Search

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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.

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]

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]

Smart traffic engineering and intelligent transportation services are in increasing demand from governmental authorities to optimize traffic performance and thus reduce energy costs, increase the drivers' safety and comfort, ensure traffic laws enforcement, and detect traffic violations. In this paper, we address this challenge, and we leverage the use of Artificial Intelligence (AI) and Unmanned Aerial Vehicles (UAVs) to develop an AI-integrated video analytics framework, called TAU (Traffic Analysis from UAVs), for automated traffic analytics and understanding. Unlike previous works on traffic video analytics, we propose an automated object detection and tracking pipeline from video processing to advanced traffic understanding using high-resolution UAV images. TAU combines six main contributions. First, it proposes a pre-processing algorithm to adapt the high-resolution UAV image as input to the object detector without lowering the resolution. This ensures an excellent detection accuracy from high-quality features, particularly the small size of detected objects from UAV images. Second, it introduces an algorithm for recalibrating the vehicle coordinates to ensure that vehicles are uniquely identified and tracked across the multiple crops of the same frame. Third, it presents a speed calculation algorithm based on accumulating information from successive frames. Fourth, TAU counts the number of vehicles per traffic zone based on the Ray Tracing algorithm. Fifth, TAU has a fully independent algorithm for crossroad arbitration based on the data gathered from the different zones surrounding it. Sixth, TAU introduces a set of algorithms for extracting twenty-four types of insights from the raw data collected. These insights facilitate the traffic understanding using curves, histograms, heatmaps, and animations. The present work presents a valuable added value for academic researchers and transportation engineers to automate the traffic video analytics process and extract useful insights to optimize traffic performance. TAU is a ready-to-use framework for any Transportation Engineer to better understand and manage daily road traffic (video demonstrations are provided here: https://youtu.be/wXJV0H7LviU and here: https://youtu.be/kGv0gmtVEbI). The source code is available at: https://github.com/bilel-bj/TAU. [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]

Traffic guidance compliance behavior is influenced by many factors. The present study investigates the effect of Variable Message Sign (VMS) location on guidance compliance behaviors of drivers. Based on the State, Operator, and Result (SOAR) cognitive architecture, a SOAR agent framework of drivers’ traffic guidance compliance behavior is developed. The formation mechanism and the changes in the law of traffic guidance compliance behaviors of drivers, as well as the key factors of VMS location that affect drivers’ compliance behaviors are studied. These factors include the visual perception of drivers, memory representation, decision cycle, and learning mechanisms. Finally, traffic guidance compliance behavior based on the SOAR cognitive architecture is simulated multiple times to verify the effectiveness of different VMS locations. The simulation results show that setting the VMS a bit further away from the downstream intersection achieves better guidance effect. [ABSTRACT FROM AUTHOR]

Road transport is one of the main land transport modes providing flexible door to door services. New type of control of road traffic flows in urban intersections is modelled in this article. Furthermore the synchronizing possibilities of intersections are investigated as well. Cost of CO2, CO2 CH, NOx, PM and value of travel time had been used by the authors in order to estimate the cost of road users as a basis of control. The article presents advice for optimal control and gives simulation results based on the emission and delay based costing. Traffic flow parameters, such as traffic flow concentration and traffic flow speed are presented based on real traffic data of investigated intersections. In this article not only single intersections were investigated, but a chain of intersections in order to analyze the recovery potential of synchronization reserves. [ABSTRACT FROM AUTHOR]

Vehicular communications networks are envisioned for the access to drive-thru Internet and IP-based infotainment applications. These services are supported by roadside access routers (ARs) that connect vehicular ad hoc networks (VANETs) to external IP networks. However, VANETs suffer from asymmetric links due to variable transmission ranges caused by mobility, obstacles, and dissimilar transmission power, which make it difficult to maintain the bidirectional connections and to provide the IP mobility required by most IP applications. Moreover, vehicular mobility results in short-lived connections to the AR, affecting the availability of IP services in VANETs. In this paper, we study the secure and timely handover of IP services in an asymmetric VANET and propose a multihop-authenticated Proxy Mobile IP (MA-PMIP) scheme. MA-PMIP provides an enhanced IP mobility scheme over infrastructure-to-vehicle-to-vehicle (I2V2V) communications that uses location and road traffic information. The MA-PMIP also reacts, depending on the bidirectionality of links, to improve availability of IP services. Moreover, our scheme ensures that the handover signaling is authenticated when V2V paths are employed to reach the infrastructure so that possible attacks are mitigated without affecting the performance of the ongoing sessions. Both analysis and extensive simulations in OMNeT++ are conducted, and the results demonstrate that the MA-PMIP improves service availability and provides secure seamless access to IP applications in asymmetric VANETs. [ABSTRACT FROM AUTHOR]

