مدلسازی و پیش بینی ترافیک با استفاده از شبکه ی عصبی پایه و شبکه ی عصبی موجک و به کارگیری سه الگوریتم فراابتکاری ژنتیک، ازدحام ذرات و رقابت استعماری جهت بهینه سازی

جهت رفتارسنجی ترافیک در کلانشهرها، علاوه بر نیاز به داده های ترافیکی به روز و حجیم، نیاز به ارائه و استفاده از روش هایی برای تحلیل رفتار ترافیک جهت پیش بینی آن است. موضوع پیش بینی ترافیک، از این جهت مورد توجه است که می توان با رسیدن به این هدف، حمل و نقل را رونق بخشید و از بار اقتصادی که هر ساله بر جوامع مختلف وارد می شود، کم کرد. به همین دلیل، باید به دنبال راه حل هایی برای پیش بینی ترافیک بود. در این میان، استفاده از علمی به نام شبکه های عصبی می تواند بسیار کابردی باشد. در این پژوهش، ابتدا سامانه ای طراحی شد تا بتواند داده های ترافیکی مورد نیاز پژوهش را جمع آوری کند. موضوع دستیابی به داده های ترافیکی همواره معضلی بوده است که تمامی دغدغه مندان این حوزه با آن مواجه بوده اند. از این رو در پژوهش حاضر ابتدا با طراحی یک سامانه جهت جمع آوری داده های ترافیکی به رفع معضل مورد نظر پرداخته شد. در مرحله ی بعد، داده های ترافیکی جمع آوری شده فراخوانی می گردد و با استفاده از اعمالی که برای استاندارد سازی و نرمالیزه سازی آن انجام شد، میزان خطا با استفاده از داده های تست و آموزشی محاسبه گردید. در این مرحله، میزان خطا با مطلوبیت 72 درصد به دست آمد. در مرحله ی بعدی، تحلیل داده های ترافیکی با استفاده از ترکیب شبکه عصبی پایه و موجک انجام شد و میزان خطا محاسبه شد. نتایج نشان میدهد که محاسبات با استفاده از تبدیلات موجک دقیق تر است ولی به دلیل تعداد ورودی های کمتر، مقادیر خطا با استفاده از داده های تست و آموزشی، 28 درصد محاسبه گردید. به عبارت دیگر میزان مطلوبیت حدود 72 درصد به دست آمد. در مرحله ی آخر هم داده های ترافیکی جمع آوری شده با استفاده از الگوریتم های بهینه سازی، بهینه شدند و بهترین نقطه با کمترین خطای ممکن برای هر الگوریتم بهینه سازی، محاسبه گردید.

Traffic Modeling and Prediction Using Basic Neural Network and Wavelet Neural Network Along with Traffic Optimization Using Genetic Algorithm, Particle Swarm, and Colonial Competition

It is a fact that people are often looking for a way that combines the parameters of shortness, low cost, and low energy consumption. Hence traffic is one of the most influential factors in choosing the route to reach the destination. It can be said that people often prefer along with low traffic than a short one with heavy traffic. Therefore, it is clear that the main criterion for choosing a route is the traffic situation in the relevant route. Traffic has become a major social problem in all societies today. Understanding the causes of traffic and its aggravation parameters can reduce traffic problems. Meanwhile, the issue of traffic forecasting has become a goal among different nations. Since traffic can be predicted, it is possible to avoid wasting energy and time, which has become a crisis in metropolises today. But predicting traffic conditions and behavior, especially in large cities, requires management, planning, and using technologies such as GIS. In recent years, the urban transportation network has become more complex in modern societies. The reason for this is the creation of different infrastructures with the motivation of creating more convenience for the movement of citizens. The high complexity, multilayered nature, and multistructured nature of the urban, transportation network do not make it easy for citizens to move, and these factors may even confuse citizens more than just moving from one place to another. There is a direct link between transportation and traffic. So far, urban plans have been made to improve the traffic situation. The variability of the parameters affecting the traffic situation and its direct impact on the traffic problem has always been a big problem for different communities. Therefore, these parameters should be identified and the role of each of them on the traffic situation should be measured. Then it is possible to improve the traffic situation. To achieve this issue, the role of Geographic Information System (GIS) to solve problems that have a specific spatial and temporal dimension (such as traffic) should not be overlooked. This highlights the need for the present research to collect traffic data. If the goal of the research is achieved, it will save time, money, and energy at least. To measure traffic behavior in metropolitan areas and to achieve uptodate traffic data, there is a need to provide and use methods to analyze traffic behavior. By achieving this goal, transportation can be prospered and the economic burden can be reduced on different communities every year. For this reason, solutions for traffic forecasting should be sought. In the meantime, the use of science called neural networks can be very practical. In this research, first, a system was designed to collect the traffic data required for the research. The issue of access to traffic data has always been a problem, which is concerned in this area. Therefore, in the present study, first, by designing a system for collecting traffic data, the desired problem was solved. In the next step, the collected traffic data is called and normalized. Then the error rate was calculated using test and training data. At this stage, the error rate was 72%. In the next step, traffic data analysis was performed using a combination of baseline and wavelet neural network, and the error rate was then calculated. The results show that using wavelet transforms is more accurate, but the error values ​​were calculated using test and training data, 28% due to the smaller number of inputs. In other words, the desirability rate was about 72%. Finally, the collected traffic data were optimized using optimization algorithms and the best point was calculated with the least possible error for each optimization algorithm.