Modeling correlation and heterogeneity in crash rates by collision types using full bayesian random parameters multivariate Tobit model

Bayesian hybrid gamma-GPD model for extreme traffic conflict threshold determination in the peak over threshold approach

Extreme value theory (EVT) models have been frequently utilized to estimate crash risk from traffic conflicts with the peak over threshold commonly used to identify conflict extremes. However, a common problem for the peak over threshold method is the selection of a suitable threshold to distinguish general and extreme conflicts. Subjective and arbitrary selection of the threshold in peak over threshold method can result in bias and unstable estimation results. The primary objective of the study is to propose a hybrid modelling approach for the threshold determination in peak over threshold method. The hybrid model consists of a joint gamma distribution and generalized Pareto distribution (GPD). The gamma distribution is used to fit general conflicts while the GPD is used to fit extreme conflicts. Specially, discontinued, continued and differentiable gamma-GPD models are developed with the threshold being treated as a model parameter. Traffic conflict data collected from three signalized intersections in the city of Surrey, British Columbia were used for the study. The modified time to collision (MTTC) was employed as conflict indicator. The Bayesian approach was employed to estimate the threshold as well as other hybrid gamma-GPD model parameters. The results show that the discontinued gamma-GPD model is superior to the continued and differentiable gamma-GPD models for determining the threshold in terms of crash estimation accuracy and model fit. The crash estimates using the threshold determined by the hybrid gamma-GPD model outperform those estimated based on the traditional quantile plots method, indicating that the superiority of the proposed threshold determination approach based on gamma-GPD hybrid model. The proposed hybrid gamma-GPD model could determine the threshold parameter in peak over threshold method for traffic conflicts extremes automatically in an objective and quantitative way. It contributes to existing peak over threshold method for producing reliable crash estimation.

Experimental investigation of the multianticipation mechanism in commercial SAE level 2 automated driving vehicles and associated safety impact

Extensive experimental analyses concerned with Adaptive Cruise Control (ACC) have clearly shown that such systems have failed to deliver the promise of safe and traffic-flow effective car-following. On the contrary, large reaction times and poor string stability performances characterize commercial ACCs. While a huge research line is investigating the introduction of communication among vehicles to overcome the mentioned limitation, market adoption of connectivity-enhanced vehicles is struggling. In this context, an alternative approach based on multiple vehicle anticipation using RADAR only has emerged. Multianticipation is definitely not a new concept within the transportation community. However, until now, it was mainly associated with human driving. In the present manuscript, we demonstrate instead how, at least, one vehicle manufacturer has implemented multianticipation on a commercial vehicle. Following an in-house carried out testing campaign, we give an experimental characterization of the functioning of such a system including the potential impact on the flow and safety using a state-of-the-art fuzzy-logic safety performance model. The first results demonstrate that the vehicle under test reacted to one additional vehicle in front of the leader vehicle. Moreover, the actual realization appears to mainly target safety applications whereas there is only a marginal benefit on the string stability characteristics of the system. While we recorded a marginal string stability improvement (about 10 %), the minimum TTC was twice as large when multianticipation occurred with respect to the cases when that was not activated. Relevant Fuzzy Surrogate Safety Metrics further supported the safety argument.

A study on diversion behavior in weaving segments: Individualized traffic conflict prediction and causal mechanism analysis

Lane change behavior disrupts traffic flow and increases the potential for traffic conflicts, especially on expressway weaving segments. Focusing on the diversion process, this study incorporating individual driving patterns into conflict prediction and causation analysis can help develop individualized intervention measures to avoid risky diversion behaviors. First, to minimize measurement errors, this study introduces a lane line reconstruction method. Second, several unsupervised clustering methods, including k-means, agglomerative clustering, gaussian mixture, and spectral clustering, are applied to explore diversion patterns. Moreover, machine learning methods, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Attention-based LSTM, eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), are employed for real-time traffic conflict prediction. Finally, mixed logit models are developed using pre-conflict condition data to investigate the causal mechanisms of traffic conflicts. The results indicate that the K-means algorithm with four clusters exhibits the highest Calinski-Harabasz and Silhouette scores and the lowest Davies-Bouldin scores. With superior classification accuracy and generalization ability, the LSTM is used to develop the personalized traffic conflict prediction model. Sensitivity analysis indicates that incorporating the diversion patterns into the LSTM model results in an improvement of 3.64% in Accuracy, 7.15% in Precision, and 1.34% in Recall. Results from the four mixed logit models indicate significant differences in factors contributing to traffic conflicts within each diversion pattern. For instance, increasing the speed difference between the target vehicle and the right preceding vehicle benefits traffic conflict during acceleration diversions but decreases the likelihood of traffic conflicts during deceleration diversions. These results can help traffic engineers propose individualized solutions to reduce unsafe diversion behavior.

Injury severity analysis of e-bike riders in China based on the in-vehicle recording video crash data: a random parameter ordered logit model

This study investigates the impacts of various factors on e-bike riders' injury severity in crashes with motor vehicles, based on the in-vehicle recording video crash data in China. Variables from human factors, vehicle characteristics, road conditions, and environmental attributes are extracted from the video, especially for drivers and riders' illegal and avoidance behaviour before the crash, and sun shade canopy use. Results of mixed logit models reveal that drivers' speeding, running red lights, slow-down and swerve behaviour, light trucks, heavy trucks, and buses have significantly varied impacts on riders' injury. Moreover, both drivers and riders' illegal behaviour leads to an increased injury, while their avoidance behaviour before crashes can protect riders. In addition, types of visual obstacles, accidents occurring at night, large vehicles' involvement, and the application of sunshade canopies by riders increased the probability of severe injury, while helmet use can protect riders in accidents with motor vehicles.

Analyzing the factors influencing speeding behavior based on quasi-induced exposure and random parameter logit model with heterogeneity in means

INTRODUCTION

Speeding behavior is a major threat to road traffic safety, which can increase crash risks and result in severe injury outcomes. Although several studies have been conducted to analyze speeding crashes and relevant influential factors, the heterogeneity of variables has not been fully explored. Based on the traffic crash data extracted from the Crash Report Sampling System, the study aims to identify the factors that influence speeding driving with the consideration of variable heterogeneity.

METHOD

Quasi-induced exposure technique is adopted to identify the disparities in the propensities of speeding for various driving cohorts. The random parameter logit model with heterogeneity in means is employed to examine the factors impacting speeding behavior.

RESULTS

Results indicate that: (a) driving cohorts such as young drivers, male drivers, passenger cars, and pickups appear to have higher propensities of engaging in speeding driving; (b) the propensity of speeding is higher when the driver is drinking, distracted, changing lanes, negotiating a curve, driving in lighted condition, and on curved roads; and (c) the random parameter logit model with heterogeneity in means has better performance as opposed to that without heterogeneity in means.

CONCLUSIONS

Speeding behavior can be influenced by various factors in terms of driver-vehicle characteristics, physical condition, driving actions, and environmental conditions.

PRACTICAL APPLICATIONS

The findings could serve to develop effective countermeasures to reduce speeding behavior and improve traffic safety.

