Traffic Risk Research Articles

COVID Vaccine Hesitancy and Risk of a Traffic Crash

BackgroundCoronavirus disease (COVID) vaccine hesitancy is a reflection of psychology that might also contribute to traffic safety. We tested whether COVID vaccination was associated with the risks of a traffic crash. MethodsWe conducted a population-based longitudinal cohort analysis of adults and determined COVID vaccination status through linkages to individual electronic medical records. Traffic crashes requiring emergency medical care were subsequently identified by multicenter outcome ascertainment of all hospitals in the region over a 1-month follow-up interval (178 separate centers). ResultsA total of 11,270,763 individuals were included, of whom 16% had not received a COVID vaccine and 84% had received a COVID vaccine. The cohort accounted for 6682 traffic crashes during follow-up. Unvaccinated individuals accounted for 1682 traffic crashes (25%), equal to a 72% increased relative risk compared with those vaccinated (95% confidence interval, 63-82; P < 0.001). The increased traffic risks among unvaccinated individuals extended to diverse subgroups, was similar to the relative risk associated with sleep apnea, and was equal to a 48% increase after adjustment for age, sex, home location, socioeconomic status, and medical diagnoses (95% confidence interval, 40-57; P < 0.001). The increased risks extended across the spectrum of crash severity, appeared similar for Pfizer, Moderna, or other vaccines, and were validated in supplementary analyses of crossover cases, propensity scores, and additional controls. ConclusionsThese data suggest that COVID vaccine hesitancy is associated with significant increased risks of a traffic crash. An awareness of these risks might help to encourage more COVID vaccination.

Risk Assessment Model and Sensitivity Analysis of Ordinary Arterial Highways Based on RSR–CRITIC–LVSSM–EFAST

In this paper, in order to evaluate the traffic safety status of ordinary arterial highways, identify the sources of safety risks, and formulate safety development countermeasures for arterial highways to reduce accident risks, a combination method involving rank-sum ratio (RSR), criteria importance though intercriteria correlation (CRITIC), and least squares support vector machine (LVSSM) is adopted. The traffic safety risk index system and risk assessment model of ordinary arterial highways with two dimensions of risk severity and accident severity are established. Based on the global sensitivity analysis of the extended Fourier amplitude sensitivity test (EFAST), the resulting risk assessment model for ordinary arterial highways is proposed. Combined with the current traffic safety situation of ordinary arterial highways in Weinan City, Shaanxi Province, China, data collection and analyses were carried out from the perspectives of traffic operation status, personnel facilities management, road environment characteristics, and accident occurrence patterns. The results show that the risk level of ordinary arterial highways can be obviously divided into warning areas, control areas, and prompt areas. The proportion of roads through villages and the number of deceleration facilities belong to the highly sensitive indicators of the S107 safety risk, which need to be emphatically investigated. This analysis method based is on the RCLE (RSR-CRITIC-LVSSM-EFAST) risk assessment model and has high operability and adaptability. It can be adaptively divided according to the requirements of risk-level differentiation, and the road risk classification can be displayed more intuitively, which is conducive to formulating targeted improvement measures for arterial highway safety and ensuring the safe and orderly operation of arterial highway traffic.

A Statistical Simulation Model for the Analysis of the Traffic Flow Reliability and the Probabilistic Assessment of the Circulation Quality on a Freeway Segment

