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We experimentally study particle scale dynamics during segregation of a bidisperse mixture under oscillatory shear. Large and small particles show an underlying asymmetry that is dependent on the local particle concentration, with small particles segregating faster in regions of many large particles and large particles segregating slower in regions of many small particles. We quantify the asymmetry on bulk and particle scales, and capture it theoretically. This gives new physical insight into segregation and reveals a similarity with sedimentation, traffic flow, and particle diffusion. [ABSTRACT FROM AUTHOR]

Traffic density can be accurately measured by counting the number of vehicles within 1 km; however, it is often calculated between macroscopic traffic parameters using the fundamental equation because of difficulty of observing traffic density directly in the field. Measuring density in this way may be inaccurate and may bias the analysis because the relationship between these traffic parameters can vary across the study sites. The purpose of this study is to find a method for measuring traffic density from aerial photography that is easy and accurate, and for this purpose, we investigated whether the measuring length (i.e., the length of a section of roadway from which observations of traffic are simultaneously collected) can be shorter than 1 km and yet retain the same measured traffic density. We divided an aerial photograph into several 20-m unit sections, counted the number of vehicles manually, and examined measured traffic density according to central limit theory. According to the results of this study, with the number of 20-m unit sections for observing traffic density at 15 (the measuring length is 300 m), the measured traffic density was almost the same as the density of a representative section of 1 km. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In urban contexts, accessibility measures are often utilized to represent the interactions among land use, transport and people’s trips to services and other facilities. Accessibility is generally evaluated using conventional traffic measures such as travel time and distance, but traffic volume, which is a product of the travel trips induced by people’s needs, may also be positively related to accessibility. This study tests the hypothesis “can freeway traffic volume information facilitate urban accessibility assessment?” Traffic volume based accessibility models are proposed to investigate this relationship and test the hypothesis. The results indicate that the answer to this question is a qualified “yes”, as there is a statistically significant relationship between traffic volume differences and accessibility. The coefficients of the models can potentially be used as a reference to guide the urban planning process. The limitations of the proposed models can be addressed by deploying Intelligent Transportation Systems (ITS) traffic sensors on ramps and arterial roadways. [ABSTRACT FROM AUTHOR]

Network-wide traffic measurement is of interest to network operators to uncover global network behavior for the management tasks of traffic accounting, debugging or troubleshooting, security, and traffic engineering. Increasingly, sophisticated network measurement tasks such as anomaly detection and security forensic analysis are requiring in-depth fine-grained flow-level measurements. However, performing in-depth per-flow measurements (e.g., detailed payload analysis) is often an expensive process. Given the fast-changing Internet traffic landscape and large traffic volume, a single monitor is not capable of accomplishing the measurement tasks for all applications of interest due to its resource constraint. Moreover, uncovering global network behavior requires network-wide traffic measurements at multiple monitors across the network since traffic measured at any single monitor only provides a partial view and may not be sufficient or accurate. These factors call for coordinated measurements among multiple distributed monitors. In this paper, we present a centralized optimization framework, LEISURE (Load- EqualIzed mea SUREment), for load-balancing network measurement workloads across distributed monitors. Specifically, we consider various load-balancing problems under different objectives and study their extensions to support both fixed and flexible monitor deployment scenarios. We formulate the latter flexible monitor deployment case as an MILP (Mixed Integer Linear Programming) problem and propose several heuristic algorithms to approximate the optimal solution and reduce the computation complexity. We evaluate LEISURE via detailed simulations on Abilene and GEANT network traces to show that LEISURE can achieve much better load-balanced performance (e.g., 4.75\times smaller peak workload and 70 \times smaller variance in workloads) across all coordinated monitors in comparison to a naive solution (uniform assignment) to accomplish network-wide traffic measurement tasks under the fixed monitor deployment scenario. We also show that under the flexible monitor deployment setting, our heuristic solutions can achieve almost the same load-balancing performance as the optimal solution while reducing the computation times by a factor up to 22.5\times in Abilene and 800\times in GEANT. [ABSTRACT FROM AUTHOR]

The sensor location problem is that of locating the minimum number of traffic sensors at intersections of a road network such that the traffic flow on the entire network can be determined. In this paper, we provide a new necessary condition on the location of these sensors to enable the traffic flow throughout the network to be computed. This condition is not sufficient in general, but we show that for a large class of problem instances, the condition is sufficient. Many typical road networks are included in this category, and we show how our condition can be used to inform traffic sensor placement. [ABSTRACT FROM AUTHOR]

Mechanism of randomization describes behaviors of velocity randomization, but ignoring individual differences in traffic such as psychosocial, cultural and other social complexity. In this paper, 1) building multi-level model of roads based on the complexity of the analytical community, and turn factors of randomization into basic elements, especially the personalized factors, expanding the architecture of Agent in the traditional homogenization cellular automata model, 2) integrating those factors by the Multi-Agent collaboration. Describing the differences of randomization of individual cellular, making it more detail in traffic psychology, culture and other social complexity, the whole randomization behaviors of simulation transport system will be closer to reality. Finally, through designing simulation systems according to the specific situations in the related literature, analyzing and integrating factors of randomization, the results of speed - density and flow - density have a higher degree of compliance with the actual situation than the data of original paper to prove the advantages of personalized mechanism of randomization which have been built in this paper. [ABSTRACT FROM AUTHOR]

Considering lane partial repair or close caused by an accident, ships have to change to another lane to move on. On the base of cellular automata model on AIS-based for partial reduction waterway, a numerical example is presented to analyze the effect to waterway transit capacity by different precaution area length and ship arrival rate, and the function of ship density-velocity (flux) is obtained; finally, suggestions are given to increase the waterway transit capacity. The simulation method and conclusions could help to explain some complex phenomena of marine traffic. [ABSTRACT FROM AUTHOR]

