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• The model simulates car-following and lane-changing behaviors in a unified framework. • The risk field-based behavior model has the potential to diverse simulation scenarios. • The model's parameters have transparent, interpretable effects on simulations. • The underlying mechanisms of drivers' path and trajectory planning are elucidated. The modeling of driving behavior is pivotal for the accurate simulation of traffic scenarios and for providing human-like decision-making of autonomous driving systems. Car-following (CF) and lane-changing (LC) behaviors are continuous maneuvers within traffic flow, generally modeled separately in the literature. The coherence between these two behaviors may be ignored, leading to unrealistic behavioral simulations. Therefore, this paper establishes a risk field-based driving behavior model for two-dimensional motion, ensuring coherent modeling of CF and LC behaviors under a unified framework. First, a risk quantification method is developed to calculate the risk in two-dimensional scenarios, accounting for risk over the preview time. A cubic polynomial is applied to generate path curves that align with vehicle dynamics. Second, the enhanced behavior model primarily comprises two integral components: path and trajectory planning. These two components aim to identify the path or trajectory that maximizes the benefit while meeting the desired risk. Third, the maximum acceptable risk, representing a higher risk than the desired risk, is defined to facilitate path adjustment and avoid frequent path adjustment. Finally, the proposed model is proved through comparisons with existing models using driving data. Several cases are employed for further analysis to show the model's rationality and potential in various aspects. This study develops the previous risk field-based behavior model from one-dimensional to two-dimensional scenarios, furnishes a unified framework for elucidating driving behavior in various scenarios, and contributes to the progress of behavior modeling. [ABSTRACT FROM AUTHOR]

• The effects of cognitive load (CL) and automation level (AL) on drivers' takeover quality (TQ) was investigated by a driving simulator study. • Driver's subjective workload reduced with increased AL. • Higher AL lead to a less high-quality takeover. • CL improves drivers' ability to follow instructions and TQ in critical highly automated driving condition. Mixed control by driver and automated system will remain in use for decades until fully automated driving is perfected. Thus, drivers must be able to accurately regain control of vehicles in a timely manner when the automated system sends a takeover request (TOR) at its limitation. Therefore, determining the factors that affect drivers' takeover quality at varying levels of automated driving is important. Previous studies have shown that visually distracting secondary tasks impair drivers' takeover performance and increase the subjective workload. However, the influence of purely cognitive distracting secondary tasks on drivers' takeover performance and how this influence varies at different levels of automation are still unknown. Hence, a 5 (driving modes) × 3 (cognitive secondary tasks) factorial design with the within-subject factors was adopted for this driving simulator experiment. The sample consisted of 21 participants. The participants' subjective workloads were recorded by the NASA-Task Load Index (NASA-TLX). Results showed that compared to manual driving conditions, the drivers' subjective workloads were significantly reduced in both partially and highly automated driving conditions, even with a TOR, confirming the benefit of the automated driving system in terms of reducing the driving workload. Moreover, the drivers exhibited a lower takeover behavior quality at high levels of automation than manual driving in terms of increased reaction time, abnormal performance, standard deviation of lane position, lane departure probability, and reduced minimum of time to collision. However, at the highly automated driving condition, the drivers' longitudinal driving safety and ability to follow instructions improved when performing a highly cognitive secondary task. This phenomenon possibly occurred because automated driving conditions lead to an underload phenomenon, and the execution of highly cognitive tasks transfers drivers into moderate load, which helps with the drivers' takeover performance. [ABSTRACT FROM AUTHOR]

• This is the first paper to study the impact of manual driving experience on takeover performance in level 3 automation. • The interaction effects among driving experience, takeover time budget, and secondary task were also studied. • Rich manual driving experience cannot make takeover process of experienced drivers more stable under conditions of eye-off-road. • Novice drivers have poor takeover stability and adaptability, but their longitudinal collision risk is not deteriorated by the lack of driving experience. With level 3 automated vehicles poised to appear on the roads soon, takeover remains a major challenge. At present, the effect of manual driving experience on takeover performance is unknown. Therefore, a simulator study was conducted to investigate the influence of driving experience (novice and experienced) on takeover performance in different takeover time budgets (7 s and 5 s) and in combination with a visual secondary task (i.e., surrogate reference task). Data from 48 young and middle-aged participants consisting of 24 novice and 24 experienced drivers were used for this study. Researchers found that the overall stability of evasive maneuvers by novice drivers was considerably worse than that by experienced drivers. A detailed analysis showed that the influence of driving experience on takeover stability was mainly reflected in longitudinal control rather than lateral control. A significant interaction between driving experience and visual secondary task showed that the latter had a substantial impact on the takeover stability of experienced drivers but not on that of novice drivers. Researchers also found that rich manual driving experience cannot make the takeover process of experienced drivers more stable than that of novice drivers under conditions of eye-off-road. In addition, no significant difference was found between novice and experienced drivers in automation disengagement time, takeover time and minimum time to collision. Results indicate that novice drivers have poor takeover stability and weak adaptability, but their longitudinal collision risk is not deteriorated by the lack of manual driving experience. [ABSTRACT FROM AUTHOR]

Aspect sentiment classification is an important research topic in natural language processing and computational linguistics, assisting in automatically review analysis and emotional tendency judgement. Different from extant methods that focus on text sequence representations, this paper presents a network framework to learn representation from concurrence-words relation graph (LRCWG), so as to improve the Macro-F1 and accuracy. The LRCWG first employs the multi-head attention mechanism to capture the sentiment representation from the sentences which can learn the importance of text sequence representation. And then, it leverages the priori sentiment dictionary information to construct the concurrence relations of sentiment words with Graph Convolution Network (GCN). This assists in that the learnt context representation can keep both the semantics integrity and the features of sentiment concurrence-words relations. The designed algorithm is experimentally evaluated with all the five benchmark datasets and demonstrated that the proposed aspect sentiment classification can significantly improve the prediction performance of learning task. [ABSTRACT FROM AUTHOR]

Text classification is an important task in machine learning. Specifically, deep neural network has been shown strong capability to improve performance in different fields, for example speech recognition, objects recognition and natural language processing. However, in most previous work, the extracted feature models do not achieve the relative text tasks well. To address this issue, we introduce a novel multi-task learning approach called a hybrid representation-learning network for text classification tasks. Our method consists of two network components: a bidirectional gated recurrent unit with attention network module and a convolutional neural network module. In particular, the attention module allows for the task learning private feature representation in local dependence from training texts and that the convolutional neural network module can learn the global representation on sharing. Experiments on 16 subsets of Amazon review data show that our method outperforms several baselines and also proves the effectiveness of joint learning multi-relative tasks. [ABSTRACT FROM AUTHOR]

