Traffic Deviation Research Articles

The design and analysis of optimal descent profiles using real flight data

This paper aims to evaluate the vertical profile efficiency of arrival traffic at Trabzon Airport in terms of fuel consumption, carbon dioxide (CO2), hydrocarbon (HC), carbon monoxide (CO), and nitrogen oxide derivatives (NOx). For this purpose, 3-deg descent profiles were created using the parameters obtained from real flight data under the near idle thrust and clear configuration restrictions and these were then compared to the reference real flight profiles. The cruise section was also considered while comparing the designed continuous descent profiles to baseline scenarios. According to the results of our analyses, the altitudes at which landing traffic deviates from the optimum profile is especially around 6000ft and after the top of descent point. It was not possible to note a high level of vertical inefficiency in the vertical profile of arrival traffic, however, holding maneuvers, waypoint restrictions play an important role in overall descent time, fuel consumption, and emissions.

Traffic flow rate on Kigali roads: a case of national roads (RN1 and RN3)

Traffic flow study plays a key important in various functionalities of cities all over the world. The study of traffic flow is also viewed as an essential facility of the country when it wants to establish traffic operations patterns in the progress of road planning. Blockages are accountable for a sequence of harmful effects such as loss of time, scheduling difficulties, carbon dioxide production, and risks of accidents as well as external expenses. Besides, inadequate transportation facilities and increased traffic volume hamper urban development, especially in developing nations. The objective of the study was to assess the traffic flow state in two selected national roads of Kigali city. The traffic data were collected from 5 am to 8 pm on two National Roads (RN1 and RN3). The relationship between density and flow rate was analyzed using the fundamental diagram of traffic flow. It was found that the peak hours were from 6 am to 8 am and 5 pm to 8 pm. The highest number of vehicles counted were motorcycles due to shortcuts taken to reduce travel time. The results on RN3 revealed a proportion increase of traffic flow and density in the free-flow regime from 0 to maximum flow of 3346.6 veh/h correspondent to a critical density of 114.9 veh/km. However, in the congested zone regime, there was a decrease in traffic flow and an increase in density. It was found that the curve of flow versus density tended to increase on-road RN1. This study proposed the promotion of public transport and e-commerce as strategies to mitigate the congestion. Also, further research may be carried out on all roads of Kigali city, to provide the level of congestion useful for traffic deviation accordingly.

Application of Traction Power Supply for Metro Rail Transit Based on Electromagnetic Sensing Technology

With the gradual acceleration of modernization, metro projects have been opened for the development and construction of urban areas. How to ensure the smooth operation of subway project in public service has become the focus of urban public safety service research. In view of this content, this paper studies the subway field of urban rail transit operation and improves the public safety of urban rail transit operation. Firstly, the factors affecting the track traction power supply are analyzed, the anti‐interference technology of ring array electromagnetic sensor is added, and the research scheme is formulated according to the track potential research standard to ensure the operation of the power supply system. Then, the electromagnetic sensor traction algorithm is used to detect the subway track power supply signal, and the working characteristics and advantages of single electromagnetic induction and double electromagnetic induction are analyzed. The experimental results show that compared with the traditional electromagnetic sensor power supply signal detection algorithm, the content of this paper can judge whether the power supply is normal according to electromagnetic induction, detect whether the subway traffic deviates from the track traction coordinates, and understand the size of the deviation from the track. It improves the efficiency of traction power supply detection, stabilizes the running track of subway traffic, and is conducive to the protection and development of urban public safety.

Identify factors that caused false and violation by motorcycle rider

A motorcycle is a vehicle that is widely used by Indonesia community. However, increasing of motorcycle users were not followed by a good driving behaviour, the increasing was directly proportional to increase in the number of traffic accidents. The motorcycle is one of the dominant causes of the traffic accident in Indonesia. This research aims to identify the causes of the deviation and violation in driving that conducted by motorcyclists. The characteristics of driving and the type of deviation committed by motorcyclists collected using Driver Behavior Questionnaire (DBQ). The respondents were motor drivers who have a motorcycle driving license (SIM) in 8 districts in Cilegon. The data processing used the factor analysis method to identify the factors that caused traffic deviation by the motorcyclists. This research find seven factors that caused the deviation and violation by drivers, the dominant factor that explained 16.857 % deviation in riding is lack knowledge of traffic rules. Next factors were vehicle speed exceeds the threshold, the emotions, hasty, careless, not paying attention to the health, and not focus in driving.

