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A distributed firewall is a security application that monitors and controls traffic on an organization's network. While centralized firewalls are used against attacks coming from outside a network, distributed firewalls are considered for inside attacks from internal networks such as wireless access and VPN tunnel. Distributed firewalls use policies, which are stated by rules, to find anomalous packets. However, such static rules may be incomplete. In this case, by monitoring firewall logs, the anomalies can be detected. Such logs become big when networks have high traffic, but their hidden knowledge contains valuable information about existing anomalies. In this paper, to detect the anomalies, we extract patterns from big data logs of distributed firewalls using data mining and machine learning. The proposed method is applied to big logs from distributed firewalls in a real security environment, and results are analyzed. [ABSTRACT FROM AUTHOR]

Identification of accident black spots has gained tremendous popularity among road agencies and safety specialists for evaluating and subsequently enhancing road traffic safety. However, there is still limited understanding of the internal relationship between black spots and microscopic vehicle kinetic parameters. To address this gap, this paper describes a project that was undertaken using the real-time tire force data (kinetic response) obtained from road experiments on Wenli Expressway. First, factor analysis was applied to extracted three independent indicators (power-braking, handling stability, and ride comfort) from seven original kinetic indicators with multiple collinearities. Afterward, the main indicators were given vehicle kinetic meaning by analyzing the characteristics of original indicators associated with them. A compelling correlation was established among kinetic parameters, vehicle running qualities, and accident risk. Additionally, an integrated evaluation framework was established to identify accident black spots based on applying ordered logit models and PLS-entropy-TOPSIS approaches. The recognition results exhibited that the overall recognition accuracy obtained by the latter was found to be comparable to that achieved using the previous one. The compound evaluation model proposed in this paper has been proven to present many advantages for black spot identification. It is evidently clear from the findings that the vehicle kinetic parameters have significant correlations with road accident risk. This paper could provide some insightful knowledge for identifying and preventing the black spots from ameliorating traffic safety. [ABSTRACT FROM AUTHOR]

Movement-specific vehicle classification and counting at traffic intersections is a crucial component of various traffic management activities. In this context, with recent advancements in computer-vision-based techniques, cameras have emerged as a reliable data source for extracting vehicular trajectories from traffic scenes. However, classifying these trajectories by movement type is quite challenging, as characteristics of motion trajectories obtained this way vary depending on camera calibrations. Although some existing methods have addressed such classification tasks with decent accuracies, the performance of these methods significantly relied on the manual specification of several regions of interest. In this study, we proposed an automated classification method for movement-specific classification (such as right-turn, left-turn and through movements) of vision-based vehicle trajectories. Our classification framework identifies different movement patterns observed in a traffic scene using an unsupervised hierarchical clustering technique. Thereafter, a similarity-based assignment strategy is adopted to assign incoming vehicle trajectories to identified movement groups. A new similarity measure was designed to overcome the inherent shortcomings of vision-based trajectories. Experimental results demonstrated the effectiveness of the proposed classification approach and its ability to adapt to different traffic scenarios without any manual intervention. [ABSTRACT FROM AUTHOR]

With the explosive growth of cloud computing applications, the east-west traffic among servers has come to occupy the dominant proportion of the traffic in data center networks (DCNs). Cloud computing tasks need to be executed in a distributed manner on multiple servers, which exchange large amounts of intermediate data between the adjacent stages of each multi-stage task. Therefore, the congestion in DCNs can reduce the processing performance when conducting multi-stage tasks. To address this, the relationship between the blocking performance and the traffic load can be adopted as a theoretical basis for network planning and traffic engineering. In this paper, the traffic load correlation between edge links and aggregation links is considered, and an iterative blocking performance analysis method is proposed for two-layer tree-type DCNs. The simulation results show the good accuracy of the proposed method with respect to the theoretical results especially in the blocking rate range below 4% and with over-subscription ratio 1.5. [ABSTRACT FROM AUTHOR]

Vehicular driver behaviour is an important factor for vehicular speed and safety at horizontal curves, few studies have attempted to understand this aspect. In this regard, the objective of the present study is to evaluate the vehicular speed characteristics with the effect of driver behaviour at horizontal curves. The study uses real traffic environments where vehicle speed, driver behaviour (lane change or not) and number of vehicles in the opposite lane (approaching vehicles in the opposite lane) data were recorded by video graphic technique. A total of four horizontal curves with different radiuses on two-lane undivided two-way roadways have been studied. Further, the curve characteristics (radius, length of curve, etc.) data were collected from a preliminary survey. The data was analyzed with a multiple linear regression model by considering vehicle speed as the dependent variable and radius of curve, driver lane change behaviour, number of vehicles in the opposite lane, type of vehicle, and shoulder width as independent variables. The study discovered that driver lane change behavior, number of vehicles in the opposite lane, vehicle type in the opposite lane, and shoulder width all have significant effects on vehicle speed characteristics at horizontal curves, in addition to the radius of the curve. These findings are useful for the design of horizontal alignment and evaluating countermeasures for vehicular driver safety at horizontal curves. [ABSTRACT FROM AUTHOR]