Geographically weighted random forests for macro-level crash frequency prediction

Machine learning models such as random forests (RF) have been widely applied in the field of road safety. RF is a prominent algorithm, overcoming the limitations of using a single decision tree such as overfitting and instability. However, the traditional RF is a global concept, and thus may fail to capture spatial variability. In macro-level analysis of road safety, the relationship between crash frequency and risk factors can vary spatially. To address this issue, we employ a modified RF algorithm, named geographically weighted random forest (GWRF). Based on the data from London at the level of Middle-super-output-area (MSOA), the predictive performances of RF and GWRF are compared using mean absolute error (MAE) and root mean square error (RMSE). Moreover, considering MSOAs are geographically connected with each other, several factors related to the discrepancies between adjacent zones are also included in the models. Our results indicate that GWRF outperforms the traditional RF and GWR when an appropriate bandwidth is selected. We further explore the effects of multicollinearity on model performance. The results show that prediction accuracy of GWRF models are not susceptible to the multicollinearity. However, the importance values of those variables with multicollinearity may reduce. Finally, and of equal importance, it is found that the importance of each explanatory variable varies across zones. The density of minor road makes the highest contribution to crash frequency in downtown area, while the crash frequency in peripheral area is more sensitive to the discrepancy of road environment between MSOAs. With such information, road safety interventions can be designed and implemented according to the locally important factors, avoiding thus general guidelines addressed for the entire city.

How do people perceive driving risks in small towns? A case study in Central Texas

The number of studies investigating the relationship between perceived and objective traffic risk from drivers' perspective is limited. This study aims to investigate this dynamic within an understudied transportation environment - small towns in Texas, USA, defined as incorporated places with a population of less than 50,000. A web-based survey was distributed to six small towns in central Texas to ascertain perceptual traffic risk factors and personal characteristics. A participatory GIS exercise was also conducted to collect where high-risk locations were perceived and to correlate them to high crash zones. This study spatially examined the relations between perceived and observed risk locations and statistically identified a set of contributing factors which could make crash-intensive areas more perceivable by road users. The results indicated that road users' perceived risk locations are not always associated with high crash rates. The match rate between perceived and observed risk locations varied significantly across studied sites. We found that some personal and built environment factors significantly impacted people's sensitivity to perceiving crash-intensive locations. The binary logistic regression model was accurate (74.13%) in highlighting whether a perceived risk location matches observed risk locations. The results emphasize the importance of considering perceived and objective risk simultaneously to gain a better understanding of traffic risk mitigation, especially in underserved small towns.

A hybrid approach of random forest and random parameters logit model of injury severity modeling of vulnerable road users involved crashes

Vulnerable road users (VRUs) involved crashes are a major road safety concern due to the high likelihood of fatal and severe injury. The use of data-driven methods and heterogeneity models separately have limitations in crash data analysis. This study develops a hybrid approach of Random Forest based SHAP algorithm (RF-SHAP) and random parameters logit modeling framework to explore significant factors and identify the underlying interaction effects on injury severity of VRUs-involved crashes in Shenyang (China) from 2015 to 2017. The results show that the hybrid approach can uncover more underlying causality, which not only quantifies the impact of individual factors on injury severity, but also finds the interaction effects between the factors with random parameters and fixed parameters. Seven factors are found to have significant effect on crash injury severity. Two factors, including primary roads and rural areas produce random parameters. The interaction effects reveal interesting combination features. For example, even though rural areas and primary roads increase the likelihood of fatal crash occurrence individually, the interaction effect of the two factors decreases the likelihood of being fatal. The findings form the foundation for developing safety countermeasures targeted at specific crash groups for reducing fatalities in future crashes.

Are car safety systems associated with more speeding violations? Evidence from police records in Israel

Over the past decade, the popularity of installing advanced driver-assistance systems (ADAS) in cars has increased markedly. However, the effectiveness of ADAS is subject to debate, primarily because these systems intervene in drivers' perceptions and actions and could lead to adaptive behavior. Using complete national data for the installation of three leading safety systems and speeding tickets issued over the course of an entire year, allowed us to pinpoint the impact of these safety systems at a national level. Employing zero-inflated negative binomial regression models, we found that the installation of the three safety systems was associated with higher number of speeding tickets. These findings are in line with the literature that indicates adaptive behavior in the context of risk. However, when we accounted for the proneness to commit other traffic violations, the effect of the safety systems on the prevalence of speeding tickets was evident only for those prone to violations. Further research should be conducted to identify which drivers will be more likely to be affected and under what circumstances and safety system types.

Modeling spatiotemporal interactions in single-vehicle crash severity by road types

INTRODUCTION

Spatiotemporal correlations have been widely recognized in single-vehicle (SV) crash severity analysis. However, the interactions between them are rarely explored. The current research proposed a spatiotemporal interaction logit (STI-logit) model to regression SV crash severity using observations in Shandong, China.

METHOD

Two representative regression patterns-mixture component and Gaussian conditional autoregression (CAR)-were employed separately to characterize the spatiotemporal interactions. Two existing statistical techniques-spatiotemporal logit and random parameters logit-were also calibrated and compared with the proposed approach with the aim of highlighting the best one. In addition, three road types-arterial road, secondary road, and branch road-were modeled separately to clarify the variable influence of contributors on crash severity.

RESULTS

The calibration results indicate that the STI-logit model outperforms other crash models, highlighting that comprehensively accommodating spatiotemporal correlations and their interactions is a recommended crash modeling approach. Additionally, the STI-logit using mixture component fits crash observations better than that using Gaussian CAR and this finding remains stable across road types, suggesting that simultaneously accommodating stable and unstable spatiotemporal risk patterns can further strengthen model fit. According to the significance of risk factors, there is a significant positive correlation between distracted diving, drunk driving, motorcycle, dark (without street lighting), and collision with fixed object and serious SV crashes. Truck and collision with pedestrian significantly mitigate the likelihood of serious SV crashes. Interestingly, the coefficient of roadside hard barrier is significant and positive in branch road model, but it is not significant in arterial road model and secondary road model.

PRACTICAL APPLICATIONS

These findings provide a superior modeling framework and various significant contributors, which are beneficial for mitigating the risk of serious crashes.

Traffic conflict assessment using macroscopic traffic flow variables: A novel framework for real-time applications

The present study develops a comprehensive traffic conflict assessment framework using macroscopic traffic state variables. To this end, vehicular trajectories extracted for a midblock section of a ten-lane divided Western Urban Expressway in India are used. A macroscopic indicator termed "time spent in conflict (TSC)" is adopted to evaluate traffic conflicts. The proportion of Stopping distance (PSD) is adopted as a suitable traffic conflict indicator. Vehicle-to-vehicle interactions in a traffic stream are two-dimensional, highlighting that the vehicles interact simultaneously in lateral and longitudinal dimensions. Therefore, a two-dimensional framework based on the influence zone of the subject vehicle is proposed and employed to evaluate TSCs. The TSCs are modeled as a function of macroscopic traffic flow variables, namely, traffic density, speed, the standard deviation in speed, and traffic composition, under a two-step modeling framework. In the first step, the TSCs are modeled using a grouped random parameter Tobit (GRP-Tobit) model. In the second step, data-driven machine learning models are employed to model TSCs. The results revealed that intermediately congested traffic flow conditions are critical for traffic safety. Furthermore, macroscopic traffic variables positively influence the value of TSC, highlighting that the TSC increases with an increase in the value of any independent variable. Among different machine learning models, the random forest (RF) model was observed as the best-fitted model to predict TSC based on macroscopic traffic variables. The developed machine learning model facilitates traffic safety monitoring in real-time.