• We design a hybrid structure neural network (MTVAE) for sentiment classification. • We combine with generative model, five-point classification and binary classification for training simultaneously. • Experimental results show that our multi-task learning model outperforms most of state-of-the-art approaches. With the rapid growth of the big data, many approaches in the representation of text for sentiment classification have been successfully proposed in natural language processing. However, these approaches remedy this problem based on single-task supervised objectives learning and do not consider their relative of multiple tasks. Based on these defects, in this work, we consider these tasks are relative, and use weight-shared parameters for learning the representation of text in neural network model, we introduce and study a multi-task approach with variational auto-encoder generative model (MTVAE) by jointly learning them. Experimental results on six subsets of Amazon review data show that the proposed approach can effectively improve the sentiment classification accuracy by other relative tasks. [ABSTRACT FROM AUTHOR]

CO chemisorption has been studied on TiO2(110) under surface conditions where oxygen anion vacancy sites are not present (oxidized surface), compared to conditions where the vacancy sites are present (annealed surface). The binding energy of CO on the nondefective TiO2(110) surface is 9.9 kcal/mole in the limit of zero coverage. CO...CO repulsive interactions have been observed at higher coverages. When anion vacancy sites are produced under controlled annealing conditions in vacuum at 900 K, a significant increase in the desorption temperature of a portion of the chemisorbed CO is observed. This observation, coupled with measurements showing that defective TiO2(110) does not have enhanced CO chemisorption capacity, suggests that CO adsorbs more strongly on lattice Ti sites in the vicinity of anion vacancy sites. It is postulated that enhanced CO bonding occurs via the interaction of the O moiety of CO with the anion vacancy site while primary adsorbate bonding occurs via the C moiety to Ti lattice sites. Neither CO2 production nor oxygen exchange in CO occurs when CO desorbs from defective TiO2(110). [ABSTRACT FROM AUTHOR]

We have investigated the adsorption and thermal conversion of molecular oxygen (O2) states on the TiO2(110) surface by making use of the distinct photodesorption behavior of each adsorption state. Oxygen chemisorbs at the oxygen vacancy defect sites on the annealed TiO2(110) surface at 105 K to a saturation coverage of less than 0.12 monolayers (ML), producing mostly the α-O2 species which is observed to undergo slow photodesorption. Upon heating this surface to above 250 K, the α-O2 is converted to the β-O2 state which can photodesorb at a significantly higher rate. The β-O2 species dissociates above 400 K to produce atomic oxygen, eliminating the oxygen anionic vacancies. Both the α- and β-photodesorption processes have a threshold energy at the TiO2 band gap (3.1 eV), indicating a substrate excitation mediated process. The photodesorption time-profile is fitted with an exponential decay function with a cross section of ∼8×10-17 cm2 for the α-O2 and ∼1.5×10-15 cm2 for the β-O2 species at a photon energy of 3.94 eV. © 1995 American Institute of Physics. [ABSTRACT FROM AUTHOR]

We report the first experimental observation of two chemisorption states for molecular oxygen on a TiO2(110) surface containing anion vacancy sites. The first molecular species can be photoactivated to oxidize coadsorbed CO to CO2 (α channel) and undergoes slow photodesorption. The second molecular oxygen species only undergoes fast photodesorption (β channel). Conversion from α-O2, to β-O2 occurs upon heating the surface to above 200 K. © 1995 American Institute of Physics. [ABSTRACT FROM AUTHOR]

We have used multiple internal reflection infrared spectroscopy to investigate the interaction of digermane with Ge(111) at temperatures between 104–600 K. Digermane predominantly adsorbs molecularly on the surface below 120 K, displaying a vibrational spectrum similar to that of condensed digermane. At temperatures between 120–150 K, digermane dissociates via Ge–Ge bond scission to form adsorbed GeH3. Chemisorbed germyl GeH3 has a distinct symmetric deformation vibration at ∼772 cm-1, compared to a value of 721 cm-1 for molecularly adsorbed Ge2H6. At 200 K, Ge2H6 adsorption produces surface GeH3, GeH2, and GeH species with stretching vibrations at 2063, 2023, and 1968 cm-1, respectively. The surface GeH2 species is also identified by a characteristic scissor mode at ∼830 cm-1. Adsorption at 300 and 400 K produces only GeH2 and GeH, with a much lower concentration of GeH2 at 400 K. The surface GeH2 and GeH species are also generated by the successive decomposition of GeH3 upon heating. All surface hydrogen desorbs at ∼600 K. [ABSTRACT FROM AUTHOR]

Studies the adsorption and reaction of tetraethoxysilane with hydroxylated silicon oxide using transmission infrared spectroscopy. Importance of dielectric thin films; Methodology of the study; Discussion on the result of the study.

The schedule of urban road network recovery caused by rainstorms, snow, and other bad weather conditions, traffic incidents, and other daily events is essential. However, limited studies have been conducted to investigate this problem. We fill this research gap by proposing an optimal schedule for urban road network repair with limited repair resources based on the greedy algorithm. Critical links will be given priority in repair according to the basic concept of the greedy algorithm. In this study, the link whose restoration produces the ratio of the system-wide travel time of the current network to the worst network is the minimum. We define such a link as the critical link for the current network. We will re-evaluate the importance of damaged links after each repair process is completed. That is, the critical link ranking will be changed along with the repair process because of the interaction among links. We repair the most critical link for the specific network state based on the greedy algorithm to obtain the optimal schedule. The algorithm can still quickly obtain an optimal schedule even if the scale of the road network is large because the greedy algorithm can reduce computational complexity. We prove that the problem can obtain the optimal solution using the greedy algorithm in theory. The algorithm is also demonstrated in the Sioux Falls network. The problem discussed in this paper is highly significant in dealing with urban road network restoration. [ABSTRACT FROM AUTHOR]

