Average Delay Research Articles

Research on optimization method for traffic signal control at intersections in smart cities based on adaptive artificial fish swarm algorithm

The transportation environment of smart cities is complex and ever-changing, and traffic flow is influenced by various factors. With the increase of traffic flow in smart cities, optimizing traffic intersection signal control has become an important method to improve traffic efficiency and reduce congestion. To this end, a smart city traffic intersection(SCTI) signal control optimization method based on adaptive artificial fish swarm algorithm was studied. Establish the Equation of state of traffic flow at SCTIs to understand the actual traffic flow at SCTIs. On this basis, design SCTI signal control parameters, with the minimum average delay and average number of stops as objective functions, and construct an optimization model for SCTI signal control. By combining chaotic search theory and adaptively improving the artificial fish swarm algorithm, based on the adaptive artificial fish swarm algorithm, the intelligent city traffic intersection signal control optimization model is solved to achieve intelligent city traffic intersection signal control optimization. The experimental results show that the average delay of this method is 7.8 ms, the average number of stops is 2, and the travel time is 68.4 s s. Thus, it is proved that the method in this paper has a good optimization effect of traffic signal control at smart city intersections, which can improve the optimization efficiency of traffic signal control at smart city intersections and reduce traffic congestion at smart city intersections.

Efficient design approaches to CMOS full adder circuits

In the field of high-performance chip designs, many applications have the ability to tolerate small errors through the use of approximate full adders. By appropriately reducing the corresponding precision, the performance of hardware implementation can be greatly improved. The adders are commonly applied in digital signal processors (DSP), communication systems, image processing, etc., to quickly complete addition estimation operations. This paper proposes the designs of 8 approximate adders and compares their performance with existing circuit structures. Simulation results demonstrate that the approximate adders proposed in the first group (i.e., LCAFA1∼LCAFA3) achieve an average area reduction of 21.44 % and an average delay reduction of 42.85 %. In the second group, the proposed approximate adders LCAFA4∼LCAFA8 achieve an average power reduction of 28.35 %, an average area reduction of 17.39 %, an average delay reduction of 84.25 %, and an average decrease in the power-area-delay product (PADP) of 68.26 %.

Temperature regulation for liquid-cooled fuel cell based on adaptive sliding mode control

The liquid cooling system (LCS) of the proton exchange membrane (PEM) fuel cell suffers from large time delay, coupling, uncertainties, and various disturbances, making it susceptible to temperature regulation overshoot and control oscillation. This article focuses on a composite control scheme for the LCS that has effectively addressed the aforementioned issues. We first propose a two-layer control approach for the pump to minimize coupling, and an average delay model (ADM) for radiator whose parameter uncertainties are removed by equilibrium optimizer (EO). To overcome disturbances, an adaptive sliding mode control (ASMC) based on extended state observer (ESO) is presented, and its finite-time convergence is demonstrated. Additionally, the indirect regulation for the stack temperature is designed to considerably reduce the time delay in the fan control loop. Co-simulation results indicate that the proposed ASMC significantly suppresses the chattering phenomenon compared with regular sliding mode control (SMC). In contrast to incremental fuzzy control, the proposed scheme possesses superiorities in response speed, tracking accuracy, anti-disturbance, as well as suppression of overshoot and oscillation.

Traffic Signal Timing Scheme Based on the Improved Harris Hawks Optimisation

With the continuous increase of urban vehicles, traffic congestion becomes severe in the metropolitan areas and higher car utilisation areas. The traffic signal timing scheme can effectively alleviate traffic congestion at intersections. We need to make a profound study in the traffic signal timing. An optimisation model is established, which not only takes the average delay time of vehicles, the number of vehicle stops and the traffic capacity, but also takes the exhaust emissions as the evaluation indexes. The model is too complex and involves too many variables to be solved by using multi-objective programming. Thus, the Harris Hawks Optimisation (HHO) with few parameters and high search accuracy was used to solve the model. To avoid the disadvantages of poor search performance and easy to fall into local optimisation of the Harris Hawks Algorithm, multi-strategy improvements were introduced. The experimental effects show that during the peak hours of traffic flow, the improved algorithm can reduce the average vehicle delay by 36.7%, the exhaust emission by 31.2% and increase the vehicle capacity by 41.6%. The above indicators have also been upgraded during the low peak stage.

