Multi-factor coupled friction coefficient map spatiotemporal modeling and driving risk fusion for rainy roads.

The pavement skid resistance, namely the vehicle tire-road contact friction, plays important roles on pavement response, pavement deterioration, traffic operation, and traffic safety. One may find higher tire-road friction surface course is needed or applicable as an effective safety countermeasure for reducing potential traffic crashes and/or collision severity at certain roadway locations, in particular, the ramp emerging or weaving area where foreseeable sideswipe or rear-end collisions might take place between a merging vehicle and another vehicle from the outside lane. A driver should decelerate when merging become difficult as trained in a licensing procedure for avoiding potential collisions. If the required deceleration length is shortened upon avoiding a potential collision, the number of collisions at the onramp should decrease and the collisions if occurred would be less severe. The tire-road frictional coefficient if raised high enough could dramatically reduce the required deceleration length prior to collision, and the high friction surface treatment (HFST) has been introduced in practice for the last decade to shorten the required distance to stop a moving vehicle for both wet and dry road surface conditions. It is of significant interest to examine quantitatively, based on a proposed integrated framework, the effect of a high frictional surface course on reducing the needed deceleration distance, in particular, on an onramp merging/weaving section constructed with limited right of way.

Variable speed limits (VSLs) and ramp metering (RM) have been proved to be effective alternatives to alleviate the traffic congestions occurred on freeways in many studies. However, no research has so far evaluated the efficiency of the above strategies on urban expressways in Chinese cities, where the early onset breakdown often occurs at the bottlenecks because of different driver behaviour from those reported in overseas. Hence, this study was intended to propose an integrated control strategy of VSL and RM to dynamically postpone the early onset breakdown of on‐ramp bottlenecks based on its occurring probability. The proposed strategy is capable of simultaneously optimising mainline speed and RM rate based on the modified METANET model and the ALINEA model. Meanwhile, an online simulation platform that integrates VISSIM, Visual Basic and MATLAB programs was developed for the purpose of a close‐to‐reality evaluation. Empirical results on urban expressways in Shanghai show that the average travel time and the speed difference were significantly reduced by 8.6 and 41.4%, respectively; the pre‐queue flow at the bottleneck was increased by 14.7%, which indicates that the early onset breakdown could be efficiently postponed or prevented by the use of the proposed control strategy.

Deep learning models for short-term travel speed prediction on urban expressways, such as the convolutional neural network (CNN), still present several limitations in multiscale spatiotemporal feature extraction. Hence, in this paper, three hybrid CNN models are proposed to improve the basic CNN model with regard to three target aspects for short-term (i.e., 5 min) travel speed prediction on urban expressways. More specifically, long short-term memory (LSTM), AutoEncoder (AE), and Inception module are incorporated into the basic CNN model to capture multiscale spatiotemporal features of travel speed data effectively and improve the accuracy and robustness of the basic CNN model. Based on loop detector data collected on the Yan’an expressway in Shanghai, the proposed hybrid CNN models are trained and tuned. To validate the improvements on the target aspects, a comprehensive comparison is conducted using a classical statistical model (i.e., autoregressive integrated moving average), a typical shallow neural network model (i.e., artificial neural network), and two basic deep learning models (i.e., recurrent neural network and CNN). Results show that the prediction accuracies of all the proposed hybrid CNN models exceed 96% and the mean absolute errors are less than 2.5 km/h, which are superior to other models. In terms of target improving aspects, two new metrics were introduced, and the proposed models, especially the AE–CNN model, showed better robustness under various input data structures and traffic states. The LSTM–CNN model outperformed the other models in learning time-series features, and the Inception–CNN model is superior in reproducing the dynamics of traffic congestion patterns on urban expressways.

A method for identifying rear-end collision risks using inductive loop detectors

Urban expressways connect with local surface/arterial roads tightly through off-ramps, then lead to the increased traffic volume during peak hours, traffic flows near off-ramps usually become chaotic. Therefore traffic flow behavior at off-ramps is critical to improve traffic management of urban expressways with better analysis. Drivers' queue-jump behavior near the off-ramp occurs frequently in the city of developing country, which contributes to the traffic complexity. However, the existing microscopic traffic simulation usually neglected drivers' queuejump behaviors. So, multiple cellular automata based simulation model was built, which specifically targets on queue jump behaviors and the traffic spillback near expressway off-ramp, which the car is contained multiple cellular. Queue-jump rules are incorporated into the car-following and lane changing logics in the model. Using the model, different configurations around off-ramp in combination was simulated. The results show that, easy to recognizable pavement markers is designed to reduce the number of discretionary lane-changes, and multiple cellular cars in simulation reflects the traffic state better.

