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Urban-rural integration is the critical process of urbanization, while during this process there are various corresponding problems generated, safety is the most important one, especially at urban-rural fringe areas due to mixed traffic flow and complicated land use. This study intended to investigate the injury severity interaction between urban area and urban-rural fringe area, and explore the correlation within injury severity levels and heterogeneity attributed to unobserved factors. To address the correlation and heterogeneity issues, a grouped random parameters seemingly unrelated bivariate (SUB) probit model was proposed, in which the SUB probit model addressed the correlation of residuals, while the random parameters model accommodated the heterogeneity due to unobserved factors. By comparing the pooled with random parameters models, the results showed that random parameters SUB probit model performed better than the pooled one, and the dataset collected from 2013 to 2017 in Chengdu, China was adopted to illustrate the proposed model. It is found that crash location, speed limit and age of person injured are significant for injury severity in urban and urban-rural fringe areas, but crash form plays a significant role in urban area while number of persons involved should be paid more attention due to injury severity in urban-rural fringe area. Some empirical suggestions are presented to improve the safety in urban and urban-rural fringe areas. [ABSTRACT FROM AUTHOR]

This study is intended to investigate the influencing factors of injury severity by considering the heterogeneity issue of unobserved factors at different arterials and the spatial attributes in geographically weighted regression models. To achieve the objectives, geographically weighted panel logistic regression model was developed, in which the geographically weighted logistic regression model addressed the injury severity from the spatial perspective, while the panel data model accommodated the heterogeneity attributed to unobserved factors from the temporal perspective. The geo-crash data of Las Vegas metropolitan area from 2014 to 2016 was collected, involving 27 arterials with 25,029 injury samples. By comparing the conventional logistic regression model and geographically weighted logistic regression models, the geographically weighted panel logistic regression model showed preference to the other models. Results revealed that four main factors, human-beings (drivers/pedestrians/cyclists), vehicles, roadway, and environment, were potentially significant factors of increasing the injury severity. The findings provide useful insights for practitioners and policy makers to improve safety along arterials., 1. Introduction During the last decade, there have been a variety of different approaches and perspectives [1-3] presented in safety evaluation, and regression analysis has been widely applied to investigate [...]

In recent years the pedestrian deaths have been declining, but the pedestrian–vehicle death rate in Croatia is still pretty high. This study intended to investigate the injury severity of pedestrian–vehicle crashes and identify the influencing factors. To achieve this goal, the dataset was firstly collected from Traffic Accident Database System maintained by the Ministry of the Interior, Republic of Croatia from 2015 to 2019, and then latent cluster analysis was employed to identify homogenous clusters from heterogeneous dataset. Based on the classified dataset, unbalanced panel mixed ordered probit model was proposed. By analyzing the classes with different vehicles, the proposed model revealed a more complete understanding of significant variables and showed beneficial performance from the goodness-of-fit, while capturing the impact of exogenous variables to vary among different places, as well as accommodating the heterogeneity issue due to unobserved effects. Findings revealed that the proposed model can be considered as an alternative to determine the factors of injury severity and to deal with the heterogeneity issue. The results may provide potential insights for reducing the injury severity of pedestrian-vehicle crashes. [ABSTRACT FROM AUTHOR]

Intelligent traffic control and guidance system is an effective way to solve urban traffic congestion, improve road capacity and guarantee drivers' travel safety, while short-term traffic flow prediction is the core of intelligent traffic control and guidance system. To investigate the long-term memory and the dynamic feature of short-time traffic flow time series, a hybrid model was proposed by integrating autoregressive fractionally integrated moving average (ARFIMA) model and nonlinear autoregressive (NAR) neural network model to predict short-time traffic flow, in which ARFIMA model can address the long-term memory of linear component and NAR neural network can accommodate the dynamic feature of nonlinear residual component. First, the ARFIMA model was employed to predict the linear component of traffic flow, and the results were compared with those of autoregressive integrated moving average (ARIMA) model. Next, the NAR neural network model was adopted to forecast the nonlinear residual components, and the weighted results were considered as the predicted flow of the hybrid model. The proposed hybrid model was validated by using the cross-sectional traffic flow data in California freeways obtained from the open-access PeMS database. The results showed that the ARFIMA model considering the long-term memory can effectively predict the short-term traffic flow, and the prediction accuracy of the hybrid model is better than that of the singular models. The findings provide an alternative for the short-term traffic flow prediction with lower error and higher accuracy. [ABSTRACT FROM AUTHOR]