Measuring the traffic quality and congestion level is fundamental in highway engineering, and several decades of studies and research have pursued this specific objective, especially for freeways. Nowadays, smart technologies on personal devices and information shared by users have made available various online information platforms that provide dynamic representations of the use of the road network. If, on the one hand, these tools provide a simple and direct representation of the quality of circulation, on the other hand, their aggregated information is only partial for those dealing with traffic and highway engineering. This branch of engineering relies on multidimensional knowledge of traffic flow phenomena, and only through their in-depth knowledge, we can assess traffic quality and congestion risk. After identifying the different approaches for analyzing in quantitative terms the traffic quality on the freeway, the paper deepens the reliability approach. From this point of view, the paper aims to unite the two perspectives in the literature, namely, the probabilistic analysis of traffic instability with the characterization of speed random processes and the analysis of breakdowns with the survival analysis. For this purpose, the work outlines a procedure based on the estimation and simulation of ARIMA models for speed random processes in a freeway section, particularly on the leftmost lane, to assess the traffic reliability function. Applying the Product Limit Method to the Monte Carlo simulation results makes it possible to obtain probabilistic assessments of congestion, considering the Level of Service density limits defined in the Highway Capacity Manual. Its application to a case study makes it possible to illustrate the application of the method, which can be easily applied to historical and near-real-time data using a continuous flow of information.

Failure Mode and Effects Analysis (FMEA) for Traffic Risk Assessment Based on Unbalanced Double Hierarchy Linguistic Term Set

Failure Mode and Effects Analysis (FMEA) for Traffic Risk Assessment Based on Unbalanced Double Hierarchy Linguistic Term Set

A Framework for Auditing Multilevel Models using Explainability Methods

Multilevel models (MLMs) are increasingly deployed in industry across different functions. Applications usually result in binary classification within groups or hierarchies based on a set of input features. For transparent and ethical applications of such models, sound audit frameworks need to be developed. In this paper, an audit framework for technical assessment of regression MLMs is proposed. The focus is on three aspects: model, discrimination, and transparency &amp; explainability. These aspects are subsequently divided into sub-aspects. Contributors, such as inter MLM-group fairness, feature contribution order, and aggregated feature contribution, are identified for each of these sub-aspects. To measure the performance of the contributors, the framework proposes a shortlist of KPIs, among others, intergroup individual fairness (DiffInd_MLM) across MLM-groups, probability unexplained (PUX) and percentage of incorrect feature signs (POIFS). A traffic light risk assessment method is furthermore coupled to these KPIs. For assessing transparency &amp; explainability, different explainability methods (SHAP and LIME) are used, which are compared with a model intrinsic method using quantitative methods and machine learning modelling. Using an open-source dataset, a model is trained and tested and the KPIs are computed. It is demonstrated that popular explainability methods, such as SHAP and LIME, underperform in accuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution (negative versus positive contribution on the outcome). For other contributors, such as group fairness and their associated KPIs, similar analysis and calculations have been performed with the aim of adding profundity to the proposed audit framework. The framework is expected to assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit providers, users, and assessment bodies, as defined in the European Commission’s proposed Regulation on Artificial Intelligence, when deploying AI-systems such as MLMs, to be future-proof and aligned with the regulation.

A Framework for Auditing Multilevel Models using Explainability Methods

Multilevel models (MLMs) are increasingly deployed in industry across different functions. Applications usually result in binary classification within groups or hierarchies based on a set of input features. For transparent and ethical applications of such models, sound audit frameworks need to be developed. In this paper, an audit framework for technical assessment of regression MLMs is proposed. The focus is on three aspects: model, discrimination, and transparency &amp; explainability. These aspects are subsequently divided into sub-aspects. Contributors, such as inter MLM-group fairness, feature contribution order, and aggregated feature contribution, are identified for each of these sub-aspects. To measure the performance of the contributors, the framework proposes a shortlist of KPIs, among others, intergroup individual fairness (DiffInd_MLM) across MLM-groups, probability unexplained (PUX) and percentage of incorrect feature signs (POIFS). A traffic light risk assessment method is furthermore coupled to these KPIs. For assessing transparency &amp; explainability, different explainability methods (SHAP and LIME) are used, which are compared with a model intrinsic method using quantitative methods and machine learning modelling.&#x0D; Using an open-source dataset, a model is trained and tested and the KPIs are computed. It is demonstrated that popular explainability methods, such as SHAP and LIME, underperform in accuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution (negative versus positive contribution on the outcome). For other contributors, such as group fairness and their associated KPIs, similar analysis and calculations have been performed with the aim of adding profundity to the proposed audit framework. The framework is expected to assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit providers, users, and assessment bodies, as defined in the European Commission’s proposed Regulation on Artificial Intelligence, when deploying AI-systems such as MLMs, to be future-proof and aligned with the regulation.

