New roundabouts are innovation and revolutionary in roundabout design, with the risk of incidents in these roundabouts far less than traditional common roundabouts. These types of roundabouts control the traffic flow at the entrance and exit of the roundabout, with the guidance of drivers to isolated lines before entering the roundabout and guidance to the spiral lines inside the roundabout as channelized. Another advantage of these roundabouts is the much more balanced division of traffic flows than traditional ones. In Iran, in view of the many problems of capacity, flow and safety of intersections and intersections, this type of roundabout can be very useful in certain conditions. In this research, how these roundabouts are compared and also the comparison of different input capacities based on the origin - destination demand matrix for new roundabouts and common roundabouts of two lanes with the help of Aimsun trafficsimulation software have been investigated. For this purpose, the values of traffic indicators of delay time, density, flow, stop time and travel time of computer simulations for new roundabouts and common roundabouts have been investigated and compared. According to the demand matrix of the origin-destination models loaded in the software and comparisons done, the optimal model for the highest capacity and the lowest delay time and travel time is presented. In this simulation, 8 to 16 percent increase in traffic flow and a decrease of 34 to 59 percent for travel time and delay time in the new roundabouts is shown in comparison with the common two-lane roundabouts.
1) Transmodeler software is a multi-function trafficsimulation package developed by the United States Caliper company for urban traffic planning and simulation. The specific flow of the bus priority simulation on the non-coordinated phase of the Changjiang Road by Transmodeler is shown in the Figure.2. The distance between the five intersections of the green belt is 350m, 270m, 450m, 245m respectively, the loss time of the green light for each phase is 4s, the common cycle of the signal is 94s, the velocity of the green band is 47.5Km/h, and the two directions of the bandwidth is 25s, and the direction of the Changjiang Road is the direction of coordinated phase. The bus flow detector is set in the import of the intersection before the stop line 80m. And the bus priority control on the coordinated phase also includes two control modes of lately breaking green light and early turning on green light. The lane saturation rate set in the Transmodeler is 2000pcu/h, the average passenger loaded by bus is 30 people and by car is 1.5 people, the conversion rate of bus is 2 to the standard car. The current basic flow of intersections in the Changjiang Road is captured by the video device and acquired by algorithm. In order to verify and evaluate the benefit of implementing bus priority on the non-coordinated phase of the main road signal, this paper selects the per capita delay, the total delay, the average delay and the total parking number output by Transmodeler as the evaluation index.
we have got surveyed the introduction of TraCI and highlight analyzed that it is prominently speedier to couple a gadget investigate machine with the road guests test device instead of forming pursue records first and run the gadget reenactment in the ensuing degree, regardless of whether or never again or now not we need not issue with a control hover for the length of execution. utilizing the TraCI interface one can likewise even test convoluted VANET circumstances, for instance, incidents (for instance through utilizing stopping fabulous autos at a chose time second) or copying hazardous road conditions (e.G., with the guide of utilizing modifying the speed of vehicles which have really 'saw' such conditions). We starting at now use TraCI to complete and view absolutely stand-out VANET applications proposed through C2C-CC . In fate work, we can loosen up TraCI to help a greater grouped type of gadget and street site online guests test frameworks.
Basic design includes five parts: master module, fault sensing circuit, RF(receiver and transmitter), motor and LCD. The block diagram of the project is shown in figure 4. PIC16F877A is selected as microcontroller for the master module. PIC(Peripheral Interface Controller) as 8Kx14 Flash program memory, high performance RISC based CPU, low power consumption and it has up to 14 sources of interrupts. Fault sensing circuit used to find the defects and sends information to the respective systems through RF(Radio- Frequency). Motor drive is used to control the speed of the motor.
