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In order to improve road safety, modern vehicles are equipped with smart sensors and Vehicle-to-Everything (V2X) communication technologies that facilitate the exchange of data (e.g., location, speed, road hazards) with other vehicles, the road infrastructure, and pedestrians, thus extending the range of perception beyond the capabilities of on-board sensors. All these data can be processed by a Collision Avoidance service deployed in a mobile edge computing (MEC) platform to guarantee low latency in the detection and localization of road hazards. In this paper, we propose a Collision Avoidance service based on Vanetza, an open-source ETSI ITS protocol stack, and demonstrate its operation using already developed experimental On-Board Units (OBUs) that communicate with the Collision Avoidance service over UDP and MQTT to exchange ETSI ITS messages encoded in ASN.1 and JSON format, respectively. We measure the application-level latency and we observe the benefit of our proposed approach in terms of latency reduction by a factor of 12 with respect to the literature. © 2022 IEEE.

Vehicle-to-everything (V2X) communication is contributing towards the realization of futuristic vehicular networks such as Internet-of-Vehicles (IoV). The IoV is expected to usher in a new direction of intelligence and networking to achieve the goal of intelligent transport systems, which rely on the secure exchange of messages between vehicles and infrastructure. However, the transmission of false/incorrect data by malicious vehicles may cause serious damages on road safety. Therefore, it is crucial to detect safety-threatening incorrect information and mitigate potentially detrimental effects on road users. In this paper, we propose a reinforcement learning (RL)-based misbehavior detection approach for V2X scenarios. In our method, the RL-based detection model processes V2X data broadcast by vehicles as time-series at the roadside units, and classifies incoming data as misbehaving or genuine. We evaluate the proposed RL-based approach for detection of various attack types using an open-source dataset, and compare its performance against recent work in misbehavior detection. Our scheme is able to detect all types of misbehavior with a superior recall of 0.9970 and an F1 score of 0.9845, yielding a significant improvement over the benchmarks. Our research outcomes further reveal that misbehaving vehicles can be detected with a great accuracy of 0.9882 by exploiting real-time V2X information. © 2022 IEEE.

Objective: To investigate the burden of pedestrian injuries, including pedestrian fall injuries (PFI), compared to other transport-related injuries in Sweden and document their characteristics in terms of demographics, causes, type of injuries, and severity level with a focus on long-term consequences. Methods: Data were retrieved from the national Swedish Traffic Accident Data Acquisition register. A total of 361,531 fatalities and injuries were reported by emergency hospitals during 2010–2019, of which 127,804 were pedestrians (35%). We assessed the magnitude of PFIs and conducted comparative analyses to assess differences compared to other types of road users regarding sex, age, severity level, injury circumstances, hospital care, causes of accidents, and type of injuries. Results: Pedestrians were the second largest group of traffic-related deaths in Sweden after car occupants and accounted for just over a quarter of all fatal accidents in the road traffic environment. Of the total number of pedestrian fatalities, three out of four have been in collision accidents and the others in fall-related accidents. In terms of injuries, pedestrians were the largest group among all road users, regardless of the type of accident. PFIs accounted for a third of all injuries in the road traffic environment and nearly half of all injuries resulting in permanent medical impairment (i.e., 2.2 times more long-term consequences among PFIs compared to injured car occupants). Females (particularly middle-aged and older) and older adults were overrepresented, and most PFIs occurred on urban and municipal roads. The causes were often related to maintenance (e.g., slippery surfaces such as ice, snow, leaves or gravel together with uneven pavements and roads are the cause three out of four of PFIs). Among collision injuries, the representation was almost equal for sex and age. Conclusions: Injuries and fatalities among pedestrians are a considerable issue in the road traffic environment in Sweden. Contrary to other traffic groups, the incidence has not decreased over time, meaning that this issue must be met with specific measures and address the specific risk factors they are associated with. Practical Application: Including fall accidents in the definition of traffic accidents increases the chances of getting better information about the accidents and taking preventive measures.

While developing technology and industrialization factors increase production, they also lead to an increase in energy consumption at the same time. The transportation sector, which is a branch of industrialization, has an important place on the basis of sector in energy consumption. In this study, energy consumption in the transportation sector has been examined, especially in the USA, where freight transport by road has an important place, it has a high potential. Within the scope of the study, energy consumption prediction modeling is made by using artificial neural networks (ANN) adaptive neuro-fuzzy inference system (ANFIS) and Simple Membership Functions and Fuzzy Rules Generation Technique (Fuzzy SMRGT) from artificial intelligence techniques. Artificial intelligence methods were also compared with multivariate linear regressions and multivariate regressions types. Interaction, pure quadratic and quadratic methods were used as multiple nonlinear regression. In the modeling, energy consumption was estimated by taking the highway network length, the number of vehicles and the number of drivers as independent variables. When comparing the prediction models, the determination coefficient (R-2), the root-mean-square error (RMSE) and the average percentage error (APE) performance criteria were taken into consideration. In addition, it was shown that the models performed well based on the metrics in the testing phase. When the performances of the models were compared, it was seen that two models obtained remarkable results. According to performance criteria, the best model is obtained by Fuzzy SMRGT and ANFIS methods. R-2, RMSE, APE values of the best models are Fuzzy SMRGT (0,978; 208,08; % 0,79) and ANFIS (0,969; 282,69; % 1,06), respectively. The Fuzzy SMGRT and ANFIS models have slightly better performance than MLR, MR, ANN models. It is aimed to use the developed models in the evaluation and management of transportation and energy policies.