• The effects of DAVL assignments, inter-vehicle distances of AV platoons, and AV platoon speed on the car-following performance of nearby MV drivers were studied. • The DAVL assignments and the inter-vehicle distances of AV platoons significantly affected the car-following safety and driving stability of nearby MV drivers. • The AV platoon speed significantly affected the car-following safety and mental workload of MV drivers in the vicinity. Deploying dedicated lanes for automated vehicles (AVs) can effectively alleviate the coordination issues between AVs and manual vehicles (MVs). However, AV platoons running on dedicated AV lanes (DAVLs) have a prominent collective behavior characteristic of small inter-vehicle distance. The nearby MV drivers' imitation of this characteristic may reduce their car-following time headway (THW). The researchers conducted a simulation experiment to investigate the influence of DAVL assignments, inter-vehicle distances of AV platoons and AV platoon speed on the car-following performance of nearby MV drivers. The data of mean THW, standard deviation of THW, standard deviation of lateral position, standard deviation of velocity, standard deviation of horizontal gaze position and mean saccadic peak velocity were collected from 36 participants. Statistical analysis results show that the three factors considerably affected the MV drivers' car-following performance. In particular, the MV drivers showed a worse car-following safety but a better driving stability when the left lane was dedicated to AVs than when the right lane was dedicated to AVs (Note the experiments were done in a drive-on-the-left environment.). With respect to the inter-vehicle distances of AV platoons, the MV drivers' car-following safety was poorer under the 4 m condition than that under the 10 and 18 m conditions. In addition, the MV drivers showed a poorer car-following safety and bore a larger mental workload when driving next to the AV platoons running at 110 km/h. This study may provide some suggestions for DAVLs. Assigning the right lane of a three-lane motorway as the DAVL may have a slighter negative impact on the nearby MV drivers in China. In terms of traffic management in DAVLs, the inter-vehicle distance of AV platoons can be reduced to 10 m, and the speed of AVs should not be higher than the design speed of adjacent MV lanes. [ABSTRACT FROM AUTHOR]

Objective: This study aims to investigate the crossing behavior of straight-moving drivers when they encounter other straight-moving drivers at unsignalized intersections in China.Background: In China, when two vehicle drivers encounter at an unsignalized intersection, neither driver completely stops his or her vehicle in most cases. Instead, one driver gradually approaches the intersection and dynamically decides to either yield or preempt by gaming with the other vehicle. This process increases the probability of accidents.Method: A total of 305 crossing cases were collected at an unsignalized intersection in Kunming City. Motion parameters were extracted from the video detection program designed by our research group. Based on a logistic regression method, we analyzed decision making moment of straight-moving drivers under crossing conditions, established crossing behavior models, and identified the main factors that affected drivers' decisions. In all, 68 cases observed at a separate intersection were used to validate the established models.Results: For crossing processes at unsignalized intersections in China, straight-moving drivers from the right side completed preemptive/yielding decisions at 1.3 s before reaching the crossing point. However, the majority of straight-moving drivers from the left side completed decisions at 1.1 s before reaching the crossing point. The most important parameter that influenced the drivers' decisions was the difference between the speeds of the two vehicles.Conclusion: An effective method for preventing traffic conflict between two straight-moving drivers is to control the speeds of the vehicles before they enter the intersection.Application: This study explores crossing behavior of straight-moving drivers and provides significant insights for controlling driver behavior. [ABSTRACT FROM AUTHOR]

• A risk field model independent on yellow duration is proposed to quantify the risk constraints of traffic lights on vehicle movement. • A driving behavior model framework is established to explain the approaching behaviors to signalized intersections from the perspective of human behavioral mechanism. • By considering drivers' desired risks, the probability of passing the stop line during yellow period is obtained by simulation and compared with that calculated by existing models. • Considerations regarding the superiority of modeling based on human behavioral mechanisms compared to data-driven modeling are presented. The stop/go decisions made by drivers who are approaching signalized intersections during yellow period will affect the safety and efficiency of intersections. Existing research mostly modeled drivers' decision-making behaviors using real-world driving data, while these datasets were collected in different traffic flows and road environments, and it is difficult to develop models suitable for different intersections. Aiming at explaining the approaching behaviors to signalized intersections from the perspective of human behavioral mechanism, this study establishes a driving behavior model framework, including a risk field model of dynamic traffic control elements independent on yellow duration, and a trajectory planning model constructed according to the risk homeostasis theory and preview-follower theory. Probabilities of passing the stop line during yellow period and the distribution of acceleration and deceleration rates when passing are obtained in the simulation by the Monte Carlo method. Results show the validity of the proposed model and its applicability to drivers with different desired risks. Compared to the proposed model, drivers are more inclined to use smaller acceleration rates or greater deceleration rates when entering intersections in observed cases. The intervention of reaction time may decrease the probabilities of passing. This study is an indispensable supplement to our previous study, contributing a unified model based on risk quantification to comprehensively describe the risk of the traffic environment, and is an attempt to promote the development of driving behavior models. [ABSTRACT FROM AUTHOR]

Many accidents occurring at signalized intersections are closely related to drivers’ decisions of running through intersections during yellow light, i.e., yellow-light running (YLR). Therefore it is important to understand the relationships between YLR and the factors which contribute to drivers’ decision of YLR. This requires collecting a large amount of YLR cases. However, existing data collection method, which mainly relies on video cameras, has difficulties to collect a large amount of YLR data. In this research, we propose a method to study drivers’ YLR behaviors using high-resolution event-based data from signal control systems. We used 8 months’ high-resolution data collected by two stop-bar detectors at a signalized intersection located in Minnesota and identified over 30,000 YLR cases. To identify the possible reasons for drivers’ decision of YLR, this research further categorized the YLR cases into four types: “in should-go zone”, “in should-stop zone”, “in dilemma zone”, and “in optional zone” according to the driver’s location when signal turns to yellow. Statistical analysis indicates that the mean values of approaching speed and acceleration rate are significantly different for different types of YLR. We also show that there were about 10% of YLR drivers who cannot run through intersection before traffic light turns to red. Furthermore, based on a strong correlation between hourly traffic volume and number of YLR events, this research developed a regression model that can be used to predict the number of YLR events based on hourly flow rate. This research also showed that snowing weather conditions cause more YLR events. [ABSTRACT FROM AUTHOR]

Highlights: [•] The merging process at unsignalized intersection in China is illustrated. [•] We use classification tree method to analyze drivers’ dynamic decision making. [•] We indentify the motion parameters which influence drivers’ decision significantly. [•] Drivers finish decisions between 1.3 and 1.5s before reaching the merging point. [•] This study suggests effective way to avoid traffic conflicts. [ABSTRACT FROM AUTHOR]

