occupancy into account in protocol design (e.g. IPv6 Routing Protocol for LLNs (RPL)) to reduce the number of dropped packets at the buffer. Also, simulation results show that the value of the reassembly timeout parameter has significant impact on network performance when congestion occurs as an IPv6 packet is fragmented by SICSlowpan adaptation layer. In , Teo et al. propose a new reassembly mechanism called Multi-Reassemblies Buffer Management System (MR- BMS) for 6LoWPAN networks. MR-BMS consists of three components: buffer manager, list of reassembly buffer and IP packet buffer. When a new fragment arrives at a node, the buffer manager creates a new reassembly buffer to store the incoming fragment and starts a reassembly timer. After that, if the next incoming fragment belongs to the packet of the first fragment, it is stored in the same buffer. Otherwise, a new reassembly buffer is created to store the incoming fragment. However, the authors do not show the importance of the reassembly timeout parameter value in heavy data traffic conditions.
In the past six years, Nigeria has embraced the Global System for Mobile Communication (GSM). As of today there are four different operators, namely, MTN, CELTEL (Formerly ECONET), Globacom, and Mtel. The limited facility vis-à-vis the large number of subscribers has resulted in high-level congestion. This paper identifies the areas on the GSM network where congestion occurred. The causes of congestion on GSM network and congestion comparative analysis on each of the GSM networks in Nigeria are also presented.
In a second part of this paper the congestion inefficiency was estimated and the scores of three distinct approaches were compared, respectively the FGL, CGL and TS approaches. In the FGL approach, the different assumptions (CRS and VRS) to measure congestion indicate some differences between them. When the all sample is considered, the congestion proportions diverge between 4.4% (CRS) and 3.6% (VRS). The FGL congestion results are considerably higher when only the congested hospitals are taken into account, varying between 7.8% (CRS) and 8.1% (VRS). CGL approach revealed congestion signs a little higher than the previous ones. An average congestion value of 4.8% was obtained when the whole sample was considered and 9.0% only for the congested hospitals. Moreover, this method allows for the perception of the input(s) contribution to the congestion score. In our case, OOPEX was identified as the most important contributor. TS approach, reflecting slightly different scores from the previous ones, shows signs of congestion of 3.3% and 8.2% when the set of Portuguese hospitals is considered and when the sample is restricted to the congested ones, respectively.
The aim of this study is to assess the potential use of Bluetooth data for traffic monitoring of arterial road networks. Bluetooth data provides the direct measurement of travel time between pairs of scanners, and intensive research has been reported on this topic. Bluetooth data includes “Duration” data, which represents the time spent by Bluetooth devices to pass through the detection range of Bluetooth scanners. If the scanners are located at signalised intersections, this Duration can be related to intersection performance, and hence represents valuable information for traffic monitoring. However the use of Duration has been ignored in previous analyses. In this study, the Duration data as well as travel time data is analysed to capture the traffic condition of a main arterial route in Brisbane. The data consists of one week of Bluetooth data provided by Brisbane City Council. As well, micro simulation analysis is conducted to further investigate the properties of Duration. The results reveal characteristics of Duration, and address future research needs to utilise this valuable data source.
The solid lines are obtained using the exact results given by (3.8) ; the dashed lines are the approximations based on an Erlang distribu- tion with k = 100 phases. It can be seen that for k = 100 the capacities determined by our approximations are very close to the exact values. There is still a small difference visible, which is probably acceptable for all practical purposes. If a higher accuracy is desired, it is straightfor- ward to increase k which results in more accurate approximations. Ev- ery data point can be computed nearly instantaneously; this remains the case when performing similar calculations for more complex variants of the model considered in this example. This is obviously a great advan- tage of our analysis compared to microsimulations.
Design routability is a major concern in the ASIC design flow, particularly with today’s increasingly aggressive process technology nodes. Increased die areas, cell densities, routing layers, and net count all contribute to complex interconnect requirements, which can significantly deteriorate performance, and sometimes lead to unroutable solutions. Congestionanalysis and optimization must be performed early in the design cycle to improve routability. This paper presents a congestion estimation algorithm for a placed netlist. We propose a net-based stochastic model for computing expected horizontal and vertical track usage, which considers routing blockages. The main advantages of this algorithm are accuracy and fast runtime. We show that the congestion estimated by this algorithm correlates well with post- route congestion, and show experimental results of subsequent congestion optimization based this algorithm.
Abstract— with the emergence of wireless network the rapid growing traffic is starting to experience heavy utilization and network congestion. Congestion in network may arise when network node is carrying more data than it can handle and keep the load below the capacity. Several experiments and researches have been proposed for network traffic congestionanalysis in the field of computer network. This paper provides an overview of category provided by congestion detection and congestion avoidance by using neural network. It also includes hoe neural network uses for congestion detection to avoid congestion or effortlessness congestion in network. Computer network have experienced a rapid growth over the past few years and with that growth have come stern congestion problems. This paper concentrates on analysis of congestion to reduce the chances of network failure. It explores the congestion detection and congestion avoidance techniques of various authors and their conclusion has been summarised.