With the continuous expansion of the city size and the improvement of people's economic level, the travel is more and more convenient. However, the traffic pressure is bigger. By forecasting the demand of the city bus network, we can formulate the scientific operation plans and allocate the bus resources reasonably. This can meet the daily travel and obtain higher income. In this paper, we propose ARMA predictive model based on the empirical mode decomposition. This model solves the premise that the sequences in the traditional time sequence analysis method must be smooth. Then it can improve the prediction accuracy. Finally, we apply the model to forecast the short-term traffic flow. The last example shows that the model has the pertinence and validity. [ABSTRACT FROM AUTHOR]

This paper presents a simple spatial equilibrium model for a linear monocentric city to investigate the effects of rationing and pricing on morning commuters' travel cost and modal choice behavior in each location. Under rationing and pricing, every day in the morning peak hour, each commuter is classified as either "free" or "rationed". "Free" commuters are allowed to use the highway without paying the toll, whereas "rationed" commuters can avoid the toll only if they travel by transit. Each day, a fraction of commuters are rationed in their free use of the highway, and the rationing fractions are determined systematically so that everyone is equally rationed in a given period. It is found that Pareto-improving rationing and pricing scheme might be obtained as a combination of the rationing degree and the toll associated with rationing. Extension to the rationing and pricing scheme with cordon and park-and-ride service has been made. Cordon and park-and-ride might help in improving the efficiency of rationing and pricing strategy although remains its Pareto-improving property. [ABSTRACT FROM AUTHOR]

This paper shows the relationship between flow, generalized origin-destination (OD), and alternative route flow from a set of ordinal graph trajectories. In contrast to traffic assignment methods that employ OD matrix to produce flow matrix, we use ordinal trajectory on a network graph as input and produce both the generalized OD matrix and the flow matrix, with the alternative and substitute route flow matrices as additional outputs. By using linear algebra-like operations on matrix sets, the relationship between network utilization (in terms of flow, generalized OD, alternative route flow, and desire line) and network structure (in terms of distance matrix and adjacency matrix) are derived. [ABSTRACT FROM AUTHOR]

Traffic-related emissions depend on traffic volume, traffic flow composition, and fleet age, as well as urban housing density and meteorological conditions. Thus, software technology and high-quality (applicable) input databases are the key prerequisites for multidisciplinary and multiparameter analysis of the impact of traffic on air quality. A series of studies conducted in Novi Sad and the use of modern technology for data measurement and storage enabled improved assessment of the impact on air quality. This paper presents the simulation results pertaining to the influence of traffic on NO2 and PM10 emissions at the most heavily congested city streets, based on the surrounding housing and meteorological conditions in Novi Sad. The results of this type of research have multiple benefits, including application in the development of air quality plans and testing environmental effects of different transport policy measures. [ABSTRACT FROM AUTHOR]

A transit network, visualized as a graph, can be evaluated using indicators such as connectivity, coverage, directness, and complexity, among others, based on the relationships between network elements. This study focuses on the analysis of interconnected and operationally complicated bus networks, a shortcoming of existing approaches tailored to simpler, metro networks. A new procedure is proposed for drawing bus networks as graphs, by disaggregating them into sub-networks at the traffic analysis zone level. As well, improved network connectivity indicators are proposed which incorporate the influence of bus operational characteristics. The effect of bus route transfers is analyzed by introducing intermediate walking transfer edges. The contribution of this research will provide transit agencies with quantitative measures to analyze the network characteristics and the related operational attributes at a zonal sub-network level across the agency's coverage area. The proposed methodology was demonstrated by applying it to the Greater Vancouver Regional District public transportation system. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of speed-flow-density relations is an essential requirement in planning, design and operation of transportation systems. Many researchers have investigated the nature of speed-flow-density relationships, commonly known as stream models, under homogeneous traffic conditions. However, those models may not be the best under Indian traffic conditions. Indian traffic differs significantly from homogeneous traffic and may need a different treatment for analysis. Not much research has been reported on this important aspect so far from India. The present study is an attempt to develop an optimal traffic stream model suitable for the heterogeneous traffic flow condition existing in India, taking Chennai as a case study. A two-regime speed-density relationship was developed using a conventional optimisation algorithm. Using this, the speed-flow and flow-density relationships were theoretically derived. The results indicate that the proposed model is able to represent the steady-state macroscopic behaviour of the heterogeneous and less lane-disciplined traffic stream under study with reasonable accuracy. [ABSTRACT FROM AUTHOR]

Highlights: [•] A common numerical framework for second-order traffic flow models is presented. [•] The ow models are written in conservation or balance law form. [•] The relaxation approximation transforms the nonlinear systems to semi-linear ones. [•] High-resolution finite volume discretizations (in space and time) are utilized. [ABSTRACT FROM AUTHOR]