Modelling injury severity in single-vehicle crashes using full Bayesian random parameters multinomial approach

Single-vehicle (SV) crash severity model considering spatiotemporal correlations has been extensively investigated, but spatiotemporal interactions have not received sufficient attention. This research is dedicated to propose a superior spatiotemporal interaction correlated random parameters logit approach with heterogeneity in means and variances (STICRP-logit-HMV) for systematically characterizing unobserved heterogeneity, spatiotemporal correlations, and spatiotemporal interactions. Four flexible interaction formulations are developed to uncover the spatiotemporal interactions, including linear structure, Kronecker product, mixture-2 model, and mixture-5 model. Four candidate approaches-random parameters logit (RP-logit), RP-logit with heterogeneity in means and variances (RP-logit-HMV), correlated RP-logit-HMV (CRP-logit-HMV), and spatiotemporal CRP-logit-HMV (STCRP-logit-HMV)-are also established and compared with the proposed model. SV crash observations in Shandong Province, China, are employed to calibrate regression parameters. The model comparison results show that (1) the performance of the RP-logit-HMV model outperforms the RP-logit model, implying that capturing heterogeneity in the means and variances can strengthen model fit; (2) the CRP-logit-HMV model and the RP-logit-HMV model are comparable; (3) the STCRP-logit-HMV model outperforms the CRP-logit-HMV model, implying that addressing the spatiotemporal crash mechanisms is beneficial to the overall fitting of the crash model; (4) the STICRP-logit-HMV model performs better than the STCRP-logit-HMV model and this finding remains stable across different interaction formulations, indicating that comprehensively reflecting the spatiotemporal correlations and their interactions is a promising approach to model SV crashes. Among the four interaction models, the STICRP-logit-HMV model with mixture-5 component maintains the best fit, which is a recommended approach to model crash severity. The regression coefficients for young driver, male driver, and non-dry road surface are random across observations, suggesting that the influence of these factors on SV crash severity maintains significant heterogeneity effects. The research results provide transportation professionals with a superior statistical framework for diagnosing crash severity, which is beneficial for improving traffic safety.

Spatial influence of engineering construction on traffic accidents, a case study of Jinan

Due to urban construction, engineering transport vehicles are gradually increased on roads, which might speed up traffic accident risks. To investigate the influence of urban construction on traffic accidents, this paper adopted 1977 traffic accidents of engineering transport vehicles and 220 engineering construction projects for correlation analysis. First, considering three degrees (Major, Ordinary and Minor) of accidents, the spatial autocorrelation test of engineering transport vehicle accidents is carried out by using spatial econometric. Then to further evaluate and analyze the spatial regression model, the optimal model is selected to analyze the spatial influence of the floor area of different types of engineering construction projects on the accidents of engineering transport vehicles. The results show that the accident of engineering transport vehicles itself is spatially dependent, that is, the higher the severity of the accident, the more concentrated it is in space, and there is a significant spatial positive correlation with engineering construction projects. And the floor areas of synthetic land, residential land, commercial land and land for roads and transportation facilities have significant spatial effects on engineering transport vehicle accidents, and the indirect effects are also concerned. The increase of floor area of roads and transportation facilities is more likely to induce accidents of engineering transport vehicles. For every 10,000 square meters of the floor area of roads and transportation facilities, there are 12.66 accidents of engineering transport vehicles in the region and its neighboring areas.

Estimate of the magnitude of risky and protective behaviors associated with road traffic injuries in capitals participating in the Life in Traffic Project of Brazil

BACKGROUND

Brazil occupies the fifth position in the ranking of the highest mortality rates due to RTI in the world. With the objective of promoting traffic safety and consequently reducing deaths, Brazil created the Life in Traffic Project (LTP). The main goal of LTP is reducing 50% of RTI deaths, by promoting interventions to tackle risk factors, such as driving under the influence of alcohol and excessive and/or inappropriate speed. Thus, the aim of this study was to estimate the magnitude of risky and protective factors for RTI in capitals participating in the LTP in Brazil. We estimated these factors according to sociodemographic (age group, sex, education, race and, type of road user).

METHODS

A total of 5,922 car drivers and motorcyclists from 14 Brazilian capitals participating in the LTP were interviewed. Data collection was carried out in sobriety checkpoints at night and consisted of the administration of an interview and a breathalyzer test. Risky and protective behaviors associated with RTI were investigated. Covariates of the study were: age, sex, education, race and, type of road user. Poisson multiple regression analysis was used to assess the relationship between variables of interest.

RESULTS

The prevalence of individuals with positive blood alcohol concentration (BAC) was 6.3% and who reported driving after drinking alcohol in the last 30 days was 9.1%. The others risky behaviors reported were: driving at excessive speed on roads of 50 km/h, using a cell phone for calls while driving, using a cell phone to send or read calls while driving, running a red light. Use of seatbelts and helmets showed prevalence above 96,0% Use of seatbelts showed prevalence of 98.6% among car drivers, and helmet use was described by 96.6% of motorcycle drivers. Most risky behaviors were more prevalent in younger age groups (except BAC measurement higher in older participants), in males (except for cell phone use), in participants with higher education level and without a driver's license.

CONCLUSION

Excessive speed and driving under the influence of alcohol, defined as priorities within the LTP, need more consistent interventions, as they still have considerable prevalence in the cities investigated. The factors described such as cell phone usage and passing red traffic lights should also need to be prioritized as a focus on promoting traffic safety.

An empirical analysis of factors contributing to roadway infrastructure damage from expressway accidents: A Bayesian random parameters Tobit approach

This paper presents an empirical analysis of factors contributing to roadway infrastructure damage from expressway accidents, using a Bayesian random parameters Tobit model. The accident data collected from Kaiyang Expressway, China in 2014 and 2015 are used for the empirical analysis. The results of parameter estimation in the proposed model indicate that: the effects of vehicle types are significantly heterogeneous across observations, and that the effects of horizontal curvature, time of day, vehicle registered province, and accident type are also significant but homogeneous across observations. The marginal effects of these contributing factors are calculated to explicitly quantify their impacts on road infrastructure damage. According to the analysis results, some strategies pertaining to safety education, traffic enforcement, roadway design, and intelligence transportation technology are advocated to reduce road infrastructure damage from expressway accidents.

Spatial-Temporal Pattern and Influencing Factors of Ecological Efficiency in Zhejiang-Based on Super-SBM Method

The traditional meaning of ecological efficiency generally considers only the ratio of economic output to environmental input. This paper expands the meaning and the evaluation system of ecological efficiency from the perspective of improving people's livelihoods. Not only are the discharge of wastewater, waste gas, and solid waste included in the undesired output, but the output index also takes full account of the overall development of the economy, innovation, society and the environment from the perspective of high-quality development. Under the assumption of variable returns to scale, a super-efficiency slack-based measure model based on the undesirable output and Malmquist index is introduced to measure the spatial and temporal variation of ecological efficiency of Zhejiang Province in China, and the panel Tobit method is used to study the key factors affecting ecological efficiency. The results include the four following findings: (1) In the past 12 years, the ecological efficiency of Zhejiang Province has steadily increased, except in 2019 and 2020, when seven cities in Zhejiang Province experienced a decline or near stagnation due to the impact of the economic slowdown and the COVID-19 epidemic. (2) The ecological efficiency of Zhejiang demonstrates a severe regional imbalance, showing a high level in the northeast and a low level in the southwest. (3) Malmquist index analysis shows that the improvement of ecological efficiency in Zhejiang Province has shifted from mainly relying on the dual drivers of pure technical efficiency and scale efficiency in the early stage to relying on technological progress in the later stage. (4) Tobit regression analysis shows that industrialization structure, Theil index, and traffic activity have a significant positive effect on ecological efficiency.

