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Walking has attracted increasing interest in the social sciences and humanities. Yet only rarely have studies in those disciplines considered the subject of traffic, leaving governing and regulations to traffic specialists and engineers. Primarily using conceptual reflections but intersecting these with an analysis of historical traffic regulations in Estonia as they are related to contemporary framings, this article introduces the pedestrian as an element of a traffic system who is governed to control her body in legally prescribed ways. Directing attention also to language, as the vignette from Estonia shows the existence of a particular word "liikleja" for the ways in which pedestrians are seen as being subject to the traffic system as disciplined elements of the traffic machine. The article describes such perspective as "traffic discourse" which is contrasted with the more holistic view of pedestrians present in walking studies ("walking discourse"). [ABSTRACT FROM AUTHOR]

Macroscopic traffic flow modeling is essential for describing and forecasting the characteristics of traffic flow. However, the classic Lighthill–Whitham–Richards (LWR) model only provides equilibrium values for steady‐state conditions and fails to capture common stochastic variabilities, which are a necessary component of accurate modeling of real‐time traffic management and control. In this paper, a stochastic LWR (SLWR) model that randomizes free‐flow speed is developed to account for the stochasticity incurred by the heterogeneity of drivers, while holding individual drivers' behavior constant. The SLWR model follows a conservation law of stochastic traffic density and flow and is formulated as a time‐dependent stochastic partial differential equation. The model is solved using a dynamically bi‐orthogonal (DyBO) method based on a spatial basis and stochastic basis. Various scenarios are simulated and compared with the Monte Carlo (MC) method, and the results show that the SLWR model can effectively describe dynamic traffic evolutions and reproduce some commonly observed traffic phenomena. Furthermore, the DyBO method shows significant computational advantages over the MC method. [ABSTRACT FROM AUTHOR]

In the conventional human driving environment, due to poor traffic information transmission and the inability to actively control vehicles, variable speed limits (VSLs) and ramp metering (RM) strategies often fail to achieve the desired results when dealing with the merging zone of an expressway under high traffic demand. To improve traffic efficiency, an integrated control method for traffic flow in the merging area of expressways is proposed by combining connected and autonomous vehicle (CAV) active lane change (LC) technology with traditional VSL and entrance RM strategies. This method uses an upgraded cell transmission model (CTM) to estimate the state of the mainline and ramp input traffic volume and then uses the genetic algorithm (GA) to solve for the optimal combination of the mainline speed limit, number of mainline vehicles changing lanes, and ramp regulation under maximized flow in the merging zone. This integrated control strategy incorporating VSL, LC, and RM (VSL-LC-RM) enables synergistic control of the mainline and ramp traffic entering the merging zone in time-space. Finally, simulation experiments are conducted with the mainline two-lane and on-ramp singlelane expressway merging zone scenarios. The results show that compared with the VSL strategy and the strategy incorporating only VSL and RM (VSL-RM), the proposed VSL-LC-RM strategy can enhance the regulation of traffic flow, improve the flow of merging bottlenecks, and make full use of the road space-time resources. [ABSTRACT FROM AUTHOR]

Congestion is an important issue in Internet of Things (IoT) networks with constrained devices and a growing number of applications. This paper investigated the problem of congestion control for burst traffic in such networks. We highlight the shortcomings of the current constrained application protocol (CoAP) in its inability to support burst traffic and rate control. Subsequently, we propose an analytical model for CoAP burst traffic and a new rate-control algorithm for CoAP to avoid congestion. A CoAP sender increases or decreases the transmission rate depending on the congestion detection. Using simulations, we compared the performance of the proposed algorithm with the current CoAP in various traffic scenarios. Experimental results show that the proposed algorithm is efficient for burst traffic and provides better performance in terms of delay, throughput, retransmission, packet duplication, and packet loss compared to CoAP. [ABSTRACT FROM AUTHOR]

In this paper, a new method is proposed to detect traffic regulations at intersections using GPS traces. The knowledge of traffic rules for regulated locations can help various location-based applications in the context of Smart Cities, such as the accurate estimation of travel time and fuel consumption from a starting point to a destination. Traffic regulations as map features, however, are surprisingly still largely absent from maps, although they do affect traffic flow which, in turn, affects vehicle idling time at intersections, fuel consumption, CO 2 emissions, and arrival time. In addition, mapping them using surveying equipment is costly and any update process has severe time constraints. This fact is precisely the motivation for this study. Therefore, its objective is to propose an automatic, fast, scalable, and inexpensive way to identify the type of intersection control (e.g., traffic lights, stop signs). A new method based on summarizing the collective behavior of vehicle crossing intersections is proposed. A modification of a well-known clustering algorithm is used to detect stopping and deceleration episodes. These episodes are then used to categorize vehicle crossing of intersections into four possible traffic categories (p1: free flow, p2: deceleration without stopping events, p3: only one stopping event, p4: more than one stopping event). The percentages of crossings of each class per intersection arm, together with other speed/stop/deceleration features, extracted from trajectories, are then used as features to classify the intersection arms according to their traffic control type (dynamic model). The classification results of the dynamic model are compared with those of the static model, where the classification features are extracted from OpenStreetMap. Finally, a hybrid model is also tested, where a combination of dynamic and static features is used, which outperforms the other two models. For each of the three models, two variants of the feature vector are tested: one where only features associated with a single intersection arm are used (one-arm model) and another where features also from neighboring intersection arms of the same intersection are used to classify an arm (all-arm model). The methodology was tested on three datasets and the results show that all-arm models perform better than single-arm models with an accuracy of 95% to 97%. [ABSTRACT FROM AUTHOR]

The article focuses on the need for space traffic management. It underscores the differences between space and Earth traffic, suggesting that traditional air traffic regulations are not directly applicable to space, and lays the groundwork for an argument that COLREGs offer a suitable framework for addressing space traffic issues and are crucial for the future of space travel.