Traffic conflicts in the lane-switching sections at highway reconstruction zones

Introduction: There are designated sections for lane-shifting in several highway reconstruction and expansion zones. Similar to the bottleneck sections of highways, these sections are characterized by poor pavement surface conditions, disorderly traffic flow, and high safety risk. This study examined the continuous track data of 1,297 vehicles collected using an area tracking radar. Method: The data from the lane shifting sections were analyzed in contrast with the regular section data. Further, the single-vehicle attributes, traffic flow factors, and the respective road characteristics in the lane-shifting sections were also taken into account. In addition, the Bayesian network model was established to analyze the uncertain interaction between the various other influencing factors. The K-Fold cross validation method was used to evaluate the model. Results: The results showed that the model has a high reliability. The analysis of the model revealed that the significant influencing factors in decreasing order of their influence on the traffic conflict are: the curve radius, cumulative turning angle per unit length, standard deviation of the single-vehicle speed, vehicle type, average speed, and the standard deviation of the traffic flow speed. The probability of traffic conflicts is estimated to be 44.05% when large vehicles pass through the lane- shifting section while it is 30.85% for small vehicles. The probabilities of traffic conflict are 19.95%, 34.88%, and 54.79% when the turning angles per unit length are 0.20 °/m, 0.37 °/m, and 0.63 °/m, respectively. Practical Applications: The results support the view that the highway authorities help reduce traffic risks on lane change sections by diverting large vehicles, implementing speed limits on road sections, and increasing the turning angle per unit length of vehicles.

Vulnerability of road transportation networks under natural hazards: A bibliometric analysis and review

The vulnerability of the road transportation networks' infrastructures worldwide that can adversely be affected by the extreme weather conditions needs comprehensive assessment for reliable performance, mitigating the traffic hindrance and risks of accidents. Despite many efforts a clear understanding of the vulnerability of road transportation networks remains deficient. Based on this fact, this work bibliometrically analyzed the vulnerability of road transportation networks using 635 state-of-the-art articles published (Scopus database) during 1975–2020. The main purpose was to examine all-inclusively the evolution of diverse scientific studies in this field to ascertain the future research trends and benefits. In addition, the recent research progress and future drifts concerning the vulnerability of transportation networks were evaluated based on the published papers contributed by authors from various nations and co-incidences of authors’ keywords. The proposed bibliometric analyses disclosed that the publications related to the vulnerability in transportation networks have significantly been increased since 2013. Approximately 70.6% countries showed research collaborations or partnership with at least one country. The USA and China presented a significant increase in the research trends in this area. It was found that the vulnerability studies have evolved significantly to include multi-dimensional evaluations, optimization approaches, multi-objective algorithm in order to minimize the disaster impacts and a GIS-based framework to create automated solution on the impacts of transportation network due to natural disasters. There has been a gradual acceptance that the benefit-cost ratios associated with vulnerability reduction are greatly favorable for the mitigation of the disaster risk to make economic sense.

A Geographical and Temporal Risk Evaluation Method for Red-Light Violations by Pedestrians at Signalized Intersections: Analysis and Results of Suzhou, China.