Study on Risk of Long-Steep Downgrade Sections of Expressways Based on a Fuzzy Hierarchy Comprehensive Evaluation

The long-steep downgrade sections of expressways are characterized by a large elevation difference, poor horizontal and vertical alignment, and the easy failure of brakes on large trucks. They are sections with a high overall operation safety risk. It is necessary to strengthen the research on traffic risk evaluation. In order to study the traffic safety risks of long-steep downgrade parts of expressways, the fuzzy hierarchical comprehensive evaluation method is used to establish the calculation model. First, an evaluation index system including the target level, rule level, first-level index level and second-level index level is established. The qualitative and quantitative indicators are processed by the set value statistical method and the linear standard method, respectively, so that all indicators can be quantitatively evaluated together. Then, each indicator is assigned a score and divided into five risk levels, and a ridge-shaped fuzzy distribution is used to constitute a membership function for each level. A hierarchical structure model is established with the analytic hierarchy process to determine the affiliation between the upper and lower levels, and the relative weight of each level to the upper level also can be obtained. Finally, according to the hierarchical relevance of each evaluation indicator, a three-level fuzzy comprehensive evaluation model is constructed. The traffic risk evaluation level for long-steep downgrade sections can be obtained, and the probability of the corresponding risk evaluation level can be calculated. Through the risk evaluation of the long-steep downgrade sections of the Fuzhou Yinchuan Expressway in China, this shows that the risk evaluation conclusion obtained by using this evaluation method is consistent with the actual traffic safety situation, which shows that the traffic safety risk evaluation model based on a fuzzy hierarchy comprehensive evaluation is operable.

Estimating mountainous freeway crash rate: Application of a spatial model with three-dimensional (3D) alignment parameters

The road alignment is a three-dimensional (3D) curve in nature. In this study, we quantitatively examine the effect of 3D road alignment on traffic safety on mountainous freeways. Geometric parameters of 3D curvature and torsion in mathematics are derived to characterize the 3D road curve. Based on the coordination of different horizontal and vertical elements, 3D road alignment is divided into twelve types of combined alignment. For each alignment combination, the 3D curvature and torsion are calculated according to the differential geometry theory. Regarding crash statistical modeling, the Bayesian spatial Tobit (BST) model is developed to accommodate possible spatial correlation of traffic crashes among adjacent freeway segments. The Bayesian Tobit (BT) model is also built for comparison. A 118-km mountainous freeway associated road geometric features, traffic volume with three years of crash data is used as a case study. The result from the model comparison shows the BST model outperforms the BT model in terms of goodness-of-fit. Parameter estimation result for the BST model shows that the differences of average 3D curvature (and torsion) between adjacent segments have statistically significant effects on the crash rate of the segment, indicating it is necessary to consider three-dimensional alignment parameters in estimating mountainous freeway crash rate. Moreover, by comparing the predicted crash rate calculated by the BST model and the observed crash rate, the result shows the proposed BST model can provide a reliable prediction for freeway crash rates of different combined alignments. This study provides new insight on the effect of road geometric design on traffic safety but also deepens our understanding of spatial correlations in freeway crash modeling.

Impacts of Real-Time Traffic State on Urban Expressway Crashes by Collision and Vehicle Type

With the rapid development of urban expressway systems in China in recent years, traffic safety problems have attracted more attention. Variation of traffic flow is considered to have significant impact on the safety performance of expressways. Therefore, the motivation of this study is to explore the mechanism of how the variation of traffic flow measurements such as average speed, speed variation and traffic volume impact the crash risk. Firstly, the crashes were classified according to crash type and vehicles involved: and they are labeled with rear-end collisions or side-impact collisions, they are labeled with heavy-vehicle related collisions or light-vehicle related collisions as well. Then, the corresponding crash data were aggregated based on the similarity of traffic flow conditions and types of crashes. Finally, a random effect negative binomial model was introduced to consider the heterogeneity of the crash risk due to the variance within the traffic flow and crash types. The results show that the significant influencing factors of each type of crashes are not consistent. Specifically, the percentage of heavy vehicles within traffic flow is found to have a negative impact on rear-end collisions and light-vehicle-related collisions, but it has no obvious correlation with side-impact collisions and heavy-vehicle-related collisions. Average speed, speed variation and traffic volume have an interactive effect on the crash rate. In conclusion, if the traffic flow is with higher speed variation within lanes and is with lower average speed, the risk of all types of crashes tends to be higher. If the speed variation within lanes decreases and the average speed increases, the crash risk will also increase. In addition, if the traffic flow is under the conditions of higher speed variation between lanes and lower traffic volume, the risk of rear-end collisions, side-impact collisions and heavy-vehicles related collisions tend to be higher. Meanwhile, if the speed variation between lanes decreases and the traffic volume increases, the crash risk is found to increase as well.

Influence factors on injury severity of bicycle-motor vehicle crashes: A two-stage comparative analysis of urban and suburban areas in Beijing

OBJECTIVE

More attention should be given to bicycle-motor vehicle (BMV) crashes, as cyclists are at a higher risk of suffering injuries than motor vehicle users in a crash. This study aims to explore the factors influencing the injury severity of bicycle-motor vehicle (BMV) crashes in Beijing (China) and discusses the commonalities and differences between the urban and suburban areas.

METHODS

Information regarding 1,136 crashes between bicycles and motor vehicles were collected using police reported data from 2014 to 2015. A two-stage approach integrating random parameters logit (RP-logit) model and two-step clustering (TSC) algorithm was proposed to investigate the significant influence factors and their combination characteristics. Specifically, the RP-logit model was first used to identify the significant influence factors of urban and suburban areas, and then the TSC algorithm was applied to reveal the combination characteristics of significant influence factors for the fatal crashes.

RESULTS

Five factors were found to be statistically significant and had random effects on the injury severity in urban areas, i.e., type of motor vehicle, motor vehicle license ownership, type of bicycle, signal control mode and lighting condition; and seven factors were found to be statistically significant on the injury severity in suburban areas, i.e., type of motor vehicle, motor vehicle license ownership, physical isolation facility, signal control mode, weather, visibility and lighting condition. Based on TSC, the combination of significant factors showed different characteristics for fatal crashes in urban and suburban areas, in which two types of the scene including five factors should be concerned in urban areas while one type of scene containing four factors in suburban areas.

CONCLUSIONS

The results suggest that different influence factors and individual heterogeneity exist in the RP-logit model for injury severity analysis of BMV crashes in urban and suburban areas. It shows that in urban areas, heavy truck, light truck and bus significantly increase the likelihood of fatal injury than that of suburban areas. These findings can provide valuable reference information for BMV crashes response, such as heavy truck restriction, to facilitate regional safety measures for urban and suburban areas.