Background: This study aimed to determine the rates of road traffic injuries and deaths as well as the factors affecting their levels in the Al-Najaf governorate for 3 years (2017-2019). Materials and Methods: Data were collected and analysed from the annual report of traffic crashes statistics of the Iraqi Ministry of Planning/Central Statistical Organisation. Al-Najaf governorate was selected as the study model for the period between 2017 and 2019. This selection was based on its urban growth, important location and standing for religious tourism. Results: Of the 8824, 9852 and 10,753 crashes reported in Iraq in 2017, 2018, and 2019, Al-Najaf governorate recorded 1057 (12%), 1041 (10.6%) and 1148 (10.7%), respectively. The percentage of injuries in Al-Najaf governorate to the total of injuries in Iraq decreased as follows: (11.9%), (10.2%) and (9.9%) for 2017, 2018 and 2019 respectively, while the mortality rate decreased as follows: (8.4%), (7.7%) and (7.1%), respectively. The number of deaths and injuries on main roads and highways was higher. Besides, the rates of injuries and deaths among males were higher than in females. Driving at excessive speed was the most statistically significant factor leading to a high proportion of deaths and injuries. Conclusion: This study showed a decrease in the deaths and injuries caused by road traffic crashes during the 3 years 2017-2019 in the Al-Najaf Governorate, Iraq. It seems that driving rules and regulations need to be revised so that they can have more deterrent power in preventing injuries and deaths from driving accidents. [ABSTRACT FROM AUTHOR]

Vehicle driving safety is an important performance indicator for vehicles, and there is still much room for development in the active safety control of electric vehicles. A vehicle rollover is an important road traffic safety problem, as rollover accidents cause serious casualties and huge economic losses. It is very easy for vehicles in high-speed sharp turns or high-speed overtaking to roll over; in order to improve the vehicle in these conditions with the anti-rollover stability, this study proposed a real-time motor control strategy, mainly through the acquisition of vehicle attitude data and the use of multi-sensor fusion on the vehicle running state for real time. The lateral load transfer rate was used as the vehicle rollover evaluation index, and the test results indicate that when the real-time rollover index exceeds the set limit safety threshold, the motor speed is reduced through active control so that the vehicle avoids rollover accidents, or the risk of rollover is reduced. The STM32F103RET6 was used as the main chip for hardware design, control board fabrication, control program software design, and joint testing of software and hardware. The tests and data analysis prove that the motor control strategy is reliable in real time and can significantly improve the active safety of electric vehicles. [ABSTRACT FROM AUTHOR]

Cities world-wide have taken the opportunity presented by the COVID-19 pandemic to improve and expand pedestrian infrastructure, providing residents with a sense of relief and pursuing long-standing goals to decrease automobile dependence and increase walkability. So far, due to a scarcity of data and methodological shortcomings, these efforts have lacked the system-level view of treating sidewalks as a network. Here, we leverage sidewalk data from ten cities in three continents, to first analyse the distribution of sidewalk and roadbed geometries, and find that cities present an unbalanced distribution of public space, favouring automobiles at the expense of pedestrians. Next, we connect these geometries to build a sidewalk network –adjacent, but irreducible to the road network. Finally, we compare a no-intervention scenario with a shared-effort heuristic, in relation to the performance of sidewalk infrastructures to guarantee physical distancing. The heuristic prevents the sidewalk connectivity breakdown, while preserving the road network's functionality. While public space is traditionally shared by pedestrian and cars, the distancing measures imposed by the Covid-19 pandemic have underlined the need for wider pedestrian spaces. Here, the authors take a complex network approach to analyze sidewalk data from cities across the world to evidence a strong unbalance in the space distribution between cars and pedestrian, and propose a strategy to improve urban walkability within a socially distancing context and without compromising road traffic. [ABSTRACT FROM AUTHOR]

Road accidents are one of the most relevant causes of injuries and death worldwide, and therefore, they constitute a significant field of research on the use of advanced algorithms and techniques to analyze and predict traffic accidents and determine the most relevant elements that contribute to road accidents. The research of road accident prediction aims to respond to the challenge of offer tools to generate a more secure mobility environment, and ultimately, save lives. This paper aims to provide an overview of the state of the art in the prediction of road accidents through machine learning algorithms and advanced techniques for analyzing information, such as convolutional neural networks and long short-term memory networks, among other deep learning architectures. Furthermore, in this article, a compendium and study of the most used data sources for the road accident forecast is made. And a classification is proposed according to its origin and characteristics, such as open data, measurement technologies, onboard equipment and social media data.For the analysis of the information, the different algorithms employed to make predictions about road accidents are listed and compared, as well as their applicability depending on the types of data being analyzed, along with the results obtained and their ease of interpretation and analysis.The best results reported by the authors are obtained when two or more analytic techniques are combined, in such a way that analysis of the obtained results is strengthened. Among the future challenges in road traffic forecasting lies the enhancement of the scope of the proposed models and predictions by the incorporation of heterogeneous data sources, that include geo spatial data, information from traffic volume, traffic statistics, video, sound, text and sentiment from social media, that many authors concur that can improve the precision and accuracy of the analysis and predictions.