Autonomous vehicles are expected to display human-like behavior, at least to the extent that their decisions can be intuitively understood by other road users. If this is not the case, the coexistence of manual and autonomous vehicles in a mixed environment might affect road user interactions negatively and might jeopardize road safety. To this end, it is highly important to design algorithms that are capable of analyzing human decision-making processes and of reproducing them. In this context, lane-change maneuvers have been studied extensively. However, not all potential scenarios have been considered, since most works have focused on highway rather than urban scenarios. We contribute to the field of research by investigating a particular urban traffic scenario in which an autonomous vehicle needs to determine the level of cooperation of the vehicles in the adjacent lane in order to proceed with a lane change. To this end, we present a game theory-based decision-making model for lane changing in congested urban intersections. The model takes as input driving-related parameters related to vehicles in the intersection before they come to a complete stop. We validated the model by relying on the Co-AutoSim simulator. We compared the prediction model outcomes with actual participant decisions, i.e., whether they allowed the autonomous vehicle to drive in front of them. The results are promising, with the prediction accuracy being 100% in all of the cases in which the participants allowed the lane change and 83.3% in the other cases. The false predictions were due to delays in resuming driving after the traffic light turned green. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This work was supported by the Austrian Ministry for Climate Action, Environment, Energy, Mobility, Innovation, and Technology (BMK) Endowed Professorship for Sustainable Transport Logistics 4.0., IAV France S.A.S.U., IAV GmbH, Austrian Post AG, and the UAS Technikum Wien. It was additionally supported by the Zero Emission Roll-Out?Cold Chain Distribution_877493.

The environment of the vehicle can significantly influence the driving situation. Which conditions lead to unsafe driving behaviour is not always clear, also not to a human driver, as the causes might be unconscious, and thus cannot be revealed by expert interviews. Therefore, it is important to investigate how such situations can be reliably detected, and then search for their triggers. It is conceivable that such insecure situations (e.g. near-accidents, U-turns, avoiding obstacles) are reflected, for example, as anomalies in the movement trajectories of road users.Collecting real world traffic data in driving studies is very time consuming and expensive. However, a lot of roads or public areas are already monitored with video cameras. In addition, nowadays more and more of such video data is made publicly available over the internet so that the amount of free video data is increasing. This research will exploit the use of such kind of opportunistic VGI. In the paper the first step of an automatic analysis are presented, namely: to introduce a real time processing pipeline to extract road user trajectories from surveillance video data.

the article considers a series of issues connected with the assessment of orderliness of regional road safety provision systems. Success in the improvement of manufacturing processes management depends a lot on the level of system orderliness. This article presents a method of statistical modeling of the orderliness of regional road safety provision systems by assessing the information entropy in the cause-effect chain ≪Population ( P ) - The number of vehicles ( N ) - The number of road accidents ( DTP ) - The number of deaths in road accidents ( PG )≫. It was found that the range of actual values of relative entropy H n for regional road safety provision systems is [0.7161; 0.9787]. According to the results of held statistical modeling, the classification of regions of the Russian Federation by the orderliness of road safety provision systems processes was developed. During the discussion of the results an assumption, that information entropy of road safety provision systems is determined by resource availability of industry, was made. © Published under licence by IOP Publishing Ltd.

In this study, we present a bilevel programming model in which upper level is defined as a biobjective problem and the lower level is considered as a stochastic user equilibrium assignment problem. It is clear that the biobjective problem has two objectives: the first maximizes the reserve capacity whereas the second minimizes performance index of a road network. We use a weighted-sum method to determine the Pareto optimal solutions of the biobjective problem by applying normalization approach for making the objective functions dimensionless. Following, a differential evolution based heuristic solution algorithm is introduced to overcome the problem presented by use of biobjective bilevel programming model. The first numerical test is conducted on two-junction network in order to represent the effect of the weighting on the solution of combined reserve capacity maximization and delay minimization problem. Allsop & Charlesworth's network, which is a widely preferred road network in the literature, is selected for the second numerical application in order to present the applicability of the proposed model on a medium-sized signalized road network. Results support authorities who should usually make a choice between two conflicting issues, namely, reserve capacity maximization and delay minimization. © 2019 Ozgur Baskan et al.

Objective: Analyze the road crashes in Cartagena (Colombia) and the factors associated with the collision and severity. The aim is to establish a set of rules for defining countermeasures to improve road safety. Methods: Data mining and machine learning techniques were used in 7894 traffic accidents from 2016 to 2017. The severity was determined between low (84%) and high (16%). Five classification algorithms to predict the accident severity were applied with WEKA Software (Waikato Environment for Knowledge Analysis). Including Decision Tree (DT-J48), Rule Induction (PART), Support Vector Machines (SVMs), Naïve Bayes (NB), and Multilayer Perceptron (MLP). The effectiveness of each algorithm was implemented using cross-validation with 10-fold. Decision rules were defined from the results of the different methods. Results: The methods applied are consistent and similar in the overall results of precision, accuracy, recall, and area under the ROC curve. Conclusions: 12 decision rules were defined based on the methods applied. The rules defined show motorcyclists, cyclists, including pedestrians, as the most vulnerable road users. Men and women motorcyclists between 20–39 years are prone in accidents with high severity. When a motorcycle or cyclist is not involved in the accident, the probable severity is low. © 2019, Springer Nature Switzerland AG. for first author was covered by (CEIBA)?Gobernaci?n de Bol?var (Colombia). We thank the Administrative Department of Traffic and Transportation (DATT) in the accompaniment and support of the information required for this investigation.