A Spatial Autoregressive Quantile Regression to Examine Quantile Effects of Regional Factors on Crash Rates

Spatial autocorrelation and skewed distribution are the most frequent issues in crash rate modelling analysis. Previous studies commonly focus on the spatial autocorrelation between adjacent regions or the relationships between crash rate and potentially risky factors across different quantiles of crash rate distribution, but rarely both. To overcome the research gap, this study utilizes the spatial autoregressive quantile (SARQ) model to estimate how contributing factors influence the total and fatal-plus-injury crash rates and how modelling relationships change across the distribution of crash rates considering the effects of spatial autocorrelation. Three types of explanatory variables, i.e., demographic, traffic networks and volumes, and land-use patterns, were considered. Using data collected in New York City from 2017 to 2019, the results show that: (1) the SARQ model outperforms the traditional quantile regression model in prediction and fitting performance; (2) the effects of variables vary with the quantiles, mainly classifying three types: increasing, unchanged, and U-shaped; (3) at the high tail of crash rate distribution, the effects commonly have sudden increases/decrease. The findings are expected to provide strategies for reducing the crash rate and improving road traffic safety.

A random parameters with heterogeneity in means and Lindley approach to analyze crash data with excessive zeros: A case study of head-on heavy vehicle crashes in Queensland

This study performed statistical analyses to identify likely crash contributing factors for Head-on Fatal and Serious Injury (FSI) collisions involving heavy vehicles (HVs) on the Queensland state road network. Head-on HV collisions are associated with the largest number of fatalities compared to other crash types in Queensland. However, there is limited relevant literature regarding this type of crashes. Recent studies on road safety research have focused on variants of random parameters models to capture unobserved heterogeneity that may influence the occurrence of crashes. Among such models, random parameters with heterogeneity in means has recently provided better results and has become popular. However, this study illustrates a potential limitation regarding the use of these models without explicitly factoring for excessive zero crash observations. To address this potential limitation, a random parameters with heterogeneity in means and a Lindley distribution is introduced in this study to factor for the unobserved heterogeneity using additional variables as well as site-specific variation from excessive zero crash observations. Results showed that a Poisson model with random parameters and heterogeneity in means using a Lindley distribution outperformed multiple alternative state-of-the-art specifications in terms of fit as well as overall prediction ability. The analyses using the proposed modelling approach revealed factors likely to affect the likelihood of Head-on FSI crashes involving HVs in Queensland including volume, segment length, period of analysis, terrain type being rolling, curve (moderate/sharp/very sharp) longer than 50% of the corresponding segment length, rural single carriageway with high (>=100 kph) and medium (>=50 and <100 kph) speed limits, and urban single carriageway. Unobserved heterogeneity regarding the parameter for road curvature was explained using rolling terrain type as an explanatory variable. This study has explained variation in the means of random parameters for a road attribute using the effect of a geometric variable, in which several stakeholders are primarily interested.

Examining Bayesian network modeling in identification of dangerous driving behavior

Traffic safety problems are still very serious and human factor is the one of most important factors affecting traffic crashes. Taking Next Generation Simulation (NGSIM) data as the research object, this study defines six control indicators and uses principal component analysis and K-means++ clustering methods to get the driving style of different drivers. Then use the Bayesian Networks Toolbox (BNT) and MCMC algorithm to realize the structure learning of Bayesian network. and parameter learning was completed through Netica software. Finally, the vehicle-based traffic crash risk model was created to conduct sensitivity analysis, posterior probability inference, and simulation data was used to detect the feasibility of the model. The results show that the Bayesian network modeling can not only express the relationship between the crash risk and various driving behaviors, but also dig out the inherent relationship between different influencing factors and investigate the causes of driving risks. The results will be beneficial to accurately identify and prevent risky driving behavior.

Detection of Geometric Risk Factors Affecting Head-On Collisions through Multiple Logistic Regression: Improving Two-Way Rural Road Design via 2+1 Road Adaptation

This study aims to characterize locations on two-way rural roads where head-on crashes are more likely to occur, attending to geometric road design factors. For this purpose, a case-control study was carried out using multiple logistic regression models with variables related to road design parameters, considering several scenarios. The dataset corresponding to cases (places where crashes have occurred) was collected on Spanish “1+1” rural roads over a four-year period. The controls (places where no crashes have occurred in the period) where randomly selected through a specific ad hoc designed method. The obtained model identifies risk factors and allows the computation of the odds of a head-on collision on any specific road section: width of the pavement (when it exceeds 6 m), width of the lanes (for intermediate widths between 3.25 and 3.75 m) and tight curves (less than 250 m of radius) are identified as factors significantly increasing the odds of a crash, whereas a paved shoulder is a protective factor. The identified configurations on two-way rural roads may be susceptible to transformation into “2+1” roads to decrease the odds of a head-on crash, thus preventing possible serious injuries and enhancing transportation safety.

Velocity Prediction Based on Vehicle Lateral Risk Assessment and Traffic Flow: A Brief Review and Application Examples

Forecasting future driving conditions such as acceleration, velocity, and driver behaviors can greatly contribute to safety, mobility, and sustainability issues in the development of new energy vehicles (NEVs). In this brief, a review of existing velocity prediction techniques is studied from the perspective of traffic flow and vehicle lateral dynamics for the first time. A classification framework for velocity prediction in NEVs is presented where various state-of-the-art approaches are put forward. Firstly, we investigate road traffic flow models, under which a driving-scenario-based assessment is introduced. Secondly, vehicle speed prediction methods for NEVs are given where an extensive discussion on traffic flow model classification based on traffic big data and artificial intelligence is carried out. Thirdly, the influence of vehicle lateral dynamics and correlation control methods for vehicle speed prediction are reviewed. Suitable applications of each approach are presented according to their characteristics. Future trends and questions in the development of NEVs from different angles are discussed. Finally, different from existing review papers, we introduce application examples, demonstrating the potential applications of the highlighted concepts in next-generation intelligent transportation systems. To sum up, this review not only gives the first comprehensive analysis and review of road traffic network, vehicle handling stability, and velocity prediction strategies, but also indicates possible applications of each method to prospective designers, where researchers and scholars can better choose the right method on velocity prediction in the development of NEVs.

Analysing the main and interaction effects of commercial vehicle mix and roadway attributes on crash rates using a Bayesian random-parameter Tobit model

In previous research, the effects of commercial vehicle proportions (CVP) on overall crash propensity have been found to be significant, but the results have been varied in terms of the effect direction. In addition, the mediating or moderating effects of roadway attributes on the CVP-vs-safety relationships, have not been investigated. In addressing this gap in the literature, this study integrates databases on crashes, traffic, and inventory for Hong Kong road segments spanning 2014-2017. The classes of commercial vehicles considered are public buses, taxi, and light-, medium- and heavy-goods vehicles. Random-parameter Tobit models were estimated using the crash rates. The results suggest that the CVP of each class show credible effects on the crash rates, for the various crash severity levels. The results also suggest that the interaction between CVP and roadway attributes is credible enough to mediate the effect of CVP on crash rates, and the magnitude and direction of such mediation varies across the vehicle classes, crash severity levels, and roadway attribute type in four ways. First, the increasing effect of taxi proportion on slight-injury crash rate is magnified at road segments with high intersection density. Second, the increasing effect of light-goods vehicle proportion on slight-injury crash rate is magnified at road segments with on-street parking. Third, the association between the medium- and heavy-goods vehicle proportion and killed/severe injury (KSI) crash rate, is moderated by the roadway width (number of traffic lanes). Finally, a higher proportion of medium- and heavy-goods vehicles generally contributes to increased KSI crash rate at road segments with high intersection density. Overall, the findings of this research are expected not only to help guide commercial vehicle enforcement strategy, licensing policy, and lane control measures, but also to review existing urban roadway designs to enhance safety.