Red-light violations of pedestrians crossing at signal intersections is one of the key factors in pedestrian traffic accidents. Even though there are various studies on pedestrian behavior and pedestrian traffic conflicts, few focus on the risk of different crosswalks for the violating pedestrian group. Due to the spatio-temporal nature of violation risk, this study proposes a geographical and temporal risk evaluation method for pedestrian red-light violations, which combines actual survey and video acquisition. First, in the geographical-based risk evaluation, the pedestrian violation rate at signal intersections is investigated by Pearson correlation analysis to extract the significant influencing factors from traffic conditions, built environment, and crosswalk facilities. Second, in the temporal-based risk evaluation, the survival analysis method is developed to quantify the risk of pedestrian violation in different scenarios as time passes by. Finally, this study selects 16 typical signalized intersections in Suzhou, China, with 881 pedestrian crosswalk violations from a total size of 4586 pedestrians as survey cases. Results indicate that crossing distance, traffic volume on the crosswalk, red-light time, and crosswalk-type variables all contribute to the effect of pedestrian violation from a geographical perspective, and the installation of waiting refuge islands has the most significant impact. From the temporal perspective, the increases in red-light time, number of lanes, and traffic volume have a mitigating effect on the violations with pedestrian waiting time increases. This study aims to provide a development-oriented path by proposing an analytical framework that reconsiders geographical and temporal risk factors of violation. The findings could help transport planners understand the effect of pedestrian violation-related traffic risk and develop operational measures and crosswalk design schemes for controlling pedestrian violations occurring in local communities.

Traffic Inflow and Outflow Forecasting by Modeling Intra- and Inter-Relationship Between Flows

Forecasting traffic inflows and outflows is crucial for intelligent transportation applications such as traffic management and risk assessment. Recently, deep learning models, which focus on capturing spatio-temporal correlations between stations (locations) by constructing Spatio-Temporal Feature Learners (STFL), have achieved promising performance in traffic inflows and outflows prediction. However, two unresolved issues limit the performance of these models. i) dynamic and heterogeneous intra- and inter-relationships between flows are ignored, and ii) the STFL in these models cannot capture the global information. To address the above issues, we propose a novel deep Spatio-Temporal Network framework based on Multi-Relational learning (MR-STN) for predicting traffic inflows and outflows. Specifically, a multi-relational learning module is designed to comprehensively model three kinds of relationships between flows while extracting diverse spatio-temporal features. In this module, an enhanced STFL is developed to capture both local and global information. Then, a feature fusion module is introduced to extract fused features for inflows and outflows respectively via a gated fusion mechanism. On this basis, the prediction module uses fusion features to generate future inflows and outflows. Finally, we implement the proposed framework with four state-of-the-art graph-based deep spatio-temporal models to demonstrate its generality and superiority. Extensive experiments on three datasets show that the proposed framework can significantly boost the performance of existing models.

Preparation and evaluation of a fluorinated nano-silica superhydrophobic coating for cement pavement

To reduce the traffic risk of pavement icing, a fluorinated nano-silica superhydrophobic coating for cement pavement was prepared with fluorinated nano-silica, carbon black, and waterborne epoxy resin. The component and spraying dosage were analyzed for optimal performance by combining the fluorination verification, component contents determination, and spraying dosage optimization. The engineering performance was evaluated for applicability, basing anti-icing performance and pavement durability. The test results showed that the fluorine group has successfully drafted on the nano-silica. The determined content of nano-silica is 75 wt% and of carbon black is 17.5 wt% of waterborne epoxy resin. The optimal application amount is 0.5 L/m2 to balance the requirement of superhydrophobic property and skid resistance. Meanwhile, the prepared coating has a good engineering performance. It can boost 70 % ability to repulse incoming water droplets, delay 40 % time of ice formation, and increase 40 % ratio of ice-layer removal compared with the uncoated. It can also maintain the basic appearance and superhydrophobic property after about 150,000 times wheel abrasion or three high-low temperature cycles. This demonstrates that the prepared coating has a good potential for practical use.

Assessment of Chennai traffic using a queuing model

The key problem of most Tamil Nadu towns is traffic congestion due to the rising number of vehicles on the road. In order to reduce traffic risks, higher fuel consumption and the production of unburned hydrocarbons in the surrounding area require adequate analysis of traffic movements. The queuing-based analytical approach is used in this research to analyze the flow of vehicle traffic. The M/M/1 queuing model is used as the aggregated trivial resolution model to analyze the traffic approaching the Koyambedu intersection for arrival and waiting time to define the spatial and temporal effects of traffic on various days. The adopted approach defines the important factors that cause traffic congestion and will also refer to the large scenario for deciding the traffic flow pattern.