While traffic speed data and travel time estimates are increasingly more available from commercial vendors, they are not sufficient for proper management and performance evaluation of transportation networks. Effective traffic control and demand management requires information about volumes, which is provided by fixed location sensors, such as loop detectors or cameras, and those are sparse. This paper proposes a method for estimating route choice using sparse flow measurements and estimated speed on the road network based on compressed sensing technology widely used in image processing, where from a handful of scattered pixels, a full image is recovered. What is known includes flows at origins and at selected links of the road network, where the detection is present; speed estimates are available for all network links. We find coefficients that split origin flows among routes starting at those origins. The advantage of the proposed methodology is that it does not rely on simulation that is prone to calibration errors but only on measured data. We also show how vehicle flows can be estimated at links with no detection, which enables computing performance measures for road networks lacking complete sensor coverage. Finally, we propose a method for selecting plausible routes between origins and destinations. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new vehicle detection equipment called Smart Road Stud (SRS) based on Anisotropic magneto-resistive (AMR) sensor is proposed. • The novel algorithm of two-step extraction has been explained to get the signal of magnetic field perturbation generated by vehicle. • The novel method to distinguish extracted signals corresponding to one or two vehicles has been detailed to improve traffic volume. • Fields test including two different types of road has been implemented to verify the reliability of proposed method. Anisotropic Magneto-Resistive (AMR) sensing is one of the most burgeoning techniques for traffic volume measurement to support effective traffic management. This is because of its small size, low power consumption and environmental resilience. This paper presents a vehicle detection algorithm based on a new type of AMR sensor called Smart Road Stud (SRS), which can detect the Magnetic Field Intensity (MFI) disturbance generated by passing vehicles. The proposed two-step process can extract the disturbance signal from MFI time series with a simple determination of threshold and a more widespread applicability. However, when vehicle temporal headway is short, an extracted signal may be generated by more than one vehicle. For this case, a method to recognize the number of vehicles the signal corresponds to is proposed also. The results show that the accuracy of traffic volume measurement is higher than 97% when detecting traffic volume in two adjacent lanes. [ABSTRACT FROM AUTHOR]

With the rapid growth of link speed, obtaining detailed traffic statistics becomes much more difficult. In order to reduce the resource consumption of measurement systems, more and more passive traffic measurement employs sampling at the packet level. Packet sampling has become an attractive and scalable method to measure flow data on high-speed links. However, knowing the length distributions of traffic flows passing through a network link is very useful for network operation and management. In this paper, we consider the problem of estimating flow length distributions based on sampled flow data. This paper introduces two algorithms for estimating flow length distributions, fitting estimation and factoring estimation. The fitting estimation uses piecewise Pareto distribution to fit the original traffic for a small sampling period. The factoring estimation is used for a large sampling period. It first factorizes the large sampling period into a product of some smaller integer factors, then iteratively invokes fitting estimation or other algorithms such as $${\textit{EM}}$$ in the arranged order of the factors. Evaluations of the proposed algorithms on large Internet traces obtained from several sources demonstrate that they have high measurement accuracy with low computation overhead. The algorithms allow us to recover the complete flow length distributions. [ABSTRACT FROM AUTHOR]

As one of the key components for the railway transportation system, the Train Operation Diagram can be greatly influenced by many extrinsic and intrinsic factors. Therefore, the railway train flow has shown the strong nonlinear characteristics, which makes it quite difficult to take further relative studies. Fortunately, the cellular automaton model has its own advantages in solving nonlinear problems and traffic flow simulation. Considering the mixed features of multispeed running trains on the passenger dedicated lines, this paper presents a new train model under the moving block system with different types of trains running with the cellular automaton idea. By analyzing such key factors as the maintenance skylight, the proportion of the multispeed running trains, and the distance between adjacent stations and departure intervals, the corresponding running rules for the cellular automaton model are reestablished here with. By means of this CA model, the program of train running system is designed to analyze the potential impact on railway carrying capacity by various factors; the model can also be implemented to simulate the actual train running process and to draw the train operation diagram by computers. Basically the theory can be applied to organize the train operation on the dedicated passenger lines. [ABSTRACT FROM AUTHOR]

Short-time traffic flow prediction is necessary for advanced traffic management system (ATMS) and advanced traveler information system (ATIS). In order to improve the effect of short-term traffic flow prediction, this paper presents a short-term traffic flow multistep prediction method based on similarity search of time series. Firstly, the landmark model is used to represent time series of traffic flow data. Then the input data of prediction model are determined through searching similar time series. Finally, the echo state networks model is used for traffic flow multistep prediction. The performance of the proposed method is measured with expressway traffic flow data collected from loop detectors in Shanghai, China. The experimental results demonstrate that the proposed method can achieve better multistep prediction performance than conventional methods. [ABSTRACT FROM AUTHOR]

Travel times on the urban roadways are intrinsically uncertain. For known traffic conditions, a wide travel time distribution can be observed. Among all the components of travel times, delays incurred when approaching intersections constitute a large part of travel times that vehicles experience in urban trips. In this article, a model is presented for the delay distribution function for an urban trip with two fixed-time controlled intersections. Most parameters of the model are related to traffic control and flows, which can be directly calibrated from observations. The overflow queue distribution provides important parameters in the delay distribution function that have to be calibrated indirectly from traffic measurements, for example, from the measured delays and flows. Based on the directly observed and estimated model parameters, the delay distribution can be reconstructed. This article discusses the calibration procedure for delays. Two parameter estimation methods, namely, maximum likelihood (ML) and least squares (LS), are applied to estimate the overflow queue distribution from sample data simulated by VISSIM. Results show that estimation accuracy is not so susceptible to the choice of the estimation methods, sampling techniques, and sample size. The proposed delay model is able to successfully capture the delay process. [ABSTRACT FROM AUTHOR]

The study aims to develop a novel evolution model of speed perturbations for quantifying the probability of the traffic flow breakdown on congested freeway flow. The model is applicable to freeway operation and management. Time headway, driver behavior, and state transitions with stochastic nature are the most important factors for the breakdown and recovery of a disturbance. Two types of drivers, 'adventurers' and 'conservatives,' and two styles of reacting behavior, 'copy' and 'shift' are defined to characterize driving behavior. The time headway distributions for arriving and discharging traffic are distinguished for the existence of the 'hysteresis' phenomenon. The Monte Carlo simulation is carried out to obtain the estimation of the probability of breakdown (PB). The calculated PB is a monotonically increasing function of freeway flow with an 'S' shape. The simulation results indicate that the proposed model can describe the changes in spatial extent and magnitude of a disturbance, which have potential use for future works and field applications. [ABSTRACT FROM AUTHOR]