signal timing plans, survey respondents indicated that ATCSs are only tools for traffic management and they need to be supervised and controlled by skilled engineering staff. Proper training and acquisition and retention of expertise within an agency were reported as the most important factors for allevi- ating institutional barriers for ATCS deployment. ATCS opera- tions are often not perceived as being difficult; however, it appears that ATCS users are not often given the opportunity to learn how to fully operate their systems. One of the reported operational problems indicated a lack of the basic knowledge for operating an ATCS. A majority of the ATCS users rely on in-house expertise, which is more an indication of not having adequate resources to hire outside support than that ATCS users are fully trained to control and operate their systems. In general, ATCS users would like to acquire additional expertise; however, the agencies do not have enough financial resources to acquire comprehensive training, and most of the agencies are short staffed. ATCSs are considered more operationally demanding than conventional traffic signal systems; however, agencies are not able to support these systems in the same way they support conventional traffic signal systems. Unlike con- ventional systems that are maintenance-intensive, ATCSs require more emphasis on the expertise necessary to operate their sophisticated operations. This switch in the type of labor (from maintenance to operations), which is needed to support proper ATCS operations, is often not recognized by an agency until the ATCS is already deployed. This inability to recognize the need for additional operational expertise in a timely man- ner can adversely affect the ATCS performance. If the agency is disappointed with the performance, it will be reluctant to expand on the existing system or to procure new ATCSs.
In this paper, three algorithms are proposed and tested through extensive simulations using a discrete event model for traffic light controller. In the way, three control parameters were identified namely; green-interval, cycle and sequence of entry openings. To study the proposed algorithms, two simulation experiments were conducted. In the first one, the proposed algorithms (AW Adaptive, AW Predictive and AW VariableC) were compared with fixed green-interval baseline algorithm (AW Fixed). Results showed that AW VariableC outperformed all other algorithm including baseline algorithm due to its high adaptability to the variations in
In the present paper we have described several extensions, which have been implemented into SUMO in order to reflect mixed traffic conditions, where automated and manual vehicles coexist in one scenario. More specifically, we have implemented an ACC model, which is often applied to model automated controllers (Milanés & Shladover, 2014; Xiao, Wang, & van Arem, 2017) and a generic mechanism to impose perception errors upon an arbitrary car-following model. Further, we have presented simulation results for two scenarios where the new models have been applied to evaluate the effects of different TM measures for transition areas, where an increased amount of ToCs can be expected to occur. These results suggest that both cases bear the potential for considerable benefits if the TM is applied. In the simulation the TMC significantly increased either traffic safety (see Section 3.1) or traffic efficiency (see Section 3.2).
Our protocol evaluations are based on the simulation using OPNET simulator. The scale up network model consists of thirty nodes distributed randomly in a space of 250m X 250m. The channel speed of the wireless LAN is also set to 2Mbps. The simulation parameters have been reported in Table 1. Fig. 2 is a snapshot of the proposed network model considers for simulation. In order to enable direct, fair comparisons between the protocols, it was critical to challenge the protocols with identical loads and environmental conditions. Each run of the simulator accepts as input a scenario file that describes the exact motion of each node and the exact sequence of packets originated by each node, together with the exact time at which each change in motion or packet origination is to occur. We pre-generated 35 different scenario files with varying movement patterns and traffic loads (FTP), and then ran all three routing protocols against each of these scenario files. Since each protocol was challenged in an identical fashion, we can directly compare the performance results of the three protocols. For all simulations, the same movement models were used, and the number of traffic sources was fixed at 30. Fig. 2 shows a model of nodes used to simulate different ad hoc network protocols.
The compensation effectiveness of the active power filter is corroborated in a 2 kVA experimental setup. A six-pulse rectifier was selected as a nonlinear load in order to verify the effectiveness of the current harmonic compensation. A step load change was applied to evaluate the transient response of the dc voltage loop. Finally, an unbalanced load was used to validate the performance of the neutral current compensation. Because the experimental implementation was performed on a dSPACE I/O board, all I/O Simulink blocks used in the simulations are 100% compatible with the dSPACE system capabilities. The complete control loop is executed by the controller every 20 μs, while the selected switching state is available at 16 μs. An average switching frequency of 4.64 kHz is obtained.