Although the occurrence of road accidents and the number of road accident casualties in almost all Polish voivodeships has decreased over the last few years, the rate of this change varies considerably from region to region. To provide a better understanding of such a tendency, panel data regression models are proposed to conduct this pilot research which evaluates the relative performance of Polish regions in terms of their road traffic safety. Panel data are multi-dimensional data which involve measurements over time. In the research, a voivodeship is a unit analysed at a group level, whereas a year is a unit analysed at a time level. A two-way error component regression model has been applied to survey the impact of regressors, the group effects, and time effects on a dependent variable. The analysis has been conducted using data acquired from the Statistics Poland Local Data Bank website, as well as from the General Directorate for National Roads and Motorways. The panel data from 16 regions in Poland and the 2012-2018 period have been investigated. The examined models refer to road traffic safety indices defined based on the following characteristics: the number of road accidents, the number road fatalities, and the number of people injured. The results of all the three models indicate a negative effect as regards the GDP per capita, (car) motorisation rate, the indicator of government expenditure for current maintenance of national roads, and the road length per capita. A positive association has been found between the truck motorisation rate and the indicator of local government expenditure on roads. The impact of the region's urbanisation indicators on road safety is ambiguous as, on the one hand, its increase causes a reduction in the road accident and accident injury indices, but, on the other hand, it produces a rise in the accident fatality index. In the models, the significance of time effects has been identified; a decreasing time trend suggests a general improvement in road safety from year to year. Most of the group effects have turned out to be highly significant. However, the effects differ as regards both the road accident and the accident injury indices in magnitude and direction. [ABSTRACT FROM AUTHOR]

Traffic congestion is common in most cities. It not only affects people's normal travel time but also causes more traffic crashes. To solve the traffic congestion and reduce the corresponding hazards, it is necessary to quickly detect the location of traffic congestion. In view of the fact that the trajectory data record the temporal and spatial information of moving objects, this article presents two methods for the real-time detection of traffic congestion through real-time processing of trajectory data. One is to use distributed densit-based spatial clustering of applications with noise (DBSCAN) clustering to detect the location of traffic congestion. The other is to perform distributed topology analysis of trajectory data to find congested areas. Finally, extensive experiments that involved using three real datasets to simulate both real-time detection methods on Spark Streaming demonstrated the efficiency of the two methods. [ABSTRACT FROM AUTHOR]

Abstract: This article describes a method for compressing position and identification data for files containing comprehensive trajectory records of all vehicles traversing the same roadway segment over an arbitrary time period, in such a way that no loss of information content occurs. Such complete trajectory records are important for the study of traffic flow theory and could become increasingly relevant as test data against which to study the behavior of autonomous vehicles in a mixed traffic environment. Compression steps include differential encoding, motion prediction, and parsimonious binary storage, plus steps that are unique to the context of vehicle trajectories, including vehicle ID numbers and lane occupancy. The effectiveness of the compression is due, in part, to the strong correlations between positions (and speeds) of vehicles traveling near each other, as well as recognition that certain trajectory artifacts change with spatial and temporal frequencies much lower than the sampling rates. The algorithm is demonstrated on two sets of publicly available complete trajectory records, and compression performance statistics are given. Compression ratios in the range 10:1 to more than 20:1 are achieved for the sample files. [ABSTRACT FROM AUTHOR]

This is a study of a major provincial road in South Africa's most densely settled and economic active province, Gauteng. The road plays a key role in Gauteng's transport network. The road is subjected to high traffic volumes, especially from trucks, as well as motorists using the road for a daily work commute. This study shows there is a misalignment between what the road was designed for and its present usage patterns, due to the road being poorly managed in terms of traffic volumes and load, as well as, weak maintenance regimes. The continuous traffic overloading has resulted in pavement distress and premature road surface failure, making road rehabilitation costly. As data records management and data collection procedures are weak, current road managers are unable to make appropriate rehabilitation decisions. Cumulatively, current usage and management patterns result in an unsustainable situation, whereby this major transport route is unable to function optimally, jeopardising those businesses and residents who rely on it and placing a significant maintenance burden on the public sector. This has resulted in a call to subject sections of it to a controversial and costly electronic tolling system. This study, rather, calls for better road management instead. [ABSTRACT FROM AUTHOR]

This paper considers the problem of moving object evasion from detection in the plane. Detection is performed by a system of heterogenous observers that consists of one sensor and a group of detectors. We establish the optimal evasion law in explicit form for the case when the level of transmitted signal has power-type dependence on the evader's speed. The paths to evade the system of observers and penetrate through the system are constructed. [ABSTRACT FROM AUTHOR]

This paper consider the coordination problem for the boundaries of control polygons in large-scale network structures; coordination is performed among different types of network partitions into such polygons. We define conditions ensuring smaller control costs in the case where the polygon boundaries of one partition type coincide with those of another partition type. The solution of this class of problems is topical for the analysis of traffic control and infrastructure maintenance in transport networks (particularly, railway networks). [ABSTRACT FROM AUTHOR]

Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks. [ABSTRACT FROM AUTHOR]

Historic traffic information is valuable in transportation analysis and planning, e.g., evaluating the reliability of routes for representative source-destination pairs. Also, it can be utilized to provide efficient and effective route-search services. In view of these applications, we propose the k traffic-tolerant paths ( TTP) problem on road networks, which takes a source-destination pair and historic traffic information as input, and returns k paths that minimize the aggregate (historic) travel time. Unlike the shortest path problem, the TTP problem has a combinatorial search space that renders the optimal solution expensive to find. First, we propose an exact algorithm with effective pruning rules to reduce the search time. Second, we develop an anytime heuristic algorithm that makes 'best-effort' to find a low-cost solution within a given time limit. Extensive experiments on real and synthetic traffic data demonstrate the effectiveness of TTP and the efficiency of our proposed algorithms. [ABSTRACT FROM AUTHOR]