Mainstream traffic flow control (MTFC) on freeways, enabled via variable speed limits (VSL), is a traffic management tool capable of avoiding or, at least, postponing congestion formation on freeway bottlenecks, and may complement other existing measures. Two simple feedback controllers for local MTFC via VSL are proposed. Both controllers are evaluated and compared to a previously developed local MTFC feedback cascade controller and to a sophisticated optimal control approach. All feedback controllers rely on readily available real-time measurements, are simple and robust, and take into account a number of practical and safety restrictions; therefore, they are suitable for implementation in the field as a new or as part of an existing intelligent transportation systems infrastructure. The evaluations are conducted for a hypothetical freeway network by use of a validated second-order macroscopic traffic flow model. The results show that the feedback controllers exhibit a satisfactory control behavior and, indeed, approach the optimal control results. Recommendations for the operation of the feedback controllers are given. [ABSTRACT FROM AUTHOR]

Highlights: [•] Quasi-dynamic o–d flow estimator assumes constant o–d shares and varying generation profiles for a given period. [•] This assumption revealed to be acceptable in a real test site, even for constant distribution shares in the whole day. [•] The quasi-dynamic estimator outperforms significantly the simultaneous estimator. [•] Using quasi-dynamic estimates as historical seeds allows the Kalman filter to provide better o–d flow estimates. [•] The aggregation of quasi-dynamic o–d flows across time slices is an effective way to obtain o–d flows for larger time horizons. [ABSTRACT FROM AUTHOR]

Valuable information for estimating the traffic flow is obtained with current GPS technology by monitoring position and velocity of vehicles. In this paper, we present a proof of concept study that shows how the traffic state can be estimated using only partial and noisy data by assimilating them in a dynamical model. Our approach is based on a data assimilation algorithm, developed by the authors for chaotic geophysical models, designed to be equivalent but computationally much less demanding than the traditional extended Kalman filter. Here we show that the algorithm is even more efficient if the system is not chaotic and demonstrate by numerical experiments that an accurate reconstruction of the complete traffic state can be obtained at a very low computational cost by monitoring only a small percentage of vehicles. [ABSTRACT FROM AUTHOR]

Real-time information about the dynamic variation of turning rates at intersections is essential for achieving a more efficient traffic control and management system. Limitations in obtaining measurements of turning movements have drawn attention to the need for alternative methods for estimation of turning rates. In a large-scale urban network, characteristics such as disparate signal plans and various intersection layouts make defining a generic model for the entire network challenging. For this reason, most of the proposed methods are impractical for real-field implementation. The application of existing optimization-based methods is also limited by computational complexity, the need for pre-defined assumptions and insufficient stationary traffic measurements (e.g. loop detectors and plate recognition cameras) in urban networks. In this study, we take advantage of probe vehicles to propose a hybrid method that noticeably reduces the need for stationary measurements in the network. This hybrid method consists of two core stages: signal processing, and physical data-driven modeling to consecutively estimate turning rates. In the signal processing phase, we use wavelet transformation to extract fundamental components of inferred floating car turning rates. These components are then used as inputs to a turning rate estimation model. Using these decomposed components of floating car turning rates instead of raw input helps to balance the effect of low and high-frequency signal components and leads to a more accurate estimation. Investigation of the proposed method in a microscopic simulation environment reveals the ability of the hybrid model to detect turning rate variations even when a severe incident occurs. • A hybrid data-driven method is used for network-wide turning rate estimation. • The method makes use of floating car data for turning rate estimation. • A small proportion of stationary measurements is needed for the training purpose. • Deployment of wavelet transformation allows access to more accurate information. • The method makes a balance between the effect of low and high-frequency changes. [ABSTRACT FROM AUTHOR]

• Estimate the traffic demand integrating information provided by traditional and new technologies. Explore the inherent properties of extended coverage information. • Deal with splitting ratios of demand flows among paths identified by coupled Bluetooth sections. • Derivate the analytical solving procedure. • Increase of the intercepted path flows or spatial coverage leads to more accurate estimates. • Assess the effectiveness of the proposed method on a study network in Rome, Italy. Recent advances in technology have made available numerous new monitoring systems that collect updated traffic measurements both in fixed locations and over specific corridors or paths. Such recent technological developments point to challenging and promising opportunities for Origin-Destination (OD) traffic demand estimation and forecast. Therefore, the aim of this paper is to study how to exploit available information detected by new monitoring devices in the estimation of traffic demand. Starting from the formulation proposed by Spiess (1987, 1990), in this paper a new method to estimate the traffic demand by means of Bluetooth data is proposed. It explores inherent properties of this information in an off-line (and static) context, where mathematical formulation of the estimation problem can be derived. The effectiveness of the proposed method has been investigated in an extensive plan of experiments carried out both on test networks and on a study network consisting in a part of the city of Rome, Italy, obtaining promising results in both applications. [ABSTRACT FROM AUTHOR]