With the rapid progress of urbanization and continuous increasing of automobiles, expressway on‐ and off‐ramp area becomes the bottleneck, and recurrent congestion occurs frequently. In order to solve the problem of traffic jam caused around off‐ramps, various methods have been employed. Among all, mainline variable speed limit (VSL) control accounts for some proportion. In this study, mainline VSL adjustment of off‐ramp upstream is investigated with the reinforcement learning algorithm under the connected vehicles environment to alleviate the traffic congestion. First, the assumptions are made to be suitable for the traffic conditions of mainline VSL control on off‐ramp upstream, and then VSL algorithm based on reinforcement learning is presented, and Q‐learning is chosen as the main algorithm. Next, the state space, action space, and reward function required by Q‐learning are constructed orderly, and the related parameters are labelled. After that, according to the platform based on Python and VISSIM, three schemes, free control (Scheme 0), mainline VSL adjustment of off‐ramp upstream based on rule (Scheme 1), and mainline VSL adjustment of off‐ramp upstream based on Q‐learning algorithm (Scheme 2), are designed, and the three schemes are simulated and compared quantitatively to reflect the off‐ramp travel efficiency. The results indicate that mainline dynamic VSL adjustment of off‐ramp upstream based on Q‐learning algorithm performs the best in terms of general and specific indexes. The results provide potential insights for relieving the traffic congestion and traffic flow control under CAVs environment. [ABSTRACT FROM AUTHOR]

With the progress of urbanization and urban-rural integration (URI), the interaction between URI and urban-rural transport network is becoming stronger, thus it is necessary to investigate the interactive relationship between URI and transport network. By assuming certain functions and principles, evolution model was proposed to explain the relationship between URI space development scale and urban-rural transport network scale, and then by considering the interaction between URI and transport network, dynamic evolution model was presented. Stability analysis was performed to verify the interaction relations with three patterns and elasticity simulation provided the optimal value of urban-rural transport network scale configuration. The results may provide potential insights on URI development and transport network. [ABSTRACT FROM AUTHOR]

This study intended to (1) investigate the pedestrian injury severity involved in traffic crashes; and (2) address the heterogeneity issue at signalized intersections. To achieve the objectives, Bayesian binary and ordinal quantile regression models were proposed to address the pedestrian injury severity at signalized intersections. The suitability of the proposed method was illustrated with the Hong Kong dataset from 2008 to 2012 and 376 signalized intersections involving 2090 pedestrian-related crashes are selected. It's found that age, injury location, pedestrian special circumstance, pedestrian contributory and the presence of Tram/LRT stops and right turning pocket are significant variables. The results indicated that both Bayesian binary and ordinal quantile regression models not only provide a more comprehensive and in-depth understanding of the relationship between pedestrian injury severity and the explanatory variables, but also highlight the heterogeneity issue for the data collected at different locations and different times without many assumptions. The goodness-of-fit of the proposed models outperforms existing mean models, while the Bayesian binary quantile model provides a better fit than the Bayesian quantile regression for ordinal model. The results can benefit the pedestrian facilities improvement/management and guide a much safer pedestrian environment. [ABSTRACT FROM AUTHOR]

This study intended to investigate the crash injury severity from the insights of the novice and experienced drivers. To achieve this objective, a bivariate panel data probit model was initially proposed to account for the correlation between both time-specific and individual-specific error terms. The geocrash data of Las Vegas metropolitan area from 2014 to 2017 were collected. In order to estimate two (seemingly unrelated) nonlinear processes and to control for interrelations between the unobservables, the bivariate random-effects probit model was built up, in which injury severity levels of novice and experienced drivers were addressed by bivariate (seemingly unrelated) probit simultaneously, and the interrelations between the unobservables (i.e., heterogeneity issue) were accommodated by bivariate random-effects model. Results revealed that crash types, vehicle types of minor responsibility, pedestrians, and motorcyclists were potentially significant factors of injury severity for novice drivers, while crash types, driver condition of minor responsibility, first harm, and highway factor were significant for experienced drivers. The findings provide useful insights for practitioners to improve traffic safety levels of novice and experienced drivers. [ABSTRACT FROM AUTHOR]

With the development of big data technology and the improvement of deep learning technology, data-driven and machine learning application have been widely employed. By adopting the data-driven machine learning method, with the help of clustering processing of data sets, a recurrent neural network (RNN) model based on Keras framework is proposed to predict the injury severity in urban areas. First, with crash data from 2014 to 2017 in Nevada, OPTICS clustering algorithm is employed to extract the crash injury in Las Vegas. Next, by virtue of Keras' high efficiency and strong scalability, the parameters of loss function, activation function and optimizer of the deep learning model are determined to realize the training of the model and the visualization of the training results, and the RNN model is constructed. Finally, on the basis of training and testing data, the model can predict the injury severity with high accuracy and high training speed. The results provide an alternative and some potential insights on the injury severity prediction.