Accurate predictions of the characteristics of urban streets in particular with respect to the typical traffic situations are crucial for numerous real world applications such as navigation, scheduling of logistic and public transportation services as well as high-level planning of infrastructure which may include planning of construction sites or even changes of the road topology. However, this information may be hard to obtain, especially in complex urban road networks where interdependencies between roads are highly present. In addition, accurate and recent traffic data is not always available, especially for uncommon situations like large-scale public events, traffic accidents or construction sites. This work demonstrates how to employ historical traffic datasets in conjunction with other, infrastructure related data, to derive a deeper understanding of urban traffic behaviour. In particular this paper provides the following contributions: (1) the generation of meaningful features to describe the segments in urban road networks; (2) an unsupervised machine learning approach that identifies similar segments based on those features; (3) a supervised approach to predict unknown features of the segments and, finally, (4) an extensive evaluation of the extracted road characteristics and the proposed methods using real-world data. The resulting clusters reveal the similarities of the street segments and give a different perspective on the road network and the traffic situation, respectively. The experiments on the classification approach demonstrate that unknown features can be predicted with a good quality.

SMARTCOMP 2018 IEEE International Conference on Smart Computing, Taormina, ITALIE, 18-/06/2018 - 20/06/2018; The ability to detect critical spots in transportation networks is fundamental to improve traffic operations and road-network resilience in smart cities. Real-Time monitoring of these networks, especially in very large metropolitan areas, is a compelling challenge due to the complexity of computing robustness metrics. This paper presents a framework for identifying vulnerabilities in very-large road networks. The framework adopts graph-based modeling of road networks and exploits big-data techniques and technologies for processing such large and complex graphs. First, we use the framework to prove the existence of a significant correlation between global efficiency and betweenness centrality. Then, we focus on an efficient algorithm, integrated in the framework, to rank the nodes according to this metric for finding potential vulnerabilities of a road network. To keep computation time under a 'quasi' real-Time threshold, a fast, requirement-driven, approximated strategy for computing betweenness centrality is adopted. The evaluation shows that the algorithm, integrated in the framework, exhibits a very good approximation for the most critical nodes, thus being well-suited for on-line operational monitoring.

Disruptive events occur on road networks on a daily basis and affect traffic flow. Resilience analysis aims to assess the consequences of such disruptions by quantifying the ability of a network to absorb and react to adverse events. In this paper, we advance a methodological approach based on resilience stress testing and a dynamic mesoscopic simulator. We aim to identify and rank the links most critical to the overall performance of the road network, taking into account the dynamic properties of road traffic and focusing on day-to-day disruptions. As a metric to quantify road network performance in the presence of such disruptions, we use the increase in overall travel cost. We then compare our approach with purely topological approaches. We discuss the advantages and drawbacks of the different analyzed metrics. We prove that link ranking varies greatly depending on the metric. Specifically, the proposed stress testing methodology can produce very accurate results by taking into account demand and congestion, but requires computationally intensive simulations, being therefore prohibitive even on medium-sized networks. Conversely, purely static topological metrics can be inaccurate if they do not take into account traffic demand and network dynamics. Our study highlights the need for joining traditional traffic-agnostic topological resilience analysis with demand-aware dynamic stress testing techniques.

Research reveals that visual information forms the major portion of the received data for driving. At night -owing to the, sometimes scarcity, sometime inhomogeneity of light- the human physiology and psychology experiences a dramatic alteration. It is found that although the likelihood of accident occurrence is higher during the day due to heavier traffic, the most fatal accidents still occur during night time. How can road safety be improved in limited lighting conditions using DMD-based high resolution headlamps? DMD-based pixel light systems, utilizing HID and LED light sources, are able to address hundreds of thousands of pixels individually. Using camera information, this capability allows 'glare-free' light distributions that perfectly adapt to the needs of all road users. What really enables these systems to stand out however, is their on-road image projection capability. This projection functionality may be used in co-operation with other driver assistance systems as an assist feature for the projection of navigation data, warning signs, car status information etc. Since contrast sensitivity constitutes a decisive measure of the human visual function, here is then a core question: what distributions of luminance in the projection space produce highly visible on-road image projections? This work seeks to address this question. Responses on sets of differently illuminated projections are collected from a group of participants and later interpreted using statistical data obtained using a luminance camera. Some aspects regarding the correlation between contrast ratio, symbol form and attention capture are also discussed.

The article considers issues of assessment of the relation between road accidents severity and psychoemotional state of Russian society. In the context of the hypothesis about the significant impact of people's awareness of the value of life on their ordinary behavior, four subtasks were solved. Firstly, the difference in road accidents severity between all road accidents and road accidents that happened in alcohol intoxication state was established. Secondly, indicators of the psychoemotional state of people from different Russian regions were identified. Thirdly, dependencies Road accidents severity = f(Psychoemotional state of people from Russian region) for two cases - All road accidents and Road accidents that happened in alcohol intoxication state were defined. Finally, it was found that degree of impact of alcohol intoxication on road accidents severity depends on the psychoemotional state of people from different Russian regions. Produced results can be used in the development of regional programs of road safety management taking into account features of the psychoemotional background in different subjects of Russia. © The Authors, published by EDP Sciences, 2018.

Early detection of congestion is essential to traffic management. Conventional loop or microwave detectors provide flow and speed information but involve significant delays and they offer sparse coverage. To improve this situation, other data sources must be used. This paper investigates the potential use of floating car data (FCD) and ETC (electronic toll collection). These are trajectory data along the routes. DSRC (dedicated short-range communication) data is also collected for the same routes. Assuming that the fused data is able to calculate accurate road operation characteristics, the vehicle number (OBU-ID) is used as the key parameter for ETC data and DSRC data fusion. Road speed can also be obtained by the analysis of FCD. These speeds can be analyzed in order to obtain the road link speed of temporal and spatial variation. A pilot location shows that data fusion could help improve the accuracy of road characteristics. © 2018 American Society of Civil Engineers. Didi Chuxing; Shanghai International Automobile City Group Co., Ltd.; Transportation and Development Institute (T and DI) of the American Society of Civil Engineers (ASCE); Zhengzhou Yutong Group Co., Ltd.