Inclement weather, such as heavy rain, significantly affects road traffic flow operation, which may cause severe congestion in road networks in cities. This study investigates the effect of inclement weather, such as rain events, on traffic flow and proposes an integrated model for traffic flow parameter forecasting during such events. First, an analysis of historical observation data indicates that the forecasting error of traffic flow volume has a significant linear correlation with mean precipitation, and thus, forecasting accuracy can be considerably improved by applying this linear correlation to correct forecasting values. An integrated online precipitation-correction model was proposed for traffic flow volume forecasting based on these findings. We preprocessed precipitation data transformation and used outlier detection techniques to improve the efficiency of the model. Finally, an integrated forecasting model was designed through data fusion methods based on the four basic forecasting models and the proposed online precipitation-correction model. Results of the model validation with the field data set show that the designed model is better than the other models in terms of overall accuracy throughout the day and under precipitation. However, the designed model is not always ideal under heavy rain conditions. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, a rear-end collision control model is proposed using the fuzzy logic control scheme. Through detailed analysis of car-following cases, our fuzzy control system is established with reasonable control rules. Furthermore, a genetic algorithm is introduced into the fuzzy rules refining process to reduce the computational complexity while maintaining accuracy. Numerical results indicate that our genetic algorithm-optimized fuzzy logic controller outperforms the traditional fuzzy logic controller in terms of better safety guarantee and higher traffic efficiency. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Through this paper the author aims to study and find solutions for automatic detection of traffic light position and for automatic calculation of the waiting time at traffic light. The first objective serves mainly the road transportation field, mainly because it removes the need for collaboration with local authorities to establish a national network of traffic lights. The second objective is important not only for companies which are providing navigation solutions, but especially for authorities, institutions, companies operating in road traffic management systems. Real-time dynamic determination of traffic queue length and of waiting time at traffic lights allow the creation of dynamic systems, intelligent and flexible, adapted to actual traffic conditions, and not to generic, theoretical models. Thus, cities can approach the Smart City concept by boosting, efficienting and greening the road transport, promoted in Europe through the Horizon 2020, Smart Cities, Urban Mobility initiative. [ABSTRACT FROM AUTHOR]

Increasing availability of floating car data both historic in the form of trajectory data-sets and real-time in the form of continuous data stream paves the way for data driven traffic simulations. While historic data-sets can be used to construct spatiotemporal models of different roads, the continuous data streams from probe vehicles can be used for purposes such as current traffic-state estimation, incident detection and predicting short term evolution of traffic. A service which incorporates the aforementioned features will be invaluable for advanced traffic management and information services. In this paper we present a thorough analyses of using probe vehicles for reconstructing traffic state in real time, by employing detailed agent based microscopic traffic simulations of several scenarios on real world expressway. [ABSTRACT FROM AUTHOR]

Embedded transaction design inside solid state drives (SSDs) is an attractive way to support system consistency with low overhead due to the no-overwrite property of flash memory. Recent research proposes shadow paging variants to reduce write traffic to SSDs for longer lifetime while leveraging the random performance of SSDs. However, writes in transactions usually are small, and the page-aligned shadow paging protocols still incur high write amplification, which hurts SSD lifetime. In this paper, we propose an embedded transaction protocol, DiffTx, which differentially logs partial page updates in a write-ahead logging way and writes full page updates in a shadow paging way, aiming at low write amplification. DiffTx further improves transaction performance using two techniques. First, by clustering mapping metadata of full page updates with differential data of partial page updates in a single record, DiffTx tracks pages of each transaction at low overhead. Second, DiffTx removes the write ordering on commit by delaying the completeness check of writes, leveraging the clean-state write property of flash memory. Experiments show that DiffTx improves throughput by 25.9 percent and halves write traffic on average compared to TxFlash, a typical embedded transaction protocol for flash memory. [ABSTRACT FROM AUTHOR]

Among real-system applications of AI, the field of traffic simulation makes use of a wide range of techniques and algorithms. Especially, microscopic models of road traffic have been expanding for several years. Indeed, Multi-Agent Systems provide the capability of modeling the very diversity of individual behaviors. Several professional tools provide comprehensive sets of ready-made, accurate behaviors for several kinds of vehicles. The price in such tools is the difficulty to modify the nature of programmed behaviors, and the specialization in a single purpose, e.g. either studying resulting ows, or providing an immersive virtual reality environment. Thus, we advocate for a more exible approach for the design of multi-purpose tools for decision support. Especially, the use of geographical open databases offers the opportunity to design agent-based traffic simulators which can be continuously informed of changes in traffic conditions. Our proposal also makes decision support systems able to integrate environmental and behavioral modifications in a linear fashion, and to compare various scenarios built from different hypotheses in terms of actors, behaviors, environment and ows. We also describe here the prototype tool that has been implemented according to our design principles. [ABSTRACT FROM AUTHOR]

We present a unified system for vehicle tracking and classification which has been developed with a data-driven approach on real-world data. The main purpose of the system is the tracking of the vehicles to understand lane changes, gates transits and other behaviors useful for traffic analysis. The discrimination of the vehicles into two classes (cars vs. trucks) is also required for electronic truck-tolling. Both tracking and classification are performed online by a system made up of two components (tracker and classifier) plus a controller which automatically adapts the configuration of the system to the observed conditions. Experiments show that the proposed system outperforms the state-of-the-art algorithms on the considered data. [ABSTRACT FROM AUTHOR]