With rapid development of the urbanization, how to improve the traffic lights efficiency has become an urgent issue. The traditional traffic light control is a method that calculates a series of corresponding timing parameters by optimizing the cycle length. However, fixing sequence and duration of traffic lights is inefficient for dynamic traffic flow regulation. In order to solve the above problem, this study proposes a traffic light timing optimization scheme based on deep reinforcement learning (DRL). In this scheme, the traffic lights can output an appropriate phase according to the traffic flow state of each direction at the intersection and dynamically adjust the phase length. Specifically, we first adopt Proximal Policy Optimization (PPO) to improve the convergence speed of the model. Then, we elaborate the design of state, action, and reward, with the vehicle state defined by Discrete Traffic State Encoding (DTSE) method. Finally, we conduct experiments on real traffic data via the traffic simulation platform SUMO. The results show that, compared to the traditional timing control, the proposed scheme can effectively reduce the waiting time of vehicles and queue length in various traffic flow modes. [ABSTRACT FROM AUTHOR]

Secondary roads often suffer from diverted traffic trying to avoid congestion on major motorways. In this paper we study the traffic problems of a small town that is located parallel to a congested motorway and suffers from such diverted traffic (high local accident risks, local congestion and other nuisances). We assume that the motorway and the secondary road through the local town are under the jurisdiction of a different authority. A local government controls local accident risks and congestion using non-price measures such as speed bumps, traffic lights and explicit access restrictions for through traffic. A 'federal' government can control traffic levels on the motorway using tolls. We show the following results. First, competition between the federal and local authority leads to a Nash equilibrium where the toll is too high and there is too much traffic calming compared to the second-best social optimum. Second, if the local government uses traffic calming measures, imposing a federal toll on the main road is welfare-reducing, unless congestion on the main road is severe and accident risks and other traffic nuisances in the small town are unimportant. Third, traffic diverted from the main road to the local community gives the latter strong incentives to close the local road for through traffic, even when it is socially undesirable to do so. Fourth, if the access restriction only applies to through traffic by trucks, the conflict between federal and local authorities disappears: both will agree on restricting truck access. A numerical application using a two-link network between Leuven and Brussels (the highway and an alternative road passing through local communities) illustrates the theoretical results. [ABSTRACT FROM AUTHOR]

Loss of the insulin-stimulated glucose uptake in muscle is a crucial event participating in the defect of whole-body metabolism in type 2 diabetes. Therefore, identification by Pavarotti et al. (Biochem. J (2021) 478 (2): 407-422) of complexin-2 as an important contributor to glucose transporter 4 (GLUT4) translocation to muscle cell plasma membrane upon insulin stimulation is essential. The present commentary discusses the biological importance of the findings and proposes future challenges and opportunities. [ABSTRACT FROM AUTHOR]

The vigorous progress in artificial intelligence and the widespread availability of computing power and big data have resulted in remarkable achievements in applying deep learning methods to traffic engineering. However, the complexity of deep learning models poses a significant challenge to their interpretability, despite their scalability to big data. As a result, academia has shown a keen interest in interpreting these intricate models. To address these limitations, this study introduces the Gated Recurrent Convolution Network (GRCN) for predicting spatiotemporal crash risks. Moreover, to transform the opaque nature of the GRCN into a transparent framework, SHapley Additive Explanations (SHAP) was employed, a game theory-based approach. The application of SHAP analysis not only enhances the explainability of the GRCN model but also reveals complex causal relationships regarding crash risks, using extensive datasets encompassing taxi trips data, law enforcement, and weather conditions. The study incorporates both local and global interpretability analyses at the grid level. Additionally, it indicates that intersection red light violations tend to elevate crash risks. The findings further demonstrate that adverse weather conditions, such as low visibility and strong crosswinds, negatively affect driving behavior and correlate with higher crash risks. However, the presence of cloudy skies is associated with a decrease in crash risks. By shedding light on the intricate dynamics of crash risk factors, this study contributes to the field of traffic engineering and emphasizes the importance of interpretability in deep learning models. The insights gained from this research can inform the development of effective interventions and policies aimed at reducing crash risks and improving overall transportation safety. [Display omitted] • Introducing a novel model that advances the field of spatiotemporal crash risk prediction. • Proposing a method rooted in game theory to enhance the interpretability of the model. • Delving into the causality inference of traffic law enforcement violations on crash risk. • Investigating the causality inference related to the impact of weather conditions on crash risks. [ABSTRACT FROM AUTHOR]

Accessibility, robustness, and connectivity are the salient structural properties of networks. The labelling of networks with numeric numbers using the parameters of edge or vertex weights plays an eminent role in the study of the aforesaid properties. The systems interlinked in a network are transformed into a graphical network, and specific numeric labels assigned to the converted network under certain rules assist us in the regulation of data traffic, bandwidth, and coding/decoding of signals. Two major classes of such network labellings are magic and antimagic. The notion of super a , 0 edge-antimagic labelling on networks was identified in the late nineties. The present article addresses super a , 0 edge antimagicness of union of the networks' star S n , the path P n , and copies of paths and the rooted product of cycle C n with K 2 , m . We also provide super a , 0 edge-antimagic labelling of the rooted product of cycle C n and planar pancyclic networks. Further, we design a super a , 0 edge-antimagic labelling on a pancyclic network containing chains of C 6 and three different symmetrically designed lattices. Moreover, our findings have also been recapitulated in the shape of 3- D plots and tables. [ABSTRACT FROM AUTHOR]