A balanced energy efficient virtual backbone construction algorithm in wireless sensor networks

A steady increase in the number of Wireless Sensor Networks (WSN) applications in day-to-day life lead to the design of new algorithms and protocols to solve their various issues such as energy efficiency, topology control and data gathering. Energy efficiency can be achieved by focusing on the communication part, since the communication cost is higher than that of the computation. The use of Connected Dominating Set (CDS) as the virtual backbone for data transmission towards the Base Station improves the network lifetime. Therefore, a Balanced Energy Efficient Virtual Backbone Construction (BEE-VBC) algorithm in WSN is proposed in this paper using the well-known heuristic Genetic Algorithm (GA). This work emphasizes on the design of fitness function using multiple factors such as energy, coverage of neighbors, reliability, data rate, and traffic deviation. The CDS construction using the maximum fitness nodes, provides network longevity. This ensures a steady connectivity among the dominator nodes and path towards the BS, thus improving the quality of the solution. The simulation experiments have been carried out and compared with a few CDS-based and clustering-based algorithms to show the performance improvement achieved in the proposed work.

Fatigue reinforcement during repainting for two motorway bridges

A large number of metallic structures have already reached or even exceeded the limit of their initial design fatigue life. All this paper reports and investigates about the various works carried out in order to extend the fatigue life of existing motorway bridges located in the north of France.The studied bridge is composed with two independent decks of a French motorway carrying heavy traffics for 40 years. The slabs of both bridge decks are very fine: steel-concrete composite slabs associating 8 mm steel plate and 100 mm concrete layer according to the original design of Charles Brignon, who was also a pioneer in France of modern fatigue design for bridges, using rounded gussets to attach transverse beams, which reduces the stress concentration factor at the crossing point of the flanges.The structures of the bridges were recently strengthened to extend their fatigue resistance by three different methods which are:Addition of a continuous welded steel plate inclined outside of the edge girders to increase the safety at the Ultimate Limit State (ULS) and the robustness in fatigue of the bottom steel flanges. The inclined additional plate supported by the webs and the free bottom flangesPost weld treatment with Tungsten Inert Gas (TIG) dressing of the welded cross-beams connection gussets were performed.Bonding of CFRP carbon fibres on the bottom steel flanges of the main beams at the extremities of the welded existing cover-plates. These fibres need a high elastic modulus of 400 GPa.The interactions between both fatigue and anticorrosion life extension works are discussed. An innovative option of mixed complete-partial sandblasting methods was proved more economic than the usual complete sandblasting method and performed. The initial conception with two independent decks carrying the highway showed itself particularly relevant for the implementation of heavy repairs. The traffic deviation on the other deck allowed to work on each deck without stopping the traffic.

Refueling station location problem with traffic deviation considering route choice and demand uncertainty

Construction of refueling stations in a traffic network is a major step toward the promotion of hydrogen fuel vehicles in the metropolitan areas. This research provides a framework for the refueling demand uncertainty and the effect of travelers' deviation to refuel considerations in the network. First, considering the refueling demand uncertainty of the hydrogen fuel vehicles market, we propose a discrete, robust optimization model in which refueling demand is formulated as an uncertainty set during planning horizon. A cutting plane algorithm is adopted to solve this robust centralized planning model. Numerical results demonstrate that the robust model can lead to more reliable design compared to the nominal plan. Second, a link-based bi-level program is proposed in which the lower level problem describes the user equilibrium traffic condition characterized by the locations of refueling stations. This model is solved using a computationally efficient genetic algorithm. Numerical examples indicate that as the refueling demand share increases, total system travel time, for both refueling and non-refueling users, increases. This increase is more pronounced in low levels of refueling demand share compared to high levels.