The calculating method for fuel consumption (FC) was put forward and calibrated and the characteristics of the fuel consumption on intersections were analyzed based on 18 groups of vehicular operating data which were acquired from the test experiment of a single vehicle along the urban arterial roads in Harbin, China. The results obtained show that 50.36% of the fuel consumption for the test vehicle was used at the area of intersections compared with 28.9% of the influencing distance and 68.5% of the running time; and 78.4% of them was burnt at the stages of acceleration and idling. Meanwhile, the type (c) of the vehicular operating status was illustrated to be the worst way of reducing fuel consumption, the causes were analyzed and four improvement strategies were put forward. [ABSTRACT FROM AUTHOR]

The transport modelling represents one of the main line of the transport engineering. The transport model of observed area is perceived in its final step. The related professions (traffic planners, ecologists, municipality officials …) need specific data such as traffic volumes, modal split, travel time, travel cost … Output data has to be exported to the different time intervals. A transport engineer must cover whole process of transport model creating. Generally it is a long term process. The transport modelers must detailed specify the conditions which are needs to transport model using. The output data must be calibrated and validated. Mentioned two steps are oftentimes underestimated mainly for financial reasons. The transport models are classified by the scale modeled data. The practical application of the transport forecast theory is not easy. The transport engineer must walk through the principled problems. The article is aimed to the description of real two transport models of bordering Slovak regions. Main part of the article describe the transport data gathering system and its implementation in to the transport forecast calculation (calibration, validation). [ABSTRACT FROM AUTHOR]

This paper proposes a fuzzy logic model for assessing the mobility of road transport networks from a network perspective. Two mobility attributes are introduced to account for the physical connectivity and road transport network level of service. The relative importance of the two mobility attributes has been established through the fuzzy inference reasoning procedure that was implemented to estimate a single mobility indicator. The advantage of quantifying two mobility attributes is that it improves the ability of the mobility indicator developed to assess the level of mobility under different types of disruptive events. A case study of real traffic data from seven British cities shows a strong correlation between the proposed mobility indicator and the Geo distance per minute, demonstrating the applicability of the proposed fuzzy logic model. The second case study of a synthetic road transport network for Delft city illustrates the ability of the proposed network mobility indicator to reflect variation in the demand side (i.e. departure rate) and supply side (i.e. network capacity and link closure). Overall, the proposed mobility indicator offers a new tool for decision makers in understanding the dynamic nature of mobility under various disruptive events. [ABSTRACT FROM AUTHOR]

Traffic state estimation is a key problem with considerable implications in modern traffic management. A simple, general, and complete approach to the design of urban network traffic state and phase estimator has been developed in this paper. A uniform traffic state dynamic estimation method structure is designed which consists of three steps. (1) Floating-car data and radio frequency identification data preprocessing method is proposed to remove the abnormal data and finish the map matching process. (2) Section speed estimation method is proposed based on the degree of confidence. (3) Traffic phase identification method is proposed based on the estimated section speed. A number of simulation and field investigations have been conducted to test the estimator performance. The investigation results indicate that the proposed approach is of high accuracy and smoothness on the section speed estimation and effectively eliminates the influence of abnormal data fluctuations and insufficient data. And the traffic phase identification method can effectively filter out the abnormal distortion of estimated section speed around the threshold value and modify the phase step of traffic status caused by abnormal data. [ABSTRACT FROM AUTHOR]

The breakdown phenomenon at freeway has both practical and theoretical interests. Many researchers have done great efforts on it. As a result, the stochastic concept for freeway capacity was defined and some approaches were developed. However, due to the fact that it is impossible to observe the value of the capacity, it is difficult to obtain the exact traffic capacity distribution. In this paper, traffic operational degradation concept was introduced and degradation model was developed to describe traffic breakdown process. A stochastic degradation parameter is introduced to explain the phenomena of traffic breakdown occurring at various flow rate levels. Traffic breakdown probability and traffic capacity distribution were derived theoretically. Based on the surveyed data, the experimental model was obtained by using the bootstrap method. The results indicate that Weibull distribution function can be used to describe traffic capacity stochastic nature. [ABSTRACT FROM AUTHOR]

Introduction Large truck crashes have significantly declined over the last 10 years, likely due, in part, to the increased use of onboard safety systems (OSS). Unfortunately, historically there is a paucity of data on the real-world efficacy of these devices in large trucks. The purpose of this study was to evaluate the two OSSs, lane departure warning (LDW) and roll stability control (RSC), using data collected from motor carriers. Method A retrospective cohort approach was used to assess the safety benefits of these OSSs installed on Class 7 and 8 trucks as they operated during normal revenue-producing deliveries. Data were collected from 14 carriers representing small, medium, and large carriers hauling a variety of commodities. The data consisted of a total of 88,112 crash records and 151,624 truck-years that traveled 13 billion miles over the observation period. Results The non-LDW cohort had an LDW-related crash rate that was 1.917 times higher than the LDW cohort ( p = 0.001), and the non-RSC cohort had an RSC-related crash rate that was 1.555 times higher than the RSC cohort ( p < 0.001). Conclusions The results across analyses indicated a strong, positive safety benefit for LDW and RSC under real-world conditions. Practical applications The results support the use of LDW and RSC in reducing the crash types associated with each OSS. [ABSTRACT FROM AUTHOR]