Peroxisomes are organelles that perform a wide range of essential metabolic processes. To ensure that peroxisomes are optimally positioned in the cell, they must be transported by both long- and short-range trafficking events in response to cellular needs. Here, we review our current understanding of the mechanisms by which the cytoskeleton and organelle contact sites alter peroxisomal distribution. Though the focus of the review is peroxisomal transport in mammalian cells, findings from flies and fungi are used for comparison and to inform the gaps in our understanding. Attention is given to the apparent overlap in regulatory mechanisms for mitochondrial and peroxisomal trafficking, along with the recently discovered role of the mitochondrial Rho-GTPases, Miro, in peroxisomal dynamics. Moreover, we outline and discuss the known pathological and pharmacological conditions that perturb peroxisomal positioning. We conclude by highlighting several gaps in our current knowledge and suggest future directions that require attention. [ABSTRACT FROM AUTHOR]

We study a future Internet networking paradigm where instead of congestion control an open loop traffic control is applied. We aim to give theoretical foundations for data transfer controlled only by the access points of the network. The key characteristics of networks without congestion control are stability and efficiency addressed in this paper. We consider the queue length processes of data-flows on directed graphs. The stability is characterized by the ergodicity of these processes and the efficiency of the network is measured by the Price of Anarchy. Under restrictions on the input traffic rates we derive an achievable efficiency limit in a stable network for very general conditions, namely, for any network topology and for any buffer management policy. Moreover, we show that even for cyclic networks, which usually cause severe instability in networks, an upper bound for the loss of efficiency can be given independently of the size of the network under a fair AQM buffer management policy. Furthermore, for monotonic networks we present a reasonable choice for setting access capacities. Our results demonstrate that with a proper setting of access capacities of incoming flows the congestion collapse of the Internet can be avoided even without congestion control. [ABSTRACT FROM AUTHOR]

In order to improve the route planning efficiency of vehicle route chain in Internet of vehicles and avoid traffic congestion by optimizing dynamic vehicle route planning, a dynamic vehicle route guidance model under Internet of vehicles based on global adaptive optimization scheduling is proposed. The regional grid segmentation method is adopted to design and plan nodes of dynamic vehicles under Internet of vehicles, and dynamic vehicle route planning is performed by constructing a network system through road segments and intersections and the travel time on roads as control constraint parameter; the particle swarm optimization algorithm is adopted to adaptively optimize dynamic vehicle routes, and a dynamic vehicle route guidance model under Internet of vehicles is constructed by taking the individual particle pheromone as guidance parameter to optimize the guidance route planning capability of vehicle routes. Simulation results show that the method proposed in this paper can increase traffic volume by 42.8% and shorten the travel time by more than 50% in dynamic vehicle route guidance under Internet of vehicles. [ABSTRACT FROM AUTHOR]

Urban road dynamic impedance calculation is the important basis of dynamic traffic assignment and real-time traffic management and control scheme. The current impedance calculation is mostly based on BPR function, and the BPR function is derived from macroscopic statistical laws, in which the microscopic characteristics of traffic flow are insufficiently described. In order to more accurately express the change laws of traffic impedance at the microscopic level, a stochastic dynamic traffic assignment algorithm based on road impedance function is designed to analyze the time impedance of congested roads under random dynamic traffic assignment under different conditions of road network saturation. Compared with the current model, the comprehensiveness and portability are greatly improved. The results show that the impedance calculation error of this method is less than 10% when the load degree of the road is lower than 0.85, which proves that the method has good precision under unsaturated flow conditions. [ABSTRACT FROM AUTHOR]

We consider a strongly coupled ODE-PDE system representing moving bottlenecks immersed in vehicular traffic. The systems can be used to represent the effect of autonomous vehicles immersed in the bulk traffic. The PDE consists of a scalar conservation law modeling the traffic flow evolution and the ODE models the trajectory of a slow moving vehicle. The moving bottleneck influences the bulk traffic flow via a point flux constraint, which is given by an inequality on the flux at the slow vehicle position. We prove uniqueness and continuous dependence of solutions with respect to initial data of bounded variation. The proof is based on a new backward in time method established to capture the values of the norm of generalized tangent vectors at every time. The results are the first step to study control problems for traffic flow via the action of autonomous vehicles. [ABSTRACT FROM AUTHOR]

The study of traffic flow bifurcation control analysis is of great significance for understanding the essential evolution law of traffic flow, controlling traffic flow with practical means and alleviating traffic congestion. This paper describes the influence of lateral distance compensation in the process of lane change, and adjusts the traffic flow from the perspective of bifurcation control, which can capture the characteristics of traffic flow better, so as to describe the practical significance of the actual traffic phenomenon better. For example, the green ratio of traffic lights in the road, the planning and design of traffic signs, and the guidance of traffic condition prediction in ITS. In addition, the traffic flow is introduced and applied to the traffic model from the perspective of random function, and the bifurcation control is carried out according to the bifurcation behavior in traffic. That is, the bifurcation characteristics of the system are modified by the designed feedback controller, and the appearance of the equilibrium point of the system is adjusted to make it move forward, backward or disappear, so as to prevent or alleviate traffic congestion. There are few researches on bifurcation control of traffic flow model with lateral distance compensation. This paper introduces stochastic function based on lateral compensation model to study Hopf bifurcation phenomenon and Hopf bifurcation control. Firstly, the existence condition of Hopf bifurcation at the equilibrium point in the model is proved theoretically. Then, a feedback controller is designed to control the amplitudes of the Hopf bifurcation and the limit cycles formed by the Hopf bifurcation. Finally, the theoretical results are verified by numerical simulation. The research shows that by adjusting the control parameters in the feedback controller, the influence of boundary conditions on the stability of the traffic system is fully described, the effect of unstable focus and saddle point on the system is inhibited, and the traffic flow is slowed down. In addition, the unstable bifurcation points can be eliminated, and the amplitude of the limit cycle formed by Hopf bifurcation can be adjusted, so as to achieve the stability of the control system. • A new continuum model is proposed taking into account the lateral offset compensation. • Introducing the traffic flow into the traffic model from the perspective of the random function. • The designed feedback controller corrects the bifurcation characteristics of the system and adjusts the appearance of the balance point of the system to make it forward, backward or disappear, so as to prevent or reduce traffic jams. [ABSTRACT FROM AUTHOR]