Road vehicles equipped with appropriate measurement equipment, computing facilities, data storage and data communication capabilities can be considered valuable data sources for the description and quantitative characterisation of road traffic. The data obtained from these vehicles provide valuable direct and indirect information pertaining to traffic states and various aspects of traffic safety in respect of the analysed road network. In this study, trucks' abrupt braking events, detected by the trucks' on-board safety protection units, were analysed. The road locations of the detected abrupt braking events can be characterised by a number of features ranging from the specific traffic regulations (e.g., speed limits) in force to the socio-cultural environment of the location. The abrupt braking data evidence was used for identification and description of non-trivial interactions of drivers, trucks and roads. Some of the more interesting results and conclusions of the experiments are reported herein. [ABSTRACT FROM AUTHOR]

The presence or absence of the necessary road sections with visibility distance passing on a two-lane road, affects the quality of traffic flow as it affects the capacity, the level of service, accident and operating speed. Therefore, it is very important that a two-lane road has sections with passing sight distance, equally important is also the formula that indicates how many running meters are needed to perform the maneuver safely and the procedure to be performed to the geometric design planes to the determine the road sections in which there is this distance. In considering the formulations that calculate this distance, there are many variables, of which it is necessary to emphasize on some variables and let other variables fixed, taking conditions in which there is no influence of these. That is why data were taken from the maneuver in which a car passing another car, how many seconds of data used by drivers to overtake in places where other variables such as the slope of the road are not of great importance, because very small. It also discusses Colombian regulations related to the topic. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to explore the interrelationship between driver distraction and characteristics of driver behavior associated with reduced highway traffic efficiency. Background: Research on the three-phase traffic theory and on behavioral driving suggests that a number of characteristics associated with efficient traffic flow may be affected by driver distraction. Previous studies have been limited, however, by the fact that researchers typically do not allow participants to change lanes, nor do they account for the impact of varying traffic states on driving performance. Methods: Participants drove in three simulated environments with differing traffic congestion while both using and not using a cell phone. Instructed only to obey the speed limit, participants were allowed to vary driving behaviors, such as those involving forward following distance, speed, and lane-changing frequency. Results: Both driver distraction and traffic congestion were found to significantly affect lane change frequency, mean speed, and the likelihood of remaining behind a slower-moving lead vehicle. Conclusions: This research suggests that the behavioral profile of "cell phone drivers," which is often described as compensatory, may have far-reaching and unexpected consequences for traffic efficiency. Application: By considering the dynamic interplay between characteristics of traffic flow and driver behavior, this research may inform both public policy regarding in-vehicle cell phone use and future investigations of driving behavior. [ABSTRACT FROM AUTHOR]

Abstract: Multi-agent systems allow the simulation of complex phenomena that cannot easily be described analytically. Multi-agent approaches are often based on coordinating agents whose actions and interactions are related to the emergence of the phenomenon to be simulated. In this article, we focus on road traffic simulation, specifically the design of a road traffic simulation tool able to deal realistically with road junctions. We propose a multi-agent behavioral model based on (i) the opportunistic individual behaviors that describe the norm violation and (ii) the anticipatory individual abilities of simulated drivers that allow critical situations to be detected. Our proposition has been validated for different traffic scenarios. Specifically, we simulated the traffic in a real intersection and then compared the simulated traffic flow with the real flow to highlight the relevance of our approach. [Copyright &y& Elsevier]

By road traffic accident prevention generally we understand a set of measures that either directly or indirectly influence road users so as to reduce their conflict behavior in road traffic, which mostly results in harm to health or damage to objects. This preventive activity takes many forms including: the creation of sale traffic environments through traffic engineering measures; education and training of individual road users, including training in driving schools and testing the applicants for licenses; good quality legislation regulating and monitoring the technical condition of vehicles and roads. Punitive enforcement, too, can be considered prevention. The basic general rule for work in the field of road traffic safety accident prevention is the "3 E" rule: Education, Enforcement, Engineering. Sometimes this is extended to "4 E" by including Evaluation. These 3 or 4 rules express the necessity for a complex approach to the problem. The analysis of the reliability of individual accident prevention systems should refer to the tripartite basis: the person, the means of transport, and the environment; and it is made possible by theoretical models based on the analysis and evaluation of errors. Within the framework of analyzing preventive measures, the research was focused on selecting workers, education of road users, training programs and working conditions. [ABSTRACT FROM AUTHOR]