On top of that, traffic congestionanalysis of the town has exposed that auto-rickshaw, cycle rickshaw, bicycle and motor cycle are the popular traffic mode and traffic flow becomes so intensive during morning, noon and evening. Starting and ending of office time for employment and working time for different professionals, schooling time, shopping hours, festivals and seasonal variation etc. have influence on traffic flow of the town. It has also been found that Ataikula road is the most congestion-prone route than A.H road and the level of services of all intersections is F except TM intersection. There is a rotary at TM intersection which provides a comparatively better level of services to the travelers of the town.
According to Kuboye (2010), congestion is the unavailability of network to the subscriber at the time of making a call. Congestion was described as a situation that arises when the number of calls emanating or terminating from a particular network is more than the capacity the network is able to cater for at a time (Mughele et al., 2012). There are various reasons for which traffic congestion can occur, depending on switch facilities, exchange equipment and transmission link. Traffic congestion mainly occurs due to inadequate capacity of equipment and improper network manage- ment. Some of the effects of congestion on the network systems are queuing, slow speed, poor throughput and poor network among the mobile wireless communication. Consequently, efforts are made by various researchers on the congestionanalysis of mobile network providers.
This report shows how congestion has changed in San Francisco between 2010 and 2016 using well-established metrics such as vehicle hours of delay (VHD), vehicle miles travelled (VMT), and average speeds. It also estimates how much different factors, including TNCs, employment growth, population growth, and changes to the transportation system such as the addition of bike lanes and transit red carpet lanes, contribute to these changes in congestion. The data used to develop this report comes from several sources. Changes in measures of congestion are based on INRIX data, a commercial dataset which combines several real-time GPS monitoring sources with data from highway performance monitoring systems. TNC information is based on the profile of local TNC usage in San Francisco documented in the TNCs Today report. The original TNC data was gathered by researchers at Northeastern University from the Application Programming Interfaces (APIs) of Uber and Lyft, and subsequently processed into imputed in-service and out-of-service trips by Transportation
However, it should be stressed that equity effects are often discussed in a short-term or narrow way, simply comparing the situation before and after charges are introduced. But congestion charges are not an instrument for redistributing income: it is a way to make the price of driving more “correct”, to make it better reflect the true social cost of driving. Generally speaking, consumer prices for services and goods are almost always the same for everyone, regardless of income or wealth (for very good reasons). The desire for increased income equity is instead usually handled by taxation and welfare systems; not even essential goods such as food and clothes are usually subsidized (except in special cases). If the default position therefore is that prices are, generally, equal for everyone, then it is natural to argue that the distributional effects of corrective taxes – taxes which are introduced to make the prices “right” in the sense that they reflect full social costs – are in fact essentially irrelevant. At least, one should realize that arguing against such corrective taxes with equity arguments is logically equivalent to arguing that the good in question (car travel in rushing hours, in this case) should be subsidized for equity reasons – and this is often a much less persuasive or intuitively appealing argument.
I now turn to discussing some related literature. Arnott (2013) provides a re- cent and comprehensive review of the bathtub literature. Here I review just the part of the literature that is most closely related to the current paper. In a very in- fluential paper, Walters (1961) used the fundamental diagram of traffic flow to de- rive a supply curve for highway travel. In combination with a demand curve, this allowed him to consider equilibria and optima of travel demand. Agnew (1976) constructs a dynamic model of a general congestion-prone system using a conges- tion technology that is similar to the present. However, the demand rate is fixed and constant, which means that important aspects of urban traffic congestion are not represented. Moreover, Agnew’s version of the congestion technology does not allow for distances traveled, which is another fundamental aspect of urban congestion that is treated in the present analysis.
This is illustrated in Fig. 6, in which each vehicle is represented by a trajectory in time and space, and the slope of the trajectory falls as congestion increases. A typical performance curve (or fundamental diagram) is derived for a rectangular space-time domain ABCD, and includes the impact of some drivers who start before the time period, some who complete their journeys within it, and some who have not completed their journeys by the time that it ends. It is clear from Fig. 6 that this does not represent the time incurred by those drivers who start their journeys in the time period AB. To do this, and hence derive a supply curve, we need a quadrilateral time- ABC D F 6. This was the approach which we adopted in our earlier work (May et al, 2000), and which was accepted
As we discussed earlier, content providers or CDNs have additional degrees of freedom to control congestion, by hosting content (e.g. web pages, video, streaming music) at locations close to its consumers, and strategically selecting from which source to transmit the content, in order to affect congestion, improve performance, or reduce the cost of the transmission. Effective use of this strategy requires a sufficient number of uncongested paths to reach a CDN’s customers, at a reasonable price. Of course, for business reasons, a content provider might also choose a lower cost path instead of a less congested path to its destination. In abstract terms, when a content provider directly connects to an access ISP (peering, whether revenue-neutral or paid), the two parties have asymmetric options for congestion control. The two parties jointly decide which paths to provision, and then the content provider picks which of these paths to use. Both decisions can either create or mitigate congestion. These degrees of freedom suggest that if regulators are contemplating intervention to deal with observed congestion, they should first try to facilitate cooperation among existing players to resolve the congestion and deliver content efficiently.