Objective: The aim of the research was to assess the prevalence, effects and risk factors for motor vehicle accidents (MVA) in the years 2004-2017 in Poland.Method: We merged secondary data from the Prevention and Analysis Office of Road Traffic Headquarters of Police and Central Statistical Office of Poland.Results: Our results suggest that several thousand people are killed on Polish roads annually, and tens of thousands are injured. Road crashes represent the leading cause of death for Polish men up to 44 years of age. The most common causes of road crashes in Poland include failure to comply with the road traffic rules and low driving skills. We also found drivers who poorly assessed road situations, roads characterized by a lack of adequate road infrastructure, and many vehicles in poor condition. Road crashes have become a significant public health and social problem globally. Drivers caused most MVA in Poland in the years 2004-2017, whereas the underlying cause was inadequate speed regarding the road traffic condition as well as not respecting the right-of-way. Despite various measures that are being taken to improve safety on Polish roads, the number of the dead and wounded as a result of road accidents is still high and the losses to the society are considerable as well.Conclusion: It is necessary to continue multidirectional actions to improve safety on the roads in Poland resulting in a systematic increase in the level of road traffic security. [ABSTRACT FROM AUTHOR]

Variable speed limit (VSL) is becoming recognized as an effective way to improve traffic throughput and road safety. In particular, methods based on traffic state prediction exhibit promising potential to prevent future traffic congestion and collisions. However, field observations indicate that the traffic state prediction model results in nonnegligible error that impacts the next step decision making of VSL. Thus, this paper investigates how to eliminate this prediction error within a VSL environment. In this study, the traffic state prediction model is a second-order traffic flow model named METANET, while the VSL control is model predictive control (MPC) based, and the VSL decision is discrete optimized choice. A simplified version of the switching mode stochastic cell transmission model (SCTM) is integrated with the METANET model to eliminate the prediction error. The performance of the proposed method is assessed using field data from a VSL pilot test in Edmonton, Canada, and is compared with the prediction results of the baseline METANET model during the road test. The results show that during the most congested period the proposed SCTM-METANET model significantly improves the prediction accuracy of regular METANET model. [ABSTRACT FROM AUTHOR]

The effectiveness of speed limit enforcement (SLE) is a critical factor in reducing the global burden of fatalities and injuries due to road crashes. A random-parameter ordered-probit model was developed to explore the relationship between the effectiveness of SLE and different explanatory variables using data from a 2013 World Health Organization global road safety report. An attempt was made to understand the complex interactions between SLE, socio-economic factors, other critical key risk factors and their enforcement levels. Socio-economic status, public policies and road safety legislation and its enforcement were found to be significantly associated with different level(s) of SLE (very low, low, medium and high enforcement levels). Out of seven significant variables, two variables – effectiveness of seatbelt enforcement and road safety audits of new roads – were found to be normally distributed random parameters, suggesting that their effects vary in different countries. The model findings provide preliminary insights into how socio-economic characteristics, road safety policies and traffic laws can be associated with SLE. [ABSTRACT FROM AUTHOR]

Service matching aims to find the information similar to a given query, which has numerous applications in web search. Although existing methods yield promising results, they are not applicable for transportation. In this paper, we propose a multilevel matching method based on semantic technology, towards efficiently searching the traffic information requested. Our approach is divided into two stages: service clustering, which prunes candidate services that are not promising, and functional matching. The similarity at function level between services is computed by grouping the connections between the services into inheritance and noninheritance relationships. We also developed a three-layer framework with a semantic similarity measure that requires less time and space cost than existing method since the scale of candidate services is significantly smaller than the whole transportation network. The OWL_TC4 based service set was used to verify the proposed approach. The accuracy of offline service clustering reached 93.80%, and it reduced the response time to 651 ms when the total number of candidate services was 1000. Moreover, given the different thresholds for the semantic similarity measure, the proposed mixed matching model did better in terms of recall and precision (i.e., up to 72.7% and 80%, respectively, for more than 1000 services) compared to the compared models based on information theory and taxonomic distance. These experimental results confirmed the effectiveness and validity of service matching for responding quickly and accurately to user queries. [ABSTRACT FROM AUTHOR]