Highlights: [•] The crossing process at unsignalized intersections in China is illustrated. [•] We use classification tree method to analyze drivers’ decision making. [•] We indentify the motion parameters which influence drivers’ decision significantly. [•] This study sheds lights on drivers’ decision model suited for China. [Copyright &y& Elsevier]

• Consider multiple dependent variables through a single experiment, to evaluate differential contribution of key factors to takeover behavior. • Consider the mediating role of trust in the formation and evolution of takeover behavior. • Takeover time budget, together with traffic density and driver characteristics, determined drivers' takeover quality directly. • Driver characteristics and takeover time budget strongly influenced the takeover time directly. • Trust as an intermediary variable, explained a major portion of the variance in the takeover time. The takeover process in level 3 automated driving determines the controllability of the functions of automated vehicles and thereby traffic safety. In this study, we attempted to explain drivers' takeover performance variation in a level 3 automated vehicle in consideration of the effects of trust, system characteristics, environmental characteristics, and driver characteristics with a structural equation model. The model was built by incorporating drivers' takeover time and quality as endogenous variables. A theoretical framework of the model was hypothesized on the basis of the ACT-R cognitive architecture and relevant research results. The validity of the model was confirmed using data collected from 136 driving simulator samples under the condition of voluntary non-driving-related tasks. Results revealed that takeover time budget was the most critical factor in promoting the safety and stability of takeover process, which, together with traffic density, drivers' age and manual driving experience, determined drivers' takeover quality directly. In addition, the pre-existing experience with an automated system or a similar technology and self-confidence of the driver, as well as takeover time budget, strongly influenced the takeover time directly. Apart from the direct effects mentioned above, trust, as an intermediary variable, explained a major portion of the variance in takeover time. Theoretically, these findings suggest that takeover behavior could be comprehensively evaluated from the two dimensions of takeover time and quality through the combination of trust, driver characteristics, environmental characteristics, and vehicle characteristics. The influence mechanism of the above factors is complex and multidimensional. In addition to the form of direct influence, trust, as an intermediary variable, could reflect the internal mechanism of the takeover behavior variation. Practically, the findings emphasize the crucial role of trust in the change in takeover behavior through the dimensions of subjective trust level and monitoring strategy, which may provide new insights into the function design of takeover process. [ABSTRACT FROM AUTHOR]

The expansion of online social networks has led to attracting a significant number of users. Meanwhile, the analysis of these networks has raised new perspectives and challenges due to the large amount of information. One of these challenges in the analysis of social networks is the problem of link prediction. The purpose of this problem is to find links that have not yet been observed, but may exist in the future. There are many solutions for link prediction on monoplex networks. However, many real social networks model communication in multiple layers. In recent years, link prediction in multilayer social networks has attracted the attention of many researchers. A solution for multilayer networks involves taking into account the information of all layers to make predictions for a target layer. Among the link prediction techniques based on monoplex networks, local random walk is very popular. This paper presents an extended version of this technique for multilayer social networks considering reliable routes and weighted links. Our proposed algorithm as Weighted Reliable Local Random Walk (WRLRW) simultaneously uses interlayer and intralayer information to predict links in a target layer of a multilayer network. WRLRW adjusts link weights based on multimodal and topological features and forms reliable routes between users by multiplying link weights. We demonstrated the effectiveness of WRLRW to address the link prediction problem on real-world multilayer networks through numerical simulations. WRLRW has better performance compared to link prediction techniques based on monoplex networks as well as state-of-the-art methods based on multilayer networks. According to the results of the experiments, WRLRW obtains 3.2% and 2.5% better average f-measure compared to SEM-Path (Structural, Ego-Paths and Meta-Paths features) and MLRW (Multiplex Local Random Walk), respectively. [ABSTRACT FROM AUTHOR]

Presents information pertaining to the rapid thermal chemical vapor deposition (RTCVD) of polysilicon. Information on the capacity of the simulator; What role the computer simulation plays; Basis of the mathematical models for equipment and materials processes.

Studies contamination control for gas delivery from a liquid source in semiconductor manufacturing. Experimental procedure used; Results of the study.

Presents information on optimization as it relates to the semiconductor manufacturing industry. Investigation into rapid thermal chemical vapor deposition (RTCVD); Details on the requirements of environmentally-conscious manufacturing (ECM); Indepth look at the dynamic simulator description.

Knowledge Base Question Answering (KBQA) task is an important research direction in natural language processing. Due to the flexibility and ambiguity of natural language, users' questions often have more complex query types and richer semantic information. To address this issue, this paper proposes the GS-CBR-KBQA model, a Case-Based Reasoning model tailored for KBQA to improve the semantic parsing accuracy and interpretability of natural language questions. The model integrates Knowledge-oriented Programming Language (KoPL) reasoning graphs with query information, employing a Graph Auto-Encoder and the RoBERTa pretrained language model for a highly effective case retrieval. This integration leads to a more robust knowledge retrieval and application approach, particularly innovative in capturing the relationships within KoPL graphs. The model addresses explicitly complex questions such as multi-hop reasoning and questions involving intricate entity relationships. Finally, our extensive experiments show that the model performs excellently in accuracy and F1 metrics on benchmark datasets such as WebQSP and ComplexWebQuestions, particularly in complex question-answering. The code of our model is available at https://anonymous.4open.science/r/GS-CBR-KBQA. [ABSTRACT FROM AUTHOR]

Spectral clustering has been demonstrated to often outperform K-means clustering in real applications because it improves the similarity measurement of K-means clustering. However, previous spectral clustering method still suffers from the following issues: 1) easily being affected by outliers; 2) constructing the affinity matrix from original data which often contains redundant features and outliers; and 3) unable to automatically specify the cluster number. This paper focuses on address these issues by proposing a new clustering algorithm along with the technique of half-quadratic optimization. Specifically, the proposed method learns the affinity matrix from low-dimensional space of original data, which is obtained by using a robust estimator to remove the influence of outliers as well as a sparsity regularization to remove redundant features. Moreover, the proposed method employs the ℓ2,1-norm regularization to automatically learn the cluster number according to the data distribution. Experimental results on both synthetic and real data sets demonstrated that the proposed method outperforms the state-of-the-art methods in terms of clustering performance. [ABSTRACT FROM AUTHOR]