Assessment of the Robustness of Signal Timing Plans in an Arterial Corridor Through Seasonal Variation of Traffic Flows

Arterial traffic signal systems, predominantly in the United States, deploy multiple signal timing plans to account for daily variability of traffic demand. Those types of traffic flow deviations should be anticipated when timing plans are designed and, therefore, serviced satisfactorily. When traffic flow patterns are no longer predictable, a predetermined time-of-day (TOD) plan may no longer be the optimal one. This research aimed to examine signal timing optimality by applying a method similar to the selection of a traffic responsive plan to recognize automatically the best timing plan suited to current traffic conditions. The proposed method attempted to determine whether the optimality of signal timing settings could have been effectively estimated when systematic detector counts of the major approach were available. The study used 4 months of data from field microwave detectors coupled with data of turning-movement counts obtained over several days. The findings show that TOD signal timing plans mainly depended on adequate data collection that best describes a specific set of traffic conditions. Thus, the designed plan was as optimal as the related traffic information was reliable, whereas a problem arose in the case of limited-availability and low-quality data. New technologies are capable of collecting and storing massive amounts of data. Even if the granularity of collected data is low, the data can be used to improve traffic performance (i.e., reduce corridor delay). This realization could be of particular importance to traffic agencies that have installed, or plan to install, new field devices.

Human error tolerant anomaly detection based on time-periodic packet sampling

This paper focuses on an anomaly detection method that uses a baseline model describing the normal behavior of network traffic as the basis for comparison with the audit network traffic. In the anomaly detection method, an alarm is raised if a pattern in the current network traffic deviates from the baseline model. The baseline model is often trained using normal traffic data extracted from traffic data for which all instances (i.e., packets) are manually labeled by human experts in advance as either normal or anomalous. However, since humans are fallible, some errors are inevitable in labeling traffic data. Therefore, in this paper, we propose an anomaly detection method that is tolerant to human errors in labeling traffic data. The fundamental idea behind the proposed method is to take advantage of the lossy nature of packet sampling for the purpose of correcting/preventing human errors in labeling traffic data. By using real traffic traces, we show that the proposed method can better detect anomalies regarding TCP SYN packets than the method that relies only on human labeling.

Capacitated Refueling Station Location Problem with Traffic Deviations Over Multiple Time Periods

In this paper, we consider the problem of locating refueling stations in a transportation network via mathematical programming. The proposed model is applicable for several alternative fuel types and is particularly suitable for hydrogen fuel. We assume that a central planner, a hydrogen manufacturer or a government agency, determines the locations of the refueling stations for a given intra-city transportation network while accounting for multi-period travel demand, nonlinear refueling station operational cost, and the deviations of travelers from their shortest routes to refuel. Incorporating demand patterns over multiple periods allows us to account for both short- and long-term variation in hydrogen refueling demand (the former due to time of day, and the latter future hydrogen fuel cell vehicle growth). It also helps us model the changes in user preferences (station and route choices) and traffic conditions over different periods. To account for refueling station operational cost in making investment decisions, we introduce a staircase marginal cost function. In addition, the model explicitly considers station and route choices of travelers as they may deviate from their original paths to refuel, incurring additional costs and affecting the number and locations of refueling stations. We formulate this problem as a multi-period, mixed-integer model with constant link travel time and staircase operational cost at refueling stations. We applied two well-known solution algorithms, branch-and-bound and Lagrangian relaxation, to solve the problem. Our analysis shows that although we are able to solve the refueling station location problem to optimality with branch-and-bound, the Lagrangian relaxation approach provides very good results with less computational time. Additionally, our numerical example of Mashhad, Iran demonstrates that locating refueling stations with considering multi-period traffic patterns (as opposed to single-period) results in minimum network-wide traffic congestion and lower user and agency costs over a planning horizon.