a small welfare gain, but at the price of raising a revenue that is much larger than the welfare gain. Thus car drivers will lose substantially from road pricing if road pricing revenues are not returned to them. The intermediate case just reaches hypercongestion at the middle of the peak when road pricing is not in place. In this case, road pricing can produce a larger welfare gain but the revenue from pricing is still much larger than the welfare gain. The flow profile is not much affected by road pricing in this case, which means that simply counting traffic will not easily reveal that road pricing has had any effect. However, the flow without pricing results with a high density of cars traveling at a low speed, while the same flow in the presence of pricing results with a low density of cars traveling at a high speed. In the most congested case, the capacity of the bathtub is only slightly smaller than in the intermediate case, but the congestion outcomes are quite different. Without road pricing, traffic flow has two maxima, one at the beginning of the peak and one at the end. In between, flow drops noticeably due to hypercongestion. Road pricing maintains flow above capacity and thus produces a large welfare gain. The revenue from pricing is a little larger than the welfare gain, which means that drivers would still lose if revenues were not returned to them. Also in this case, pricing has remarkable effects on the timing of trips. Travelers who ex ante travel early in the peak are induced by pricing to travel such that they complete their trips earlier than otherwise. Thereby they free capacity for travelers in the middle of the peak, who then gain from higher speed. The increase in speed is so large that capacity is also freed for the early travelers, some of whom can depart later than they would without pricing, even if they arrive earlier. The same, seemingly paradoxical phenomenon emerges for travelers at the end of the peak who, in the presence of pricing, will depart later but still arrive earlier than they would have without pricing.
Packet drop occurs only if there is severe con- gestion in the router and when the buﬀer over- ﬂows. So, with packet-drop we have four diﬀer- ent levels of congestion indication and appro- priate action could be taken by the source TCP depending on the level of congestion. The four levels of congestion are summarized in Table 1. The marking of CE, ECT bits is done using a multilevel RED scheme. The RED scheme has been modiﬁed to include another thresh- old called the mid th , in addition to the min th and max th . If the size of the average queue is between min th and mid th , there is incipient congestion and the CE, ECT bits are marked as ’’10’’ with probability p 1 . If the aver- age queue is between mid th and max th , there is moderate congestion and the CE, ECT bits are marked as ’’11’’ with probability p 2 . If the average queue is above the max th all pack- ets are dropped. The packet dropping policy of RED is shown in Fig. 1. The modiﬁed packet marking/dropping policy of MECN is shown in
The other solution to the network access congestion problem is to divide the users into clusters or groups, and the users in the same cluster have to go through a gateway or aggregator to access the BS, which makes the access procedure become a two-hop Aloha protocol. Fig. 7 shows a two-hop M2M network, where we assume only the users generate the requests and the gateways are only responsible for collecting and forwarding the request to the base station. Assume there are 𝐼
In this paper, we reviewed two approaches that are available in the DEA literature for evaluating congestion. The congestion model introduced by Cooper et al.  determines the congested inputs and the amounts of congestion. This model, first determines the projection point of the DMU under evaluation and then by assessing the projection point, it finds the maximum value that can augmented to the projection's inputs and remain in 𝑇 𝑁𝐸𝑊 . This model suffers from an occurrence of multiple solutions.
erences that comprise the - - trip timing preferences of Arnott/Vickrey as a limiting case. The main difference concerns the treatment of trip distance. Arnott uses Vickrey’s assumption that outflow from the bathtub is proportional to den- sity. 6 This requires that remaining trip lengths at any time in the bathtub have an exponential (i.e. memoryless) distribution. This assumption can be interpreted as saying that each driver’s trip length is random with a certain distribution and that drivers do not know their trip lengths at the time they make their departure decisions, which is somewhat awkward. This paper merely assumes that drivers choose departure time optimally knowing their trip length and the speed at which they will travel and the physics of the model keeps track of the distance driven for each driver. Arnott ( 2013 ) finds that the efficiency gain from congestion tolling might be smaller than the toll revenue when congestion is light and larger, even much larger, when congestion is severe.