Gaze tracking patterns on different types of traffic risk in early-stage Alzheimer’s disease

Many fatal accidents are caused by the driver’s inability, upon the occurrence of sudden events, to detect hazards in time, an ability that is based on the driver’s eye movement patterns, the detection of involved persons, events, objects, as well as the length of time spent on noticing same. This study intends to compare risk perception and decision-making in high-risk traffic situations between individuals with AD and controls by exploring gaze tracking and hazard detection patterns. The study included 16 individuals with early-stage AD and 26 controls. During the experiment, participants underwent several vision and cognitive tests and watched twelve videos of high-risk traffic situations while their eye movement patterns were being recorded by the eye tracking device, in order to understand how cognitive abilities affect eye movement patterns during high-risk traffic situations. The results revealed poorer cognitive abilities and diminished risk perception in individuals with AD compared with the controls and thus an inability to predict potential accidents. In terms of high-risk traffic situations involving pedestrians, traffic violations by others and view-blocking events, a significant difference in gaze patterns was found between individuals with AD and healthy seniors. The contribution of this study is in elaborating the effect of different traffic event type and specific cognitive abilities on traffic risk-detection in individuals with AD. The results of this study may aid in the formulation of driver licensing policies and the design of traffic scenarios; they also elucidate the driving behaviors of individuals with AD.

A Method for Locational Risk Estimation of Vehicle-Children Accidents Considering Children's Travel Purposes.

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.

Comparing Resampling Algorithms and Classifiers for Modeling Traffic Risk Prediction.

Road infrastructure has significant effects on road traffic safety and needs further examination. In terms of traffic crash prediction, recent studies have started to develop deep learning classification algorithms. However, given the uncertainty of traffic crashes, predicting the traffic risk potential of different road sections remains a challenge. To bridge this knowledge gap, this study investigated a real-world expressway and collected its traffic crash data between 2013 and 2020. Then, according to the time-spatial density ratio (Pts), road sections were assigned into three classes corresponding to low, medium, and high risk levels of traffic. Next, different classifiers were compared that were trained using the transformed and resampled feature data to construct a traffic crash risk prediction model. Last, but not least, partial dependence plots (PDPs) were employed to interpret the results and analyze the importance of individual features describing the geometry, pavement, structure, and weather conditions. The results showed that a variety of data balancing algorithms improved the performance of the classifiers, the ensemble classifier superseded the others in terms of the performance metrics, and the combined SMOTEENN and random forest algorithms improved the classification accuracy the most. In the future, the proposed traffic crash risk prediction method will be tested in more road maintenance and design safety assessment scenarios.

Reading between the Regulations: Parking Requirements, Planners’ Perspectives, and Transit

This article reports on local planers’ perspectives on metropolitan parking requirements. Workplace parking requirements, which are often in excess of demand, influence parking pricing and urban form. In turn, these affect transit demand and transit service potentials. These connections have led researchers and policy-makers to call for changes, but the perspectives of planners who create the parking requirements are not well understood. Using southern California cities as a study area, a telephone survey revealed that most parking requirements are driven by concerns about traffic mitigation, spillover parking, and risk avoidance. These factors push parking requirements in the direction of oversupply. The article proposes methods to reduce the risk of changing parking requirements and develops a typology of approaches for change. Transit agencies will benefit if they play a role in reforming local parking requirements.

Review of “Dead Reckoning: Air Traffic Control, System Effects, and Risk”

Review of “Dead Reckoning: Air Traffic Control, System Effects, and Risk”

Vehicle Driving Risk Prediction Model by Reverse Artificial Intelligence Neural Network.