Objectives: This cross-sectional study assessed one-year period prevalence of road traffic crashes (RTCs) and examined the factors associated with RTCs among young adults in Kuwait.Design and Settings: During December 2016, 1500 students enrolled in 15 colleges of Kuwait University were invited to participate in the study. Students 18 years old or older and who drive by themselves were eligible. Data were collected using a structured self-administered questionnaire. One-year period prevalence of RTCs (≥1 vs. none) was computed. Multivariable log-binomial regression model was used to identify the risk factors associated with one-year period prevalence of RTCs.Results: Of 1500 invited individuals, 1465 (97.7%) participated, of which 71.4% (1046/1465) were female, 56.4% (804/1426) were aged between 21 and 25 years, and 67.1% (980/1460) were Kuwaitis. One-year period prevalence of RTC was 38.9%. The final multivariable log-binomial regression model showed that after adjusting for the influences of other variables in the model, participants were more likely to have had at least one RTC during the past year, if they habitually sped over limit (adjusted PR = 1.19; 95% confidence interval (CI): 1.04-1.36), crossed a red light (adjusted PR = 1.33; 95% CI: 1.16-1.52), or if they have had three or more speeding tickets (adjusted PR = 1.40; 95% CI: 1.13-1.73) compared to those who reportedly had no RTC during the same period.Conclusion: One-year period prevalence of RTCs among university students in Kuwait, though relatively lower than the reported figures in similar populations elsewhere in the region, is yet high enough to warrant diligent attention. Habitual speeding, having had three or more speeding tickets, and the practice of crossing a red light were significantly and independently associated with at least one RTC during the past year. Targeted education and enforcement of existing traffic laws may reduce the RTCs frequency in this relatively young population. Future studies may look at impact of such interventions. [ABSTRACT FROM AUTHOR]

Abstract: This article proposes a novel methodology that uses the bi‐level programming formulation to calibrate the expected total demand and the corresponding demand variability of traffic networks. In the bi‐level formulation the upper‐level is either a new maximum likelihood estimation method or a least squares method and the lower‐level is the strategic user equilibrium assignment model (StrUE) which accounts for the day‐to‐day demand volatility. The maximum likelihood method proposed in this article has the ability to utilize information from day‐to‐day observed link flows to provide a unique estimation of the total demand distribution, whereas the least squares method is capable of capturing link flow variations. The lower‐level StrUE model can take the total demand distribution as input, and output a set of link flow distributions which can then be compared to the link‐level observations. The mathematical proof demonstrates the convexity of the model, and the sensitivity to the prediction error is analytically derived. Numerical analysis is conducted to illustrate the efficiency and sensitivity of the proposed model. Some possible future research is discussed in the conclusion. [ABSTRACT FROM AUTHOR]

Abstract: This article proposes an extension of the strategic user equilibrium proposed by Waller and colleagues and Dixit and colleagues. The proposed model relaxes the assumption of proportional Origin‐Destination (O‐D) demand, as it accounts for users’ strategic link choice under independently distributed O‐D demands. The convexity of the mathematical formulation is proved when each O‐D demand is assumed to follow a Poisson distribution independently; link flow distributions and users’ strategic link choice are also proved to be unique. Network performance measures are given analytically. A numerical analysis is conducted on the Sioux Falls network. A Monte Carlo method is used to simulate network performance measures, which are then compared to the results computed from the analytical expression. It is illustrated that the model is capable of accounting for demand volatility while maintaining computation efficiency. [ABSTRACT FROM AUTHOR]

Abstract: The prediction of bus arrival time is important for passengers who want to determine their departure time and reduce anxiety at bus stops that lack timetables. The random forests based on the near neighbor (RFNN) method is proposed in this article to predict bus travel time, which has been calibrated and validated with real‐world data. A case study with two bus routes is conducted, and the proposed RFNN is compared with four methods: linear regression (LR), k‐nearest neighbors (KNN), support vector machine (SVM), and classic random forest (RF). The results indicate that the proposed model achieves high accuracy. That is, one bus route has the results of 13.65 mean absolute error (MAE), 6.90% mean absolute percentage error (MAPE), 26.37 root mean squared error (RMSE) and 13.77 (MAE), 7.58% (MAPE), 29.01 (RMSE), respectively. RFNN has a longer computation time of 44,301 seconds for a data set with 14,182 data. The proposed method can be optimized by the technology of parallel computing and can be applied to real‐time prediction. [ABSTRACT FROM AUTHOR]

Abstract: Connected vehicles are able to proactively change speed to adapt to the prevailing traffic condition. Even in the mixed traffic environment, connected vehicles may function as leading vehicles, hence influencing the driving pattern of following nonconnected vehicles. This article proposes a cell‐based simulation approach to model the proactive driving behavior of connected vehicles. First, a state variable of connected vehicles is introduced to track the trajectory of connected vehicles. Then the exit flow of cells containing connected vehicles is adjusted to simulate the proactive driving behavior, such that the traffic light is green when the connected vehicle arrives at the signalized intersection. The second part of the article conducts numerical tests to examine the effect of the proactive driving behavior of connected vehicles. Extensive test results consistently show that the presence of connected vehicles contributes significantly to the smoothing of traffic flow and vehicular emission reductions in the network. [ABSTRACT FROM AUTHOR]

Real-time detailed vehicle trajectory data can be utilized to estimate traffic parameters for improving the efficiency of traffic control systems. Due to the low frequency of GPS data, the original vehicle trajectory obtained from the GPS device may be incomplete. This results in information loss or parameter estimation errors. In order to obtain more accurate traffic information from vehicle trajectory data, it is necessary to reconstruct or complete the original vehicle trajectory. However, for the real-time trajectory reconstruction there are two issues that need to be addressed. One is the real time original trajectory construction. The other is the original trajectory reconstruction. In this paper, a distributed traffic management framework and a data-driven trajectory reconstruction algorithm are proposed to address these two issues. GPS data taken from vehicles travelling on the segment are first delivered into the local traffic management center to achieve the map-matching for constructing the original vehicle trajectories. These original trajectories are reconstructed in real time on the local center using the proposed data-driven trajectory reconstruction method which is based on rough set theory. To obtain a more detailed reconstructed vehicle trajectory, reasonable condition and decision attributes are selected to establish a trajectory decision system based on the historical trajectory dataset. Discernibility based attribute weight is calculated using rough set theory to select the trajectory decision rules for vehicle trajectory reconstruction. Simulations illustrate that the proposed real-time data-driven trajectory reconstruction method can achieve vehicle trajectory reconstruction using the historical traffic trajectory dataset. [ABSTRACT FROM AUTHOR]