STONE: A streaming DDoS defense framework

Distributed Denial-of-Service (DDoS) attacks aim at rapidly exhausting the communication and computational power of a network target by flooding it with large volumes of malicious traffic. In order to be effective, a DDoS defense mechanism should detect and mitigate threats quickly, while allowing legitimate users access to the attack’s target. Nevertheless, defense mechanisms proposed in the literature tend not to address detection and mitigation challenges jointly, but rather focus solely on the detection or the mitigation facet. At the same time, they usually overlook the limitations of centralized defense frameworks that, when deployed physically close to a possible target, become ineffective if DDoS attacks are able to saturate the target’s incoming links.This paper presents STONE, a framework with expert system functionality that provides effective and joint DDoS detection and mitigation. STONE characterizes regular network traffic of a service by aggregating it into common prefixes of IP addresses, and detecting attacks when the aggregated traffic deviates from the regular one. Upon detection of an attack, STONE allows traffic from known sources to access the service while discarding suspicious one. STONE relies on the data streaming processing paradigm in order to characterize and detect anomalies in real time. We implemented STONE on top of StreamCloud, an elastic and parallel-distributed stream processing engine. The evaluation, conducted on real network traces, shows that STONE detects DDoS attacks rapidly, provides minimal degradation of legitimate traffic while mitigating a threat, and also exhibits a processing throughput that scales linearly with the number of nodes used to deploy and run it.

A97 New metro line in Turin: an analysis of the impacts on local mobility and road traffic accidents

Abstract Background Several studies have analysed impacts of new metro lines in dense urban areas. This transport mode is able to move travellers from private car to a more efficient and less time-consuming solution. This shift determines lower traffic congestion rates, leading to reduced traffic accidents. Few studies have also applied an equity lens, to understand who is really benefitting from these improvements: results show that ethnic minorities, economically disadvantaged, children and women are the most exposed to injuries. Methods We analyse impacts of the construction of the last metro section in Turin (Italy), highlighting effects during the construction phase, and after the opening. We analyse data on road traffic injuries in relation to the improved accessibility offered by the metro line, and how impacts are distributed among the population. We considered an impact area of 500 m around subway stations. Time series (2004-2013) of road traffic accidents in the whole city has been compared to the one of accidents occurred in the selected area (pre-during-post intervention). A Bayesian change-point (BCP) approach has been used to identify where each time series undergoes sudden changes. Finally, a Poisson regression analysis has been performed in order to verify if road accidents in the subway area have a significantly greater reduction than the ones occurred in the whole city. Results BCP algorithm shows that the trend of road traffic accidents in the subway area had an increase in august 2006, and then decreased at the beginning of 2008. Taking the whole city as comparison, we can notice a reduction in the beginning of 2008 but no significant change around 2006. Moreover, the Poisson regression showed no significant reduction of accident rates in the area when compared to the whole city in the period 2004-2013. When considering only the main street of the subway line (via Nizza), the model showed a more marked reduction, accompanied by a car traffic reduction of 3%. Conclusions While the opening in 2006 of the first section of the Turin metro significantly reduced injuries from road accidents on the corresponding area, preliminary results for this last section seem to indicate a modest impact. The increase of accidents in the subway area from august 2006 can be related to the opening of working sites, and consequent traffic deviations. Shift of travellers from private car to underground has not been wide as expected. The missing interconnection with other transport modes could partly explain this.

Short-Term Highway Traffic State Prediction Using Structural State Space Models

This research proposes a short-term highway traffic state prediction method based on a structural state space model, with the intention to provide a robust approach for obtaining accurate forecasts of traffic state under both recurring and non-recurring conditions. True traffic state is decomposed to three components, namely, regular traffic pattern, structural deviation, and random fluctuation. Particularly, the structural deviation term reflects the change of true traffic state from regular (historical) pattern, due to unexpected capacity reduction and/or demand variations. A polynomial trend is adopted to describe the temporal evolution of structural deviations across different time intervals. We derive an analytical form of structural deviations in a single bottleneck case based on cumulative flow count diagrams. The proposed model is incorporated in a Kalman filtering-based algorithmic framework, together with an adaptive scheme for determining the variances of random errors. A set of numerical experiments was conducted on two test beds in the northern Taiwan highway network. Experimental results show that the proposed approach is particularly superior to an ordinary Kalman filtering method presented in the literature under non-recurring conditions, highlighting the advantage of combining both the polynomial trend model and historical pattern into the proposed short-term traffic state prediction approach.