Early detection of congestion is essential to traffic management. Conventional loop or microwave detectors provide flow and speed information but involve significant delays and they offer sparse coverage. To improve this situation, other data sources must be used. This paper investigates the potential use of floating car data (FCD) and ETC (electronic toll collection). These are trajectory data along the routes. DSRC (dedicated short-range communication) data is also collected for the same routes. Assuming that the fused data is able to calculate accurate road operation characteristics, the vehicle number (OBU-ID) is used as the key parameter for ETC data and DSRC data fusion. Road speed can also be obtained by the analysis of FCD. These speeds can be analyzed in order to obtain the road link speed of temporal and spatial variation. A pilot location shows that data fusion could help improve the accuracy of road characteristics. © 2018 American Society of Civil Engineers. Didi Chuxing; Shanghai International Automobile City Group Co., Ltd.; Transportation and Development Institute (T and DI) of the American Society of Civil Engineers (ASCE); Zhengzhou Yutong Group Co., Ltd.

The article considers the role of the transportation system in the sustainable development of cities. The article pays special attention to the development dynamics of road transport in Russia and of the emissions of air pollutants by motor vehicles. The role and significance of fuel taxes in the system of government revenue are investigated. The author analyzes the existing system of fuel taxes in the Russian Federation and concludes that the mechanism of taxing motor fuel is based on consumption and does not take into account the adverse impact on the environment and the energy content of fuels. The author provides arguments for changing the mechanism of calculating the rate of the excise tax on motor fuel so that it factors in its energy efficiency and carbon dioxide emissions. A conclusion is drawn that the introduction of the proposed adjustments would produce a substantial fiscal impact on government revenues and would make it possible to eliminate the existing distortions for two competing sources of energy (petrol versus diesel fuel); would encourage the use and development of public transport and help reduce the emissions of air pollutants. The objective of the study is to work out proposals for improving fuel taxation that would also contribute to the sustainable development of cities. © 2018 Institute of Physics Publishing. All rights reserved. The work was supported by the Russian Foundation for Basic Research, contract No. 17-22-21001.

BIGDATA 2017 IEEE International Conference on Big Data, Boston, ETATS-UNIS, 11-/12/2017 - 14/12/2017; Betweenness centrality is a metric widely used in several domains (social, biological, transportation, computer) to identify critical nodes of networks. Its exact computation is very demanding, with an O(nm) time complexity for unweighted graphs (where n is the number of nodes and m is the number of edges). Such complexity becomes an obstacle to the adoption of betweenness centrality for continuous monitoring of critical nodes in very large networks. Several solutions have been proposed to reduce computation time, mainly via parallelism, approximation or incremental recalculation. In this paper, we propose an algorithm for computing approximated values of betweenness that allows for tuning its performance on the basis of a tolerable error. The algorithm aims at reducing the number of single-source shortest-paths explorations via a pivot-based technique that exploits topological properties of graphs and clustering. It is evaluated by identifying the vulnerabilities (critical nodes) of a real-world, very-large road network. The evaluation shows that the approximation error does not significantly affect the most critical nodes, thus making the algorithm well-suited for on-line operational monitoring of road networks.

The article considers issues of identification of all-Russian and regional (on the example of Kemerovo region) road safety provision (RSP) systems orderliness. Research presents methods of identification of road safety provision system orderliness. The main idea of this methodic is the calculation of relative entropy of formation process of final result of cause-effect chain «Population – The number of vehicles – The number of road accidents – The number of victims – The number of deaths in road accidents» performance. Statistics of road accident rate during 2004…2018 was analyzed for the calculation of relative entropy. The result of research concludes that the level or road safety provision system increases in Russia generally and Kemerovo region specifically. Meanwhile the development of this process in Kemerovo region lags relatively to all-Russian one.

Date of Conference: 12-15 Dec. 2016 Conference name: IEEE 16th International Conference on Data Mining Workshops (ICDMW), 2016 While research on time-varying graphs has attracted recent attention, the research community has limited or no access to real datasets to develop effective algorithms and systems. Using noisy and sparse GPS traces from vehicles, we develop a time-varying road network data set where edge weights differ over time. We present our methodology and share this dataset, along with a graph manipulation tool. We estimate the traffic conditions using the sparse GPS data available by characterizing the sparsity issues and assessing the properties of travel sequence data frequency domain. We develop interpolation methods to complete the sparse data into a complete graph dataset with realistic time-varying edge values. We evaluate the performance of time-varying and static shortest path solutions over the generated dynamic road network. The shortest paths using the dynamic graph produce very different results than the static version. We provide an independent Java API and a graph database to analyze and manipulate the generated time-varying graph data easily, not requiring any knowledge about the inners of the graph database system. We expect our solution to support researchers to pursue problems of time-varying graphs in terms of theoretical, algorithmic, and systems aspects. The data and Java API are available at: http://elif.eser.bilkent.edu.tr/roadnetwork. © 2016 IEEE.