An extensive body of research has found that angry and aggressive driving are both significantly related to crash involvement. There has also been a large body of research investigating the situational factors related to angry and aggressive driving, but one interesting question that has not yet been answered is whether the enforcement of traffic laws causes or reduces angry and aggressive driving. The independent region of Northern Kosovo represents a unique opportunity to investigate the impact of a lack of traffic enforcement on driving behaviour. Therefore, the present study set out to investigate whether the presence of traffic enforcement has a significant impact on the level of driver anger and aggressive driving. Registered owners of motor vehicles in Northern Kosovo and Serbia were both sent a questionnaire which contained the 28-item Dula Dangerous Driving Index (DDDI) and the 21-item UK Driving Anger Scale (UKDAS). This found that anger was higher in two of the four driving anger factors (direct hostility and progress impeded) and two of the three DDDI factors (risky driving & aggressive driving). Furthermore, the present study found that the lack of police enforcement was a significant predictor of both aggressive and risky driving, even after the driving anger and demographic variables had been partialled out. Therefore, it appears that introducing or increasing traffic enforcement may be one method of reducing aggressive and risky driving behaviour. [ABSTRACT FROM AUTHOR]

Autonomous Vehicle (AV) technology is quickly becoming a reality on US roads. Testing on public roads is currently undergoing, with many AV makers located and testing in Silicon Valley, California. The California Department of Motor Vehicles (CA DMV) currently mandates that any vehicle tested on California public roads be retrofitted to account for a back-up human driver, and that data related to disengagements of the AV technology be publicly available. Disengagements data is analyzed in this work, given the safety-critical role of AV disengagements, which require the control of the vehicle to be handed back to the human driver in a safe and timely manner. This study provides a comprehensive overview of the fragmented data obtained from AV manufacturers testing on California public roads from 2014 to 2017. Trends of disengagement reporting, associated frequencies, average mileage driven before failure, and an analysis of triggers and contributory factors are here presented. The analysis of the disengagements data also highlights several shortcomings of the current regulations. The results presented thus constitute an important starting point for improvements on the current drafts of the testing and deployment regulations for autonomous vehicles on public roads. [ABSTRACT FROM AUTHOR]

In this work, moving synchronized flow patterns (MSP) in empirical city traffic at signals have been observed which was predicted by Kerner (2013, 2014). The single vehicle data has been obtained through aerial observations. Based on this microscopic data, we have found trajectories for all vehicles together with vehicle speeds as functions of time and space. This has allowed us to find empirical MSPs in oversaturated city traffic as well as to perform microscopic analysis, that revealed the following empirical spatiotemporal features of MSPs: (i) MSPs emerge upstream of the queue at the signal and then they can propagate through the complete road section, (ii) merging of several MSPs into a single MSP, (iii) dissolving MSPs and (iv) spontaneous emerging of MSPs faraway upstream of the signal, (v) spontaneous emerging of moving queue within an MSP that is far away upstream of the signal. As a base for this work, we used an unmanned aerial vehicle (UAV) to record videos of inner city traffic situations and supervised tracking methods to gather the vehicle trajectories. [ABSTRACT FROM AUTHOR]

This article studies a capacitated fixed-charge multiperiod location–transportation problem in which, while the location and capacity of each facility must be determined immediately, the determination of the final production and distribution of products can be delayed until actual orders are received in each period. In contexts where little is known about future demand, robust optimization, namely using a budgeted uncertainty set, becomes a natural method for identifying meaningful decisions. Unfortunately, it is well known that these types of multiperiod robust decision problems are computationally intractable. To overcome this difficulty, we propose a set of tractable conservative approximations for the problem that each exploit to a different extent the idea of reducing the flexibility of the delayed decisions. While all of these approximation models outperform previous approximation models that have been proposed for this problem, each also has the potential to reach a different level of compromise between efficiency of resolution and quality of the solution. A row generation algorithm is also presented to address problem instances of realistic size. We also demonstrate that full flexibility is often unnecessary to reach nearly, or even exact, optimal robust locations and capacities for the facilities. Finally, we illustrate our findings with an extensive numerical study where we evaluate the effect of the amount of uncertainty on the performance and structure of each approximate solution that can be obtain d. [ABSTRACT FROM AUTHOR]

We study an emergency response network design problem that integrates relief (supply) and evacuation (demand) sides under disaster location and intensity uncertainties which, in turn, dictate uncertainty in terms of the location and amount of demand. Representing these uncertainties by discrete scenarios, we present a stochastic programming framework in which two second stage objectives, the average and worst case costs, are combined. In our model, we minimize, over all of the scenarios, the fixed costs of opening supply centers and shelters, and the weighted sum of average and worst case flow costs. Thus, the model gives the decision maker the flexibility to put relative emphasis on the worst case and average flow cost minimization and explore outcomes in terms of total costs and network configurations. To solve large scale instances with varying relative weights, we devise alternative Benders Decomposition approaches. We implement these by using an advanced callback feature of the solver while simultaneously incorporating several performance-enhancing steps that help to improve runtimes significantly. We conduct a detailed computational study to highlight the efficiency of our proposed solution methodology. Furthermore, we apply our approach in a realistic case study based on Geographical Information Systems data on coastal Texas and present interesting insights about the problem and the resulting network structures for varying weights assigned to objectives. [ABSTRACT FROM AUTHOR]