Traffic flow measurement is very important for traffic management systems. However, the existing traditional measurement approaches are highly time-consuming and expensive to continuously gather the required data and to maintain the corresponding equipment, such as loop detectors and video cameras. On the other hand, many services on the web propose to estimate automobile travel time taking into account traffic conditions thanks to crowd sourced data (Floating Car Data). This work proposes to reconstruct, from estimated travel time, traffic flows using machine learning method. In particular, we evaluate the capacity of Gaussian Process Regressor (GPR) to address this issue. After obtaining estimated travel time on a given route, a clustering process shows that travel duration profiles in each day can be associated to different "types of day". Then, different regressors are trained in order to estimate traffic flows from travel duration. In the "multi-model" variant, we trained a Regressor for each type of day. Conversely, in the "single model" variant, only one Regressor is trained (the type of day is not taken into account). This is an innovative work to estimate and reconstruct the traffic flow in transportation networks with machine learning method from aggregated Floating Car Data (FCD). A series of experiments are conducted to compare the estimated traffic flows, obtained by the proposed single model and multi-model, and the real ones from actual sensors. The obtained results show that both single model and multi-models can capture the tendency of real traffic flows. Furthermore, the performance can be improved by regulating parameters in GPR machine learning model, such as half width of sample window and sample size (a whole week or only weekdays), and multi-models can highly increase the performance compared with the single model. Therefore, the proposed GPR machine learning and FCD based new method can replace those traditional loop detectors for the measurement of traffic flow. • Application of Machine Learning based model to rebuild the traffic flow relationship. • Construction and training of multi-models to reduce estimated traffic flow error. • Demonstration of Gaussian Process Regressor to achieve the lowest error. [ABSTRACT FROM AUTHOR]

Travel time prediction could be applied to various fields and purposes. For traffic managers, travel time prediction is a fundamental part of traffic system operation. Its results may assist traffic management department in adjusting traffic flow through time-dependent rules. From the travellers' viewpoints, travel time prediction saves travel time and improves reliability through the selection of travel routes pre-trip and en route to optimize travel plans. A large number of research efforts on travel time prediction have been conducted, but trip travel time prediction is relatively limited, compared with link travel time. Travellers are more interested in specific trip travel time than average link travel time. The advances in positioning technologies, such as Global Positioning System (GPS), make it possible to collect a large number of vehicle trajectories which cover the whole road network as long as data are enough and is growing as an alternative data set for travel time prediction as well as other traffic studies. In view of these, we extend the conventional methodology of link travel time prediction to trip using historical trajectory data from taxis in urban road network. This article basically consists of the following several parts, extracting origins and destinations, searching for matched trips, testing for normal distribution, detecting and removing outliers, predicting travel time in a statistic way, and evaluating the reliability of prediction results. Experiments based on taxi data in Shenzhen are conducted and the results are evaluated. [ABSTRACT FROM AUTHOR]

This study proposes a methodology to determine the optimal cross-sectional design of a high-occupancy vehicle (HOV) facility for safety performance by evaluating the effects of each relevant design element on safety and tradeoffs among multiple design variables. This aspect of geometric design is critical because it is often difficult to acquire sufficient right-of-way for retrofitting HOV lanes to existing freeway systems with recommended cross-sectional design. Detailed collision data of concurrent-flow buffer-separated HOV lanes, along with their geometric features and traffic flow data, were analyzed to establish collision predictive models by injury types for HOV and the adjacent general-purpose lanes. These models were used to determine the set of cross-sectional design elements that minimizes the expected collision occurrences. As a case study, a real freeway corridor where a conversion from contiguous to buffer-separated types was recently completed was selected to demonstrate the applicability of the proposed method. The evaluation through this case study shows that the proposed methodology is useful in determining the cross-sectional design of HOV facilities for safety based on selective use of available geometric space. [ABSTRACT FROM AUTHOR]

Delay, which can be defined as the difference between interrupted and uninterrupted travel times through an intersection, is one of the most important performance indicators for intersection analysis. It can arise from several types of conditions like intersection geometry, drivers' perceptions, traffic characteristics etc. Minimum delay, which can be stated as the delay faced by a minor stream driver when the minor stream flow is nearly zero, is a base delay measure of an unsignalized intersection that should be analyzed in depth. In this study, an appropriate minimum delay equation is derived for single-lane traffic circles in İzmir, Turkey. For this purpose, observations are made at seven approaches of five single-lane traffic circles. Many types of model structures like exponential, power, best subsets regression etc. are constituted and compared with the existing approaches. Results have shown that, geometry of a traffic circle has an important effect on minimum delay and should be considered in analysis. In the study, the effect of entering vehicles from the minor approach is also investigated. A new delay model that depends on the entry flow is also suggested. [ABSTRACT FROM AUTHOR]

The Traffic and Urban Mobility System (Sistema de Información de Tráfico y Movilidad Urbana - SITYMUR) is a tool used to collect positional data from circulating agents on the roads, in order to generate traffic and mobility information in near real time. This issue raises important technological challenges concerning communications, data processing and publication of the results. This article describes these challenges and discusses the effectiveness of solutions proposed by showing a number of traffic and speed results for Bogotá as well as suggesting possible alternatives for improvement. [ABSTRACT FROM AUTHOR]

Abstract: From 2005 to 2009 there was a 40% decrease in the number of days on which the European daily limit value of PM10 was exceeded at traffic locations in European cities. Yet, in many of these cities, air quality is still not in compliance with the European Air Quality Directive and additional traffic measures are planned. Our study shows that elemental carbon (EC) is a more appropriate indicator than PM2.5 and PM10 for evaluating the impact of traffic measures on air quality and health. The modelled improvement in EC concentration was translated in life years gained as a result of a traffic measure. This was investigated for a speed management zone on a motorway in the city of Rotterdam. Eighty-five per cent of those living within 400 m of the motorway gained 0–1 months of life expectancy and another 15% gained 1–3 months, depending on their distance from the motorway. In addition, EC was used to evaluate a low emission zone in Amsterdam, specifically for those living along inner-urban roads with intense traffic levels. The zone only restricts heavy duty vehicles with Euro emission class 0 to 2, Euro 3 older than eight years or more recent Euro 3 without diesel particulate filter. The results indicate a population-weighted, average gain of 0.2 months in life expectancy as compared with a maximum potential gain of 2.9 months. It is concluded that on motorways speed management is an effective measure, while a low emission zone as implemented in our case study, is less effective to reduce health effects of road traffic emissions. For inner-urban roads reduction of traffic volume seems the most effective traffic measure for improving air quality and health. [Copyright &y& Elsevier]