Enhancing Quality of Service and Energy Efficiency in Wireless Sensor Networks through TDMA-Based Topology and Cross-Layer Approaches

It is necessary to continuously monitor the environment with the sensors in delay-sensitive wireless sensor network applications. The continuous monitoring operation results in higher energy consumption by the sensor nodes; wireless sensor networks are also subject to numerous difficulties, including latency, packet loss, and throughput. These are the most important constraints in wireless sensor networks (WSNs) and need to improve for better quality communication. In this research, quality of service is related to numerous systems that have very sensitive parameters such as end-to-end delay, packet loss, throughput, energy consumption, latency, and average delay. Therefore, it is necessary for the sensors to transmit reliable data within a reasonable timeframe. To find out better communication results, we have used the TDMA (Time Division Multiple Access) approaches in our paper to improve packet delivery ratio (PDR), latency, and throughput. The NS2 simulator is used to test communication between nodes. Latency, the packet delivery ratio (PDR) of transferring data packets, and throughput are measured. The TDMA approach is used in our proposed topology, and we can see a specific area where nodes are connected to communicate with one another. Each node is communicating with the other node using two different paths. Each node's bandwidth, latency, and PDR will be different. Hence, in one path, a TDMA approach is implemented, which results in improved node performance in terms of latency, packet delivery ratio, and bandwidth.

Channel Bonding Effects of the IEEE802.11n Standard on the WLANs Performance

The evolution of wireless communication technologies has led to the widespread adoption of Wireless Local Area Networks (WLANs). IEEE 802.11n provides many enhancements in physical and MAC layers such as (channel bonding(CB), Multiple Input/Output (MIMO), frame aggregation and block acknowledgment) to achieve higher throughput and improved reliability. As a result, CB is a key technique for enhancing the performance of wireless network. It is frequently used in various fields, such as workplace networks and live streaming. This research paper focuses on analyzing the effect of channel bonding on the performance of the IEEE802.11n WLANs, especially in two terms (throughput and delay). By using the OPNET (Riverbed) Modular version 17.5, five various number of nodes (5, 10, 15, 18 and 30 nodes) uniform topologies are modeled and simulated to investigate the WLANs performance for various MIMO up to 4 SS. Simulation outcomes indicated that applying of CB enhanced both delay and velocity performance. Specifically, in the scenarios (30 nodes), for MIMO (4×4) spatial streams, the average throughput value for channel bonding is (157.28 Mbps) compared to (87.41 Mbps) for no-channel bonding, and the average delay value for channel bonding is (0.0476 Sec) compared to (0.06762 Sec) for no-channel bonding. The enhancement values in the performance of WLANs are 42% and 79.9% for latency and throughput (velocity), respectively.

A Study of Adjacent Intersection Correlation Based on Temporal Graph Attention Network

Traffic state classification and relevance calculation at intersections are both difficult problems in traffic control. In this paper, we propose an intersection relevance model based on a temporal graph attention network, which can solve the above two problems at the same time. First, the intersection features and interaction time of the intersections are regarded as input quantities together with the initial labels of the traffic data. Then, they are inputted into the temporal graph attention (TGAT) model to obtain the classification accuracy of the target intersections in four states—free, stable, slow moving, and congested—and the obtained neighbouring intersection weights are used as the correlation between the intersections. Finally, it is validated by VISSIM simulation experiments. In terms of classification accuracy, the TGAT model has a higher classification accuracy than the three traditional classification models and can cope well with the uneven distribution of the number of samples. The information gain algorithm from the information entropy theory was used to derive the average delay as the most influential factor on intersection status. The correlation from the TGAT model positively correlates with traffic flow, making it interpretable. Using this correlation to control the division of subareas improves the road network’s operational efficiency more than the traditional correlation model does. This demonstrates the effectiveness of the TGAT model’s correlation.

Analyzing Delay and CO Emissions: A Simulation Study of the Median U-Turn Method at Intersections

To improve traffic efficiency and reduce pollutant emissions at urban road intersections, VISSIM software was used to simulate traffic states to compare the median U-turn method with the direct left-turn method under various traffic volumes and left-turn ratios. Based on the average delay and CO emissions, suitable conditions were identified for using the median U-turn method at intersections. The results show that there are three critical left-turn ratio boundary curves named Rdcri(1), Rdcri(2), and Rdcri(3) based on the relatively smaller average delay and there is a critical left-turn ratio boundary curve RCOcri based on the lower average CO emissions at the intersection when the through traffic volume is in the range of 0–3000 veh/h and the left-turn ratio is in the range of 0–4. The median U-turn method is considered applicable at the intersection when the through traffic volumes are in the range of 0–87 veh/h, 87–400 veh/h, 400–416 veh/h, 416–934 veh/h, and 934–3000 veh/h, respectively, and, accordingly, the left-turn ratios are in the range of 0–Rdcri(2), 0–Rdcri(2) or Rdcri(1)–4, 0–4, 0–Rdcri(3), and RCOcri–Rdcri(3). These findings can provide a reference for traffic managers to organize the left-turn traffic at an intersection reasonably.