India has shown rapid economic development and this has forced the country to adopt smart traffic management and surveillance process. The intelligent traffic management and surveillance is the basic need for development of smart cities in India. The Intelligent traffic surveillance includes the detection of moving vehicles, estimation of their speed and extracting its registration number. This paper proposes an efficient and novel approach for the detection of moving vehicles as well as estimation of their speeds for detection of any speed limit violation by using a single camera in daylight or properly illuminated environment. The proposed approach detects and tracks the vehicle passing through the surveillance area and maintains the record of vehicles position. The Region of Interest (ROI) of the approach is the dark shaded area formed under the vehicles road clearance area instead of the vehicles. The average detection accuracy achieved by proposed approach is 93.32%. This is on an average 7.54% higher than other existing approaches. The proposed approach uses cropping operation to minimize the scope of any false positive detection on both sides of road. The average tracking accuracy of the proposed approach is about 92.2%. The tracking accuracy achieved by the proposed approach is on an average 2.2% higher than the other leading approaches. The average speed estimation error in proposed approach is +2.2 km/h which is lower than other existing leading approaches. The proposed approach is also time and space efficient over the existing approaches. [ABSTRACT FROM AUTHOR]

The article highlights the role of traffic enforcement, analyzes its efficacy to deter dangerous driving, and suggests a new framework for traffic enforcement suited to the goals of Vision Zero. It suggests that primary goal of Vision Zero-related traffic enforcement must be to create long-lasting deterrence of dangerous driving behavior.

In general, the use of superelevation and changes in a road's transverse slope are typically based on the road's design speed to provide safety and comfort for a vehicle driving on a horizontal curve. However, due to the difference between operating and design speeds, there has always been uncertainty in determining the margin of safety using superelevation. This paper discusses the assessment of the safety margins obtained from the application of superelevation in horizontal-curve design. The assessment uses geometric design guides and a reliability index to determine any uncertainties in the design parameters. In addition, adjusted design charts of superelevation values related to the radius of horizontal curves at various levels of probability of noncompliance are provided. The results show that the adjusted superelevation values are generally greater than those derived from current standards. The adjusted design charts can help designers select an appropriate superelevation value for a particular horizontal curve for highways that have geometric constraints. These adjusted charts may also aid designers in predicting the consequences and safety margins associated with the selection of superelevation alternatives required to approve geometric designs that involve violations of the standard requirements due to environmental constraints. [ABSTRACT FROM AUTHOR]

The overall aim of this paper is to enrich the global highway safety knowledge by shedding light on the current status of road safety in Iraq. Iraq is one of the major mediumincome Arabic countries in which vehicular road transport is the essential means of mobility. Recently, there has been a noticeable increase in traffic accidents and injuries; this increase is expected to be as an intuitive result of the soaring motorization which in turned is expected to be a result of the boom in economy and population after 2003. Available evidence-based literature concerning status of traffic accidents in Iraq over the past decade (2005-2015) has been analytically reviewed. Such literature includes official statistics, traffic accident studies, and current highway safety policies and traffic laws. The discussion of the synthesis of evidence-based findings revealed several important points regarding aspects such as crash characteristics, occurrence, severity, and measures. The findings can be informative for researchers interested in studying highway safety in developing countries. Also findings can guide the government to achieve its national goal in minimizing accidents and injuries. The paper attains its significance from being the first paper among those available online with timescale, spatial coverage and analytical scope sufficient to consider the preset aim. [ABSTRACT FROM AUTHOR]

Large numbers of traffic accidents were experienced on the road networks of Arab Gulf Countries including United Arab Emirates (UAE). This had resulted in enormous loss of lives and economy. This article through using Abu Dhabi city, UAE capital as a case study is aiming to understand the reasons behind such safety problem through analysing a large accidents data-set extending over the period from 2008 to 2013. The traffic accidents data-set was obtained from Abu Dhabi traffic police department records and covers wide range of accident's attributes. A wide spectrum of accident attributes was analysed in the paper including but not limited to the time of accident, accident location, type of accident, reasons behind the accident, driver characteristics, road conditions, and many other accident attributes. A specific reasoning for each of these attributes was given by authors. Furthermore, recommendations to enhance the safety levels were introduced. [ABSTRACT FROM AUTHOR]

This study aim to determine the proportion of traffic rule infractions in Cali, Colombia, in places where a road traffic injury (RTI) occurred. Description of videotaping of sites where a person was injured in an RTI in 2009. Counts of road users and infractions were established for each road user group and were compared using a Z-test. They were found 13,491 users, distributed as follow: 8.9% were pedestrians, 4.6% cyclists, 24.6% motorcyclists, and 61.8% were automobile drivers. The most frequent traffic violation among motorcyclists was transiting on lines designated for other vehicles (55.2%). Among cyclists, the most frequent violations were transiting without a helmet (99.2%) and not wearing the designated vest (100%). Among pedestrians, crossing streets at prohibited places (77.3%), even at sites where a pedestrian bridge was present (72.7%), represented two common violations. Vulnerable road users committed more traffic infractions than automobile drivers (p < 0.001). High rates of traffic rule infractions among vulnerable road users were observed. Studies to better understand the occurrence of these behaviours and the promotion of effective interventions are warranted. [ABSTRACT FROM AUTHOR]

