scholarly journals A Review of Data Analytic Applications in Road Traffic Safety. Part 2: Prescriptive Modeling

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1096 ◽  
Author(s):  
Qiong Hu ◽  
Miao Cai ◽  
Nasrin Mohabbati-Kalejahi ◽  
Amir Mehdizadeh ◽  
Mohammad Ali Alamdar Yazdi ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Shortest Path ◽  
Traffic Safety ◽  
Road Traffic ◽  
Predictive Performance ◽  
Risk Indicators ◽  
Crash Risk ◽  
Future Research ◽  
Road Traffic Safety ◽  
Prescriptive Model

In the first part of the review, we observed that there exists a significant gap between the predictive and prescriptive models pertaining to crash risk prediction and minimization, respectively. In this part, we review and categorize the optimization/ prescriptive analytic models that focus on minimizing crash risk. Although the majority of works in this segment of the literature are related to the hazardous materials (hazmat) trucking problems, we show that (with some exceptions) many can also be utilized in non-hazmat scenarios. In an effort to highlight the effect of crash risk prediction model on the accumulated risk obtained from the prescriptive model, we present a simulated example where we utilize four risk indicators (obtained from logistic regression, Poisson regression, XGBoost, and neural network) in the k-shortest path algorithm. From our example, we demonstrate two major designed takeaways: (a) the shortest path may not always result in the lowest crash risk, and (b) a similarity in overall predictive performance may not always translate to similar outcomes from the prescriptive models. Based on the review and example, we highlight several avenues for future research.

A New Framework to Evaluate Crash Risk for Road Traffic Safety Management System

2021 ◽  
pp. 573-587
Author(s):  
Fabio Porcu ◽  
Francesca Maltinti ◽  
Nicoletta Rassu ◽  
Francesco Pili ◽  
Michela Bonera ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Management System ◽  
Safety Management ◽  
Road Traffic ◽  
Crash Risk ◽  
Safety Management System ◽  
Road Traffic Safety ◽  
New Framework

scholarly journals Perception of Law Enforcement Officers on Preventing Road Traffic Injury in Vanuatu: A Qualitative Study

2021 ◽  
Vol 9 ◽  
Author(s):  
Saen Fanai ◽  
Masoud Mohammadnezhad ◽  
Mosese Salusalu
Keyword(s):  
Risk Factors ◽  
Law Enforcement ◽  
Traffic Safety ◽  
Traffic Control ◽  
Road Traffic ◽  
Law Enforcement Officers ◽  
Daily Basis ◽  
Control Measures ◽  
Future Research ◽  
Road Traffic Safety

Background: Road Traffic Injuries (RTIs) cause approximately 1. 35 million deaths annually, and is the leading cause of death among people between ages 5 and 29. Law Enforcement Officers (LEOs) deal with Road Traffic Collisions (RTCs) and have contact with RTI victims at a daily basis, they possess an excellent perspective on preventing RTI. This study aimed to explore LEOs perceptions on risk factors and preventive measures of RTI in Vanuatu.Methods: This study employed qualitative methods that used Focus Group Discussions (FGDs) to gather data from 25 LEOs between October 14th and November 30th, 2020. Self-identified Ni-Vanuatu LEOs who have been serving for over 6 months and residing at the study setting were included in this study. Purposive sampling was used to recruit study participants from three municipalities in Vanuatu. A semi-structured open ended questionnaire was designed to guide the FGDs. Data obtained were sorted out using thematic analysis processed with some preconceived themes based on theory, and also allowing the data to determine new themes.Results: Data saturation was reached from conducting 5 FGDs with 25 LEOs who were traffic officers and municipal wardens. Five main themes and sixteen subthemes were generated from the study. The main themes include driving and alcohol, the challenges to effective enforcement, barriers to effective care and support for RTI victims, measures for road traffic control and promoting road traffic safety. The respondents perceived that addressing resources issues and the legislations on road traffic control act and vehicle regulation act will enhance prevention of RTI.Conclusion: This study explored the risk factors of RTI and the barriers to effectively prevent RTI in Vanuatu. The study also generated suggestions of a combination of road traffic control measures that could be implemented to prevent RTI. Future research should look at effective strategies of preventing RTIs in resource deficit settings.

scholarly journals Identification of Traffic Accident Hotspots using Geographical Information System (GIS)

2019 ◽  
Vol 9 (2) ◽  
pp. 4429-4438
Keyword(s):  
Information System ◽  
Geographical Information System ◽  
Statistical Methods ◽  
Traffic Safety ◽  
Traffic Accident ◽  
Road Traffic ◽  
Geographical Information ◽  
Future Research ◽  
Hotspot Analysis ◽  
Road Traffic Safety

