Traffic Simulation Analysis

Abstract: Poor road infrastructure, large population and rapidly growing economies lead to severe traffic congestion in many parts of the world. The problem is exacerbated by increased diversity in vehicle types and poor adherence to lane discipline. Existing approaches for detecting traffic congestion do not deal with the diversity of vehicle types and lack of lane discipline. In this work self-learning traffic system is practiced for accessing traffic congestion. It can be useful in controlling of vehicles according to the speed and priority of the vehicles. It can be useful in congestion management by suggesting optimal alternate route as per the vehicle type, thus saving travel time and reducing fuel consumption and emission radiation.
First, the problem cannot be easily solved using common sense, simple calculation, analytical methods, and direct experiments. The model needs to consider the system complexity that in many cases is hard to capture. If the system’s simulation is appropriate, we can then provide practical feedback to real systems, time compression or expansion, higher control, and lower costs. Thus, one question emerges: why is it necessary to simulate traffic (road network)? In order to give an answer to this query we have to keep in mind the system's complexity. Traffic simulation is necessary because this kind of application domain is inherently complex, usually formed of diverse entities (vehicles, traffic controllers, pedestrians etc.) that present different interactions reflecting social behaviours (e.g. competition, collaboration). In such case, mathematical analyses are complex and deals with traffic as a whole, using flow equations to describe vehicles and pedestrian movements. Moreover, simulation can provide comparison studies between new infrastructures, controls without interfering in the real system neither spending resources. Concerning to the latter, it can be used as training set for real systems, because of the time compressing characteristics that condense information and create hypothetical situations, in addition to all advantages aforementioned.

II. Simulation of Traffic and Transportation
Macroscopic simulation models the flow of traffic using high-level mathematical models often derived from fluid dynamics, thus it is continuous simulations. This type of simulation handles every vehicle in the same way and as group. It uses aggregate input and output variables such as speed, flow and density. Macroscopic simulators are most useful for the simulation of wide-area traffic systems, which do not require detailed modelling, such as motorway networks and interregional road networks. This approach is not very realistic because in real life there are many different types of vehicle driven by different individuals who have their own styles and behaviours. However, it is fast and accurate but is not well suited to urban models in