Under certain circumstances, drivers fail to notice changes in electronic speed limits. A video-based study was performed to reveal which countermeasures would improve drivers' ability to detect changes in electronic speed limits. Countermeasures included leaving electronic signs blank prior to a speed limit change and adding motion signals by means of flashing amber lights or a wave. A video representing a motorway was shown repeatedly to 255 participants. They were instructed to press the space bar when detecting a change. The video was viewed 13 times before the speed limit changed. Results showed that leaving signs blank prior to the change instead of displaying speed limits continuously did not alter change detection, whereas flashers and waves eroded detection of the changed speed limit. This suggests that using flashers and waves to attract attention to electronic signs in fact decreases people's ability to process the information contained in the signs. [ABSTRACT FROM AUTHOR]

Education, enforcement and engineering countermeasures are implemented to make road users comply with the traffic rules. Not all the traffic rule violations can be addressed nor countermeasures be implemented at all unsafe locations, at once, due to limited funds. Therefore, this study aims at ranking the traffic rule violations resulting in crashes based on individual ranks, such as 1) frequency (expressed as a function of the number of drivers violating a traffic rule and involved in crashes), 2) crash severity, 3) total crash cost, and, 4) cost severity index, to assist transportation system managers in prioritizing the allocation of funds and improving safety on roads. Crash data gathered for the state of North Carolina was processed and used in this study. Variations in the ranks of traffic rule violations were observed when individual ranking methods are used. As an example, exceeding authorized speed limit and driving under the influence of alcohol are ranked 1st and 2nd based on crash severity while failure to reduce speed and failure to yield the right-of-way are ranked 1st and 2nd based on frequency. To minimize the variations and capture the merits of individual ranking methods, four different composite ranks were computed by combining selected individual ranks. The computed averages and standard deviations of absolute rank differences between composite ranks is lower than those obtained from individual ranks. The weights to combine the selected individual ranks have a marginal effect on the computed averages and standard deviations of absolute rank differences. Combining frequency and crash severity or cost severity index, using equal weights, is recommended for prioritization and allocation of funds. [ABSTRACT FROM AUTHOR]

In this study, a multivariate random-parameters Tobit model is proposed for the analysis of crash rates by injury severity. In the model, both correlation across injury severity and unobserved heterogeneity across road-segment observations are accommodated. The proposed model is compared with a multivariate (fixed-parameters) Tobit model in the Bayesian context, by using a crash dataset collected from the Traffic Information System of Hong Kong. The dataset contains crash, road geometric and traffic information on 224 directional road segments for a five-year period (2002–2006). The multivariate random-parameters Tobit model provides a much better fit than its fixed-parameters counterpart, according to the deviance information criteria and Bayesian R 2 , while it reveals a higher correlation between crash rates at different severity levels. The parameter estimates show that a few risk factors (bus stop, lane changing opportunity and lane width) have heterogeneous effects on crash-injury-severity rates. For the other factors, the variances of their random parameters are insignificant at the 95% credibility level, then the random parameters are set to be fixed across observations. Nevertheless, most of these fixed coefficients are estimated with higher precisions (i.e., smaller variances) in the random-parameters model. Thus, the random-parameters Tobit model, which provides a more comprehensive understanding of the factors’ effects on crash rates by injury severity, is superior to the multivariate Tobit model and should be considered a good alternative for traffic safety analysis. [ABSTRACT FROM AUTHOR]

Real-time road traffic awareness is a challenge in an intelligent transportation system or internet of vehicles. In this paper, we provide a real-time road traffic awareness model for vehicle drivers based on the optimal multi-channel self-organized time division multiple access algorithm. A system based on this model can be implemented in all kinds of road networks including urban and rural roads or highways and cover a large area. The results indicate that this system can identify more than 5000 vehicles within an identification radius of 40 km on rural roads or highways and more than 8000 vehicles within an identification radius of 8 km on urban roads. Simulation results show that a system based on this model can meet the drivers’ requirement of obtaining road traffic status in real time. [ABSTRACT FROM AUTHOR]

As a domain of political intervention, speed is a meeting ground for projects of domination and tactics of emancipation, for logics of exclusion coexisting with various forms of collisions, intersections and amalgamations. Instead of reducing the 'need for speed' to a linear and unequivocal process, this article makes the case for thinking about speed as an arena of conflict, subject to brutal accelerations but also to at least momentary decelerations. Taking the example of Karachi, where road accidents and larger controversies around transport played a prominent role in the violent postcolonial transformations of the city, it argues that from these collisions emerged a form of political power premised on the regulation of traffic flows. It concludes that moving-power always exceeds the initial intentions of those setting it in motion as it simultaneously builds on and disrupts the forces that give every city its vitality, that is, on the vitalism of speed. [ABSTRACT FROM AUTHOR]

Currently used criteria for determining car operating efficiency in road traffic are, among others, emission of harmful substances, consumption of engine driving fuels, technical service and reliability of operation, safety of use. The operational characteristics of the car in terms of engine driving fuel consumption data is usually recognised as so-called road specific fuel consumption. An important deficiency of this approach is the failure to take into account the influence of the time on the journey's effectiveness and the final result of the entire project. To obtain a new solution in this range in the analysis, a quantity called "action", which at last will be treated as the criterion of the car operating effectiveness, was used. The quantity of action is the product of the performed work and its realisation time. Many phenomena and processes in nature take place according to the principle of "minimum of action" - this criterion can be applied in the analysis of the car's operating efficiency taking place in road traffic. An approach of this issue is presented in this article, wherein the basic data for analysis were obtained in the framework of the car tests performed at the real traffic conditions. [ABSTRACT FROM AUTHOR]