International audience; A connected road network with N nodes and L edges has K≤L edges identified as one-way roads. In a feasible direction, these one-way roads are assigned a direction each, so that every node can reach any other [Robbins ’39]. Using O(L) preprocessing time and space usage, it is shown that all feasible directions can be found in O(K) amortized time each. To do so, we give a new algorithm that lists all the strong orientations of an undirected connected graph with m edges in O(m) amortized time each, using O(m) space. The cost can be deamortized to obtain O(m) delay with O(m2) preprocessing time and space

The routing of vehicles on road transportation networks is an area of great importance to transportation planners within scientific literature. This field includes well known and studied problems like traveling salesman problems or TSP or the more realistic asymmetric variant or ATSP, whose applications extend to other areas of transport and operations research. This work studies the effect that the asymmetry of road transportation networks, geographical location and territory have over TSP and ATSP methods. We conduct comprehensive experiments in order to assess the effects that these factors have on some of the best known algorithms for the TSP/ATSP. We demonstrate that all these factors have a significant influence in solution time and quality. Furthermore, we show that the solutions obtained with Euclidean matrices and those obtained with real distance matrices differ significantly. © 2011 Elsevier Ltd. All rights reserved., The authors would like to warmly thank Prof. Keld Helsgaun, Prof. Yuichi Nagata, Profs. Boris Goldengorin and Gerold Jager, and especially Prof. Matteo Fischetti, for facilitating the code of their great algorithms. This work is partially funded by the Spanish Ministry of Science and Innovation, under the project "SMPA Advanced Parallel Multiobjective Sequencing: Practical and Theoretical Advances" with reference DPI2008-03511/DPI. The authors should also thank the IMPIVA-Institute for the Small and Medium Valencian Enterprise, for the project OSC with references IMIDIC/2008/137, IMIDIC/2009/198 and 175 and the Polytechnic University of Valencia, for the project PPAR with reference 3147.

The objective of this paper is to study and quantify the possible impact of provision of exclusive bus lane under heterogeneous traffic condition. The quantum of increase in level of service of bus due to introduction of exclusive bus lane was determined using a simulation model of heterogeneous traffic flow. The impact of introduction of an exclusive bus lane is measured in terms of reduction in speed of other categories of motor vehicles, due to the consequent reduction in road space, over a wide range of traffic volume. The main finding of the simulation experiment is the quantification of the maximum permissible volume to capacity ratio that will ensure a level of service of C for the traffic stream comprising all the motor vehicles, except the buses, on a typical six-lane and eight-lane divided urban roads. This study is also concerned with estimation of the probable shift of the personal vehicle users to bus due to provision of exclusive bus lanes. � 2010 Springer-Verlag.

The goods movement via terminals at ports is a complex operation as it involves use of different modes of transport that includes surface and marine. In this paper, we present three simulation models which can be used to understand and evaluate these operations. The macroscopic simulation model simulates the flows in and out of terminals and ports and it is integrated with a microscopic simulation model of the adjacent road network. A terminal cost model receives inputs from these two models to evaluate the cost associated with the processing of containers inside the terminal. The use of the three models is demonstrated by evaluating the impact of terrorist attack scenarios on a container terminal in LA/LB Port complex. © 2009 IFAC. 129 134 Sponsors: IFAC Technical Committee on Transportation Systems TC 7.4 Conference code: 85831 Cited By :2

[EN] The number of road safety studies that are based on driving simulators is growing significantly. The Polytechnic University of Valencia, Spain, developed a low-cost driving simulator for the assessment, training, and rehabilitation of drivers (SE2RCO). The main objective of this research was the validation of the driving simulator so that studies about road safety and highway geometric design that considered human factors could be performed. The validation was based on continuous speed profiles collected from 28 volunteers on a 30-km-long, two-lane rural road section. The same volunteers drove through the same road section built in SE2RCO. Speed data of 79 curves and 52 tangents were selected for the analysis. Comparison of the real and simulated speeds ensured the simulator's objective validity according to average and operating speeds. Two models were developed to predict field speeds from simulated speeds. Results showed that a simulated average speed lower than approximately 90 km/h was linked to a similar real average speed. For higher simulated speeds, the average speed in the real environment was lower than the simulated one. In addition, the actual operating speed was around 5 km/h lower than the operating speed in the driving simulator. Most volunteers assessed the quality and similarity of the virtual environment compared with the real world as medium or high and assessed the driving tasks similarly, thus achieving subjective validation of the simulator., The authors thank the Polytechnic University of Valencia, which subsidized the research project CONSIM-Desarrollo de un Modelo para la Evaluacion de la Consistencia del Diseno Geometric de Carreteras Convencionales Mediante Simuladores de Conduccion. The study presented here was also part of the research project titled CASEFU-Estudio Experimental de la Funcionalidad y Seguridad de las Carreteras Convencionales, which was subsidized by the Spanish Ministry of Economy and Competitiveness and the European Social Fund.

Every year, road accidents kill more than a million people and injure more than 20 million worldwide. This paper aims to offer guidance on road safety and create awareness by pinpointing the major causes of traffic accidents. The study investigates motor vehicle crashes in the Genesee Finger Lakes Region of New York State. Frequency Pattern Growth algorithm is utilized to cultivate knowledge and create association rules to highlight the time and environment settings that cause the most catastrophic crashes. This knowledge can be used to warn drivers about the dangers of accidents, and how the consequences are worse given a specific context. For instance, a discovered rule from the data states that 'most of the crashes occur between 12:00 pm and 6:00pm'; hence, it is suggested to modify existing navigation application to warn drivers about the increase in risk factor. Scopus

Cooperative systems in traffic and transport provide that vehicles communicate wirelessly with other vehicles, infrastructure (roads and related equipment) and other users (pedestrians, VRU, etc.). Technology of cooperative system allows two-way communication: V2V - vehicle to vehicle, V2I - vehicle to infrastructure, V2U-vehicle to other users (eg. Vulnerable Road User - VRU), I2U - infrastructure to other users, etc. The application of modern ICT technologies in this area aims to creating integrated solutions in the field of road safety, energy-efficient transport, more environmentally friendly solutions, etc. Some basic approaches of cooperative architecture are presented in the paper. The paper is part of the deliveries of the project "Implementation of cooperative systems in optimizing the energy efficiency of transport in the cities."