chitecture is mainly composed of autonomous on-line simula- tors which continuously monitor and model the network con- ditions and topology. Based upon the on-line model of traffic and topology, the simulators can execute simulations to eval- uate the performance of the network for a given set of proto- col parameters. The assumption is that network control proto- cols (e.g.: traffic management, routing protocols) are sensitive to traffic loads and a subset of their parameters. The goal then is to have the on-line simulation system use sophisticated parameter search methods to search for better parameter settings applica- ble to the current traffic and topology mix. In other words, the simulation system can support continuous tuning of the network based upon the on-line modeling, parameter search and simu- lation capabilities. The on-line simulation scheme uses a best- effort parameter search strategy whose emphasis is not on “full” optimization, but on continuously and increasingly moving the system towards a “better” operating point. And in this sense, on-line simulation equips the network management infrastruc- ture with pro-active, dynamic and automated management ca- pabilities.
The purpose of active training of the affected limb is to strengthen the muscle strength and endurance of the affected limb. In the control process, the impedance control algorithm with better human-computer flexibility is used to control the interaction between the robot and the patient. In the active training, it is necessary to formulate a suitable human-computer interaction trajectory according to the comprehensive assessment results of the patient's physical state, so that the patient can prolong the exercise time of the patient as much as possible and ensure the rehabilitation training effect. The normal person's lower limb output trajectory approximates the sinusoidal curve trajectory during treadmill exercise. This trajectory is the most comfortable trajectory of the human body after optimal adjustment of the human brain. Therefore, in this paper, the resistance of the lower limbs of the patient is set to the trajectory in the form of sine and cosine.
VISSIM software system can be divided into four parts: traffic supply, traffic demand, trafficcontrol facilities, data output. Traffic supply describes the physical infrastructure, including signal poles, parking facilities, bus stops, parking lots, detectors and other equipment placed on the physical infrastructure. Traffic demand generates the demand of people and vehicles running on the traffic supply. Traffic demand is determined by OD matrix and section input. The assignment model and the description of path flow are part of this module. Bus routes are defined as the sequence of sections and stations. Trafficcontrol facilities, non-Interchange intersections by trafficcontrol module to define the rules, four-way parking concession rules, the main and secondary roads through the gap acceptance priority rules, traffic lights control scheme. Data output includes dynamic demonstration, trafficcontrol status, statistical data and vehicle status.
Abstract: The proposed system is developed with the aim to reduce traffic congestion. This system is designed to provide the appropriate signal timing so that the traffic clogging is reduced. The prevailing system includes setting fixed signal timing for each lane irrespective of the number of cars present at the traffic signal. Therefore, the green signal time given to each lane would be the same even if the lane has few cars or a wide number of cars, which is one of the reasons for traffic congestion. Thus the proposed system makes use of image processing and calculates the number of cars in every lane based on the car area and appropriately renders the traffic signal timing.
The traffic lights system utilized in India are essentially pre- planned wherein the season of every path to have a green signal or light is settled. In a four-path traffic signal, one path is given a green signal at any given moment. Along these lines, the traffic light enables the vehicles of all paths to go in a grouping. In this way, the activity can progress either straight way or turn by 90 degrees. So regardless of whether the activity thickness in a specific path is the minimum, it needs to sit tight superfluously for quite a while and when it gets the green flag it pointlessly makes different paths sit tight for significantly longer lengths. Numerous techniques had me acquainted with take care of the issue of activity utilizing sensor and fluffy rationale strategies, but the issue constant illuminating the issues is still tested. This issues can overcome by utilizing Digital Signal Processing Technique i.e. image Processing.
Abstract — The optimization of traffic light control systems is at the heart of work in traffic management. Many of the solutions considered to design efficient traffic signal patterns rely on controllers that use pre-timed stages. Such systems are unable to identify dynamic changes in the local traffic flow and thus cannot adapt to new traffic conditions. An alternative, novel approach proposed by computer scientists in order to design adaptive traffic light controllers relies on the use of intelligents agents. The idea is to let autonomous entities, named agents, learn an optimal behavior by interacting directly in the system. By using machine learning algorithms based on the attribution of rewards according to the results of the actions selected by the agents, we can obtain a control policy that tries to optimize the urban traffic flow. In this paper, we will explain how we designed an intelligent agent that learns a traffic light control policy. We will also compare this policy with results from an optimal pre-timed controller.
As the backbone of the city road network, urban expressway shares large proportion of the traffic. In Beijing, major urban expressway accounts for only 8% of the total length, but carries nearly 50% of the traffic flow ; in Shanghai, only 5% bears more than 35% of the city traffic traveling. Urban expressway plays a vital role in the urban road network which gradually shifted from the large- scale infrastructure construction to refinement traffic management. With the traffic demand rapid growing, much more congestion and traffic accidents, integrated active traffic management should be introduced in the background of coordination between road and vehicle. As an important part of the active traffic management, the speed guidance control has certain positive significance to improve the expressway capacity, reduce the accident risk and decrease even eliminates traffic congestion.
With respect to transit hub distribution efficiency and passenger behavior analysis, passenger individual behavior model was firstly set up by Gipps et al. , they supposed that passenger movement obeyed the short circuit law, and put forward a simple route choice model. Helbing  illustrated the complex characteristics of passenger flow, and built social force model. Xiong. H. et al.  proposed a continuous-time random walk model for pedestrian flow walking behavior simulation. Lu. L. L. et al.  explored the effects of different walking strategies on bi-directional pedestrian flow in the channel with cellular-automata formulation. Daamen et al. [15,16] summed up that passenger flow crowd degree in hub interlayer facility as the key factor directly affected route choice behavior by analyzing the relations between interlayer facility layout and passenger path-finding behavior. Lin. Y. D. et al.  analyzed passenger flow characteristics inside of the hub, and identified passenger flow distribution bottleneck. Similarly, scholars researched on the passenger flow through corridor bottlenecks, experiments results showed that the bottleneck capacity was almost linearly increased with the width, and jamming occurred below the maximum capacity [18,19,20]. In recent researches, Duive et al.  proposed the state-of-the-art crowd motion simulation models to explain the different phenomena of crowd motion such as lane formation, stop-and-go waves, faster-is-slower effect, turbulence and zipper effect. Guillermo H. G.  built a mathematical model of the formation of lanes in crowds of pedestrians moving in opposite directions. Bandini et al.  improved the traditional floor field cellular automata model to simulate the negative interaction among pedestrians of high density. Xie. Z. Y. and Wang. S. W. et al. proposed the forecasting methods of passenger flow based on hybrid temporal-spatio forecasting model and modular neural network [24,25]. In addition, Gao. L. et al. proposed the distribution service network model of comprehensive passenger transport hub, simulation results showed that research findings were of practical significance in performance evaluation of passenger flow distribution [26,27]. Wang. S. W.  also built the transit station congestion index based on pedestrian simulation and gray clustering evaluation, which could reflect the congestion degree of transit stations.
The main objective of this project work is to develop a cost-effective and intelligent vehicle trafficcontrol system to manage the vehicles moving in different roads. The results from the simulation and experimental test rig validate its applicability for Dhaka and other cities of Bangladesh. However it can be used smoothly to the cities of other countries also. The system consists of a microcontroller embedded with the control algorithm. The algorithm decides about the vehicles numbers from sensor data and takes necessary decision to clear the road effectively.
Traffic congestion is one of the major problems of urban life. This problem is increasing day by day because of the increasing number of vehicles with limited infrastructural development. One of the oldest ways was to have a traffic police to control the traffic manually through hand signaling. But as this became quite grueling, the conventional traffic light systems were developed to controltraffic. But if a lane has more traffic congestion than the others, the existing system fails to controltraffic. To solve this problem, a real time trafficcontrol system is needed which will control the traffic.