• An effective traffic organization method is proposed for CAVs in road networks. • An effective iterative adjustment strategy is used to optimize trajectories. • A novel composite road planning strategy is designed for different demands. • The proposed method ensures safe, efficient, energy-saving, and comfortable trips. Connected automated vehicles (CAVs) have been currently considered as promising solutions for realization of envisioned autonomous traffic management systems in the future. CAVs can achieve high desired traffic efficiency and provide safe, energy-saving, and comfortable ride experience for passengers. However, in order to practically implement such autonomous systems based on CAVs, there exist several significant challenges to be dealt with, such as coupled spatiotemporal constraints on CAVs' trajectories at unsignalized intersections, multiple objectives for trajectory optimization in road segments, and heterogeneous decision-making behaviors of CAVs in road networks with highly dynamic traffic demand. In this paper, we propose a cooperative autonomous traffic organization method for CAVs in multi-intersection road networks. The methodological framework consists of threefold components: an autonomous crossing strategy based on a conflict resolution approach at unsignalized intersections, multi-objective trajectory optimization in road segments, and a composite strategy for route planning considering heterogeneous decision-making behaviors of CAVs based on social and individual benefit, respectively. Specifically, we first identify a set of potential conflict points of different CAVs' spatial trajectories at the intersection, and then design different minimum safe time headways to resolve conflicts. Under the constraints of entry and exit conditions at adjacent intersections, we propose a multi-objective optimal control model by jointly considering vehicle safety, energy conservation, and ride comfort, and then analytically derive a closed-form solution for optimizing the CAVs' trajectories. Furthermore, with the purpose to adapt dynamic traffic demand, we propose a composite strategy for route planning by coordinating heterogeneous decision-making behaviors of CAVs in road networks. Finally, extensive simulation experiments have been performed to validate our proposed method and to demonstrate its advantage over conventional baseline schemes in terms of global traffic efficiency. Additional numerical results are also provided to shed light on the impact of the proportion of CAVs with heterogeneous decision-making behaviors on the global system performance. [ABSTRACT FROM AUTHOR]

Highlights • The influencing factors of rear-end crash risk were initially analyzed by using the desired safety margin (DSM) model. • The influencing factors including upper and lower limits of DSM, acceleration and deceleration rate and response time on the car-following behavior are investigated. And simulation experiments were designed to evaluate the impact of heterogeneity of car-following behavior on rear-end crash risk. • Decreasing the lower (or upper) limit of the DSM, increasing the response time, decreasing the sensitivity coefficient for deceleration, or increasing the sensitivity coefficient for acceleration can increase rear-end crash risk. • Two critical factors affect shock waves, namely, driving behavior characteristics and proportion of different driving styles. A wide extent of driving behavior heterogeneity can attenuate shock waves and subsequently reduce rear-end crash risk. • Exploring the effect of each driving behavior parameter on rear-end crash probability is useful for urban road traffic control, and it can provide improved understanding of abnormal driving behavior characteristics to reduce rear-end crash risks. Abstract An increasing number of vehicles travel on freeways result not only in traffic congestions but also accidents. Rear-end crashes in freeways can be collectively attributed to drivers, vehicles, and road infrastructure, but driving behavior plays a key role in influencing car-following safety. This study aims to investigate the impact of heterogeneity of driving behavior on rear-end crash risk. Driving behavior depends on perceived risk levels, acceleration and deceleration habits, and driver reaction characteristics. Thus, the influencing factors of rear-end crash risk were initially analyzed by using the desired safety margin (DSM) model. Subsequently, five driving behavior parameters, including upper and lower limits of DSM, sensitivity coefficients of acceleration and deceleration, and response time, were calibrated by using the vehicle trajectories from the Next Generation Simulation I-80 datasets. Simulation experiments were designed to evaluate the impact of heterogeneity of car-following behavior on rear-end crash risk. Results showed that decreasing the lower (or upper) limit of the DSM, increasing the response time, increasing the sensitivity coefficient for acceleration, or decreasing the sensitivity coefficient for deceleration can increase rear-end crash risk. In addition, if stable and unstable driving styles coexist, then their proportions have important influences on rear-end crash risk. These results imply that two critical factors affect shock waves, namely, driving behavior characteristics and proportion of different driving styles. Thus, a potential strategy for the adjustment of the proportions of unstable driving styles can attenuate shock waves and reduce rear-end crash risk to a certain extent. Moreover, a wide extent of driving behavior heterogeneity can attenuate shock waves and subsequently reduce rear-end crash risk. Overall, driving behavior heterogeneity has an important impact on rear-end crash risk. Exploring the effect of each driving behavior parameter on rear-end crash probability is useful for urban road traffic control, and it can provide improved understanding of abnormal driving behavior characteristics to minimize rear-end crash risks. [ABSTRACT FROM AUTHOR]

Most existing studies on routing guidance only paid attention to the average path travel time, which failed to consider travel time reliability (TTR) preferences by different travelers. In this study, a moment-matching-based hybrid genetic algorithm (MHGA) is proposed to search the reliable shortest path (RSP) in stochastic road networks with link correlations. First, the goodness-of-fit results based on field data reveal that lognormal distributions are more appropriate for characterizing link travel times. The impact of topological distance (measured by the number of links) and road type on link correlations is also scrutinized. Then, a moment-matching method (MOM) is utilized to determine the parameters of the approximate path travel time distribution (TTD) by accounting for link correlations. A local search algorithm is designed to improve the search ability of the path finding algorithm. In view of travelers’ risk tolerance, the algorithm enables the provision of personalized routing guidance for individual travelers. Furthermore, to support path finding applications in a large-scale network, heuristic constraints are imposed to help reduce the computational workload and accelerate the convergence speed of the search process. Finally, numerical case studies based on synthetic networks and a real road network in Beijing are presented, and the results help demonstrate that the algorithm has good potential to solve RSP searching problems in a large-scale network with desirable efficiency. [ABSTRACT FROM AUTHOR]