Roads cause functional habitat loss, alter movement patterns and can become ecological traps for wildlife. Many of the negative effects of roads are likely to be a function of the human use of roads, not the road itself. However, few studies have examined the effect of temporally and spatially varying traffic patterns on large mammals, which could lead to misinterpretations about the impact of roads on wildlife. We developed models of traffic volume for an entire road network in south-western Alberta, Canada, and documented for the first time the response of grizzly bears Ursus arctos L to a wide range of traffic levels., Traffic patterns caused a clear behavioural shift in grizzly bears, with increased use of areas near roads and movement across roads during the night when traffic was low. Bears selected areas near roads travelled by fewer than 20 vehicles per day and were more likely to cross these roads. Bears avoided roads receiving moderate traffic (20-100 vehicles per day) and strongly avoided high-use roads (>100 vehicles per day) at all times., Synthesis and applications. Grizzly bear responses to traffic caused a departure from typical behavioural patterns, with bears in our study being largely nocturnal. In addition, bears selected private agricultural land, which had lower traffic levels, but higher road density, over multi-use public land. These results improve our understanding of bear responses to roads and can be used to refine management practices. Future management plans should employ a multi-pronged approach aimed at limiting both road density and traffic in core habitats. Access management will be critical in such plans and is an important tool for conserving threatened wildlife populations. [ABSTRACT FROM AUTHOR]

Interest in the application of computer vision techniques to automatic video-based analysis of traffic is high at present. This is due in part to the capabilities of video sensors, as well as to social demands for traffic safety. In general, these systems are cheaper and less disruptive than other kinds of devices like loop detectors for traffic monitoring. Automatic traffic surveillance is, however, still a challenging problem when we consider many of the practical difficulties involved (i.e. limited number of cameras and positions of these with respect to the scene, variable illumination and weather conditions, intrinsic complexity of analyzed traffic events, need for a real-time frame rate processing, among others). In this paper, we propose a multi-level framework for automatic analysis of complex traffic videos which present different kind of variations. The accurate and efficient extraction of relevant scene information from the video frames is performed in a hierarchical bottom-up form using the system presented. First of all, foreground moving pixels are detected in each frame using a proposed method of adaptive background subtraction. After that, these pixels are grouped into blobs if they share some common properties. Blobs detected in predefined scene entry regions are identified as vehicles and these are tracked along the controlled road area. At the upper level, some traffic monitoring statistics and also related linguistic reports on the evolution of traffic in the scene are generated periodically. Experimental results on the adaptive background method proposed, as well as regarding its integration in the multi-level traffic analysis system, are very satisfactory for the traffic videos analyzed. [ABSTRACT FROM AUTHOR]

Abstract: From a complex network perspective, this study sets out two aims around the US airport network (USAN) which is built from en-route location information of domestic flights in the US. First, we analyze the structural properties of the USAN with respect to its binary and weighted graphs, and second we explore the airport patterns, which have wide-ranging implications. Results from the two graphs indicate the following. (1) The USAN exhibits scale-free, small-world and disassortative mixing properties, which are consistent with the mainstream perspectives. Besides, we find (2) a remarkable power relationship between the structural measurements in the binary graph and the traffic measurements in the weighted counterpart, namely degree versus capacity and attraction versus volume. On the other hand, investigation of the airport patterns suggests (3) that all the airports can be classified into four categories based on multiple network metrics, which shows a complete typology of the airports. And it further indicates (4) that there is a subtle relationship between the airport traffic and the geographical constraints as well as the regional socioeconomic indicators. [Copyright &y& Elsevier]

Environmental sustainability is closely related to transportation, especially to the road network, because vehicle emissions and noise damage the environment and have adverse effects on human health. It is, therefore, important to take their effect into account when designing and managing road networks. Road network equilibrium approaches have been used to estimate this impact and to design and manage road networks accordingly. However, no comprehensive review has summarized the applications of these approaches to the design and management of road networks that explicitly address environmental concerns. More importantly, it is necessary to identify this gap in the literature so that future research can improve the existing methodologies. Hence, this paper summarizes these applications and identifies potential future research directions in terms of theories, modelling approaches, algorithms, analyses, and applications. [ABSTRACT FROM AUTHOR]

Road deposited sediments (RDS) are a valuable environmental medium for characterizing contaminant levels in urban areas; and their associated potentially toxic elements (PTEs) can directly impact both human and aquatic health. In this study, RDS were collected from 15 co-located industrial and residential roads throughout Singapore to determine the effect of land use on contaminant levels. A second pilot study was designed to quantify the efficiency of road sweeping in removing different RDS grain size fractions from industrial and residential roads. The fine fraction (<63 μm) of all RDSs was analyzed for over 40 elements. Eleven elements that reflect geogenic and anthropogenic sources were examined in detail (Al, Co, Cr, Cu, Fe, Ni, Pb, Sb, Sc, Si, and Zn). Industrial RDS had statistically higher concentrations of Co, Cr, Fe, and Ni than residential RDS. Potentially toxic elements Cu, Pb, Sb, and Zn were enriched >10-fold at all locations compared to upper continental crust values. Concentrations of Cu, Pb and Zn exceeded aquatic sediment probable effect concentration levels, suggesting they could generate a toxic response in bottom-dwelling aquatic organisms. Traffic was equally heavy at both industrial and residential sites, but large trucks and machinery comprised a larger proportion of the traffic in the industrial areas. Traffic was not significantly correlated with the PTE (i.e., Cu, Pb, Sb and Zn) concentrations. Plausible anthropogenic contaminant sources include vehicles (e.g., brake and tire wear, vehicle emissions) and several industrial activities including metal works, oil processing, and waste incineration. Street sweeping was effective in removal of large organic debris and inorganic RDS, but it was ineffective in removing the geochemically important fraction, i.e., <125 μm. [Copyright &y& Elsevier]