One of the most popular intelligent transportation systems (ITS) applications is to provide real-time road traffic congestion information to the users. Traffic density is a major congestion indicator, and because its measurement is difficult, it is usually estimated from other readily measurable parameters. Several studies have explored various approaches for density estimation for homogeneous and lane-disciplined traffic conditions. However, Indian traffic is different with its heterogeneity of traffic and absence of lane discipline. Another characteristic is the lack of access control, making automated measurement of net entry into a study section difficult. An added difficulty is that the roads in India are not yet equipped with traffic sensors, leading to limitation in data collection. The present study mainly addresses the issue of estimating traffic density in the absence of automated sensors at the side roads/ramps on Indian roadways. A lumped parameter macroscopic traffic flow model has been formulated, and using this model, a model-based estimation scheme has been designed based on the Kalman filtering technique. The only data required for implementing this method in the field are the flow passing the entry location and the spot speeds of vehicles passing through the entry and exit locations. The proposed method was corroborated using data measured from a road stretch in Chennai, and the performance was found to be satisfactory. � 2014 American Society of Civil Engineers.

This paper describes a modeling methodology adopted to simulate the flow of heterogeneous traffic with vehicles of wide ranging static and dynamic characteristics. The simulation framework for the traffic-flow model was prepared in such a way that the absence of lane discipline in mixed traffic flow conditions is taken into account. A detailed structure of the proposed model is presented. Common issues related to traffic simulation such as vehicle generation, logics for vehicular movement, etc., are described in detail in the context of heterogeneous traffic conditions. The paper also discusses the procedures adopted for validation of the proposed model and their outcomes. Finally, the details of application of the model to study the traffic flow characteristics on urban roads are also presented. Journal of Transportation Engineering ? ASCE.

The results of assessment of damage from the road accident rate in subjects of the Siberian Federal District (SFD) of the Russian Federation are presented in the article. The thesis about spatial differentiation of the Gross Regional Product (GRP) losses in different regions of the country because of people's death and injuries in the road accidents (RA) and due to formations of property and ecological damage was chosen as a working hypothesis. The calculations, carried out for 12 subjects of the SFD, confirmed this idea. The range of calculated values of economic damage from road accident rate (in % of GRP) was from 1.3 (Tomsk region) to 12.6 (Republic of Tyva) in 2015. In article the attempt to explain the received result by heterogeneous development of economics in various Russian regions is made. The consequence of it is a heterogeneous quality of people's life and quite various perception of life value by inhabitants of different regions that influences their life safety level. © 2018 Institute of Physics Publishing. All rights reserved.

This paper describes numerical and experimental results of beamforming algorithms for generation of directional sound. The intended application is a sound source for cars with the objective to warn vulnerable road users while minimising noise pollution. Nowadays, sensors exist which are able to reveal the position of the vulnerable road users, which information can be used by the warning signal generator. Based on this information, the signal generator is designed to generate the specified warning signal at the location of the vulnerable road user while the acoustic response at other locations is minimised. The directional sound beam was realised with an array of controlled acoustic moving coil sources. The paper compares different methods to generate the sound beam and investigates their effectiveness for this application by simulation and experimental results. The robustness under realistic conditions in simulations and in initial real-time experiments on a car are discussed.

This paper describes numerical and experimental results of beamforming algorithms for generation of directional sound. The intended application is a sound source for cars with the objective to warn vulnerable road users while minimising noise pollution. Nowadays, sensors exist which are able to reveal the position of the vulnerable road users, which information can be used by the warning signal generator. Based on this information, the signal generator is designed to generate the specified warning signal at the location of the vulnerable road user while the acoustic response at other locations is minimised. The directional sound beam was realised with an array of controlled acoustic moving coil sources. The paper compares different methods to generate the sound beam and investigates their effectiveness for this application by simulation and experimental results. The robustness under realistic conditions in simulations and in initial real-time experiments on a car are discussed.

This study aims to develop a simulation/optimization model for the solution to the problem of determining on-street parking places in urban road networks. The problem is dealt within the Discrete Network Design (DND) context due to the binary decision variables and the bi-level programming technique is used for the solution of the problem. The upper level represents the determination of on-street parking places while the reaction of drivers’ to the design is handled in user equilibrium manner in the lower level. The upper level problem is formulized as a non-linear mixed integer programming problem and the meta-heuristic Harmony Search (HS) optimization technique is employed for the solution. In the proposed model, VISUM traffic analysis software is utilized as the simulation tool for solving the lower level problem. The performance of the proposed model is tested on Sioux-Falls road network which has widely been used on DND studies in the previous works. Results show that determining optimal or near-optimal on-street parking places may be achieved by using the proposed model. © Springer International Publishing Switzerland 2014.

This study investigates (i) the link of land use and road design on pedestrian safety and (ii) the effect of the level of spatial aggregation on the frequency of pedestrian accidents. For this purpose, pedestrian accident frequency models were developed for New York City based on an extensive dataset collected from different sources over a period of 5 years. The assembled dataset provides a rich source of variables (land-use, demographics, transit supply, road network and travel characteristics) and two different crash frequency outcomes: total and fatal-only collision counts. Among other things, it was observed that the census tract analysis (disaggregate data) provides more insightful and consistent results than the analysis at the zip code level. The results indicate that tracts with greater fraction of industrial, commercial, and open land use types have greater likelihood for crashes while tracts with a greater fraction of residential land use have significantly lower likelihood of pedestrian crashes. Moreover, census tracts that have a greater number of schools and transit stops - which are determinants of pedestrian activity - are more likely to have greater crashes. Results also show that the likelihood of pedestrian-vehicle collision increases with the number of lanes and road width. This suggests that retrofitting or narrowing the roads could possibly reduce the risk of pedestrian crashes. � 2011 Elsevier Ltd.