Rear-end collision prediction has gained an increasing attention for safety improvement in smart cities. It is urgent to design efficient warning strategies for rear-end collisions which is one of the main causes of traffic accidents. The existing researches have been conducted to predict the collisions. Learning-based methods are proposed to solve this complicated issue, which the traditional methods are difficult to solve. However, due to some limitations in terms of the feature extraction and prediction performance, back-propagation learning methods are facing challenge. In this paper, we proposed a novel Rear-end Collision Prediction Mechanism with deep learning method (RCPM), in which a convolutional neural network model is established. In RCPM, the dataset is smoothed and expanded based on genetic theory to alleviate the class imbalance problem. The preprocessed dataset is divided into training and testing sets as the input to train our convolutional neural network model. The experimental results show that compared with the Honda, Berkeley and multi-layer perception neural-network-based algorithms, RCPM effectively improves performance to predict rear-end collisions.

The paper studies multiresolution traffic flow simulation model of urban expressway. Firstly, compared with two-level hybrid model, three-level multiresolution hybrid model has been chosen. Then, multiresolution simulation framework and integration strategies are introduced. Thirdly, the paper proposes an urban expressway multiresolution traffic simulation model by asynchronous integration strategy based on Set Theory, which includes three submodels: macromodel, mesomodel, and micromodel. After that, the applicable conditions and derivation process of the three submodels are discussed in detail. In addition, in order to simulate and evaluate the multiresolution model, “simple simulation scenario” of North-South Elevated Expressway in Shanghai has been established. The simulation results showed the following.(1)Volume-density relationships of three submodels are unanimous with detector data.(2)When traffic density is high, macromodel has a high precision and smaller error and the dispersion of results is smaller. Compared with macromodel, simulation accuracies of micromodel and mesomodel are lower but errors are bigger.(3)Multiresolution model can simulate characteristics of traffic flow, capture traffic wave, and keep the consistency of traffic state transition. Finally, the results showed that the novel multiresolution model can have higher simulation accuracy and it is feasible and effective in the real traffic simulation scenario.

In this study, an applicability of microscopic shockwave (μSW) theory is evaluated whether it is efficient in assessing the risk of rear-end collision in vehicle platoon. Two different types of μSW behavior are proposed for the evaluation; one is the car-following that shows a higher risk, whereas the other is a low-risk vehicle platooning. The numerical analysis attempts to evaluate whether the conventional risk evaluation approaches are able to distinguish those two cases; high and low risk platooning based on the proposed μSW technique. The statistical analysis showed that the behavior of μSW is capable of assessing the risk of rear-end collision of platooning vehicles.

The effects of indicating rear-end collision risk via variable message signs on traffic behaviour

How does the driver respond to rear-end risk under time-varying driving styles: A temporal instability test using the model with heterogeneity in means and variances.

This paper presents a probabilistic decision-making framework for rear-end collision avoidance systems, focusing on modeling the impact of major collision-causing factors on the occurrence of accidents. Decisions on when and how to assist drivers are made using a Bayesian network approach according to collision risk evaluation results, given a prior probabilistic knowledge. The structure of the Bayesian network model is learnt using a K2 algorithm with a practical dataset. To provide adequate response time for drivers, we also predict collision probability in the next monitoring interval using a Kalman filter model. The prediction accuracy is evaluated with different use cases and compared to real scenarios with the obtained dataset. To make our framework more relevant to practical applications, we also discuss the corresponding safety control strategies by classifying collision risk into high and low levels. In addition, the proposed model is evaluated through experiments including simulations and road tests. In the simulations, the algorithms are tested in different scenarios with various configurations of weather conditions, driver response capability, and vehicular dynamics. In order to demonstrate collision avoidance performance, the proposed model is also compared to existing schemes. In the road tests, the algorithm is embedded into an unmanned vehicle with predefined parameters to evaluate the impact of computational complexity on rear-end collision avoidance. Numerical results show that the proposed model provides an accurate estimation for car-following collision risk with a relatively low complexity, taking into account the impacts of vehicle dynamics, driver reaction capacity, and external environment on rear-end collisions.

The autonomous driving is increasingly mounting, promoting, and promising the future of fully autonomous and, correspondingly presenting new challenges in the field of safety assurance. The unexpected and sudden lane change are extremely serious causes of traffic accident and, such an accident scheme leads the multiple vehicle collisions. Extensive evaluation of recent crash data we found a crucial indication that autonomous driving systems are most prone to rear-end collision, which is the leading factor of chain crash. Learning based self-developing assessment assists the operators in providing the necessary prediction operations or even replace them. Here we proposed a Reinforcement learning-based conceptual framework for threat assessment system and scrutinize critical situations that leads to multiple vehicle collisions in autonomous driving. This paper will encourage our transport community to rethink the existing autonomous driving models and reach out to other disciplines, particularly robotics and machine learning, to join forces to create a secure and effective system.