Network Traffic Deviation Detection Based on Fractal Dimension

In this paper we examine aggregate network traffic for deviation detection. The precise and fast detection of network traffic deviation is crucial to improve the efficient operation of a network. It is often difficult to detect the time when the defects occur in a network. In this article, a new algorithm is bestowed to supervise the aggregate network traffic to fast detect the time deviation transpires in a network. This is performed by supervising the statistical attributes of the time series depicting the network conduct. The procedure examines the network conduct using fractal dimension and discrete stationary wavelet transform. In the suggested procedure, after implementing discrete stationary wavelet transform on the signal depicting the network traffic, the fractal dimension of the disintegrated signal is computed in a sliding window. Then, variations of signal fractal dimension are regarded for deviation detection. Performance of the suggested procedure is compared with that of three other existent procedures using artificial substance signal. The results show superiority of the suggested procedure in terms of preciseness compared to existent procedures.

Dispatching design for storage-centric wireless sensor networks

In a large-scale storage-centric wireless sensor network (SWSN) where data from different clusters are archived at distributed storage nodes, the dispatching design problem is to determine one or multiple storage nodes for each cluster. To achieve high data fidelity in SWSNs, the dispatching design should aim to reduce the data loss due to the network congestion and, at the same time, to prolong the network lifetime by avoiding sending excessive traffic to some particular storage nodes in order to achieve energy consumption balance among the relaying sensors around the storage node. In this paper, we propose an h-peak dispatching design for SWSN. Under such a design, regular traffic volume from each cluster will receive guaranteed data fidelity, and over-expectation traffic will receive best-effort data fidelity. We use an h-peak model to characterize the traffic deviation which assumes that at most h clusters may have over-expectation traffic simultaneously at a storage node. By incorporating h into the dispatching decision rather than assuming that all clusters may reach their traffic spikes simultaneously, the proposed h-peak dispatching design can achieve high data fidelity in SWSNs.

Robust network dimensioning for realtime services over IP networks with traffic deviation

Network planning for realtime services in traditional systems is normally performed based on a fixed traffic demand. Realtime services such as voice and video are currently being widely deployed over packet-based IP networks. Due to the lack of resource control mechanisms in IP networks, the actual traffic during network operation may easily deviate from the given mean traffic matrix values. As realtime services impose stringent quality of service (QoS) constraints, any traffic deviation inside the network might lead to severe quality degradation if any of the links provides insufficient service rate for the available traffic at any time. In this work, we highlight the significance of this problem and propose an effective approach to provide a highly robust network against traffic deviation. By means of both analytical and simulation studies, we study the distribution of realtime connections over an enterprise IP network and show how capacity needs of realtime traffic are affected when various resource control mechanisms are employed. In addition, we study the resulting tradeoff between capacity needs and traffic QoS parameters including packet loss and blocking probability.

Structure and complexity of extreme Nash equilibria

We study extreme Nash equilibria in the context of a selfish routing game. Specifically, we assume a collection of n users, each employing a mixed strategy, which is a probability distribution over m parallel identical links, to control the routing of its own assigned traffic. In a Nash equilibrium, each user selfishly routes its traffic on those links that minimize its expected latency cost. The social cost of a Nash equilibrium is the expectation, over all random choices of the users, of the maximum, over all links, latency through a link. We provide substantial evidence for the Fully Mixed Nash Equilibrium Conjecture, which states that the worst Nash equilibrium is the fully mixed Nash equilibrium, where each user chooses each link with positive probability. Specifically, we prove that the Fully Mixed Nash Equilibrium Conjecture is valid for pure Nash equilibria. Furthermore, we show, that under a certain condition, the social cost of any Nash equilibrium is within a factor of 2 h ( 1 + ɛ ) of that of the fully mixed Nash equilibrium, where h is the factor by which the largest user traffic deviates from the average user traffic. Considering pure Nash equilibria, we provide a PTAS to approximate the best social cost, we give an upper bound on the worst social cost and we show that it is NP -hard to approximate the worst social cost within a multiplicative factor better than 2 - 2 / ( m + 1 ) .

