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Statistical analysis of traffic crash frequency is significant for figuring out the distribution pattern of crashes, predicting the development trend of crashes, formulating traffic crash prevention measures, and improving traffic safety planning systems. In recent years, the theory and practice for traffic safety management have shown that road crash data have characteristics such as spatial correlation, temporal correlation, and excess zeros. If these characteristics are ignored in the modeling process, it may seriously affect the fitting performance and prediction accuracy of traffic crash frequency models and even lead to incorrect conclusions. In this research, traffic crash data from rural two-way two-lane from four counties in Pennsylvania, USA was modeled considering the spatiotemporal effects of crashes. First, a negative binomial Lindley spatiotemporal effect model of crash frequency was constructed at the micro level; Simultaneously, the characteristics and problems of excess zeros and potential heterogeneity of the crash data were resolved; Finally, the effects of road characteristics on crash frequency were analyzed. The results indicate a significant spatial correlation between the crash frequency of adjacent road sections. Compared with the negative binomial model, the negative binomial Lindley model can better handle the excess zeros characteristics in traffic crash data. The model that considers both spatial correlation and temporal conditional autoregressive effects has the best fit for the observed data. In addition, for road sections that allow passing and have a speed limitation of not less than 50 miles per hour, the crash frequency corresponding to these sections is lower due to their good visibility and road conditions. The increase in average turning angle and intersection density on the horizontal curve of the road section corresponds to an increase in crash frequency., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Single-vehicle run-off-road crashes accounts for approximately 35% of all the traffic fatalities in the U.S during the period of 2019-2021. This paper explores the association between driving speed and injury severity outcomes of single-vehicle run-off-road crashes. The single-vehicle run-off-road crash data from 2019 to 2021 on Interstate freeways in Florida are utilized, and categorized into periods of pre-, during-, and post-COVID-19 pandemic. The partially constrained temporal and temporal unconstrained random parameters logit models are developed considering three injury severity outcomes: no injury, minor injury and serious injury/fatality. Multiple variables in terms of driver, vehicle, roadway, environmental, crash, and temporal attributes are observed to significantly affect the injury severity. Moreover, temporal instability and transferability issues are validated through likelihood ratio test and out-of-sample prediction. In the partially constrained models, numerous variables such as indicators of new vehicle, male driver, and restraint-protected driving consistently yield identical parameter values across all periods, whereas various variables clearly illustrate the distinct differences across the three periods and three speed intervals. The marginal effects in the unconstrained models also display the obvious differences across three periods and three speed intervals. Moreover, the findings corroborate the increased risk outcomes linked to larger speed differences and the COVID-19 pandemic period. These results provide better understanding of the risk mechanisms underlying run-off-road crashes and furnish valuable direction for the formulation of effective safety interventions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Environment perception plays a crucial role in autonomous driving technology. However, various factors such as adverse weather conditions and limitations in sensing equipment contribute to low perception accuracy and a restricted field of view. As a result, intelligent connected vehicles (ICVs) are currently only capable of achieving autonomous driving in specific scenarios. This paper conducts an analysis of the current studies on image or point cloud processing and cooperative perception, and summarizes three key aspects: data pre-processing methods, multi-sensor data fusion methods, and vehicle-infrastructure cooperative perception methods. Data pre-processing methods summarize the processing of point cloud data and image data in snow, rain and fog. Multi-sensor data fusion methods analyze the studies on image fusion, point cloud fusion and image-point cloud fusion. Because communication channel resources are limited, the vehicle-infrastructure cooperative perception methods discuss the fusion and sharing strategies for cooperative perception information to expand the range of perception for ICVs and achieve an optimal distribution of perception information. Finally, according to the analysis of the existing studies, the paper proposes future research directions for cooperative perception in adverse weather conditions.