This paper focused on modelling Black Carbon (BC) concentrations in two busy street canyons, the Crown and Belliard Street in Brussels. The used original Operational Street Pollution Model was adapted to BC by eliminating the chemical module and is noted here as CANS BC . Model validations were performed using temporal BC data from the fixed measurement network in Brussels. Subsequently, BC emissions were adjusted so that simulated BC concentrations equalled the observed ones, averaged over the whole period of simulation. Direct validations were performed for the Crown Street, while BC model calculations for the Belliard Street were validated indirectly using the linear relationship between BC and NO x . Concerning the Crown Street, simulated and observed half-hourly BC concentrations correlated well ( r = 0.74) for the period from July 1st, 2011 till June 30th, 2013. In particular, CANS BC performed very well to simulate the monthly and diurnal evolutions of averaged BC concentrations, as well as the difference between weekdays and weekends. This means that the model correctly handled the meteorological conditions as well as the variation in traffic emissions. Considering dispersion, it should however be noted that BC concentrations are better simulated under stable than under unstable conditions. Even if the correlation on half-hourly NO x concentrations was slightly lower ( r = 0.60) than the one of BC, indirect validations of CANS BC for the Belliard Street yielded comparable results and conclusions as described above for the Crown Street. Based on our results, it can be stated that CANS BC is suitable to accurately simulate BC concentrations in the street canyons of Brussels, under the following conditions: (i) accurate vehicle counting data is available to correctly estimate traffic emissions, and (ii) vehicle speeds are measured in order to improve emission estimates and to take into account the impact of the turbulence generated by moving vehicles on the local dispersion of BC. [ABSTRACT FROM AUTHOR]

Detection of anomalous events from video streams is a challenging problem in many video surveillance applications. One such application that has received significant attention from the computer vision community is traffic video surveillance. In this paper, a Lossy Count based Sequential Temporal Pattern mining approach (LC-STP) is proposed for detecting spatio-temporal abnormal events (such as a traffic violation at junction) from sequences of video streams. The proposed approach relies mainly on spatial abstractions of each object, mining frequent temporal patterns in a sequence of videoframes toform a regular temporal pattern. In order to detect each object in every frame, the input video is first pre-processed by applying Gaussian Mixture Models. After the detection of foreground objects, the tracking is carried out using block motion estimation by the three-step search method. The primitive events of the object are represented by assigning spatial and temporal symbols corresponding to their location and time information. These primitive events are analyzed toform a temporal pattern in a sequence of videoframes, representing temporal relation between various object's primitive events. This is repeated for each window of sequences, and the support for temporal sequence is obtained based on LC-STP to discover regular patterns of normal events. Events deviating from these patterns are identified as anomalies. Unlike the traditional frequent item set mining methods, the proposed method generates maximal frequent patterns without candidate generation. Furthermore, experimental results show that the proposed method performs well and can detect video anomalies in real traffic video data. [ABSTRACT FROM AUTHOR]

In this paper, we develop a preventive congestion control mechanism applied at highway entrances and devised for Intelligent Transportation Systems (ITS). The proposed mechanism provides a vehicular admission control, regulates input traffic and performs vehicular traffic shaping. Our congestion control mechanism includes two classes of vehicles and is based on a specific priority ticket pool scheme with queue-length threshold scheduling policy, tailored to vehicular networks. In an attempt to cope with vehicular traffic fluctuation, we integrate an adaptation ticket generation module. The enhanced mechanism is then able to detect road congestion and provide performance metrics to road site units at the highway entrance. Mathematical modeling based on the embedded Markov chain method highlights the benefits of the proposed scheme. Performance analysis demonstrates the mechanism capability to accurately characterize road traffic congestion conditions, shape vehicular traffic and reduce travel time. [ABSTRACT FROM AUTHOR]

Abstract: Large control centers, as needed in road traffic, typically manage highly dynamic environments. They process vast amounts of information from heterogeneous data sources about a large number of real-world objects, which are anchored in time and space. In such systems, human operators are vulnerable to information overload and, thus, may fail to be aware of the overall meaning of available information and its implications. With BeAware, we propose a software framework that supports the development of situation awareness applications for control centers. The contribution of this paper is twofold: First, we integrate existing ontologies with spatio-temporal reasoning concepts, focusing on extensibility. We introduce meta-modeling concepts that allow us to assess and project situations and actions using semantic web technology. Second, we compare the runtime performance of the situation comprehension capabilities of a generic, ontology-driven implementation and a domain-specific relational-database-backed implementation, and discuss the strengths and shortcomings of each approach. [Copyright &y& Elsevier]

Accident data could be of various types. Count data being one of them. Effective accident analysis requires classification of count data with respect to geometric and control parameters. This paper focuses on the analysis of the effects of road width, land use, lane marking and traffic control on safety of unsignalized intersections. Intersection lying in non-urban areas with single line marking and no control on minor road were found to be the most vulnerable. The results are discussed and the recommendations are provided. [ABSTRACT FROM AUTHOR]