Risk factors associated with truck-involved fatal crash severity: Analyzing their impact for different groups of truck drivers

INTRODUCTION

Fatal crashes that include at least one fatality of an occupant within 30 days of the crash cause large numbers of injured persons and property losses, especially when a truck is involved.

METHOD

To better understand the underlying effects of truck-driver-related characteristics in fatal crashes, a five-year (from 2012 to 2016) dataset from the Fatality Analysis Reporting System (FARS) was used for analysis. Based on demographic attributes, driving violation behavior, crash histories, and conviction records of truck drivers, a latent class clustering analysis was applied to classify truck drivers into three groups, namely, ''middle-aged and elderly drivers with low risk of driving violations and high historical crash records," ''drivers with high risk of driving violations and high historical crash records," and ''middle-aged drivers with no driving violations and conviction records." Next, equivalent fatalities were used to scale fatal crash severities into three levels. Subsequently, a partial proportional odds (PPO) model for each driver group was developed to identify the risk factors associated with the crash severity. Results' Conclusions: The model estimation results showed that the risk factors, as well as their impacts on different driver groups, were different. Adverse weather conditions, rural areas, curved alignments, tractor-trailer units, heavier weights and various collision manners were significantly associated with the crash severities in all driver groups, whereas driving violation behaviors such as driving under the influence of alcohol or drugs, fatigue, or carelessness were significantly associated with the high-risk group only, and fewer risk factors and minor marginal effects were identified for the low-risk groups. Practical Applications: Corresponding countermeasures for specific truck driver groups are proposed. And drivers with high risk of driving violations and high historical crash records should be more concerned.

Real-time conflict-based Bayesian Tobit models for safety evaluation of signalized intersections

The safety of signalized intersections has traditionally been evaluated at an aggregate level by relating historical collision records for several years to the annual traffic volume and the geometric characteristics of the intersection. This is a reactive and macroscopic approach that gives little insight into how important dynamic signal cycle-related variables can affect intersection safety such as the arrival type and the shock wave characteristics. The objective of this study is to develop traffic conflict-based real-time safety models for signalized intersections using several state-of-the-art techniques. Traffic conflicts were measured by multiple indicators including time-to-collision (TTC), modified time-to-collision (MTTC), and deceleration rate to avoid collision (DRAC). Traffic conflict rate was employed as independent variable while traffic volume, queue length, shock wave area, shock wave speed, and platoon ratio of each cycle were used as covariates in the safety models. Four candidate Tobit models were developed and compared under the Bayesian framework: conventional Tobit model, grouped random parameters Tobit (GRP-Tobit) model, random intercept Tobit (RI-Tobit) model, and random parameters Tobit (RP-Tobit) model. The results showed that the GRP-Tobit model performs best with lowest Deviance Information Criteria (DIC), indicating that accounting for the unobserved heterogeneity across sites can significantly improve the model fit. The model estimation results showed that higher conflict rates were associated with various shock wave characteristics (positive sign for shock wave area, shock wave speed, and queue length) and higher traffic volume. Lower conflict rates were related with higher platoon ratio (favorable arrival patterns). The developed models can have potential applications in real-time safety evaluation, real-time optimization of signal control, and connected and autonomous vehicles (CAV) trajectories planning.

Modeling the service-route-based crash frequency by a spatiotemporal-random-effect zero-inflated negative binomial model: An empirical analysis for bus-involved crashes

Previous studies related to bus crash frequencies modeling are limited and the statistical models are usually developed at the road segment or zonal level. This study focuses on modeling crash frequencies specifically at the bus-service-route level, which is useful and important to policymakers and bus operation companies toward the improvement of the safety level of bus networks, especially for developing countries where buses are still a major mode of urban travels. Using the observed data adopted from one of the bus operating companies in Beijing, China, we proposed a spatiotemporal-random-effect zero-inflated negative binomial (spatiotemporal ZINB) model to investigate bus crash occurrence and identity key influential factors at the bus-service-route level. The model was motivated to accommodate the special statistical characteristics of the excessive zeros and, more importantly, the potential spatiotemporal correlations of the data. Three degenerated versions of this model were also developed for comparison purposes. Results indicate that the proposed spatiotemporal ZINB model is statistically superior to the others according to a comprehensive judgment based on the EAIC, EBIC, and RMSE criteria. The estimated coefficients reveal the impacts of related factors on the likelihood of bus-involved crashes from bus operation factors including total passengers, number of drivers, and proportion of male drivers as well as planning factors including route length and stop density. On the other hand, the standard deviations of the introduced structured and unstructured spatiotemporal random-effects are statistically significant indicating that the observations are correlated within each route, between neighbor routes and across years. Corresponding policy and practical implications are provided for bus operating companies and planning departments toward the improvement of bus safety.

Geometric road design factors affecting the risk of urban run-off crashes. A case-control study

Objective

Single vehicle run-off crashes in urban areas constitute a growing problem that deserves more attention from authorities and researchers. This study aims to detect geometric road design risk factors characterizing places where urban run-off crashes might happen.

Methods

A case-control study was performed in the urban area of Valladolid (Spain) with data corresponding to a four-year period. Logistic regression models were used to analyze data, considering different variables related to design parameters in the models: type of intersection, radius of curvature, width of the pavement, width of the traffic lane, number of lanes for traffic in the same direction, direction of the traffic, length of the previous straight section, distance to the previous traffic light, slope, and finally, priority regulation. Two different scenarios were investigated: intersections and curves.

Results

The Adjusted Odds-Ratio of a run-off crash was five times higher in double direction roads with median strip than in one-way urban roads, for both curves and intersections, and almost nine times higher on road sections with previous straight lengths greater than 500 meters. Specific risk factors for intersections are “number of lanes for traffic in the same direction” (the odds of a run-off crash are more than five times higher on a road with two or more lanes), “length of preceding straight section” (the odds on road sections with lengths greater than 500 meters are more than nine times that of road sections with a length of less than 150 meters). For curves, specific factors are “width of the traffic lane” (the odds of a run-off crash on curves with lanes wider than 3.75m are more than six times higher) and “priority regulation” (the odds of a run-off crash increases more than twelve times on road sections with traffic light regulation over those without any regulation).

Conclusions

The current study identifies urban road configurations that might require redesigning with the aim of decreasing the odds of a run-off crash, or the implementation of passive protective systems to mitigate their consequences. Specifically, intersections in two direction roads with median strip, more than two lanes per direction and a long preceding straight section, as well as curves with wide lanes and traffic light regulation, are the places that require attention.

Causation Analysis of Hazardous Material Road Transportation Accidents Based on the Ordered Logit Regression Model

Understanding the influence factors and related causation of hazardous materials can improve hazardous materials drivers' safety awareness and help traffic professionals to develop effective countermeasures. This study investigates the statistical distribution characteristics, such as types of hazardous materials transportation accidents, driver properties, vehicle properties, environmental properties, road properties. In total, 343 data regarding hazardous materials accidents were collected from the chemical accident information network of China. An ordered logit regression (OLR) model is proposed to account for the unobserved heterogeneity across observations. Four independent variables, such as hazardous materials drivers' properties, vehicle properties, environmental properties, and road properties are employed based on the OLR model, an ordered multinomial logistic regression (MLR) is estimated the OLR model parameters. Both parameter estimates and odds ratio (OR) are employed to interpret the impact of influence factors on the severity of hazardous materials accidents. The model estimation results show that 10 factors such as violations, unsafe driving behaviors, vehicle faults, and so on are closely related to accidents severity of hazardous materials transportation. Furthermore, three enforcement countermeasures are proposed to prevent accidents when transporting hazardous materials.

Design of a Network Optimization Platform for the Multivehicle Transportation of Hazardous Materials

With economic development, the volume of hazardous materials is increasing, and the potential risks to human beings and the natural environment are expanding. Road transportation has become the main mode of transportation for hazardous materials. Because of the specific characteristics of hazardous materials, if an accident occurs in the transportation process, it often causes mass casualties, serious property and socioeconomic damage, and damage to the ecological environment. Hence, transportation is an important part of the life cycle of hazardous materials. This paper designs an optimization platform for multidestination, multiterminal, and multivehicle networks that transport hazardous materials. The logistics module in TransCAD software is used to construct this platform. By identifying the effective transportation routes considering the transportation risk, sensitive target population, and transportation time of each road section, the entropy method can be used to fuse and obtain the comprehensive impedance value of each road section. Finally, the optimal transportation network of hazardous materials was obtained by the transportation network optimization algorithm in TransCAD. The platform can display the optimal transport program with data windows, text, and maps. The research results provide a reference for relevant departments to scientifically manage the transport of hazardous materials.

Exploratory analysis of automated vehicle crashes in California: A text analytics & hierarchical Bayesian heterogeneity-based approach

Automated vehicles (AVs) represent an opportunity to reduce crash frequency by eliminating driver error, as safety studies reveal human error contributes to the majority of crashes. To provide insights into the contributing factors of AV crashes, this study created a unique database from the California Department of Motor Vehicles 124 manufacturer-reported Traffic Collision Reports and was linked with detailed data on roadway and built-environment attributes. A novel text analysis was first conducted to extract useful information from crash report narratives. Of the crashes that could be geocoded (N = 113), results indicate the most frequent AV crash type was rear-end collisions (61.1%; N = 69) and 13.3% (N = 15) were injury crashes. These noteworthy outcomes and a small sample size motivated us to rigorously analyze rear-end and injury crashes in a Full Bayesian empirical setup. Owing to the potential issue of unobserved heterogeneity, hierarchical-Bayes fixed and random parameter logit models are estimated. Results reveal that when the automated driving system is engaged and remains engaged, the likelihood of an AV-involved rear-end crash is substantially higher compared to a conventionally-driven AV or when the driver disengages the automated driving system prior to a crash. Given the AV-involved crashes, the likelihood of an AV-involved rear-end crash was significantly higher in mixed land-use settings compared to other land-use types, and was significantly lower near public/private schools. Correlations of other roadway attributes and environmental factors with AV-involved rear-end and injury crash propensities are discussed. This study aids in understanding the interactions of AVs and human-driven conventional vehicles in complex urban environments.

The role of striking and struck vehicles in side crashes between vehicles: Bayesian bivariate probit analysis in China

OBJECTIVE

Side crashes between vehicles which usually lead to high casualties and property loss, rank first among total crashes in China. This paper aims to identify the factors associated with injury severity of side crashes at intersections and to provide suggestions for developing countermeasures to mitigate the levels of injuries.

METHOD

In order to investigate the role of striking and struck vehicles in side crashes simultaneously, bivariate probit model was proposed and Bayesian approach was employed to evaluate the model, compared to the corresponding univariate probit model.

DATA

Crash data from Beijing, China for the period 2009-2012 were used to carry out the statistical analysis. Based on the investigation with vehicles and data analysis on events, 130 intersection side crash cases were selected to form a specific dataset. Then, the influence of human, vehicles, roadway and environmental variables on crash severity was examined by means of bivariate probit regression within Bayesian framework.

RESULTS

The effects of the factors on striking vehicle drivers and struck vehicle drivers were considered separately and simultaneously to find more targeted conclusions. The statistical analysis revealed vehicle type, lane number, no non-motorized lane and speeding have the corresponding influence on the injury severity of striking vehicles, while time of day and vehicle type of struck vehicles increased the likelihood of being injured.

CONCLUSIONS

From the results it can be concluded that there indeed exists correlation between striking and struck vehicles in side crashes, although the correlation is not so strong. Importantly, Bayesian bivariate probit model can address the role of striking and struck vehicles in side crashes simultaneously and can accommodate the correlation clearly, which extends the range of univariate probit analysis. The general and empirical countermeasures are presented to improve the safety at intersections.

Impacts of experimental advisory exit speed sign on traffic speeds for freeway exit ramp

Many crashes occur around freeway exit ramp areas in China due to excessive speeds and large speed variances. Traditionally, a single posted ramp speed limit sign is installed around the physical gore area to manage the speed. To address this issue, the study presented in this paper proposes the use of an advisory exit speed sign (AESS), which is an additional exit speed limit sign positioned along the deceleration lane to accommodate the speed changes ahead of the physical gore. The study selected three sites with similar exit ramp configurations and two scenarios (with AESS/without AESS) to quantify the influences of the AESS on the speed of exiting vehicles. The speed profiles of 480 vehicles were obtained based on 12 hours of data collection. A t-test was applied to verify the reduction in mean speed between the two scenarios. The results show that the AESS in this study was effective in reducing the mean speed and 85th percentile speed, especially in the taper and deceleration lane. It was clearly seen that drivers began to decelerate in advance when the AESS was installed, which led to a smooth deceleration process, especially on the segment between the theoretical gore and the physical gore. The AESS was also helpful in reducing speeding to some extent. Although the effects of the AESS on speed reduction at curved ramps were not ideal, the speed fluctuation range tended to be more contracted when the AESS was installed. This paper provides useful information for researchers, managers, and engineers when considering the implementation of AESS.

Estimating Driving Fatigue at a Plateau Area with Frequent and Rapid Altitude Change

Due to the influence of altitude change on a driver’s heart rate, it is difficult to estimate driving fatigue using heart rate variability (HRV) at a road segment with frequent and rapid altitude change. Accordingly, a novel method of driving fatigue estimation for driving at plateau area with frequent altitude changes is proposed to provide active safety monitoring in real time. A naturalistic driving experiment at Qinghai-Tibet highway was conducted to collect drivers’ electrocardiogram data and eye movement data. The results of the eye movement-based method were selected to enhance the HRV-based driving fatigue degree estimation method. A correction factor was proposed to correct the HRV-based method at the plateau area so that the estimation can be made via common portable devices. The correction factors for both upslope and downslope segments were estimated using the field experiment data. The results on the estimation of revised driving fatigue degree can describe the driver’s fatigue status accurately for all the road segments at the plateau area with altitudes from 3540 to 4767 m. The results can provide theoretical references for the design of the devices of active safety prevention.

