Provide an automatic web-based platform for collecting traffic data

The reality is that people often search for a path that has the parameters of being short, low cost, and using the least amount of energy. However, traffic is one of the most influential factors in choosing the route to reach the destination. It can be said that people mostly prefer a long route with low traffic to a short route with heavy traffic. Thus, traffic makes many optimal path equations more confusing. In addition, it is obvious that the main criterion to select a route, among different communities, is the traffic condition on the required route. This problem highlights the significance of the present study and attempting to collect traffic data, because if the goal of the research is achieved, the least effect is saving time, money and energy. For this purpose, this study seeks to collect traffic data of Tehran province. Traffic data exist instantly, but the problem is that there is no platform for data collection and storage. The lack of an appropriate platform to store traffic data has always been a problem that has challenged researchers in this field. Therefore, a method for collecting and storing traffic data on the web platform has been discussed in the present study. The programming method in different environments has been used in this paper, as during a long period of time, the data were collected and compared to the collected traffic data. It was found that the area with heavy traffic is always overcrowded in most hours of the day, especially during the hours of the day and night when people commute to work. But the important point here is that the areas that have heavy traffic usually have heavy traffic or semi-heavy traffic during 24 hours. In other words, it is rarely observed that the areas with different traffic loads make a significant difference in the traffic condition. In terms of accuracy, it can be concluded that this study has collected traffic data with high accuracy according to the geographical location of each area on each street.