A copula-based model is developed in this study to jointly model the severity of freeway primary crashes and secondary crashes. The copula-based model can concurrently account for the severity levels in the crash and the correlation among primary-secondary crash pairs' severity. The model comprehensively considers a series of explanation variables, including temporal characteristics, crash characteristics, roadway characteristics and real-traffic conditions, and is estimated using traffic crash data from 2016 through 2019 for Los Angeles County, California. The proposed copula model is then contrasted with the traditional binary probit model and the results show a remarkable advantage of the copula model, which is evidenced by better fitting performance. It is found that weather, whether towed away, unsafe speed, collision type, road condition, terrain, road weaving and truck involvement have significant impact on primary crash severity propensity and collision type, road width, road condition, traffic volume and vehicle speed have significant impact on secondary crash severity propensity. In light of the findings, a number of countermeasures are proposed to mitigate freeway crashes, including emergency services, vehicle and roadway engineering, traffic law enforcement and driver education., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Car-following behavior is a common driving behavior. It is necessary to consider the following vehicle in the car-following model of autonomous vehicle (AV) under the background of the vehicle-to-vehicle transportation system. In this study, a safe velocity control method for AV based on reinforcement learning with considering the following vehicle is proposed. First, the mixed driving environment of AVs and human-driven vehicles is constructed, and the trajectories of the leading and following vehicles are extracted from the naturalistic High D driving dataset. Next, the soft actor-critic (SAC) algorithm is used as the velocity control algorithm, in which the agent is AV, the action is acceleration, and the state is the relative distance and relative speed between the AV and the leading and following vehicles. Then, a reward function based on state and corresponding action is designed to guide AV to choose acceleration without collision between the leading and following vehicles. Furthermore, AVs are gradually able to learn to avoid collisions between the leading and following vehicles after training the model. The test result of the trained model shows that the SAC agent can achieve complete collision avoidance, resulting in zero collision. Finally, the driving performance of the SAC agent and that of human driving are compared and analyzed for safety and efficiency. The results of this study are expected to improve the safety of the car-following process.., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

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.

In recent years, globally quantile-based model (e.g. quantile regression) and spatially conditional mean models (e.g. geographically weighted regression) have been widely and commonly employed in macro-level safety analysis. The former ones assume that the model coefficients are fixed over space, while the latter ones only represent the entire distribution of variable effects by a single concentrated trend. However, the influence of crash related factors on the distribution of crash frequency is observed to vary over space and across different quantiles. Therefore, a geographically weighted Poisson quantile regression (GWPQR) model is employed to investigate the spatial heterogeneity of variable effects crossing different quantiles. Five categories, including exposure, socio-economic, transportation, network and land use were selected to estimate the spatial effects on crash frequency. In the case study, vehicle related crashes collected in New York City were used to validate the predicted performance of the proposed models. The results show that the GWPQR outperforms the NB, QR and GWNBR for modeling the skewed distribution, reconstructing the crash distribution and capturing the unobserved spatial heterogeneity. Additionally, the significant coefficients are further used to classify all 21 variables into key, important and general parts. Then we discuss how these factors affects the regional crashes over space and distribution of crash frequency. This study confirms that the influencing factors have varying effects on different quantiles of distribution and on different regions, which could be helpful to provide support for making safety countermeasures and policies at urban regional level., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Crash injury severity analysis is useful for traffic management agency to further understand severity of crashes. A two-layer Stacking framework is proposed in this study to predict the crash injury severity: The fist layer integrates advantages of three base classification methods: RF (Random Forests), AdaBoost (Adaptive Boosting), and GBDT (Gradient Boosting Decision Tree); the second layer completes classification of crash injury severity based on a Logistic Regression model. A total of 5538 crashes were recorded at 326 freeway diverge areas. In the model calibration, several parameters including the number of trees in three base classification methods, learning rate, and regularization coefficient are optimized via a systematic grid search approach. In the model validation, the performance of the Stacking model is compared with several traditional models including the Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and Random Forests (RF) in the multi classification experiments. The prediction results show that Stacking model achieves superior performance evaluated by two indicators: accuracy and recall. Furthermore, all the factors used in severity prediction are classified into different categories according to their influence on the results, and sensitivity analysis of several significant factors is finally implemented to explore the impact of their value variation on the prediction accuracy., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Objective: Driving behavior is the key feature for determining the nature of traffic stream qualities and reflecting the risk of operating environments. However, evaluating the driving risk accurately and practically in continuous tunnels (tunnels with a space more than 250   m and less than 1000   m) still faces severe challenges due to the complex driving conditions. The objective of this study is to predict the driving risk indicators and determine different risk levels. Methods: The naturalistic driving system equipped with a road environment and driving behavior data acquisition system combined with the fixed-point test method was used for data collection in 130 tunnels on four highways. A traditional AASHTO braking model and convex hull algorithm were adopted to predict the critical safety speed and the critical time headway of each risk feature point in tunnels. According to the risk constraints under free-flow, car-following and lane-changing conditions, the average traffic flow risk index (TFRI) representing six risk levels and the safety threshold of the corresponding risk indicators were determined. Results: The findings of this study revealed that the critical safety speed at nighttime is slower than in other daytime conditions in continuous tunnels. The time headway slightly changes under 90   km/h. As the speed continues to increase, speed has a significant influence on the critical time headway. The only reliable interaction involved the different adverse weather conditions on the mean critical safety speed in the continuous tunnels (short plus long) ( F   =   9.730, p ＜0.05) and single long tunnels ( F   =   12.365, p ＜0.05). Conclusions: It can be concluded that driving behaviors significantly vary in different tunnel risk feature points and the combined effect of high speed and luminance variation may result in high driving risk. The performance validation indicted that the risk assessment level determined by the proposed approach is consistent with the real safety situations. The study provides an effective and generally acceptable method for identifying driving risk criteria that can also be applied for traffic management and safety countermeasures with a view to possible implementation in continuous tunnels.