Management Policy in Urban Rail Transit System: Trade-Off between Social Distancing and Service Efficiency Using Simulation in the Post-Epidemic Era

The past COVID-19 pandemic introduced the world to the necessity of dealing with the trade-off between minimizing probability of contagion, and providing people with services they need. This trade-off stipulates that a large person-to-person distance will reduce contagion probability, but will render service inefficient, and vice versa. This work focuses on the urban rail transit (URT) hub, as an example of a busy passenger area, from which we can derive an optimal preparedness policy to use during the pandemic time of any coronaviruses. We use simulation methodology, based on the classical social force model, to represent behaviors and characteristics of pedestrians. Passenger flow movement process is a mechanism we explore to figure out how the epidemic management policy and pedestrian psychological-related behaviors interact with the URT system. The systems’ complexity regarding contagion-prevention distances are tested over a few scenarios: before/after the outbreak, and for different person-to-person distances demonstrating different crowd levels. A case study of Xinjiekou Station, Nanjing URT, China, enables assessment of passenger management policy with person-to-person distances of 0.5 m, 1.0 m and 2.0 m. Multi-scenario performance illustrates the trade-off in dynamic between the efficiency of pedestrians’ walking behaviors and the distancing needs for preventing coronaviruses transmission. The results show that queuing length with social distancing of 1.0 m and 2.0 m is increased by 4.17% and 21.22%. The average delays in boarding are 14.1 s and 22.5 s for 1.0 m and 2.0 m, which leads to 15.29% and 22.39% increases, respectively, in comparison with ordinary social distancing of about 0.5 m.

A rail-to-rail high speed comparator with LVDS output in 0.18-[formula omitted]m SiGe BiCMOS Technology

Achieving low propagation delay in comparators under low input overdrive voltage is challenging. To overcome this difficulty, this paper presents a novel rail-to-rail high-speed comparator. By clamping the output node of the current summation circuit relative to a fixed level VC, the overdrive recovery time under large signal is successfully reduced. Moreover,by adopting a cascaded approach with multiple stages of high bandwidth and low gain,not only is the comparator’s gain enhanced,but it also acquires higher bandwidth. Ultimately, the comparator’s output is transmitted at high speed through an LVDS interface. This design is implemented using 0.18μm SiGe BiCMOS technology. Simulation results show that the comparator has a static power consumption of 26.4 mW, and for 5 mV input overdrive, the average propagation delay is about 1.09 ns.

Intersection control based on Fine-grained Phases for connected and automated vehicles

Traditional traffic signal control is one of the most widely used urban traffic control methods, due to its high efficiency, robustness, and simplicity. However, in the era of connected and automated vehicles (CAVs), it is prospective to enhance traffic control precision and achieve vehicle coordination in a more efficient manner. Focusing on an isolated intersection, this study proposes an innovative concept of Fine-grained Phase (FP) for CAV-enabled traffic control, which realizes more refined right-of-way allocation than traditional signal phases while preserving the simplicity and scalability. The development of FP jointly optimizes traffic control strategy and intersection layout design to fully utilize time-space resources, and multiple forms of FP could be generated in response to various traffic demands; meanwhile, vehicle coordination is achieved in an offline–online manner to balance solution quality and efficiency. Under the FP-based control, each lane is assigned with periodical entry time points using FP design results, then vehicles are supposed to catch up with the appropriate time points to fulfill their trips. Theoretical analyses are conducted under stationary vehicle arrival patterns, where the average delay and the intersection capacity associated with different forms of FP can be analytically acquired. For the implementation of FP-based control, we construct a general offline–online framework, consisting of an offline model that optimizes the geometry of the conflict zone at intersections and the control strategy of vehicles simultaneously, which is formulated as a mixed-integer linear programming problem, and an online FP adoption mechanism that can adapt to varying demand patterns. Under the framework, limited online computation is required, facilitating its practical real-time implementation. Simulation studies under a variety of demand settings show that FP-based control is advantageous over benchmark methods in reducing vehicle delay and also improving intersection capacity in most cases; even for cases with extremely imbalanced demands, FP-based control can admit comparable throughput, while the average delay is much smaller. In addition, results of computation time reveal that FP-based control could realize real-time implementation with negligible computation burden.