High speed weigh-in-motion (WIM) sensors are utilized as components of complex traffic monitoring and measurement systems. They should be able to determine the weights on wheels, axles and vehicle gross weights, and to help the classification of vehicles (depending on the number of axles). WIM sensors must meet the following main requirements: good accuracy, high endurance, low price and easy installation in the road structure. It is not advisable to use cheap materials in constructing these devices for lower prices, since the sensors are normally working in harsh environmental conditions such as temperatures between -40 °C and +70 °C, dust, temporary water immersion, shocks and vibrations. Consequently, less expensive manufacturing technologies are recommended. Because the installation cost in the road structure is high and proportional to the WIM sensor cross section (especially with its thickness), the device needs to be made as flat as possible. The WIM sensor model presented and analyzed in this paper uses a spring element equipped with strain gages. Using Finite Element Analysis (FEA), the authors have attempted to obtain a more sensitive, reliable, lower profile and overall cheaper elastic element for a new WIM sensor. [ABSTRACT FROM AUTHOR]

This paper presents an investigation of the accurate methodology of motorcycle equivalent units (MEUs) in mixed traffic flow by considering the characteristics of moving vehicles, such as velocity and effective space. In addition, the effective space of each kind of vehicle is computed with consideration of the influences of velocity, physical size of the subject vehicle, and the surrounding motorcycles in the proposed methodology. The field data were collected in Hanoi, Vietnam in the case study. The results indicated that the MEU values of cars, buses, minibuses, and bicycles are 3.4, 10.5, 8.3 and 1.4, respectively. On the other hand, the collection of the field data of vehicle flow and speed passing through road segments was conducted in three groups of divided roads with raised medians. The fundamental diagrams of vehicle speed-flow-density relationships, ranging from stable traffic flow to unstable conditions, were plotted in this paper. The values of capacity, maximum motorcycle flow, critical mean stream speed, and critical density of traffic flow were computed. Results indicated that the capacity values of urban roads with two, three, and four lanes per direction are 13,358, 21,725 and 24,335 (motorcycles per hour) respectively. The capacity, therefore, increases with the number of lanes of urban roads. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the effect of a new road tunnel on the concentration and distribution of traffic-related air pollution (TRAP), specifically nitrogen dioxide (NO2) and particulate matter (PM), and to determine its relationship to change in traffic flow. We used continuously recorded data from four monitoring stations at nonroadside locations within the study area and three regional monitors outside the area. The four monitors in the study area were in background locations where smaller pollutant changes were expected compared with changes near the bypassed main road. We also deployed passive samplers to assess finer spatial variability in NO2 including application of a land use regression model (LUR). The study was conducted from 2006 to 2008. Analysis of the continuously recorded data showed that the tunnel intervention did not lead to consistent reductions in NO2 or PM over the wider study area. However, there were significant decreases in NO2, NOx, and PM10 in the eastern section of the study area. Analysis of passive sampler data indicated that the greatest reductions in NO2 concentrations occurred within 100 m of the bypassed main road. The LUR model also demonstrated that changes in NO2 were most marked adjacent to the bypassed main road. These findings support the use of methods that highlight fine spatial variability in TRAP and demonstrate the utility of traffic interventions in reducing air pollution exposures for populations living close to main roads. [ABSTRACT FROM AUTHOR]

Although more attention has been attracted to benefit evaluation of Intelligent Transportation Systems (ITS) deployment, how ITS impact the traffic system and make great effects is little considered. As a subsystem of ITS, in this paper, Intelligent Transportation Management System (ITMS) is studied with its impact mechanism on the road traffic system. Firstly, the correlative factors between ITMS and the road traffic system are presented and 3 positive feedback chains are defined. Secondly, we introduce the theory of Fundamental Diagram (FD) and traffic system entropy to demonstrate the correlative relationship between ITMS and feedback chains. The analyzed results show that ITMS, as a negative feedback factor, has damping functions on the coupling relationship of all 3 positive feedback chains. It indicates that with its deployment in Beijing, ITMS has impacted the improvement of efficiency and safety for the road traffic system. Finally, related benefits brought by ITMS are presented corresponding to the correlative factors, and effect standards are identified for evaluating ITMS comprehensive benefits. [ABSTRACT FROM AUTHOR]

Abstract: Although various approaches have been proposed for modeling day-to-day traffic flow evolution, none of them, to the best of our knowledge, have been validated for disrupted networks due to the lack of empirical observations. By carefully studying the driving behavioral changes after the collapse of I-35W Mississippi River Bridge in Minneapolis, Minnesota, we found that most of the existing day-to-day traffic assignment models would not be suitable for modeling the traffic evolution under network disruption, because they assume that drivers’ travel cost perception depends solely on their experiences from previous days. When a significant network change occurs unexpectedly, travelers’ past experience on a traffic network may not be entirely useful because the unexpected network change could disturb the traffic greatly. To remedy this, in this paper, we propose a prediction–correction model to describe the traffic equilibration process. A “predicted” flow pattern is constructed inside the model to accommodate the imperfect perception of congestion that is gradually corrected by actual travel experiences. We also prove rigorously that, under mild assumptions, the proposed prediction–correction process has the user equilibrium flow as a globally attractive point. The proposed model is calibrated and validated with the field data collected after the collapse of I-35W Bridge. This study bridges the gap between theoretical modeling and practical applications of day-to-day traffic equilibration approaches and furthers the understanding of traffic equilibration process after network disruption. [Copyright &y& Elsevier]