An improved hierarchical fuzzy inference method based on C-measure map-matching algorithm is proposed in this paper, in which the C-measure represents the certainty or probability of the vehicle traveling on the actual road. A strategy is firstly introduced to use historical positioning information to employ curve-curve matching between vehicle trajectories and shapes of candidate roads. It improves matching performance by overcoming the disadvantage of traditional map-matching algorithm only considering current information. An average historical distance is used to measure similarity between vehicle trajectories and road shape. The input of system includes three variables: distance between position point and candidate roads, angle between driving heading and road direction, and average distance. As the number of fuzzy rules will increase exponentially when adding average distance as a variable, a hierarchical fuzzy inference system is then applied to reduce fuzzy rules and improve the calculation efficiency. Additionally, a learning process is updated to support the algorithm. Finally, a case study contains four different routes in Beijing city is used to validate the effectiveness and superiority of the proposed method.

Roadway elevation data is critical for a variety of transportation analyses. However, it has been challenging to obtain such data and most roadway GIS databases do not have them. This paper intends to address this need by proposing a method to extract roadway elevation data from Google Earth (GE) for transportation applications. A comprehensive accuracy assessment of the GE-extracted elevation data is conducted for the area of conterminous USA. The GE elevation data was compared with the ground truth data from nationwide GPS benchmarks and roadway monuments from six states in the conterminous USA. This study also compares the GE elevation data with the elevation raster data from the U.S. Geological Survey National Elevation Dataset (USGS NED), which is a widely used data source for extracting roadway elevation. Mean absolute error (MAE) and root mean squared error (RMSE) are used to assess the accuracy and the test results show MAE, RMSE and standard deviation of GE roadway elevation error are 1.32 meters, 2.27 meters and 2.27 meters, respectively. Finally, the proposed extraction method was implemented and validated for the following three scenarios: (1) extracting roadway elevation differentiating by directions, (2) multi-layered roadway recognition in freeway segment and (3) slope segmentation and grade calculation in freeway segment. The methodology validation results indicate that the proposed extraction method can locate the extracting route accurately, recognize multi-layered roadway section, and segment the extracted route by grade automatically. Overall, it is found that the high accuracy elevation data available from GE provide a reliable data source for various transportation applications.

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP).