The waterway in the Vistula Delta is not covered by any Traffic Control System, River Information System or Automatic Identification System. Data regarding inland waterway transport, unit traffic and transported cargo can be obtained from the Central Statistical Office and Regional Waterways Authority in Gdańsk and an observation method (Fig. 4) for determining the average speed of vessels. This information is obtained with a long time delay and can’t be used to model the traffic flow of units in real time. Considering the current use of the navigable route in the Vistula Delta, it seems that this way of acquiring data is sufficient. In the case of an increase in traffic density and density of units moving along this route, it will be necessary to introduce a monitoring system in a continuous movement of units in the studied area.
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Typically any queuing system is composed of units, referred to as customers, needing some kind of service and who arrive at a service facility, join a queue if service is not immediately available, and eventually leave after receiving the service. A server refers to mechanism that delivers service(s) to the customer. If upon arrival a “customer” finds the server busy, then she/ he may form a queue, join it or leave the system without receiving any service even after waiting for some time. According to Lartey (2014), the following configurations of vehicular traffic flow as typical queuing system are possible. They are explored in this work.
Figure 1.1 shows the funnelling issue that occurs in a network with multi-to- one uplink traffic. Suppose that A is a node that serves as data collector (also known as sink node) and nodes from B to P are sensor nodes that serve as data transmitter (also known as sensor nodes). Each of sensor nodes has different traffic intensity based on its distance from sink node. For example, a comparison between node B that is near to node A and node M that is far from node A. By assuming that every sensor node transmits a packet periodically, the node B has six packets that must be transmitted periodically. Among of them are one packet from its own packet and five packets from other nodes i.e. E, I, M, J, and N. Meanwhile, the node M only has one packet to transmit its own packet periodically.
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b) Weak tourist traffic (50,000 to 250,000 persons/one million inhabitants) is typical of countries forming slightly more than two-thirds of the portfolio consisting of 45 countries and generating less than 0.5 worth of air traffic. In the very high weighted average, which is hundred and fifty times greater than the arithmetic average, the population of many hundreds of millions in the United States, Japan, and Russia plays a decisive role. Among the national extremities, the minimum value is not greater than the minimum value in the previous group (Table 5). The vast majority of the members of this group are concentrated in the zone between the tourism intensity line of 150 thousand tourists /1 million people and traffic intensity line of 0.7 airports. The overwhelming majority of the Y-axis adjacent members are African, where tourism has only a very modest share in air traffic (because the majority of foreigners arrive on land vehicles from the neighbouring countries).
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The community abutting subproject road will be adversely affected by increase in noise level due to road development activities. Road noise depends on factors such as nature of construction activity, traffic intensity, type and condition of the vehicles plying on the road, various road construction activities increase noise levels in the impact zone of the project corridor. The construction activities such as excavation for foundations, grading of the site, construction of structures and facilities, movement of heavy vehicles, loading, transportation and unloading of construction materials produce significant noise during construction stage.
Calculations were performed for the situation of 2010 year. In order to define congestions loss, the assumption, that traffic flows in Kaunas city increased by 5 percent every year (information of Kaunas city Transport department) was done. According to „Regitra“ registration data, there was no essential change in vehicles composition, but since 2005 every year 6 percent of cars of M1 class used fluid fuel. It is necessary to take into account additionally, that traffic intensity in the main and country roads in Kaunas city has increased by 20-30 percent and approached the flow intensity in city streets. This increases probability of congestion in cities.
urgency is therefore very important to ensure that the right patient receives the right care at the right moment. The time target of 15 min for patients with life-threatening complaints (U1) appears to be met significantly less often as the distance increases. Furthermore, it appears that other factors, such as traffic intensity and home visit busi- ness, are of hardly any influence. This is probably due to the fact that the doctor interrupts his work immediately for a U1 patient and uses the bus lane, sirens, and flashing lights to get to the patient immediately. For a somewhat lower priority, such as that for U2, we see that distance does not play a role, but home visit business and traffic intensity do.
The requested traffic from the users in the network will be referred to as the offered traffic (to the network) in the follow- ing. The offered traffic levels in the hotspot cell and the sur- rounding cells are varied according to spatial traffic intensity maps that are scaled differently within the hotspot cell and the surrounding cells in order to generate different load ratios and interference situations to the area served by the hotspot cell. The traffic intensity maps are assumed to contain the av- erage offered traffic in a certain time interval (e.g. 1 h). Thus, there is no scheduling modelled explicitly and a time aver- aged resource allocation is simulated as explained in the fol- lowing section. The main simulation parameters used in the evaluation of the DSA mechanisms are given in Table 1. 2.1 DSA configurations
Abstract: This paper deals with the spatial mining of the GPS logs for finding optimal shortest path between every source & destination. The optimal route finding is based on the fuzzy logic based on shortest path calculation. The fuzzy logic is used for finding the optimal route based on various constraints like real time traffic, traffic intensity and the path with the optimal travel cost between the source to the destination. This paper is GIS based implementation of finding the optimal path with fuzzy logic and it is the comparison with the other GIS based tools for finding the shortest path between every source & destination. This fuzzy logic based shortest path algorithm is better than the classical Dijkstra’s algorithm for finding the shortest path. This fuzzy logic based shortest path algorithm outperforms all the other shortest path algorithms.
From table 1 above, the results for the three channels in the morning section shows fairly stable traffic congestion but not a smooth and good traffic flow. Also, the afternoon section the traffic intensity results for the three channels shows good stable and smooth traffic flow. During evening hours, the traffic intensity recorded smooth and stable traffic flow situation for the three channels intersection. The result from table 2 shows that the expected waiting time of vehicles in the system is more than the expected waiting time of vehicles in the queue for all the channels in each section per day. We also observed from the results that the expected numbers of vehicles in the system is more than the expected numbers of vehicles in the queue waiting to be served in each channel. The time each vehicle spent in the queue waiting for service in all the channels per session is less than the mean queue length. From fig 2; the graph explained the traffic flow intensity and its shows unstable traffic congestion in the morning and evening than the afternoon section.
Abstract. By applying spatio-temporal aggregation to traffic data consisting of vehicle trajectories, we generate a spatially abstracted transportation network, which is a directed graph where nodes stand for territory compartments (areas in geographic space) and links (edges) are abstractions of the possible paths be- tween neighboring areas. From time series of traffic characteristics obtained for the links, we reconstruct mathematical models of the interdependencies be- tween the traffic intensity (a.k.a. traffic flow or flux) and mean velocity. Graph- ical representations of these interdependencies have the same shape as the fun- damental diagram of traffic flow through a physical street segment, which is known in transportation science. This key finding substantiates our approach to traffic analysis, forecasting, and simulation leveraging spatial abstraction. We present the process of data-driven generation of traffic forecasting and simula- tion models, in which each step is supported by visual analytics techniques.
Abstract. The paper deals with the assessment of the rock- fall risk for a road stretch, in southern Italy, affected by high traffic intensity. Three qualitative rockfall risk rating systems (QRSs) which use an exponential scoring with a base of 3 were employed, and then the results were compared. The used methods are the following: the Rockfall Hazard Rating System, a modified version of this method already proposed in the past by one of the authors, and the modified version of the Colorado Rockfall Hazard Rating System. The stud- ied road stretch is about 11 km in length and is part of a very tortuous road flanked by rock slopes characterised by complex geostructural and geomechanical layouts. The road was subdivided into 56 sections, defined so as to have – as much as possible – homogeneous geological characteristics. By means of the three QRSs, it was possible to ascertain that high levels of rockfall risks are due to the lack of ditches, a very limited percentage of decision sight distance (PDSD) values and a small roadway width, whereas a subordinate factor is the hazard caused by rockfalls. Several positive and negative aspects arising from the use of the employed meth- ods are highlighted and discussed.
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Therefore, from the premise of arguments presented above, it is very much appropriate to investigate the effect of DGC without buffer on the parameters giving simulation result of Fig. 5 since DGC is meant to offset some of the tradeoffs of guard channel implementation by providing more combined channels when new call traffic load is high. It was said in  that when handover traffic intensity is low, the PGC with buffer should be used as channel assignment scheme for Mobile WiMAX network but that when handover traffic intensity is high, that the NP assignment scheme should be used. The investigation in Fig. 5 was extended to include dynamic guard channel (DGC) assignment scheme for the purpose of graphical performance comparison of the simulation results of the four channel assignment schemes as shown in Fig. 6. The simulation parameters used is shown in table 1.