The French marina of La Rochelle is one of the most important o the European Atlantic Coast with nearly 3,500 rings and a broad range of seaside activities ranging from nautical building to shiplanding activities including great international nautical events. The prospect of the creation of Marina Natural Park of the Estuary of the Gironde and the Pertuis Charentais makes us focus on the issue of the nautical frequentation in the Pertuis Archipelago. The coast of La Rochelle shelters the marina of Les Minimes, a fishing harbour (called La Pallice). Therefore the traffic is potentially important and it is necessary to know how much and what goes on in order to make the traffic safe and to regulate it properly. The rational marine planning is interesting as this coast shelters one of the most important French Oyster and Mussel production unit. So we tried to know better the frequentation of the Bay of La Rochelle focusing on the analysis of marina practices. The approach we selected relies on enquiries, counting analysis, observations from land, ad from sea as well as from the air. The first results (2008 and 2009 collects) account for situations quite different from those known up to now: there are numerous sea-outings and a huge difference between the use of sailing boats or motorboats. The selective occupation of the Bay can also be observed. Consequently, these results are liable to change the marina management. [ABSTRACT FROM AUTHOR]

Abstract: Nitrogen monoxide (NO), nitrogen dioxide (NO2) and ozone (O3) have been measured downwind (0–450 m) from two line sources in a suburban environment in a set of initial experiments designed to investigate the role of chemistry in determining the partitioning of NOx into NO and NO2 and the ability of a commercial modelling package (ADMS) to reproduce downwind speciated NOx concentrations. NO concentrations decrease more rapidly than NO2 as NO is removed both by dilution and by chemical reaction with ozone. Whilst NO is the dominant form of NOx at the roadside, NO2 concentrations decay more slowly (due to chemical production and higher background levels) and NO2 typically forms the majority of NOx by 50–100 m from the source. For a majority of modelling studies the input NO and NO2 concentrations were set to match the experimental concentrations at the initial site. The measured decays of NO and NO2 can be accurately reproduced by the ADMS dispersion model only when chemical transformation is taken into account. When NOx is estimated directly from traffic flow measurements, the modelled NO2 tends to underestimate the measured value. [Copyright &y& Elsevier]

Fuzzy logic has been recognised in the literature as an effective methodology for real-time signal control. The majority of the fuzzy controllers in the literature depend on simple logic which in turn depends on the raw data of a single detector. Their input variables are usually simple estimates of traffic measures such as flow, speed or occupancy, estimated from such single-detector readings. A room for improvement is sought in this article by developing a fuzzy logic model (FLM) that could be integrated with smarter 'processing' tools to estimate several traffic measures from multiple detectors on each approach. The estimates obtained from this processing tool are integrated as input knowledge into the FLM. The mathematical formulation of these traffic measures is presented. The fuzzy logic structure is addressed in detail. A simulation model is devised to test the effectiveness of the FLM. The results are presented and discussed in depth. [ABSTRACT FROM AUTHOR]

This article proposes a new method for data assimilation and data reconciliation problems applicable to systems modeled by conservation laws. The problem is solved directly in the equivalent format of a Hamilton-Jacobi partial differential equation, for which the solution is fully characterized by a Lax-Hopf formula. Using properties of the solution, we prove that when the data of the problem is prescribed in piecewise affine form, the resulting constraints which consist of the partial differential equation in data assimilation and reconciliation problems are convex, and can be instantiated explicitly. This property enables us to identify a class of data assimilation and data reconciliation problems that can be formulated using convex programs in standard form. We illustrate the capabilities of the method for reconstruction of highway traffic flow using experimental data generated from the Mobile Century experiment. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach to finding and predicting anomalous network states based on a flow monitoring mechanism. We assume that number of active flows can show a real network state. Moreover, the dependence between flow number and link utilisation allows us to derive an equation for the confidence interval on high-loaded network links. Experiments have been conducted that confirmed the basic position of the model and identified the anomaly network states. A software package based on this model has been created that allows the prevention of DDoS attacks. For successful operation of this software the number of active flows that single IP address can generate has been analysed. [ABSTRACT FROM AUTHOR]

The problem of development and customization of optoelectronic land traffic flow monitoring systems with account taken of specified operational conditions is examined. The methodology for automated design of algorithmic support for the main subsystem of the mentioned systems, namely, the one for vehicle registration number recognition is suggested. [ABSTRACT FROM AUTHOR]

Abstract: One of the most common and important predictors in safety performance functions (SPFs) is traffic volume which is known to be measured with uncertainty. Such measurement errors (ME) can attenuate the respective predictors’ effect and also increase dispersion. This paper proposes an approach which involves the use of a ME model based on traffic flow time series data. The model is used in conjunction with the negative binomial SPF to circumvent the bias in predicting the aggregate number of accidents during the time period under study. The proposed approach (denoted by MENB), was compared with the traditional negative binomial (NB) technique by way of Monte Carlo simulation. Furthermore, both approaches were applied to two datasets corresponding to 131 and 130 road segments in British Columbia. The full Bayes method was utilized for parameter estimation, performance evaluation and inference through the use of Markov Chain Monte Carlo (MCMC) techniques. The simulation results showed that MENB has outperformed NB when large measurement errors are present. The goodness-of-fit statistics showed that MENB has provided a slightly better fit to the data. However, in the presence of measurement errors, the NB has underestimated the predicted number of accidents for heavy traffic on long road segments and vice versa. The use of MENB is justified when the variance in volume between years is large otherwise both approaches yield comparable results. [ABSTRACT FROM AUTHOR]