The impact of residential broadband traffic on Japanese ISP backbones

This paper investigates the effects of the rapidly-growing residential broadband traffic on commercial ISP backbone networks. We collected month-long aggregated traffic logs for different traffic groups from seven major ISPs in Japan in order to analyze the macro-level impact of residential broad-band traffic. These traffic groups are carefully selected to be summable, and not to count the same traffic multiple times.Our results show that (1) the aggregated residential broad-band customer traffic in our data exceeds 100Gbps on average. Our data is considered to cover 41% of the total customer traffic in Japan, thus we can estimate that the total residential broadband traffic in Japan is currently about 250Gbps in total. (2) About 70% of the residential broadband traffic is constant all the time. The rest of the traffic has a daily fluctuation pattern with the peak in the evening hours. The behavior of residential broadband traffic deviates considerably from academic or office traffic. (3) The total traffic volume of the residential users is much higher than that of office users, so backbone traffic is dominated by the behavior of the residential user traffic. (4) The traffic volume exchanged through domestic private peering is comparable with the volume exchanged through the major IXes. (5) Within external traffic of ISPs, international traffic is about 23% for inbound and about 17% for outbound. (6) The distribution of the regional broadband traffic is roughly proportional to the regional population.We expect other countries will experience similar traffic patterns as residential broadband access becomes widespread.

Detecting Network Attacks in the Internet via Statistical Network Traffic Normality Prediction

The information technology advances that provide new capabilities to the network users and providers, also provide powerful new tools for network intruders that intend to launch attacks on critical information resources. In this paper we present a novel network attack diagnostic methodology, based on the characterization of the dynamic statistical properties of normal network traffic. The ability to detect network anomalies and attacks as unacceptable when significant deviations from the expected behavior occurs. Specifically, to provide an accurate identification of the normal network traffic behavior, we first develop an anomaly-tolerant nonstationary traffic prediction technique that is capable of removing both single pulse and continuous anomalies. Furthermore, we introduce and design dynamic thresholds, where we define adaptive anomaly violation conditions as a combined function of both magnitude and duration of the traffic deviations. Finally numerical results are presented that demonstrate the operational effectiveness and efficiency of the proposed approach under the presence of different attacks, such as mail-bombing attacks and UDP flooding attacks.

Code reservation schemes at the forward link in WCDMA

We examine resource reservation schemes for the management of orthogonal variable spreading factor (OVSF) codes at the forward link of 3G mobile communications systems employing WCDMA. Like in every multi-service network, calls with different rate requirements will perceive very dissimilar system performance at the forward link in 3G systems if no measures are taken and the channelization code tree is treated as a common pool of resources. Assuming that the traffic level for each class is known in advance, we introduce complete sharing (CS), complete partitioning (CP) and hybrid partitioning (HP) policies to manage the code tree. At the resource reservation level, we develop an efficient method to partition the available codes based on the offered traffic load of each class of calls and the size of the tree. The resulting partition is optimal in the sense that the maximum blocking probability of the different rate classes is minimized. At the call level, we use a real-time scheme to assign free codes to incoming requests, and evaluate its performance in terms of blocking probability per traffic class and utilization of codes in conjunction with the partitioning method used. It turns out that code blocking, which is encountered on this type of systems, further deteriorates the unfairness conditions at the forward link. Our simulation results show that fair access to codes by different rate calls is assured more by CP and less by HP schemes, at the expense of slightly lower code utilization at medium to high loads, compared to the CS scheme. Also, hybrid schemes absorb small traffic deviations more efficiently than CP, which is generally optimized for certain traffic mixes.