Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis

Mannering, Fred; Bhat, Chandra R.; Shankar, Venky; Abdel-Aty, Mohamed

doi:10.1016/j.amar.2020.100113

Cited by 177 publications

(57 citation statements)

References 40 publications

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“…Construct a network G = V, E according to graph theory, where V represents the set of nodes in the cooperative network and E represents the set of all connections. ey analyzed the results of the proposed method compared with other traditional methods and the running performance on the Spark platform [26,27]. rough a comprehensive analysis of the factors that affect innovation performance, it can be concluded that organizing joint invention patents is the most direct and effective way to study the knowledge sharing and collaborative innovation behavior between cooperating subjects.…”

Section: E Hierarchy Of Evolutionary Game Eorymentioning

confidence: 99%

[Retracted] Evolutionary Game Analysis of Knowledge Sharing in Low‐Carbon Innovation Network

Zheng

2021

Complexity

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Low-carbon technological innovation is the main means to develop a low-carbon economy, and network knowledge sharing and collaborative innovation is an effective model for the development of low-carbon technologies. First of all, this article adopts a decision-making experiment and evaluation laboratory method and interpretation structure model, combines the two methods, extracts the advantages of the two, and discards the shortcomings of the two, thus constructing a new optimized and upgraded interpretation structure model. We give methods to explore the main influencing factors of collaborative innovation of low-carbon technologies for online knowledge sharing. Based on the industrial network knowledge sharing and cooperation network environment, the network evolution game model of network knowledge sharing knowledge collaboration is constructed to study the rewards and punishments, the profit distribution rate, the knowledge potential difference, and the parameter pairing of the network knowledge sharing cooperation network structure in the process of network knowledge sharing and collaborative knowledge innovation. The influence of the network knowledge sharing cooperation strategy is obtained through simulation to change the size of the relevant parameters so that the network knowledge sharing cooperation agent chooses the network evolution game of the sharing strategy to realize the optimal evolutionary stable strategy. According to the simulation results, this article proposes suggestions from the following aspects, aiming to improve the overall knowledge synergy effect of the network knowledge sharing and cooperation network.

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Section: E Hierarchy Of Evolutionary Game Eorymentioning

confidence: 99%

[Retracted] Evolutionary Game Analysis of Knowledge Sharing in Low‐Carbon Innovation Network

Zheng

2021

Complexity

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“…Ignoring the “latent” effects of such unobserved factors has two important implications. Foremost, it is impossible to discern that an observed correlation between an observed explanatory factor and outcome variable is a true correlation and not a manifested effect of the unobserved factors ( Mannering, Shankar, & Bhat, 2016 ; Mannering, Bhat, Shankar, & Abdel-Aty, 2020 ; Wali & Khattak, 2020 ; Wali, Greene, Khattak, & Liu, 2018 ; Wali, Khattak, Bozdogan, & Kamrani, 2018 ). Second, the presence of such context-specific unobserved factors leads to variations/heterogeneity in the relationships between built and natural environment, chronic disease, and COVID-19 mortality.…”

Section: Introductionmentioning

confidence: 99%

Treating two pandemics for the price of one: Chronic and infectious disease impacts of the built and natural environment

Frank

Wali²

2021

Sustainable Cities and Society

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Compact walkable environments with greenspace support physical activity and reduce the risk for depression and several obesity-related chronic diseases, including diabetes and heart disease. Recent evidence confirms that these chronic diseases increase the severity of COVID-19 infection and mortality risk. Conversely, denser transit supportive environments may increase risk of exposure to COVID-19 suggesting the potential for contrasting chronic versus infectious disease impacts of community design. A handful of recent studies have examined links between density and COVID-19 mortality rates reporting conflicting results. Population density has been used as a surrogate of urban form to capture the degree of walkability and public transit versus private vehicle travel demand. The current study employs a broader range of built environment features (density, design, and destination accessibility) and assesses how chronic disease mediates the relationship between built and natural environment and COVID-19 mortality. Negative and significant relationships are observed between built and natural environment features and COVID-19 mortality when accounting for the mediating effect of chronic disease. Findings underscore the importance of chronic disease when assessing relationships between COVID-19 mortality and community design. Based on a rigorous simulation-assisted random parameter path analysis framework, we further find that the relationships between COVID-19 mortality, obesity, and key correlates exhibit significant heterogeneity. Ignoring this heterogeneity in highly aggregate spatial data can lead to incorrect conclusions with regards to the relationship between built environment and COVID-19 transmission. Results presented here suggest that creating walkable environments with greenspace is associated with reduced risk of chronic disease and/or COVID-19 infection and mortality.

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“…is section demonstrates a development from traditional statistical methods to data-driven methods. Mannering et al [52] point that the choice of analysis method for crash data should take into account the trade-off between prediction capability and the causal nature of factors contributing to accidents. Traditional statistical methods have been relatively easy to use data as it presents accuracy in prediction and rationale in causality.…”

Section: Methodological Reviewmentioning

confidence: 99%

Local or Neighborhood? Examining the Relationship between Traffic Accidents and Land Use Using a Gradient Boosting Machine Learning Method: The Case of Suzhou Industrial Park, China

Yang

Chung

Kim

2021

Journal of Advanced Transportation

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In cities, road traffic accidents are critical endangerment to people’s safety. A vast number of studies which are designed to understand these accidents’ leading causes and mechanisms exist. The widely held view is that emerging analysis methods can be a critical tool for understanding the complex interactions between land use and urban transportation. Using a case study of Suzhou Industrial Park (SIP) in Suzhou, China, this paper examines the relationship between different land use types and traffic accidents using a gradient boosting model (GBM) machine learning method. The results show that the GBM can be used as an effective accident model for a variety of research and analysis methods by (1) ranking the influential factors, (2) testing the degree of interpretation of each variable as the complexity of iterations changes, and (3) obtaining partial dependence plots, among other methods. The findings of this study also suggest that land use types—including facility points—demonstrate differing degrees of influence at two geographical scales: local level and neighborhood level. In the ranking of relative importance at both scales, the variables of education institutions, traffic lights, and service institutions are all ranked high—with a more significant influence on the occurrence of accidents. However, residential land and land use mix variables differed significantly in both scales and showed a significant deviation compared to the other results. When adjusting the complexity of the decision tree, the local level is more suitable for measuring variables such as residential areas and green parks where pedestrians and vehicles have fixed mobility periods and moderate flows. On the contrary, the nearest neighborhood level is more suitable to a small number of variables related to public service facilities at fixed locations, such as traffic lights and bus stops. In the partial dependence plots, all variables, except educational institutions and residences, show a positive correlation for accidents in the fitting process. The results of this study can ideally help inform transportation planners to reconsider transport accident occurrence rates in the context of the proximity to various land use types and public service facilities.

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Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis

Cited by 177 publications

References 40 publications

[Retracted] Evolutionary Game Analysis of Knowledge Sharing in Low‐Carbon Innovation Network

[Retracted] Evolutionary Game Analysis of Knowledge Sharing in Low‐Carbon Innovation Network

Treating two pandemics for the price of one: Chronic and infectious disease impacts of the built and natural environment

Local or Neighborhood? Examining the Relationship between Traffic Accidents and Land Use Using a Gradient Boosting Machine Learning Method: The Case of Suzhou Industrial Park, China

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