on smartphones is exploited to come up with an efficient classification algorithm. Two statuses, "Driving" or "Not driving," are monitored in the real-time manner. It enables automatic actions to help the driver safer. Also, from these data, our software can detect the crash situation. The software will then automatically send messages with the user's location to their emergency departments for timely assistance. The application will also issue the same alert if it detects a driver of a vehicle driving too long. The algorithm's quality is assessed through an average accuracy of 96.5%, which is better than the previous work (i.e., 93%).
... Some others used a combined approach to reflect the main characteristics of this phenomenon. Most studies used GIS tools (Zahran et al., 2017;Soltani and Askari, 2017;and Abdulhafedh, 2017, among many others) to map traffic crashes at different spatial levels, such as states or provinces (Erdogan, 2009;Truong et al., 2016); counties (Aguero-Valverde and Jovanis, 2006;Eckley and Curtin, 2013;Liu and Sharma, 2018), or smaller spatial level units, such as census tracts or neighbourhoods (Kingham et al., 2011;Wang and Kockelman, 2013). In highly motorized countries, crashes resulting in injuries are more likely to happen on urban than on rural roads (Elvik et al., 2009), although similar results were obtained in some developing countries, as well. ...
Sustainable urban mobility and road safety have been both a challenge and a priority at the European level for two decades now. Urban road traffic crashes are some of the most difficult issues to tackle by the local administrative planning and development authorities in Europe. The aim of this study was to enhance the focus on urban road safety by providing an illustrative spatial and temporal overview on the road crashes occurred in the cities and towns of Romania and their effects on the people involved. Data related to urban road crashes for a 12-year reference period from 2008 to 2019 were used. Results showed no significant difference in the number of road traffic crashes in 2019 compared to 2008. However, the impact on the people involved show a decrease in severity, the number of road crashes deaths in 2019 being halved compared to 2008. We note a redistribution in the occurrence of these events at the city level, for the period 2008-2019, whilst the most affected are the cities of rank 1 and 2. All rank 1 cities in Romania were detected as hotspots with a high concentration of road traffic crashes and casualties, designated as low-safety road traffic urban poles. We argued in favour of customized and relevant strategies for sustainable and safe urban transport in accordance with the particular features of the cities and towns in Romania, given the varied severity degree of the phenomenon and the specific features of road infrastructure and road traffic.