Exploring travel time distribution and variability patterns is essential for reliable route choices and sophisticated traffic management and control. State-of-the-art studies tend to treat different types of roads equally, which fails to provide more detailed analysis of travel time characteristics for each specific road type. In this study, based on a vast amount of probe vehicle data, 200 links inside the Third Ring Road of Beijing, China, were investigated. Four types of roads were covered including urban expressways, auxiliary roads of urban expressways, major roads, and secondary roads. The day-of-week distributions of unit distance travel time were first analyzed. Kolmogorov-Smirnov test, Anderson-Darling test, and chi-squared test were employed to test the goodness-of-fit of different distributions and the results showed lognormal distribution was best-fitted for different time periods and road types compared with normal, gamma, and Weibull distribution. In addition, four reliability measures, that is, unit distance travel time, coefficient of variation, buffer time index, and punctuality rate, were used to explore the day-of-week travel time variability patterns. The results indicated that urban expressways, auxiliary roads of urban expressways, and major roads have regular and distinct morning and afternoon peaks on weekdays. It is noteworthy that in daytime the travel times on auxiliary roads of urban expressways and major roads share similar variability patterns and appear relatively stable and reliable, while urban expressways have most reliable travel times at night. The results of analysis help enable a better understanding of the volatile travel time characteristics of each road type in urban network. [ABSTRACT FROM AUTHOR]

Concerns over transportation energy consumption and green-household gas (GHG) emissions have prompted a growing body of research into the influence of built environment on travel behavior. Studies on the relationship between land use and travel behavior are often at a certain aggregated spatial unit such as traffic analysis zone (TAZ), spatial issues occur among individuals clustered within a zone because of the locational effects. However, recognition of the spatial issues in travel modeling was not sufficiently investigated yet. The object of this study is twofold. First, a multilevel hazard model was applied to accommodate the spatial context in which individuals generate commuting distance. Second, this research provides additional insights into examine the effects of socio-demographics and built environment on commuting distance. Using Washington metropolitan area as the case, the built environment measures were calculated for each TAZ. To estimate the model parameters, the robust maximum likelihood estimation method for a partial function was used, and the model results confirmed the important roles that played by the TAZ and individual level factors in influencing commuting distance. Meanwhile, a comparison among the general multilevel model, single level and multilevel hazard models was conducted. The results suggest that application of the multilevel hazard-based model obtains significant improvements over traditional model. The significant spatial heterogeneity parameter indicates that it is necessary to accommodate the spatial issues in the context of commuting distance. The results are expected to give urban planners a better understanding on how the TAZ and individual level factors influence the commuting distance, and consequently develop targeted countermeasures. [ABSTRACT FROM AUTHOR]

Studying driver awareness of information, particularly risk perception, is vital to understanding driving behavior and improving traffic safety. In the dynamic interaction of a driver-vehicle-environment system, risk perception of drivers changes dynamically. In this study, we focused on drivers’ risk perception at unsignalized intersections in China and analyzed their crossing behavior with consideration of risk perception. Based on cognitive psychology theory and an adaptive neuro-fuzzy inference system, quantitative models of drivers’ risk perception were established for the crossing processes between two straight-moving vehicles from the orthogonal direction. Drivers’ acceptable risk perception levels were identified using a self-developed data analysis method. On the basis of game theory, the relationship among the quantitative value of drivers’ risk perception, acceptable risk perception level, and vehicle motion state was analyzed, then the crossing behavior models of drivers were established. Finally, the behavior models were validated using data collected from real-world vehicle movements and driver decisions. The results showed that the developed behavior models had both high accuracy and good applicability. This study would provide theoretical and algorithmic references for the microscopic simulation and active safety control system of vehicles. [ABSTRACT FROM AUTHOR]

• We propose a novel COVID-19 multiple outbreak analysis method (NCMOA). • This method divides the impact of multiple outbreaks on time and degree separately. • The differentiated air traffic recovery patterns are analyzed in the case study. • The impacts on epi -center and the nation have different critical influence factors. • The implication on policy formulation of airline and government is proposed. The spread of COVID-19 results in a significant drop in traffic levels worldwide. Quantifying the impact of multiple COVID-19 outbreaks on traffic systems is critical to developing differentiated policies in the future. This paper proposes a novel COVID-19 multiple outbreak analysis method (NCMOA), dividing the impact scope and degree under multiple COVID-19 disturbances, and using the recovery rate and accumulated loss to quantify the impacts on air passenger flow. A case study based on Chinese national air traffic flow is executed, and the recovery patterns and the differentiated disturbances are analyzed. Results show that air passenger flow recovers with a similar pattern after the first outbreak, and subsequent outbreaks cause local effects and cannot affect the overall recovery pattern. Further, the heterogeneous influence factors and trends on the epi -centers (EC) and the nation are analyzed. In addition, the methods and results of this paper quantify the impact of COVID-19 on air passenger flow at a more detailed level under multiple disturbances. They could provide a basis for differentiated policy formulation of airlines and government in the future. [ABSTRACT FROM AUTHOR]

The k -nearest neighbor (KNN) model is an effective statistical model applied in short-term traffic forecasting that can provide reliable data to guide travelers. This study proposes an improved KNN model to enhance forecasting accuracy based on spatiotemporal correlation and to achieve multistep forecasting. The physical distances among road segments are replaced with equivalent distances, which are defined by the static and dynamic data collected from real road networks. The traffic state of a road segment is described by a spatiotemporal state matrix instead of only a time series as in the original KNN model. The nearest neighbors are selected according to the Gaussian weighted Euclidean distance, which adjusts the influences of time and space factors on spatiotemporal state matrices. The forecasting accuracies of the improved KNN and of four other models are compared, and experimental results indicate that the improved KNN model is more appropriate for short-term traffic multistep forecasting than the other models are. This study also discusses the application of the improved KNN model in a time-varying traffic state. [ABSTRACT FROM AUTHOR]

It has been previously documented that under the condition of crossing an uncontrolled intersection, the decision-making process of drivers is rather complex and highly interactive: they need to decide about the timing, direction, and strategy to complete the required maneuver to avoid potential conflicts. Given the significant impact of this behavior on intersection safety and capacity, we model the merging behavior of a straight-moving vehicle facing a turning vehicle at an uncontrolled intersection in China. We expect that this model can predict the probability that a straight-moving driver has a preemptive status (i.e., arrive at the merging point before the turning vehicle). The factors which determine the drivers' decision behavior are analyzed, and then we propose a logistic regression model using a dataset collected from an uncontrolled intersection in Kunming, China. Our model reveals that the speed difference, the distance between the two vehicles, and the distance of the turning vehicle to the merging point are the major determinants of a straight-moving driver's decision. In addition, the prediction results from our model are compared with real-world observations, and better validate the decision behavior model in China. [ABSTRACT FROM AUTHOR]