Robust road-boundary extraction/tracking is one of the main problems in autonomous roadway navigation. Although the road boundary can be defined by various means including lane markings, curbs, and borders of vegetation, this paper focuses on road-boundary tracking using curbs. A vehicle-mounted (downward tilted) 2-D laser-measurement system is utilized to detect the curbs. The tracking problem is difficult because both the vehicle is moving and the target is disappearing, reappearing, and maneuvering in clutter. The interacting-multiple-model probabilistic-data-association filter (IMMPDAF) is proposed to solve the problems after detailed analysis. Track initiation, confirmation, and deletion are performed using the sequential-probability-ratio test. Extensive simulations followed by experiments in a campus environment show that the road-boundary tracking utilizing curbs is possible and robust through IMMPDAF. [ABSTRACT FROM AUTHOR]

In this paper, an iterative learning approach for the freeway density control under ramp metering and speed regulation is developed in a macroscopic level traffic environment. Rigorous analyses show that the proposed learning control schemes guarantee the asymptotic convergence of the traffic density to the desired one. The two major features of the learning-based density control are 1) less prior modeling knowledge required in the control system design and 2) the ability to reject exogenous traffic perturbations. The control schemes are applied to a freeway model, and simulation results confirm the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

The addition of interchanges, traffic signals, or unsignalized driveways to an arterial corridor increases crash risk and decreases travel speeds. As adjacent vacant land is developed, however, private landowners often desire direct access to the arterial route. Because the Virginia Department of Transportation has the authority to allow direct access provided no immediate adverse impact would occur, arterials in high-growth areas tend to have an increasing number of intersections as demands for direct access grow. This study investigated the feasibility of using the provisions of the National Environmental Policy Act (NEPA) to limit corridor access (e.g., grade-separated interchanges or at-grade intersections) through four administrative mechanisms: (1) including the limited access requirement as a condition in the record of decision; (2) effecting a contract (the Federal-Aid Project Agreement) that stipulates limiting access as a requirement; (3) implementing a supplemental agreement between interested parties that indicates how the character of the corridor shall be maintained; and (4) documenting the corridor-preservation conditions that remove the requirement of an Environmental Impact Statement. Factors that limit the effectiveness of these mechanisms include (1) the source of funds for the project; (2) the extent to which environmental concerns remain constant after the NEPA process is concluded; (3) the willingness of agencies to exercise the authority they are accorded through NEPA-related agreements; and (4) the passage of congressional legislation that enhances or restricts the scope of NEPA. Case studies of projects in Virginia where the NEPA process has helped preserve the characteristics of corridors show that the process may serve to raise barriers to adding access points but does not guarantee that access points may never be added. [ABSTRACT FROM AUTHOR]

We consider a city region with several facilities that are competing for customers of different classes. Within the city region, the road network is dense, and can be represented as a continuum. Customers are continuously distributed over space, and they choose a facility by considering both the transportation cost and market externalities. More importantly, the model takes into account the different transportation cost functions and market externalities to which different customer classes are subjected. A logit-type distribution of demand is specified to model the decision-making process of users' facility choice. We develop a sequential optimization approach to decompose the complex multi-class and multi-facility problem into a series of smaller single-class and single-facility sub-problems. An efficient solution algorithm is then proposed to solve the resultant problem. A numerical example is given to demonstrate the effectiveness and potential applicability of the proposed methodology. [ABSTRACT FROM AUTHOR]

This study deals with the sensitivity analysis of an equilibrium transportation networks using genetic algorithm approach and uses the bi-level iterative sensitivity algorithm. Therefore, integrated Genetic Algorithm-TRANSYT and Path Flow Estimator (GATPFE) is developed for signalized road networks for various level of perceived travel time in order to test the sensitivity of perceived travel time error in an urban stochastic road networks. Level of information provided to drivers correspondingly affects the signal timing parameters and hence the Stochastic User Equilibrium (SUE) link flows. When the information on road system is increased, the road users try to avoid conflicting links. Therefore, the stochastic equilibrium assignment concept tends to be user equilibrium. The GATPFE is used to solve the bi-level problem, where the Area Traffic Control (ATC) is the upper-level and the SUE assignment is the lower-level. The GATPFE is tested for six-junction network taken from literature. The results show that the integrated GATPFE can be applied to carry out sensitivity analysis at the equilibrium network design problems for various level of information and it simultaneously optimize the signal timings (i.e. network common cycle time, signal stage and offsets between junctions). [ABSTRACT FROM AUTHOR]