Abstract —The increasing concern about the energy consump- tion of telecommunication networks is driving operators to manage their equipments so as to optimize energy utilization without sacrificing the user experience. In this paper, we focus on UMTS access networks, since access devices are the main energy consumers in UMTS networks. We propose a novel approach for the energy-aware management of UMTS access networks, consisting in a dynamic network planning, that, based on the instantaneous traffic intensity, reduces the number of active access devices when they are underutilized (typically at night). When some access devices are switched off, radio coverage and service provisioning are taken care of by the devices that remain active, possibly with some small increase in the emitted power, so as to guarantee that service is available over the whole area, with the desired quality.
estimators are shown in Table 1. Trends in Table-1 clearly reveals that MLE’s and UMVUE’s of all the queue’s characteristics are constant while Bayes estimates of all the characteristics tends to be decreases as mean of the traffic intensity decrease. In yet another example on assuming = 2, = 4, y = 5, n o = 3 and varying k [the number of the
Fuzzy logic has been widely used to develop an adaptive traffic signal controller because it allows qualitative modeling of complex systems. However, existing research has developed Fuzzy Logic Traffic Signal Controller (FLTSC) based on non-mixed traffic conditions. These FLTSC are not appropriate to the mixed traffic conditions of developing countries where the traffic streams consist of different types of vehicles with a wide variation in their static, dynamic and operating characteristics. This paper describes the design and evaluation of an adaptive traffic signal controller based on fuzzy logic for an isolated four-way intersection with specific reference to mixed traffic in developing countries. The controller is designed to be responsive to real-time traffic demands. Video image processing has been proposed to capture traffic data such as maximum queue length (in meters) and average occupancy rate (in %) from each approach of the intersection. The proposed FLTSC uses maximum queue lengths and average occupancy rates collected during the previous cycle in order to estimate the number of seconds of green time required by each set of signal groups (stage) during the next cycle. The effectiveness of the proposed FLTSC was examined and analysed by the simulation program VISSIM. The performance of this controller is to be contrasted with the Fixed Time Controller (FTC) and Vehicle Actuated Controller – Extension Principle (VAC-EP) for different traffic conditions on a simulated fourway intersection. The simulation results indicate that the performance of the proposed FLTSC is generally better than the FTC and VAC-EP, especially in cases time-varying traffic.
Related work in maritime traffic management: For mar- itime traffic optimization, most current works either involve high fidelity commercial simulation tools to model micro- level navigation characteristics of vessels (Marin 2018), and expert systems and rule-based approaches to model the macro-level behavior of the traffic (Hasegawa et al. 2001; Hasegawa 1993; Ince and Topuz 2004). However, enhancing safety of navigation in a geographically constrained heavy traffic area, requires statistical modeling and learning from large amounts of historical data. Rule-based expert systems are not sufficient to resolve every possible close quarter sit- uation in the heavily trafficked Strait.
In reliability theory this ratio is also known as availability ratio. The situation become alarming when one is going for queue characteristics of the model of the same nature accomplishing the same task in varying conditions. Obviously, for overcoming the situation, it seems statistical logical to assume variations in traffic intensity represented by known suitable prior distribution. In this regard, on the repeated analysis of various queue system, we have a strong base for collecting prior information showing variations in . For example, in a Barber’s shop where the arriving individuals are the customers and the barbers are the server, this intensity may differ in respect of days in a week. Another example is represented by letter arriving at a typist’s desk, where the letter is represented the customer and typist represented the server. Following the concept some of the studies like [2, 4,5, 6 and 7] include a vast literature on some queue characteristics in the Bayesian framework in which updating the prior with experimental data has been main concern. However in the Bayesian framework, it should be recognized that the prior do have an impact on the basic distribution and therefore, the present study consider the analysis of the robustness of queue's characteristics in (M\M\1):(\FCFS) system when traffic intensity is updating in respect of prior and post prior variations.
Mid-block counts are carried out on a weekly an all busy roads to study the density of traffic frequently for about 16 hours in order to assess the total daily volume and their composition and peaking pattern as well as their direction. The vehicle counts are generally made and recorded at 15 minutes’ intervals. The volume ratio of road stretches can be obtained from these data in order to help in planning long tern strategies.
This paper provided the point definition and calculation of the intensity of the urban rail transit station based on the node degree in passenger traffic and road network model, and gave the concept and the specific calculation method of the station importance degree according to