Urban expressways usually experience several levels of service (LOS) because of the stop-and-go traffic flow caused by congestion. Moreover, multiple shock waves generate at different LOS interfaces. The dynamic of shock waves strongly influences the travel time reliability (TTR) of urban expressways. This study proposes a path TTR model that considers the dynamic of shock waves by using probability-based method to characterize the TTR of urban expressways with shock waves. Two model parameters are estimated, namely distribution of travel time (TT) per unit distance and travel distances in different LOS segments. Generalized extreme value distribution and generalized Pareto distribution are derived as distributions of TT per unit distance for six different LOS. Distribution parameters are estimated by using historical floating car data. Travel distances in different LOS segments are calculated based on shock wave theory. The range of TT along the path, which can help drivers arrange their trips, can be obtained from the TTR model. Finally, comparison is made among the proposed TTR model, generalized Pareto contrast model, which does not consider different LOS or existence of shock waves, and normal contrast model, which assumes TT per unit distance as normal distribution without considering shock wave. Results show that the proposed model achieves higher prediction accuracy and reduces the prediction range of TT. The conclusions can be further extended to TT prediction and assessment of measures to improve reliability of TT in a network. [ABSTRACT FROM AUTHOR]

Abstract: Reconstruction of pedestrian–vehicle accident is a worldwide problem. Numerous previous studies have been carried out on accidents with vehicular skid marks or definite pedestrian throw distances. However, little could be done if marks or throw distances cannot be obtained in accident reconstruction. This paper first describes the physical model of dynamic process of pedestrian head impact on windshield glazing. Some simplifications are made to obtain a better and more practical model, including discussing the support boundary conditions. Firstly, the paper modeled the relations between pedestrian impact speed and deflection of windshield glazing based on the impact dynamics and thin plate theory. Later, the relations of vehicle impact speed and deflection are discussed. Therefore, a model of vehicle impact speed versus deflection of windshield glazing is developed. The model is then verified by ten real-world accident cases to demonstrate its accuracy and reliability. This model provides investigators a new method to reconstruct pedestrian–vehicle accidents. [Copyright &y& Elsevier]

• We propose a lane change prediction model with adaptability and portability. • The proposed model introduces the decision threshold which changes according to environments. • Bayesian inference is used to update the decision threshold on time. • The adaptability and stability of the model are proved. Predicting lane change behavior of surrounding vehicles is critical for autonomous vehicles as the lane change may cause conflict between vehicles. Most of the existing lane change prediction models cannot update their model to adapt to the change of road and traffic environment, such as free and congested traffic environments and roads with different levels. Thus, those models cannot be robust. This paper aims to break this limitation and builds a lane change prediction model for surrounding vehicles based on machine learning methods. The lane change prediction model contains a basic model and an adaptive model: the basic model is a long short-term memory (LSTM) based prediction model which reflects the decision-making mode for drivers; the adaptive prediction model embeds an adaptive decision threshold on the basic model, and the threshold updates by Bayesian Inference method on time. We prove the performance of the adaptive model based on the HighD dataset, and the results are inspiring that the model achieves 93.64–97.52% accuracies for target-vehicle in the left adjacent lane and 94.30–98.01% accuracies for target-vehicle in the right adjacent lane. Besides, our proposed model is capable of different driving environments, especially when the traditional LSTM method cannot capture drivers' lane change decision well. Because of the timeliness and transferability nature of our proposed model, this model could be applied to the Advanced Driver Assistance System (ADAS) and autonomous vehicles in the future to reduce driving perception errors. [ABSTRACT FROM AUTHOR]

The extraction and analysis of driving style are essential for a comprehensive understanding of human driving behaviours. Most existing studies rely on subjective questionnaires and specific experiments, posing challenges in accurately capturing authentic characteristics of group drivers in naturalistic driving scenarios. As scenario-oriented naturalistic driving data collected by advanced sensors becomes increasingly available, the application of data-driven methods allows for a exhaustive analysis of driving styles across multiple drivers. Following a theoretical differentiation of driving ability, driving performance, and driving style with essential clarifications, this paper proposes a quantitative determination method grounded in large-scale naturalistic driving data. Initially, this paper defines and derives driving ability and driving performance through trajectory optimisation modelling considering various cost indicators. Subsequently, this paper proposes an objective driving style extraction method grounded in the Gaussian mixture model. In the experimental phase, this study employs the proposed framework to extract both driving abilities and performances from the Waymo motion dataset, subsequently determining driving styles. This determination is accomplished through the establishment of quantifiable statistical distributions designed to mirror data characteristics. Furthermore, the paper investigates the distinctions between driving styles in different scenarios, utilising the Jensen–Shannon divergence and the Wilcoxon rank-sum test. The empirical findings substantiate correlations between driving styles and specific scenarios, encompassing both congestion and non-congestion as well as intersection and non-intersection scenarios. • A quantitative method of driving performance through trajectory costs is proposed. • A general driving style extraction method for the large-scale dataset is proposed. • Driving styles of different traffic scenarios are significantly different. [ABSTRACT FROM AUTHOR]

This paper presents a safety-enhanced eco-driving strategy for connected and autonomous vehicles (CAVs), which is implemented by a hierarchical and distributed framework. The driving risk field, shockwave theory, and motion planning and control method are integrated into this framework to optimize the trajectories of CAVs on a signalized arterial under mixed traffic flow, with the aim of reducing the driving risk and fuel consumption of CAVs simultaneously, while ensuring traffic efficiency. The optimization procedure is mainly composed of two parts: long-term trajectory planning based on optimal control and short-term trajectory control based on model predictive control, which makes the strategy more adaptable to the various traffic conditions. The results show that the proposed framework can effectively reduce the safety risk that vehicles are exposed to and their fuel consumption by 18%–24% and 20%–27%, respectively. Furthermore, it reveals that conventional eco-driving strategies may result in negative safety issues when only considering the impact of preceding vehicles on the eco-CAV. However, these negative impacts can be eliminated when the impacts of following vehicles on the eco-CAV are taken into account. In addition, the sensitivity analysis on the Market Penetration Rate (MPR) of CAVs and traffic demand is performed. The results show that the framework is robust and can work under various traffic conditions (including under-saturated and over-saturated ones) and different MPRs. • Propose a safety-enhanced eco-driving strategy for CAVs under mixed traffic flow. • Develop two algorithms to alleviate intersection queuing effects for eco-driving. • While ensuring traffic efficiency, optimize CAVs' fuel consumption and driving risk. • Perform numerical experiments to assess the benefits of the proposed framework. • Reveal conventional eco-driving strategy may result in safety risks. [ABSTRACT FROM AUTHOR]

