# Heuristic Fuzzy Approach to Traffic Flow Modelling and Control on Urban Networks
###### Abstract  
Computer-aided transport modelling is essential for testing different control strategies for traffic lights. One approach to modelling traffic control is by heuristically defining fuzzy rules for the control of traffic light systems and applying them to a network of hierarchically dependent crossroads. In this paper, such a network is investigated through modelling the geometry of the network in the simulation environment Aimsun. This environment is based on real-world traffic data and is used in this paper with the MATLAB R2019a-Fuzzy toolbox. It focuses on the development of a network of intersections, as well as four fuzzy models and the behaviour of these models on the investigated intersections. The transport network consists of four intersections. The novelty of the proposed approach is in the application of heuristic fuzzy rules to the modelling and control of traffic flow through these intersections. The motivation behind the use of this approach is to address inherent uncertainties using a fuzzy method and analyse its main findings in relation to a classical deterministic approach. fuzzy controller design; multivalued fuzzy logic; transport network; microsimulation; hierarchical modelling; MATLAB Fuzzy toolbox; Aimsun [  
phenomenon is a subject of interdisciplinary interest; e.g., the theory of fluid dynamics is used to describe and compare traffic with the laws of nature. A variety of approaches, methods, models and experiments are explored worldwide and locally to solve the problems of traffic congestion and improve the utilisation of the infrastructure or to justify the need for new infrastructure [1]. There are different models related to traffic control of intersections based on queuing models [2; 3]. There are some well-written sources of information about road control strategies and traffic signal control methods [4; 5]. As far as the use of information technologies for traffic is concerned, micro-simulation scenarios are used in conjunction with big data [6]. Even most contemporary research relies on the principles established in the middle of the 20 th century by [PERSON], which is to say that even though the traffic is significantly different in terms of intensity nowadays, many discoveries and principles from the past are valid [7; 8; 9]. On a local level, scientists also investigate traffic flow in networks [10; 11]. Computer-aided transport modelling is essential for testing different control strategies for traffic lights [12; 13; 14; 15; 16; 17]. One approach to modelling traffic control is by defining fuzzy rules for the control of traffic light systems and applying them to a network of hierarchically dependent crossroads [18; 19; 20; 21]. In this paper, a network of four hierarchically dependent crossroads is investigated through modelling the geometry of the network in the simulation environment Aimsun, then by applying the MATLAB Fuzzy toolbox and fuzzy logic theory [22; 23; 24; 25]. Traffic indicators for assessing the performance of the fuzzy approach are mean queues, the throughput of the network, total travel time and delay, traffic indicators which are calculated in the simulation environment and are based on the fuzzy controller inputs and outputs. Fuzzy logic has been utilised by various researchers in the field of road traffic, including in combination with deep learning techniques for short-term traffic prediction [26; 27]. It enables dynamic modelling of traffic conditions in urban environments and is commonly used in traffic management systems through fuzzy logic controllers [28; 29]. However, there is a research gap in terms of using suitable heuristics based on expert knowledge, which is the focus of this paper. The proposed approach utilises the hierarchical dependency between the intersections that allows the same fuzzy rules to be applied to each and every intersection without an increase in the number of inputs and rules in the fuzzy system.