Catastrophic factors involved in road accidents: Underlying causes and descriptive analysis

South Korea is ranked as 4th among 34 nations of the Organization for Economic Cooperation and Development with 102 deaths in road accidents per one million population. This paper aims to investigate the factors associated with road accidents in South Korea. The rainfall data of the Korea Meteorological Administration and road accidents data of Traffic Accident Analysis System of Korea Road Traffic Authority is analyzed for this purpose. In this connection, multivariate regression analysis and ratio analysis with the descriptive analysis are performed to uncover the catastrophic factors involved. In turn, the results reveal that traffic volume is the leading factor in road accidents. The limited road extension of 1.47% compared to the 4.14% per annum growth of the vehicles is resulting in road accidents at such a large scale. The increasing proportion of passenger cars accelerate road accidents as well. 56% of accidents occur by the infringement of safety driving violations. The drivers with higher driving experience tend to have a higher accident ratio. The collected data is analyzed in terms of gender, driver experience, type of violations and accidents as well as the associated time of the accidents when they happen. The results indicate that 36.29% and 53.01% of accidents happen by male drivers in the day and night time, respectively. 29.15% of crashes happen due to safety infringement and violations of 41 to 60 years old drivers. The results demonstrate that population density is associated with the accidents frequency and lower density results in an increased number of accidents. The necessity of the state-of-the-art regulations to govern the urban road traffic is beyond dispute, and it becomes even more crucial for citizens’ relief since in our daily lives road accidents are getting more diverse.

Simulation of pedestrian evacuation route choice using social force model in large-scale public space: Comparison of five evacuation strategies

The primary objective of this study is to compare pedestrian evacuation strategies in the large-scale public space (LPS) using microscopic model. Data were collected by video recording from Tian-yi square for 36 hours in city of Ningbo, China. A pedestrian evacuation simulation model was developed based on the social force model (SFM). The simulation model parameters, such as reaction time, elasticity coefficient, sliding coefficient, et al, were calibrated using the real data extracted from the video. Five evacuation strategies, strategy 1 (S1) to strategy 5 (S5) involving distance, density and capacity factors were simulated and compared by indicators of evacuation time and channel utilization rate, as well as the evacuation efficiency. The simulation model parameters calibration results showed that a) the pedestrians walking speed is 1.0 ~ 1.5m/s; b) the pedestrians walking diameter is 0.3 ~ 0.4m; c) the frequency of pedestrian arrival and departure followed multi-normal distribution. The simulation results showed that, (a) in terms of total evacuation time, the performance of S4 and S5 which considering the capacity and density factors were best in all evacuation scenarios, the performance of S3 which only considering the density factor was the worst, relatively, and S1 and S2 which considering the distance factor were in the middle. (b) the utilization rate of channels under S5 strategy was better than other strategies, which performs best in the balance of evacuation. S3 strategy was the worst, and S1, S2 and S4 were in the middle. (c) in terms of the evacuation efficiency, when the number of evacuees is within 2, 500 peds, the S1 and S2 strategy which considering the distance factor have best evacuation efficiency than other strategies. And when the number of evacuees is above 2, 500 peds, the S4 and S5 strategy which considering the capacity factor are better than others.

Congestion Evaluation of Pedestrians in Metro Stations Based on Normal-Cloud Theory

This study aims at evaluating the congestion level of pedestrians in metro stations. Twelve hours (4 h × three facilities) of video data were collected in the channel, stairway, and platform in a metro station in the city of Ningbo, China. The indicator of GPC (grade of pedestrian crowd) was proposed to quantify the congestion level of pedestrians. Four levels of congestion (level I, level II, level III, and level IV) were determined based on the GPC. A normal-cloud (NC) model was proposed and calibrated for the evaluation of three facilities including channel, stairway, and platform. The evaluation results showed that the GPC of L1-L2 and L2-L1 in channel are level II and level I, respectively. The GPC of upward and downward of stairway are level III and level I. The GPC of platform is level IV. Crowd management countermeasures were proposed for the management of pedestrians in metro station.

Risk Assessment in Urban Large-Scale Public Spaces Using Dempster-Shafer Theory: An Empirical Study in Ningbo, China

Urban Large-scale Public Spaces (ULPS) are important areas of urban culture and economic development, which are also places of the potential safety hazard. ULPS safety assessment has played a crucial role in the theory and practice of urban sustainable development. The primary objective of this study is to explore the interaction between ULPS safety risk and its influencing factors. In the first stage, an index sensitivity analysis method was applied to calculate and identify the safety risk assessment index system. Next, a Delphi method and information entropy method were also applied to collect and calculate the weight of risk assessment indicators. In the second stage, a Dempster-Shafer Theory (DST) method with evidence fusion technique was utilized to analyze the interaction between the ULPS safety risk level and the multiple-index variables, measured by four observed performance indicators, i.e., environmental factor, human factor, equipment factor, and management factor. Finally, an empirical study of DST approach for ULPS safety performance analysis was presented.

Crash severity analysis of nighttime and daytime highway work zone crashes

Introduction

Egypt’s National Road Project is a large infrastructure project which presently aims to upgrade 2500 kilometers of road networks as well as construct 4000 kilometers of new roads to meet today’s need. This leads to an increase in the number of work zones on highways and therefore a rise in hazardous traffic conditions. This is why highways agencies are shifting towards night construction in order to reduce the adverse traffic impacts on the public. Although many studies have investigated work zone crashes, only a few studies provide comparative analysis of the difference between nighttime and daytime work zone crashes.

Methods

Data from Egyptian long-term highway work zone projects between 2010 and 2016 are studied with respect to the difference in injury severity between nighttime and daytime crashes by using separate mixed logit models.

Results

The results indicate that significant differences exist between factors contributing to injury severity. Four variables are found significant only in the nighttime model and four other variables significant in the daytime model. The results show that older and male drivers, the number of lane closures, sidewise crashes, and rainy weather have opposite effects on injury severity in nighttime and daytime crashes. The findings presented in this paper could serve as an aid for transportation agencies in development of efficient measures to improve safety in work zones.

Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model

Short-term traffic speed prediction is a key component of proactive traffic control in the intelligent transportation systems. The objective of this study is to investigate the short-term traffic speed prediction under different data collection time intervals. Traffic speed data was collected from an urban freeway in Edmonton, Canada. A seasonal autoregressive integrated moving average plus seasonal discrete grey model structure (SARIMA-SDGM) was proposed to perform the traffic speed prediction. The model performance of SARIMA-SDGM model was compared with that of the seasonal autoregressive integrated moving average (SARIMA) model, seasonal discrete grey model (SDGM), artificial neural network (ANN) model, and support vector regression (SVR) model. The results showed that SARIMA-SDGM model performs best with the lowest mean absolute error (MAE), mean absolute percentage error (MAPE), and the root mean square error (RMSE). The traffic speed prediction accuracy under different time intervals were compared based on the SARIMA-SDGM model. The results showed that the prediction accuracy improves with the increase in time interval. In addition, when the time interval is greater than 10 min, the prediction results yield stable prediction accuracy.