• Driver route choice behavior in the connected vehicle environment is investigated. • The heterogeneity of drivers in attention to fuel consumption and travel time is considered. • Driving experience and the real-time information are taken into considered. • Multi-agent simulation approach is used to describe the traffic system. Drivers can move with the real-time traffic information under the connected vehicle environment. Meanwhile, individual driving behavior (e.g., route choice) can influence the traffic state of the connected vehicle. This study contributes to investigate the relationship between the performance of traffic system and the driver route choice behavior in the connected vehicle environment. Especially, the heterogeneity of drivers in attention to fuel consumption and travel time is considered. A dynamic process of commuter day-to-day route choice behavior under the connected vehicle environment was proposed, the driving experience and the real-time information that affect the driver route choice are taken into considered simultaneously. Using the multi-agent simulation approach, the microcosmic and macroscopic behaviors of driver-vehicle-unit agents were described. A series of simulation experiments were conducted under the different percentages of drivers who pay enough attention to fuel consumption, and then simulation results were compared. The simulation results demonstrated that the performance of traffic state is better when enough drivers put emphasis on fuel consumption, and the optimal percentage of these drivers is about 60%. It is indicated that the travel time is more sensitive to the change of traffic state than fuel consumption, and the traffic system with enough drivers who care the fuel consumption could bring the better performance. This study has a guidance function to the application and promotion of eco-driving for government to optimize the structure of different types of drivers in the connected vehicle environment. [ABSTRACT FROM AUTHOR]

Reliable and accurate short-term subway passenger flow prediction is important for passengers, transit operators, and public agencies. Traditional studies focus on regular demand forecasting and have inherent disadvantages in predicting passenger flows under special events scenarios. These special events may have a disruptive impact on public transportation systems, and should thus be given more attention for proactive management and timely information dissemination. This study proposes a novel multiscale radial basis function (MSRBF) network for forecasting the irregular fluctuation of subway passenger flows. This model is simplified using a matching pursuit orthogonal least squares algorithm through the selection of significant model terms to produce a parsimonious MSRBF model. Combined with transit smart card data, this approach not only exhibits superior predictive performance over prevailing computational intelligence methods for non-regular demand forecasting at least 30 min prior, but also leverages network knowledge to enhance prediction capability and pinpoint vulnerable subway stations for crowd control measures. Three empirical studies with special events in Beijing demonstrate that the proposed algorithm can effectively predict the emergence of passenger flow bursts. [ABSTRACT FROM AUTHOR]

• A driving simulator study examined the effects of time budget and task on take-over performance for lower crash risk (LCR) and high crash risk (HCR) drivers. • LCR drivers had shorter brake reaction time compared to HCR drivers. • Reading the news and watching a video seem to have a similar effect on the drivers' workload. Understanding driver behavior of conditionally automated driving is necessary to ensure a safe transition from automated to manual driving. This study aimed to examine the difference in take-over performance between high crash risk (HCR) and lower crash risk (LCR) drivers in emergency take-over situations during conditionally automated driving. In the current simulator study, a 3 × 3 (within-subjects) factorial design was used, including the task factors (no task, reading the news, and watching a video) and time budget factors (time budget = 3 s, 4 s, and 5 s). Forty-eight participants completed a test drive on an approximately 10 km long two-way six-lane urban road. The participants firstly were in manual control and then switched to the automated driving mode at a speed of 50 km/h. The automated driving system was able to detect a broken car in the ego-lane and requested the driver to take over the control of the vehicle. There are at least one or two other vehicles or motorcycles on each side of the ego-vehicle, resulting in fewer escape paths. For the two non-handheld non-driving-related tasks (NDRTs), the participants were asked to be fully engaged in a task without any need to monitor the road environments. Each participant completed nine emergency take-over situations. The participants were classified into two groups that were labeled LCR (N ≤ 2) and HCR drivers (N ≥ 3) according to the number of accidents per driver. The results show that LCR drivers had shorter brake reaction time compared to HCR drivers. For all drivers, the engagement in a task led to longer response times, and the time budget affected the longitudinal vehicle control. In addition, the task affected the response times for LCR and HCR drivers, but only the time budget affected the longitudinal vehicle control for LCR drivers. For all drivers, LCR and HCR drivers, the time budget and task affected the safety of take-over. Especially, the two non-handheld everyday tasks seem to have a similar effect on the drivers' workload. Therefore, the HCR drivers had a lower hazard perception compared to the LCR drivers, and the factor regarding the individual difference of driving ability in take-over situations should be considered to design safe take-over concepts for automated vehicles. [ABSTRACT FROM AUTHOR]

Background: Quinine (QN) remains an effective drug for malaria treatment. However, quinine resistance (QNR) in Plasmodium falciparum has been reported in many malaria-endemic regions particularly in African countries. Genetic polymorphism of the P. falciparum Na+/H+ exchanger (pfnhe1) is considered to influence QN susceptibility. Here, ms4760 alleles of pfnhe1 were analysed from imported African P. falciparum parasites isolated from returning travellers in Wuhan, Central China.Methods: A total of 204 dried-blood spots were collected during 2011-2016. The polymorphisms of the pfnhe1 gene were determined using nested PCR with DNA sequencing.Results: Sequences were generated for 99.51% (203/204) of the PCR products and 68.63% (140/204) of the isolates were analysed successfully for the pfnhe1 ms4760 haplotypes. In total, 28 distinct ms4760 alleles containing 0 to 5 DNNND and 1 to 3 NHNDNHNNDDD repeats were identified. For the alleles, ms4760-1 (22.86%, 32/140), ms4760-3 (17.86%, 25/140), and ms4760-7 (10.71%, 15/140) were the most prevalent profiles. Furthermore, 5 undescribed ms4760 alleles were reported.Conclusions: The study offers an initial comprehensive analysis of pfnhe1 ms4760 polymorphisms from imported P. falciparum isolates in Wuhan. Pfnhe1 may constitute a good genetic marker to evaluate the prevalence of QNR in malaria-endemic and non-endemic regions. [ABSTRACT FROM AUTHOR]