The popularity of private cars has brought great convenience to citizens' travel. However, the number of private cars in society is increasing yearly, and the traffic pressure on the road is also increasing. The number of traffic accidents is increasing yearly, and the vast majority are caused by small private cars. Therefore, it is necessary to improve the traffic safety awareness of drivers and help car manufacturers to design traffic risk prediction systems. The Backpropagation neural network (BPNN) algorithm is used as the technical basis, combined with the MATLAB operation program, to simulate the driving process of the car. Dynamic predictive models are built to predict and analyze vehicle safety risks. Multiple experiments found that: (1) in various simulations, the simulation driving process of MATLAB is more in line with the actual car driving process; (2) the error between BPNN and the actual driving prediction is within 0.4, which can meet the actual needs. Predictive models are optimized to deploy and predict in various traffic situations. The model can effectively prompt risk accidents, reduce the probability of traffic accidents, provide a certain degree of protection for the lives of drivers and passengers, and significantly improve the safety of traffic roads.

Comparative Analysis of Influencing Factors on Crash Severity between Super Multi-Lane and Traditional Multi-Lane Freeways Considering Spatial Heterogeneity.

With the growth of traffic demand, the number of newly built and renovated super multi-lane freeways (i.e., equal to or more than a two-way ten-lane) is increasing. Compared with traditional multi-lane freeways (i.e., a two-way six-lane or eight-lane), super multi-lane freeways have higher design speeds and more vehicle interweaving movements, which may lead to higher traffic risks. However, current studies mostly focus on the factors that affect crash severity on traditional multi-lane freeways, while little attention is paid to those on super multi-lane freeways. Therefore, this study aims to explore the impacting factors of crash severity on two kinds of freeways and make a comparison with traditional multi-lane freeways. The crash data of the Guangzhou-Shenzhen freeway in China from 2016 to 2019 is used in the study. This freeway contains both super multi-lane and traditional multi-lane road sections, and data on 2455 crashes on two-way ten-lane sections and 13,367 crashes on two-way six-lane sections were obtained for further analysis. Considering the effects of unobserved spatial heterogeneity, a hierarchical Bayesian approach is applied. The results show significant differences that influence the factors of serious crashes between these two kinds of freeways. On both two types of freeways, heavy-vehicle, two-vehicle, and multi-vehicle involvements are more likely to lead to serious crashes. Still, their impact on super multi-lane freeways is much stronger. In addition, for super multi-lane freeways, vehicle-to-facility collisions and rainy weather can result in a high possibility of serious crashes, but their impact on traditional multi-lane freeways are not significant. This study will contribute to understanding the impacting factors of crash severity on super multi-lane freeways and help the future design and safety management of super multi-lane freeways.

A novel context-aware system to improve driver’s field of view in urban traffic networks

Principal objectives of the Intelligent Transportation Systems (ITS) are to improve traffic safety, facilitate informed traffic decision making, and enhance quality of life and services in a smart traffic environment. Vehicle crashes at urban traffic intersections are among the rudimentary sources of injuries and fatalities in the cities. According to the report of the World Health Organization (WHO), in every 25 seconds, one vulnerable road-user is being killed by a vehicle crash. Therefore, it is necessary to take a novel and smart approach for improving the safety and reducing vehicle crashes. This leads to a contextual perception and spatial awareness of driver to increase security and safety for the driver, vehicle, and road users. Autonomous vehicles collects the information from the environment through equipped sensors on the vehicle such as camera, laser, radar, and Global Navigation Satellite Systems (GNSS). The main challenge arises when the person or objects are located beyond the driver’s Field of View (FOV) and cannot be detected by embedded sensors on the vehicles. This paper proposes an Advanced Driver Assistance System (ADAS) to increase the safety on road intersections by taking advantage of existing infrastructures (e.g road camera) being used for traffic control. The aim of this research is improving the driver’s FOV using a computer vision approach (e.g background subtraction algorithm) and Location Based Service (LBS). The case study results at Tehran metropolitan demonstrate the reduction in traffic collision risk and improvement of pedestrian safety using the proposed system.