Traffic density provides an indication of the level of service being provided to the road users. When the length and speed of the vehicles in a traffic stream varies significantly, however, it has been, found that, the concept of occupancy, rather than density, is more appropriate to describe traffic concentration. This is because of the reason that occupancy takes into account the variation in the speed and length of vehicles in its measurement. The value of occupancy, however, changes with change in the detection-zone length. Also, the measure, occupancy is not appropriate under heterogeneous traffic conditions, as the traffic has no lane discipline and hence, it is necessary to consider the whole of the width of the road as single unit to analyze the traffic flow. Hence, a new concept named, "area-occupancy" is proposed. Simulation technique has been used to validate the concept of area-occupancy. To check for the validity of the concept, the occupancy and area-occupancy of a homogeneous traffic stream were related to the density of the stream and it was found that area-occupancy can be a substitute for occupancy. Then, the concept of area-occupancy was applied for heterogeneous traffic and a relationship was developed between area-occupancy and traffic stream speed. The developed relationship is found to be logical indicating the appropriateness of the area-occupancy concept for heterogeneous traffic conditions. � 2010 by Science Press.

This paper is concerned with modification and validation of a recently developed microsimulation model of heterogeneous traffic flow and application of the model to study the impact of provision of reserved bus lanes on urban roads. The impact of introduction of an exclusive bus lane is measured in terms of reduction in speed of other categories of motor vehicles, due to the consequent reduction in road space, over a wide range of traffic volume. The main finding of this paper is that if an exclusive bus lane is provided under highly heterogeneous traffic conditions (prevailing in Indian cities), then the maximum permissible volume to capacity ratio that will ensure a level of service of C for the traffic stream comprising all the motor vehicles, except the buses, in a typical eight lane divided urban road (14.5-m-wide road space per direction) is about 0.62. Through this study, a framework for justification of providing exclusive bus lane has also been defined. � 2010 ASCE.

Large-scale use of leaded gasoline was an important source of the neurotoxin lead in the European environment. After a sequence of regulations on the allowed gasoline lead content and, eventually, a ban on the use of lead additives in gasoline, road transport was no longer considered a source of atmospheric lead. Currently a discrepancy exists between measured atmospheric lead concentrations and model-predicted concentrations, suggesting that lead emissions to the atmosphere may be underestimated. Recently it was suggested that lead emission from unleaded gasoline combustion is still an important source and may (partly) fill the gap between modelled and observed atmospheric lead concentrations. In this paper we assess the plausibility of the latter suggestion by following various emission estimation methodologies. The uncertainty of lead emissions from road transport is further reduced by chemical analysis of fuel samples. The result of our assessment is that lead from road transport fuel combustion is not the missing lead source needed to fill the gap between modelled and observed lead concentrations. Road transport is still a source of lead through brake wear and a small contribution from exhaust emissions but this contributes no more than 5-8% of the EU25 total emission. © 2009 Elsevier B.V. All rights reserved.

Road traffic in India, is heterogeneous, and is characterized by the presence of vehicles with wide ranging static and dynamic characteristics. Generally, all these vehicles use the same road space without any segregation and this results in a variety of traffic mix, flows and densities over time and space. In this study, a newly developed heterogeneous traffic flow simulation model (HETEROSIM) has been used to develop, a set of speed-flow curves for traffic on road spaces of widths 7.5m, 11.0m and 14.5m (equivalent to two, three and four lanes). The developed speed-flow relationships are found to follow the standard pattern indicating the goodness of the model. These curves will be useful, in addition to estimate the capacity of the roads, to predict the traffic speed on divided roads for a given traffic volume over a wide range, which is an important input for planning of road networks and road traffic operations.

Large-scale use of leaded gasoline was an important source of the neurotoxin lead in the European environment. After a sequence of regulations on the allowed gasoline lead content and, eventually, a ban on the use of lead additives in gasoline, road transport was no longer considered a source of atmospheric lead. Currently a discrepancy exists between measured atmospheric lead concentrations and model-predicted concentrations, suggesting that lead emissions to the atmosphere may be underestimated. Recently it was suggested that lead emission from unleaded gasoline combustion is still an important source and may (partly) fill the gap between modelled and observed atmospheric lead concentrations. In this paper we assess the plausibility of the latter suggestion by following various emission estimation methodologies. The uncertainty of lead emissions from road transport is further reduced by chemical analysis of fuel samples. The result of our assessment is that lead from road transport fuel combustion is not the missing lead source needed to fill the gap between modelled and observed lead concentrations. Road transport is still a source of lead through brake wear and a small contribution from exhaust emissions but this contributes no more than 5-8% of the EU25 total emission. © 2009 Elsevier B.V. All rights reserved.

Knowing the position and speed of the vehicles on the road network in real-time is one of the major challenges that vehicle control and traffic management applications are facing. Wireless sensor networks received significant attention in the last decade and successful research put them in the forefront to answer this challenge. Wireless sensor networks are a new class of observation systems with a large amount of nodes communicating with each other via a wireless network channel. In this paper such a system is used to firstly detect a vehicle and secondly estimate its motionstate. The nodes are evenly distributed in a along the road surface and each node can detect when a vehicle passes. With the known position of the individual nodes, a motion-state of the vehicle is estimated. Three state-estimators are analyzed in this paper; a synchronous Kalman filter, an asynchronous Kalman filter and a Mixture of Gaussians. After designing these state-estimators, simulations are performed to asses their performance. © 2008 IEEE.