A Multi-Agent RL Algorithm for Dynamic Task Offloading in D2D-MEC Network with Energy Harvesting.

Delay-sensitive task offloading in a device-to-device assisted mobile edge computing (D2D-MEC) system with energy harvesting devices is a critical challenge due to the dynamic load level at edge nodes and the variability in harvested energy. In this paper, we propose a joint dynamic task offloading and CPU frequency control scheme for delay-sensitive tasks in a D2D-MEC system, taking into account the intricacies of multi-slot tasks, characterized by diverse processing speeds and data transmission rates. Our methodology involves meticulous modeling of task arrival and service processes using queuing systems, coupled with the strategic utilization of D2D communication to alleviate edge server load and prevent network congestion effectively. Central to our solution is the formulation of average task delay optimization as a challenging nonlinear integer programming problem, requiring intelligent decision making regarding task offloading for each generated task at active mobile devices and CPU frequency adjustments at discrete time slots. To navigate the intricate landscape of the extensive discrete action space, we design an efficient multi-agent DRL learning algorithm named MAOC, which is based on MAPPO, to minimize the average task delay by dynamically determining task-offloading decisions and CPU frequencies. MAOC operates within a centralized training with decentralized execution (CTDE) framework, empowering individual mobile devices to make decisions autonomously based on their unique system states. Experimental results demonstrate its swift convergence and operational efficiency, and it outperforms other baseline algorithms.

The Feasibility and Operational Performance of Implementation Median U-turn Intersections: A CRITIC Method

Currently, a growing number of cities are adopting the Median-U-Turn (MUT) intersection design to enhance road capacity and traffic efficiency. The critical question in selecting the right intersection design is how significantly the implementation of MUT can enhance intersection performance, focusing on three key aspects: intersection efficiency, capacity, and the environmental impact of the design. To address this question, an evaluation of operational performance under various prevailing conditions (roadway and control) was conducted using VISSIM, a microscopic simulation platform. This evaluation involved five scenarios: conventional intersections (with increased cycle length, grade separation with a signalized at-grade intersection, grade separation with a roundabout), MUT, and signalized crossover MUT at a dense urban arterial intersection in Amman, Jordan's capital. The performance was compared using several metrics: average control delay, number of stops, average travel speed and time, average stopped delay, Carbon Monoxide (CO) emissions, fuel consumption, and vehicle safety. The Criteria Importance Through Intercriteria Correlation (CRITIC) technique was subsequently used to select the optimal design. The findings indicate that the existing intersection configuration is the least effective, while the MUT with signals at the crossing U-turn points is the most efficient solution.

Integrated operations strategies for shared and privately-owned autonomous vehicles: A deep reinforcement learning framework

The shared autonomous vehicle (SAV), combining shared mobility and vehicle automation, will play a pivotal role in urban transportation. Effectively matching SAVs to stochastic customer demand poses a significant challenge, particularly when addressing microscopic multi-AV coordination to prevent conflicts and congestion within a network. To tackle this issue, we propose a novel traffic control scheme called the Integrated Operation Framework for Order Matching and AV Coordination (I-OFOMAC). This scheme introduces a third-party coordinator responsible for integrating online order matching for the SAV platform and real-time AV coordination for the road manager. Our approach utilizes an offline deep reinforcement learning method to train a state value neural network, which facilitates strategic order matching. Additionally, a neural network approximating bisimulation metrics is trained to enhance exploration and accelerate learning. To achieve scalability in network-level AV coordination, we adopt a scheduled transportation scheme. The coordinator solves the I-OFOMAC during each decision epoch, optimizing order matching and AV coordination. The I-OFOMAC aims to balance the goals of minimizing AV travel delay and maximizing SAV service revenue. We conduct extensive simulation studies in the realistic Wangjing network to evaluate the proposed I-OFOMAC scheme. Compared to two-step benchmark frameworks that implement order matching and AV coordination sequentially, the I-OFOMAC achieves an average increase of 13.28% in platform revenue (ranging from 2% to 22.53%) and a 4.48% reduction in average PAV travel delay (ranging from −6.55% to 20.17%).