The primary purpose of our highway system is mobility, not safety. If it were the latter, we would set speed limits much lower. A 20 mph speed limit, with governors (speed limiting devices) on all motor vehicles to prevent exceeding that speed, would certainly lower highway casualties. Of course, no one would consider such a measure. Clearly, we tacitly agree to accept a certain level of carnage in order to use the highways in ways we value. At the present time in the US, this tacit agreement says that it is acceptable to sacrifice between 40,000 and 42,000 lives annually. The primary purpose of our highway system is mobility, not safety. If it were the latter, we would set speed limits much lower. A 20 mph speed limit, with governors (speed limiting devices) on all motor vehicles to prevent exceeding that speed, would certainly lower highway casualties. Of course, no one would consider such a measure. Clearly, we tacitly agree to accept a certain level of carnage in order to use the highways in ways we value. At the present time in the US, this tacit agreement says that it is acceptable to sacrifice between 40,000 and 42,000 lives annually. By 1980, excessive regulation of railroads and competition from other modes of transportation, such as trucks and barges, had nearly killed the rail industry. The federal government tightly regulated the rates railroads could charge shippers and the railroads' ability to shed unprofitable lines. The Staggers Rail Act of 1980 ("Staggers Act") eliminated a regulatory scheme in which a government agency set rail rates that uniformly applied to shippers in favor of rates set through negotiations between a railroad and a shipper. The legislation was enacted to unleash the industry from this regulatory regime. Since 1980, the financial health of the rail industry has improved, but most railroads remain revenue inadequate—they do not earn enough revenue to maintain their current infrastructure. Even though rail traffic has grown, the rates railroads charge shippers have generally declined over the last two decades. Railroads, therefore, cannot afford to expand their infrastructure to keep pace with traffic growth because building and maintaining a rail network is very expensive. In the future, railroads will have to raise their rates, which may drive less profitable traffic to the nation's highways and waterways, or invest in infrastructure so they can handle more traffic. Government policy can help the railroads while simultaneously avoiding additional congestion on our highways and waterways, and reducing pollution, fuel consumption, and damage to the highways. The United States' railroad policy could give railroads more resources to expand their infrastructure and assure that railroads have the proper economic incentives to make the investment in infrastructure necessary to meet the demand for their services. That policy must also encourage creative financing and public-private partnerships to the same end. Finally, policies to promote investment in rail infrastructure must not result in a quid pro quo of increased regulation of the industry. Such regulation would likely negate the benefits of encouraging infrastructure investment by driving away private investment, and the industry would need more public money to survive. [ABSTRACT FROM AUTHOR]

This paper presents a theory of traffic equilibrium which involves responsive signal control policies; in this theory drivers' route choices and the control policy's choice of green times are treated in a symmetrical manner. The central theme of the paper is the iterative optimization assignment algorithm. This algorithm may be considered as a means of calculating equilibria which are consistent with a given responsive control policy. But it may also be regarded as a highly idealized model of the day to day dynamics of drivers' route choices when a responsive signal setting policy is employed; on "day" 1 the signals are held fixed and drivers settle down to an equilibrium flow pattern, on "day 2" the flow pattern is held fixed and the signals are updated according to the control policy for the fixed flow pattern, on "day" 3 the signals are held fixed and drivers settle down to an equilibrium flow pattern.... We state natural but strong conditions on the responsive control policy which guarantee that this algorithm is bound to converge to a convex set of (flow, control) pairs such that (i) the flow is a user equilibrium and (ii) the control parameters satisfy the responsive control policy; and we give a proof of convergence under these conditions--we do not seek to minimize total travel cost. Our conditions involve the delay or cost formula used; with the BPR cost formula, modified in a natural way to allow for green times, the traditional policy of choosing control parameters which minimize delay for the observed traffic pattern does satisfy these conditions in full. However, with Webster's delay formula traditional control policies are a long way... [ABSTRACT FROM AUTHOR]