Limiting the number andseverity of traffic accidents is one of the major goals of road traffic safety management.The alarming rate of road accidents globally emphasizes the importance of an effective traffic safety management system. Identification of accident hotspots is the first step towards implementation of efficient traffic safety management.Until the arrival of Geographical Information System (GIS),traffic accident analyses have been performed based ontraditional statistical methods alone. The advent of GIS-based techniques has led toimproved traffic accident analysis by employing spatial statistics,enabling engineers and researchers to account for variation in the spatial characteristics of hotspot locations in the analysis. This paper discusses the different spatial and statistical methods that are employedintraffic accident hotspots identification. An example application of Planar Kernel Density Estimation (PKDE)for hotspot identification is presented based on crash data for Des Moines city of Iowa state. The effect of varying bandwidths in creating density mapsis investigated and the optimum bandwidth to obtain distinct hotspots is identified as 500 m for the chosen study area.The paper also discusses the scope for future research in traffic accident hotspot analysis.

Safety Enhancement by Automated Driving: What are the Relevant Scenarios?

2020 ◽  
Vol 64 (1) ◽  
pp. 1686-1690
Author(s):  
Niklas Grabbe ◽  
Michael Höcher ◽  
Alexander Thanos ◽  
Klaus Bengler
Keyword(s):  
Traffic Safety ◽  
Road Traffic ◽  
Test Cases ◽  
Automated Driving ◽  
Road Traffic Safety ◽  
Human Driver ◽  
Driving Task ◽  
Promising Solution ◽  
Vehicle Automation ◽  
Safety Enhancement

Automated driving offers great possibilities in traffic safety advancement. However, evidence of safety cannot be provided by current validation methods. One promising solution to overcome the approval trap (Winner, 2015) could be the scenario-based approach. Unfortunately, this approach still results in a huge number of test cases. One possible way out is to show the current, incorrect path in the argumentation and strategy of vehicle automation, and focus on the systemic mechanisms of road traffic safety. This paper therefore argues the case for defining relevant scenarios and analysing them systemically in order to ultimately reduce the test cases. The relevant scenarios are based on the strengths and weaknesses, in terms of the driving task, for both the human driver and automation. Finally, scenarios as criteria for exclusion are being proposed in order to systemically assess the contribution of the human driver and automation to road safety.

scholarly journals Assessing inequality, irregularity, and severity regarding road traffic safety during COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lei Lin ◽  
Feng Shi ◽  
Weizi Li
Keyword(s):  
New York ◽  
Los Angeles ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Human Mobility ◽  
Critical Conditions ◽  
Road Traffic Safety ◽  
Accident Data ◽  
The Impact

AbstractCOVID-19 has affected every sector of our society, among which human mobility is taking a dramatic change due to quarantine and social distancing. We investigate the impact of the pandemic and subsequent mobility changes on road traffic safety. Using traffic accident data from the city of Los Angeles and New York City, we find that the impact is not merely a blunt reduction in traffic and accidents; rather, (1) the proportion of accidents unexpectedly increases for “Hispanic” and “Male” groups; (2) the “hot spots” of accidents have shifted in both time and space and are likely moved from higher-income areas (e.g., Hollywood and Lower Manhattan) to lower-income areas (e.g., southern LA and southern Brooklyn); (3) the severity level of accidents decreases with the number of accidents regardless of transportation modes. Understanding those variations of traffic accidents not only sheds a light on the heterogeneous impact of COVID-19 across demographic and geographic factors, but also helps policymakers and planners design more effective safety policies and interventions during critical conditions such as the pandemic.

scholarly journals Classification of Russian cities by levels of road traffic safety: specifics of required management decisions

2018 ◽  
Vol 170 ◽  
pp. 05009
Author(s):  
Artur Petrov ◽  
Daria Petrova
Keyword(s):  
Regression Analysis ◽  
Russian Federation ◽  
Traffic Safety ◽  
Road Traffic ◽  
Spatial Differentiation ◽  
Rate Heterogeneity ◽  
Accident Rate ◽  
Road Traffic Safety ◽  
Relative Disadvantage

The article considers the results of research of accident rate heterogeneity in cities-administrative centers of subjects of Russian Federation (2015, 2016). Using methods of ranging, regression analysis and spatial differentiation these cities were classified into 5 classes on the basis of relative disadvantage in road traffic safety sphere. For each group of cities differentiated recommendations on financing regional road traffic safety programs were suggested.

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