SUMMARY The precise estimation of the annual average daily traffic (AADT) is a task of significant interest for many transportation authorities and Departments of Transportation. In this study, three methods are developed to improve the assignment of short-term counts to seasonal adjustment factor (SAF) groupings: the traditional functional classification, a discriminant analysis (DA), and a new statistical approach based on a weighted coefficient of variation (WCV). The data analyzed within this study are generated from all available continuous counters within the State of Ohio between 2002 and 2006. The analysis is conducted using SAFs that are separately calculated for the total volume and the directional specific volumes of a site. The results show that the directionally based assignment errors are statistically lower at a 95% confidence interval when compared with those generated by the total volume analysis. It is also found that the hourly time-of-day factors are more important in the assignment process than the average daily traffic. The directionally based WCV produces a decline in the average mean absolute percentage error (MAPE) over the roadway functional classification by 58% and in the standard deviation of the absolute error (SDAE) by 70%. On the contrary, the directionally based DA lowers the MAPE and the SDAE by 35% and 60%, respectively. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In a traffic-aware route search (TARS), the user provides start and target locations and sets of search terms. The goal is to find the fastest route from the start location to the target via geographic entities (points of interest) that correspond to the search terms, while taking into account variations in the travel speed due to changes in traffic conditions, and the possibility that some visited entities will not satisfy the search requirements. A TARS query may include temporal constraints and order constraints that restrict the order by which entities are visited. Since TARS generalizes the Traveling-Salesperson Problem, it is an NP-hard problem. Thus, it is unlikely to find a polynomial-time algorithm for evaluating TARS queries. Hence, we present in this paper three heuristics to answer TARS queries-a local greedy approach, a global greedy approach and an algorithm that computes a linear approximation to the travel speeds, formulates the problem as a Mixed Integer Linear Programming (MILP) problem and uses a solver to find a solution. We provide an experimental evaluation based on actual traffic data and show that using a MILP solver to find a solution is effective and can be done within a limited running time in many real-life scenarios. The local-greedy approach is the least effective in finding a fast route, however, it has the best running time and it is the most scalable. [ABSTRACT FROM AUTHOR]

Abstract: We investigate replacing travel diaries with sets of call detail records (CDRs) as input data for an agent-oriented traffic simulation. Synthetic CDRs are used in order to study the effect of this substitution in isolation. We introduce an experimental design where a detailed synthetic transportation scenario with individual simulated travellers is combined with a simple model of mobile phone usage to collect synthetic CDRs. This set of artificial CDRs is then considered as input for another instance of the same traffic model, disregarding all other information. We analyse to what degree the model reproduces the base case, depending on the frequency of the available CDRs. [Copyright &y& Elsevier]

Abstract: Vehicle ownerships integral part of modern life and traffic congestion an unavoidable inconvenience. The Western countries have a far better control on the pace of number of vehicles on a road matched with supporting infrastructure. In contrast, cash strapped underdeveloped countries have a poorly built and scarce number of main roads with problems compounded by soft car loans, leases and other discounts. As a result several developing countries have been inundated with peripheral complications such as pollution and congestion undermining their economy with enormous energy bills negatively impacting respective economy. Case in point is Pakistan, where depilating infrastructure or absence outright thereof and ever more number of vehicles on the road presents a unique and highly complicated problem. One can term traffic in Sub-Continent as controlled chaos and we plan to develop an organized solution from the chaos. This presents a unique challenge in traffic management. We have developed a smart phone application when the phone is placed in vehicles, provides data for the origin and destination routes. Taking 6 parameters, which we believe mostly impacts the destination arrival time for the driver in Pakistan we propose to develop a model supported by empirical data that will enable driver to select weather they are interested in economy of fuel or economy of time in reaching their destination. We propose to plot time it takes to reach destination versus the 6 factors that determines destination arrival time. The curve will be generated for each route and from the graph median time, standard deviation as well as confidence interval will be computed. Large data will be collected and statistical analysis will be performed to verify the integrity of the model. [Copyright &y& Elsevier]

Abstract: Traffic demand can be highly correlated to the exogenous factors that exist outside the road system under study. Factors like day of week, presence of vacation or salary disbursement or economic parameters are readily available and may affect traffic flow between two countries. Collection of traffic data on a consistent basis is a cumbersome process in terms of time and resources. Considering these two factors in mind, this paper investigated the feasibility of using exogenous factors with Average Annual Daily Traffic (AADT). It was found that inclusion of AADT for traffic prediction is beneficial and further analysis will be done in the future with detailed traffic data. [Copyright &y& Elsevier]

In this paper we present a formal framework for modelling a trajectory data warehouse (TDW), namely a data warehouse aimed at storing aggregate information on trajectories of moving objects, which also offers visual OLAP operations for data analysis. The data warehouse model includes both temporal and spatial dimensions, and it is flexible and general enough to deal with objects that are either completely free or constrained in their movements (e.g., they move along a road network). In particular, the spatial dimension and the associated concept hierarchy reflect the structure of the environment in which the objects travel. Moreover, we cope with some issues related to the efficient computation of aggregate measures, as needed for implementing roll-up operations. The TDW and its visual interface allow one to investigate the behaviour of objects inside a given area as well as the movements of objects between areas in the same neighbourhood. A user can easily navigate the aggregate measures obtained from OLAP queries at different granularities, and get overall views in time and in space of the measures, as well as a focused view on specific measures, spatial areas, or temporal intervals. We discuss two application scenarios of our TDW, namely road traffic and vessel movement analysis, for which we built prototype systems. They mainly differ in the kind of information available for the moving objects under observation and their movement constraints. [ABSTRACT FROM AUTHOR]

Besides knowing that a problem exists, traffic managers and prediction systems need to know its context. Here, the authors discuss how to extend current ITS technologies to capture and process such information. [ABSTRACT FROM AUTHOR]