Application of Photovoltaic Panels as Producers of Electrical Energy With IoT Technology Based on Programmable Logic Controller

The process of converting solar cell electrical energy is a form of utilizing environmentally friendly alternative energy sources (renewable energy). The conversion system is in the form of PV panels that are installed in a location exposed to sunlight and unobstructed. Monitoring is carried out when the sun shines from morning to evening. The location of the panel is in the vehicle parking lot of the engineering faculty, while monitoring is carried out in the Control Laboratory of the Tanjungpura University Engineering Faculty, which is 300 meters away. Factors that determine the quality of PV panels are the system and dimensions. This shows that fluctuations in current (I), voltage (V) and power (P) need to be controlled so that load distribution and reserves can prevent reduced energy generation. Observation and control using Long Range Area (LoRa) radio transmission media connections, Internet of Things (IoT) and Programmable Logical Control (PLC) in real-time. Observation and test results show that energy generation has experienced an increase in temperature which has an impact on reducing power. Meanwhile, the effect of the distance between the installed panels produces an average delay of 7 milliseconds (midday conditions). Meanwhile, the Signal to Noise Ratio (SNR) value for data transmission is 30 dBm at the device frequency of 868.125 MHz. Baud rate 2400-9600 bps with a speed of 0.3 bps – 62.5 Kbps. The application of this system is a research novelty because it can determine and control the generation of electrical energy at minimum, maximum and optimum conditions with a certain distance between the observer and the installed panel.

Collaborative Optimization of Dynamic Lane Assignment and Signal Timing for Incident-Affected Intersections

Traffic accidents can lead to rapid changes in the traffic supply and demand at urban intersections, thus causing a severe traffic supply/demand imbalance during specific periods. Responding to accidents requires dynamic and accurate adjustments to optimize intersection resources collaboratively and enhance the real-time reliability and stability of traffic flows during such accidents. Currently, research on traffic incidents rarely considers real-time data-based collaborative optimization theories. Therefore, this study, supported by real-time incident detection technology and accident data, first considers the location and intensity of traffic incidents to update dynamically the changes in intersection traffic demand and supply. Subsequently, a dynamically collaborative optimization method is proposed based on lane assignment and signal timings to minimize the sum of variances of the degree of saturation of various approach lanes. Finally, various traffic demand scenarios are set, and the effectiveness of the proposed model is validated based on numerical and sensitivity analyses. The results demonstrate that compared with signal-only optimization and the highway capacity methods (HCM), the collaborative optimization method presented in this study reduces the average vehicular delay percentages by 8.54% and 16.47%, respectively. Sensitivity analysis indicates that, under various detour rates and detour modes, collaborative optimization methods have effectively mitigated the average vehicular delay at upstream intersections to varying degrees. In the context of real-time accident response, collaborative optimization methods demonstrate a capacity to promptly address the urgency of incidents occurring and maintain a sustained reduction in overall delay levels following detours.

A route network resource allocation method based on multi-objective optimization and improved genetic algorithm

In order to solve the problem of imbalance between traffic demand and airspace capacity of high-altitude air route network, reduce unnecessary delay costs, and improve air route operation efficiency, the resource allocation problem of multi-objective air route network for CTOP program is studied. Taking the affected flights in the congested area of air routes as the research object, taking into account the constraints of actual flight operation, FCA time slot resource availability limit, FCA capacity limit, etc., aiming at minimizing the total delay time of each flight and maximizing the fairness of airlines, a multi-objective optimization model for air route network resource allocation is established, and an improved NSGA-II algorithm is designed to solve the model. Based on the actual operation data of air routes in East China, the Pareto optimal solution set is obtained and compared with the traditional RBS algorithm, the average delay time is reduced by 5.49% and the average fair loss degree is reduced by 66.76%. The results show that the proposed multi-objective optimization model and the improved NSGA-II algorithm have better performance, which can take into account the fairness of each airline on the basis of reducing the total delay cost, realize the allocation of optimal flight trajectories and time slot resources, and provide a reference scheme for air traffic control resource scheduling.

A Longitudinal Multiinstitutional Study of Vulvar Lichen Sclerosus: From Childhood to Perimenopause.