In order to ascertain whether neighboring intersections can be effectively coupled on the basis of traffic behavior, observational information on vehicular platoons was collected at four sites in London, England. This information is analyzed with particular emphasis on the phasing or synchronization of neighboring inter-sections for minimum delay. At each of the four sites studied, arrival time data was collected at four positions downstream from the signalized intersection from which the vehicular platoons were emerging. This data gives a measure of how platoons diffuse as they move from one intersection to the next. The analysis indicates that the diffusion process can be taken into account in the setting of signals. In particular, total delay in vehicle-hours per hour of green as a function of offset time is calculated, and in turn, the optimal offset time that would minimize delay is shown to be a linear function of the distance from the issuing traffic signal. [ABSTRACT FROM AUTHOR]

In this paper the authors find the optimal allocation of servicing periods to a facility servicing N incoming traffic streams. In our model it is assumed that stream flows are deterministic, that there is a fixed amount of lime lost, τi , when the server switches attention from the ith to the i + 1st stream and that the objective is to minimize time average delay. [ABSTRACT FROM AUTHOR]

We consider a one-way street that intersects ii one-way (side) streets. Traffic approaching the first street is steady as are the flows on all side streets. Traffic signals are idealized as perfect on-off switches and traffic is treated as a fluid moving with a constant velocity on the main street. Total delay and total number of stops are evaluated for several types of signal coordination schemes. Some conclusions are: 1. For any given common cycle time and given splits at each inter- section, there is a choice of offsets (phases) that simultaneously minimizes both the total delay and stops, but it is not necessarily the one that produces a `through band' of maximum bandwidth. 2. There are signal settings for which some signals operate on half or a third the cycle time of other lights, which give a main Street delay equal to that for the optimal common cycle time setting but give less delay to the side street. 3. Similar models are commonly used to find maximum through bands for two-way traffic, but it is not obvious that this is a suitable objective even for one-way streets. [ABSTRACT FROM AUTHOR]

The article argues that relationship of good visual function and driving safety are innately linked and have a direct bearing on the incidence of road traffic accidents. It mentions that objective screening for vision, color vision, and visual fields under standardized testing conditions should be mandatory for obtaining and renewing the driving license in India.

KV1.5 channel function is modified by different regulatory subunits. KVβ1.3 subunits assemble with KV1.5 channels and induce a fast and incomplete inactivation. Inhibition of PKC abolishes the KVβ1.3-induced fast inactivation, decreases the amplitude of the current KV1.5–KVβ1.3 and modifies their pharmacology likely due to changes in the traffic of KV1.5–KVβ1.3 channels in a PKC-dependent manner. In order to analyze this hypothesis, HEK293 cells were transfected with KV1.5–KVβ1.3 channels, and currents were recorded by whole-cell configuration of the patch-clamp technique. The presence of KV1.5 in the membrane was analyzed by biotinylation techniques, live cell imaging and confocal microscopy approaches. PKC inhibition resulted in a decrease of 33 ± 7% of channels in the cell surface due to reduced recycling to the plasma membrane, as was confirmed by confocal microscopy. Live cell imaging indicated that PKC inhibition almost abolished the recycling of the KV1.5–KVβ1.3 channels, generating an accumulation of channels into the cytoplasm. All these results suggest that the trafficking regulation of KV1.5–KVβ1.3 channels is dependent on phosphorylation by PKC and, therefore, they could represent a clinically relevant issue, mainly in those diseases that exhibit modifications in PKC activity. [ABSTRACT FROM AUTHOR]

The article focuses on various articles published in Journal of the Transportation Research Forum, related to transportation safety. In the article "New Speed Limits on Kansas Highways: Impact on Crashes and Fatalities," by Yacoub Najjar, Eugene Russell, Robert Stokes, and Ghassan Abu-Lebdeh examine the impact of recent changes in speed limits on Kansas highways. The authors note that the National Maximum Speed Limit of 55 mph was eliminated in November 1995, thus returning the authority of establishing speed limits to the states. By the end of 1996 a total of 32 states had raised speed limits on their highways. In Kansas, speed limits of most rural, separated, multilane highways were raised to 70 mph, while on most urban interstate and two-lane rural highways the limit was increased to 65 mph. The main objective of the paper is to determine whether statistically significant changes have occurred in accident related indices such as crash, fatal crash, and fatality rates after the Kansas speed limits were increased. In the article "Seat Belt Use Compliance in Kansas," by Deogratias Eustace and Teresa Bartel examine trends in Kansas seat belt use.