Knowing the position and speed of the vehicles on the road network in real-time is one of the major challenges that vehicle control and traffic management applications are facing. Wireless sensor networks received significant attention in the last decade and successful research put them in the forefront to answer this challenge. Wireless sensor networks are a new class of observation systems with a large amount of nodes communicating with each other via a wireless network channel. In this paper such a system is used to firstly detect a vehicle and secondly estimate its motionstate. The nodes are evenly distributed in a along the road surface and each node can detect when a vehicle passes. With the known position of the individual nodes, a motion-state of the vehicle is estimated. Three state-estimators are analyzed in this paper; a synchronous Kalman filter, an asynchronous Kalman filter and a Mixture of Gaussians. After designing these state-estimators, simulations are performed to asses their performance. © 2008 IEEE.

In this paper, the following four indicators describing noise annoyance in a community are considered: the percentage of people being highly annoyed (pHA), annoyed (pA), (at least) little annoyed (p LA), and the mean of the expected individual annoyance scores (m EA) in the population concerned. These indicators are calculated for a study population on the basis of maps of the noise measure DENL, using DENL-annoyance relationships. An approach is described for comparing the effect of noise reduction, using the above mentioned noise annoyance indicators. For the municipality of Leiden, the Netherlands, the results from applying this approach indicate that the calculated efficacy of a given noise abatement policy depends to some extent on the noise annoyance indicator that is used. A lower limit of 55 dB for the noise calculations has been suggested by the EU Directive on environmental noise. This leads to a large underestimation of the noise annoyance indicators and may affect priority setting regarding noise abatement. Beside the four noise annoyance measures, two other noise quality indicators are considered: the percentage of people being highly sleep disturbed (p HS) and (AREA50) - the percentage area where DENL exceeds 50 dB. The results for Leiden show that each of these two indicators put more focus on noise from motorways than the other noise indicators considered. © S. Hirzel Verlag.

In this paper, the following four indicators describing noise annoyance in a community are considered: the percentage of people being highly annoyed (pHA), annoyed (pA), (at least) little annoyed (p LA), and the mean of the expected individual annoyance scores (m EA) in the population concerned. These indicators are calculated for a study population on the basis of maps of the noise measure DENL, using DENL-annoyance relationships. An approach is described for comparing the effect of noise reduction, using the above mentioned noise annoyance indicators. For the municipality of Leiden, the Netherlands, the results from applying this approach indicate that the calculated efficacy of a given noise abatement policy depends to some extent on the noise annoyance indicator that is used. A lower limit of 55 dB for the noise calculations has been suggested by the EU Directive on environmental noise. This leads to a large underestimation of the noise annoyance indicators and may affect priority setting regarding noise abatement. Beside the four noise annoyance measures, two other noise quality indicators are considered: the percentage of people being highly sleep disturbed (p HS) and (AREA50) - the percentage area where DENL exceeds 50 dB. The results for Leiden show that each of these two indicators put more focus on noise from motorways than the other noise indicators considered. © S. Hirzel Verlag.

Submitted by Vitor Silverio Rodrigues (vitorsrodrigues@reitoria.unesp.br) on 2014-05-27T11:24:37Z No. of bitstreams: 0 Made available in DSpace on 2014-05-27T11:24:37Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-01-01 The outdating of cartographic products affects planning. It is important to propose methods to help detect changes in surface. Thus, the combined use of remote sensing image and techniques of digital image processing has contributed significantly to minimize such outdating. Mathematical morphology is an image processing technique which describes quantitatively geometric structures presented in the image and provides tools such as edge detectors and morphological filters. Previous studies have shown that the technique has potential on the detection of significant features. Thus, this paper proposes a routine of morphological operators to detect a road network. The test area corresponds to an excerpt Quickbird image and has as a feature of interest an avenue of the city of Presidente Prudente, SP. In the processing, the main morphological operators used were threshad, areaopen, binary and erosion. To estimate the accuracy with which the linear features were detected, it was done the analysis of linear correlation between vectors of the features detected and the corresponding topographical map of the region. The results showed that the mathematical morphology can be used in cartography, aiming to use them in conventional cartographic updating processes. Universidade Estadual Paulista - Unesp Faculdade de Ciências e Tecnologia - FCT Departamento de Cartografia, Presidente Prudente, SP Mississipi State University Geosystems Research Institute, 2 Reasearch Boulevard, Starkville, MS 39759 Universidade Estadual Paulista - Unesp Faculdade de Ciências e Tecnologia - FCT Departamento de Cartografia, Presidente Prudente, SP

Summarization: Recently, signal control strategies with decentralised logic have been developed to tackle the traffic congestion problems of urban road networks. Such strategies aim at network-wide traffic flow efficiency improvement via local actions, thus low design effort and infrastructure investment. This study presents, compares, and evaluates two such innovative approaches: the job scheduling algorithm comprising the local control component of the scalable urban traffic control (SURTRAC) system and the max- or back-pressure algorithm. The approaches are also compared against traffic-responsive urban control (TUC), a well-established strategy with centralised logic. Evaluation is based on the AIMSUN simulation model of the city centre of Chania, Greece. The study results indicate that the TUC and max-pressure retain performance independently of the prevailing traffic conditions, while also being computationally simpler than job scheduling. Both decentralised approaches require frequent (high-resolution) and relatively accurate measurements; on the other hand, TUC, although less demanding in this respect, calls for communication lines between the junction controllers and the central computer. Finally, compared with both decentralised approaches, the TUC provides a signal plan sequence with less excessive differences between each other, thus fewer disturbances to the common network users. Nevertheless, for more comprehensive conclusions, more investigations, including field trials, would be needed. Presented on: IET Intelligent Transport Systems

Human actors are seen as the main capability to enhance the resilience of road transportsystems against disturbing scenarios. This paper compares different approaches to introduce users’behaviour into the resilience assessment. The consideration of the stochastic nature of human responsecombined with dynamic traffic modelling enables a comprehensive resilience assessment approach.