Abstract: In 2010, the XIX Commonwealth Games (CWG-2010) were held in India for the first time at Delhi and involved 71 commonwealth nations and dependencies with more than 6000 athletes participating in 272 events. This was the largest international multi-sport event to be staged in India and strict emission controls were imposed during the games in order to ensure improved air quality for the participating athletes as a significant portion of the population in Delhi is regularly exposed to elevated levels of pollution. The air quality control measures ranged from vehicular and traffic controls to relocation of factories and reduction of power plant emissions. In order to understand the effects of these policy induced control measures, a network of air quality and weather monitoring stations was set-up across different areas in Delhi under the Government of India's System of Air quality Forecasting And Research (SAFAR) project. Simultaneous measurements of aerosols, reactive trace gases (e.g. NO x , O3, CO) and meteorological parameters were made before, during and after CWG-2010. Contrary to expectations, the emission controls implemented were not sufficient to reduce the pollutants, instead in some cases, causing an increase. The measured pollutants regularly exceeded the National Ambient Air Quality limits over the games period. The reasons for this increase are attributed to an underestimation of the required control measures, which resulted in inadequate planning. The results indicate that any future air quality control measures need to be well planned and strictly imposed in order to improve the air quality in Delhi, which affects a large population and is deteriorating rapidly. Thus, the presence of systematic high resolution data and realistic emission inventories through networks such as SAFAR will be directly useful for the future. [Copyright &y& Elsevier]

Abstract: This paper develops two soft computing models, i.e., the multilayer feedforward network (MFN) based model and the adaptive-network-based fuzzy inference system (ANFIS) based model, to mine the traffic speed patterns/trends for a road link using the sparse historical probe vehicles (PVs) data at the same link. The two models and an additional naive arithmetical average model are tested on the field datasets obtained in some Beijing (China)'s urban expressways. The results illustrate that the soft computing based models have higher robustness to the problem of missing data and their generalization capabilities are better than the arithmetic average model. Comprehensively considering all the performance metrics suggest that the ANFIS offers the best model of traffic trends in studied links. Furthermore, the traffic trends produced by ANFIS provide us the opportunities to identify some meaningful hidden traffic speed patterns. The missing data's influence on the mined traffic speed patterns is also investigated. It is found that the reliability of mined traffic speed patterns decreases with the increasing of the missing data's percentage. Nevertheless, ANFIS based model shows great robustness to the missing data problem. [Copyright &y& Elsevier]

Query processing over uncertain data streams, in particular top-$(k)$ query processing, has become increasingly important due to its wide application in many fields such as sensor network monitoring and internet traffic control. In many real applications, multiple top-$(k)$ queries are registered in the system. Sharing the results of these queries is a key factor in saving the computation cost and providing real-time response. However, due to the complex semantics of uncertain top-$(k)$ query processing, it is nontrivial to implement sharing among different top-$(k)$ queries and few works have addressed the sharing issue. In this paper, we formulate various types of sharing among multiple top-$(k)$ queries over uncertain data streams based on the frequency upper bound of each top-$(k)$ query. We present an optimal dynamic programming solution as well as a more efficient (in terms of time and space complexity) greedy algorithm to compute the execution plan of executing queries for saving the computation cost between them. Experiments have demonstrated that the greedy algorithm can find the optimal solution in most cases, and it can almost achieve the same performance (in terms of latency and throughput) as the dynamic programming approach. [ABSTRACT FROM AUTHOR]

Abstract: The influence of biomass burning (BB) aerosols, whether of regional or local origin, on fine aerosol levels in the Barcelona urban environment (Spain) was investigated. High-time resolved data on light-absorbing aerosols and inorganic tracers in PM2.5 were combined to this end during a dedicated sampling campaign carried out in winter 2011. The evaluation of PM inorganic components and equivalent black carbon evidenced that local-scale BB emissions were not detectable, whereas a source of K, different to vehicular traffic (road dust) and construction/demolition dust re-suspension, was detectable in the urban area. Source apportionment analysis evidenced the contribution from one source traced by S (62% of the source profile) and K (16% of the source profile), which was interpreted as regional-scale transport of secondary aerosols including BB contributions. The S/K ratio for this source (S/K = 4.4) indicated transport of the polluted air masses, as occurs from the rural areas towards the Barcelona urban environment. On average for the study period, the contribution of K-related aerosols from regional BB to PM2.5 levels in the urban environment was estimated as 1.7 μg/m3 as a daily mean, accounting for 8% of the PM2.5 mass during the winter period under study. The contribution from this source to urban aerosols should be lower on the annual scale. [Copyright &y& Elsevier]

Abstract: We developed a web-based route planning tool for cyclists in Montreal, Canada, using spatial monitoring data for ambient nitrogen dioxide (NO2). With this tool, we estimated exposures to NO2 along shortest routes and lower exposure alternatives using origin-destination survey data. On average, exposures were estimated to be lower by 0.76ppb (95% CI: 0.72, 0.80) relative to the shortest route, with decreases of up to 6.1ppb for a single trip. Cumulative exposure levels (ppbkm) decreased by approximately 4%. In general, the benefits of decreased exposure could be achieved with little increase (less than 1km) in the overall route length. [Copyright &y& Elsevier]

GPS data sets of several vehicles on the road are obtained via wireless network, from which specific sections of data are picked up by filtering algorithm. Then, a variety of fusion algorithms are applied to the several sets of specific sections of data to remove the error data, to guarantee the veracity of the fused result. On the basis of valid data, traffic conditions of this section can be acquired through the smart identification algorithm, which will provide the real-time traffic information. The method presented in this paper is simple and reliable. With good performance on the experimental data, it opens a new door for information collection of urban road. [ABSTRACT FROM AUTHOR]