Various collision avoidance systems (CASs) have been developed and employed in human-operated vehicles as well as more recently in autonomous vehicles. Most of the existing CASs perform an override function to actuate automatic emergency braking in a critical situation based on the current traffic information obtained from in-vehicle sensors or short-range vehicular communications. These CASs focus on the critical situation in the vicinity of the subject vehicle, which means they may have negative influences on the subject vehicle and its following vehicles, particularly when the leader vehicle of a platoon with short headway applies harsh braking to mitigate an impending collision risk. This study proposes a risk predictive CAS (RPCAS) which executes predictive deceleration with mild braking in advance to prevent a potential rear-end collision by predicting the collision risk arising from a downstream site. To evaluate the performance of the RPCAS, the proposed system is compared with several existing CASs in various car-following cases based on a microscopic traffic simulation. The simulation results show that the RPCAS can effectively reduce the rear-end collision risk with less harsh braking compared with the existing CASs. Furthermore, the RPCAS enables vehicles arriving from upstream to anticipate a potential crash, which provides them with sufficient time to reduce their current speeds proactively. The research findings suggest that the proposed system can attenuate the negative impacts of the previous CASs in relation to traffic and vehicular safety.

The middle and lower reaches of Yangtze River (MLYR) experienced a historically high number of persistent cloudy and rainy days throughout winter 2018/2019. The persistent cold and rainy weather caused severe social impacts and economic losses. Climatic monitoring shows that a weak central Pacific (CP) El Niño event initiated in November 2018 with an increase in the sea surface temperature (SST) in the central and eastern tropical Pacific. During the mature phase of the CP El Niño, the western North Pacific Subtropical High (WNPSH) was stronger than normal and the western boundary moved westward. However, the weak CP El Niño event alone is not enough to explain the abnormally strong and northward displacement of WNPSH and associated heavy rainfall in winter of 2018/2019. This study highlights the eastward propagating Rossby wave train in the middle‐upper troposphere along with the Asian westerly jet‐induced anomalous high pressure and anticyclonic circulation over East Asia, which contributed to the intensification and northward displacement of WNPSH. The synergic influence of both the CP El Niño and Rossby wave train over Eurasia resulted in an intensified and northward shifted WNPSH, which, together with the intensification of the India Burma trough, strengthened the transport of water vapour into the MLYR basin. Accompanied by frequent outbreaks of cold air and confluence of cold and warm air over the MLYR basin, cold and rainy weather persisted in winter 2018/2019 in this region.

Leptospirosis poses a serious public health problem in many countries because about 1 million people suffer annually from this severe, often fatal, infection. At present, its importance has been increasing due to development of integration processes, globalization and intensification of economic activities, inevitable anthropogenic transformation of landscapes as well as rise in number of people with occupational risk of pathogen exposure. The burden of human leptospirosis is expected to rise with demographic shifts and climate change that result in heavy rainfall and flooding. Regarding leptospirosis, Southeast Asia, including Vietnam, is among the most vulnerable global regions. The objective of the review was to analyze available published data on the spread of leptospirosis in Vietnam. More than 100 research articles were analyzed allowing to state that in Vietnam synanthropic rodents (rats), agricultural (buffaloes, cattle, pigs) and domestic animals (cats, dogs) are the main sources of leptospirosis infection in humans. It is essential that rats (cats and dogs also) in Vietnam are used for food. Among all farm animals, pigs are of maximal importance as a source of infection in humans. In Vietnam, Leptospira prevalence in pigs varies widely, up to 73%. It depends significantly on the location, farm size, age and gender of animals, etc. A large variety of Leptospira serovars have been identified in pigs in Vietnam: Tarassovi, Bratislava, Australis, Javanica, Autumnalis, Grippotyphosa, and some others. Official Vietnam reporting of leptospirosis cases based on clinical data does not reflect the real-life incidence rate in humans. However, the available seroprevalence studies reveal that, on average, about 10% of the Vietnamese population are infected with Leptospira. Work in agriculture is the dominant risk factor for acquiring this infection. The highest Leptospira seroprevalence rates were detected in farmers, 63.2%. Serovar diversity is typical for pathogenic Leptospira circulating in Vietnam. In humans, the most frequent are antibodies to Hebdomadis, Wolffi and Icterohaemorrhagiae, but the frequency varied markedly across the country depending on the site, possibly with relation to the environment, as well as with economic activity of the local population. In Vietnam, geographic, climatic and social differences between northern, central and southern regions contribute to the specificity of local leptospirosis epidemiology. This emphasizes the advisability of developing special measures for leptospirosis prevention taking into account regional peculiarities.