The main outcome of this study was the evaluation of clinical characteristics, comorbidities, and therapeutic approaches in patients with vulvar lichen sclerosus (VLS) aged from childhood to perimenopause. Secondly, it was intended to compare these characteristics according to the menarchal status. Patients less than 45 years of age with a diagnosis of VLS from January 2002 to June 2022 in 10 referral centers were included in this retrospective longitudinal study. The univariate analysis compared the dependent variables according to menarchal status. One hundred eighty-six patients met the inclusion criteria. At diagnosis, between 25% and 40% of premenarchal patients reported signs related to subepithelial hemorrhage. A significantly greater presence of bleeding (p < .005), easy bruising (p = .028), fissures (p = .008), petechiae/splinter hemorrhages (p < .001), and bleeding/blistering or open sores (p = .011) was observed in premenarchal patients with respect to the postmenarchal group. The perineum (p = .013) and the perianal region (p < .001) were significantly more involved in the premenarchal group. Topical calcineurin inhibitors were more used in the premenarchal population (p = .004), whereas vitamin E oil and moisturizers were more used in the postmenarchal population (p = .047). Vulvar lichen sclerosus is a chronic condition that can cause vulvar changes that result in severe morbidity and affects sexual function and quality of life, even before menopause. Vulvar lichen sclerosus continues to be misdiagnosed in this population. This may lead to an average delay from symptom onset to diagnosis. Evaluating clinical manifestations of VLS in premenarchal and postmenarchal age allowed us to find different clinical characteristics between the 2 periods suggestive of the diagnosis.

A NLP-based semi-automatic identification system for delays in follow-up examinations: an Italian case study on clinical referrals

BackgroundThis study aims to propose a semi-automatic method for monitoring the waiting times of follow-up examinations within the National Health System (NHS) in Italy, which is currently not possible to due the absence of the necessary structured information in the official databases.MethodsA Natural Language Processing (NLP) based pipeline has been developed to extract the waiting time information from the text of referrals for follow-up examinations in the Lombardy Region. A manually annotated dataset of 10 000 referrals has been used to develop the pipeline and another manually annotated dataset of 10 000 referrals has been used to test its performance. Subsequently, the pipeline has been used to analyze all 12 million referrals prescribed in 2021 and performed by May 2022 in the Lombardy Region.ResultsThe NLP-based pipeline exhibited high precision (0.999) and recall (0.973) in identifying waiting time information from referrals’ texts, with high accuracy in normalization (0.948-0.998). The overall reporting of timing indications in referrals’ texts for follow-up examinations was low (2%), showing notable variations across medical disciplines and types of prescribing physicians. Among the referrals reporting waiting times, 16% experienced delays (average delay = 19 days, standard deviation = 34 days), with significant differences observed across medical disciplines and geographical areas.ConclusionsThe use of NLP proved to be a valuable tool for assessing waiting times in follow-up examinations, which are particularly critical for the NHS due to the significant impact of chronic diseases, where follow-up exams are pivotal. Health authorities can exploit this tool to monitor the quality of NHS services and optimize resource allocation.

[Registry of Members of the Association of Patients with Hereditary Angioedema of Perú].

To report the registry of the HAE Peruvian patient's association. We used the questionnaire of the Latin American HAE committee. Consent was requested from the patient's association to report the data. We report data of 63 patients, 51 Female, 12 Male, range age between 6 to 74 years. Nine under 18 years old, 5/9 between 6 to 13 years. Forty-five HAE C1-INH type I, 12 HAE-FXII, 5 HAE UNK, 1 AAE. Symptoms onset average age in 56/62 HAE patients was 16.8. In a group of 50/62 adult HAE patients, the average diagnostic delay approximately was 19.3 years. Laboratory tests: we can perform C4 complement C1-inhibitor antigenic and functional tests. Treatments: The patients have access to tranexamic acid (TA) and attenuated androgens. We do not have registered specific long-term prophylaxis treatments. We used moderate/high doses of TA, in most patients up to 6 gr i.v./in 24 hours, we start with the treatment immediately the HAE acute crisis is beginning, it helps to the HAE attacks are less symptomatic, resolves in a few days and decrease the frequency. We present 63 members of the Association of Patients with Hereditary Angioedema of Perú. We have improved blood tests for HAE diagnosis. Moderate and high doses of Tranexamic Acid are used for prophylaxis and acute crisis respectively, with acceptable response. No deaths have been reported due to HAE crisis in the patient's association.