Intelligent monitoring system

Acknowledgements xiii Chapter 1 I would like to begin by thanking my thesis supervisor, Professor Tony White. Without his generous guidance and support, this research would never have been possible. I greatly enjoyed our weekly meetings, despite (or because of) the fact that the discussion would often move quickly from work to the newest Apple products. I should also thank Professor White for the financial support he provided over the past two years, as well as the help he provided me in finding external sources of funding as well. I should also thank my family for their support over the last two years (financial and otherwise). Thanks to my Dad for constantly telling me to get a job and thank you to my Mom for trying to explain to him what it is I actually do with my time. Also, thank you to my brother who has entertained me, as always, with his constant desire to pursue a new and expensive hobby (e.g., go-karting, kayak fishing). I would also like to thank my partner, as she has been there for me whenever I needed her. She has also been happy to distract me from my work, whether I needed to be or not. For that reason, I would also like to wish her good luck as she heads to graduate school, as I will be repaying the favour. Finally, a sincere thank you must be given to the numerous people at the City of Ottawa. Without their interest and help, this work would not be the same. I look forward to working with them further as our research continues in the future.

Traffic in urban areas is mainly regularized by traffic lights, which may lead to the unnecessary long waiting times for vehicles if not efficiently configured. This inefficient configuration is unfortunately still the case in a lot of urban areas where most of the traffic lights are based on a 'fixed cycle' protocol. This paper aims to design an intelligent controller of an intersection in a specific city using associative memory with multi-connect architecture via using this structure of neural network the intelligent controller can adapt to all street cases, which may be faced during its work. Not like other controllers, this work uses small associative memory. It will learn all street traffic conditions. The controller uses virtual data about the traffic condition of each street in the intersection. Thus, in an image processing module this video camera will provide visual information. This information will be processed to extract data about the traffic jam. This data will be represented in a look-up table, then smart decisions are taken when the intersection management determines the street case of each street at the intersection based on this look-up table.

Traffic jam is a crucial issue affecting cities around the world. They are only getting worse as the population and number of vehicles continues to increase significantly. Traffic signal controllers are considered as the most important mechanism to control the traffic, specifically at intersections, the field of Machine Learning offers more advanced techniques which can be applied to provide more flexibility and make the controllers more adaptive to the traffic state. Efficient and adaptive traffic controllers can be designed using a multi-agent reinforcement learning approach, in which, each controller is considered as an agent and is responsible for controlling traffic lights around a single junction. A major problem of reinforcement learning approach is the need for coordination between agents and exponential growth in the state-action space. This paper proposes using machine learning clustering algorithm, namely, hierarchal clustering, in order to divide the targeted network into smaller subnetworks, using real traffic data of 65 intersection of the city of Ottawa to build our simulations, the paper shows that applying the proposed methodology helped solving the curse of dimensionality problem and improved the overall network performance.

To optimize the travel time of priority vehicles (PV) and fluidize road traffic in Moroccan cities, we propose in this paper a control system to manage traffic at signalized intersections in urban areas, based on a multi-agent technology and fuzzy logic. the traffic system flow is divided into two types of vehicles; priority and non-priority vehicles, and a group of lanes assigned to each type, the ordinary vehicles can exploit only the ordinary lanes, while the PV may use both the priority and the ordinary lanes. This approach aims to favor emergency vehicles and promote collective modes of transport, it acts on the durations of the traffic light phases to control the different flows. We proposed a decentralized system of regulation based on the supervision of each lane which connects two intersections to build a local view of the crossroad, and intelligent cooperation between neighboring intersections to develop an overview of the environment. The regulation and prioritization decisions are made in real-time by fuzzy logic inference, communication, collaboration, and coordination between different agents. The performance of the proposed system is validated and designed in ANYLOGIC simulator, using a virtual road network that represents a section of the Marrakesh network.

The large number of vehicles in big cities has become a serious problem in adjusting the timing of traffic signal at intersections of road networks. Traffic light control is an important factor in road traffic system. In this paper, optimization of traffic signal timing at signalized intersections on road network is presented. The movement of vehicles on road network is modeled by cellular automata while the fuzzy inference system is used to obtain optimal traffic signal timing. The performance of fuzzy inference system is determined by the delay time average at signalized intersections. Delay time is addition time required by a vehicle to pass through signalized intersection compared to road without intersection. Cellular automata and fuzzy inference system are running together through computer simulation program. The optimal signal timing from the simulation is compared to fixed time or static scheme which is obtained from historical data of six signalized intersections in Bandung...

Smart and decentralized control systems have recently been proposed to handle the growing traffic congestion in urban cities. Proposed smart traffic light solutions based on Wireless Sensor Network and Vehicular Ad-hoc NETwork are either unreliable and inflexible or complex and costly. Furthermore, the handling of special vehicles such as emergency is still not viable, especially during busy hours. Inspired by the emergence of distributed smart cameras, we present a novel approach to traffic control at intersections. Our approach uses smart cameras at intersections along with image understanding for real-time traffic monitoring and assessment. Besides understanding the traffic flow, the cameras can detect and track special vehicles and help prioritize emergency cases. Traffic violations can be identified as well and traffic statistics collected. In this paper, we introduce a flexible, adaptive and distributed control algorithm that uses the information provided by distributed smart cameras to efficiently control traffic signals. Experimental results show that our collision-free approach outperforms the state-of-the-art of the average user's waiting time in the queue and improves the routing of emergency vehicles in a cross congestion area.

Effective traffic management is the need of the hour to solve issues related to transportation of vehicles on roads considering the environment, technology, government policies, etc. Vehicles either two or four wheeler has been increasing at very phenomenal rate year by year leading to congestion, pollution and wastage of man-hours waiting at signal lights for a longer period leading to loss of productivity, reduced quality of life. This ever growing problem has to be controlled by having an efficient solution. Hence traffic survey and analysis is required to be done to introspect the traffic density and pattern of traffic movement at varying timings of the day and avoiding traffic delays at the junctions. This paper discusses various approaches for handling real time traffic by controlling the traffic signal lights accordingly.

A new trend of traffic light monitoring module is the module that uses real time visual data and a computer vision approach to reflect the traffic conditions (crowded, normal and empty). This approach determines the traffic conditions by counting the number of vehicles individually on the street with the use of complex techniques. However this gives rise to some limitations. These limitations can be tackled when a multitude of vehicles in the street is detected as a group rather than individually. Such a technique can be achieved by using the auto-associative memory

The traffic in urban areas is mainly regularized by traffic lights, which may contribute to the unnecessary long waiting times for vehicles if not efficiently configured. This inefficient configuration is unfortunately still the case in a lot of urban areas, where most of the traffic lights are based on a 'fixed cycle' protocol. To improve the traffic light configuration, this paper proposed monitoring system to be as an additional component (or additional subsystem) to the intelligent traffic light system, this component will be able to determine three street cases (empty street case, normal street case and crowded street case) by using small associative memory. The proposed monitoring system is working in two phases: training phase and recognition phase. The experiments presented promising results when the proposed approach was applied by using a program to monitor one intersection in Penang Island in Malaysia. The program could determine all street cases with different weather conditions depending on the stream of images, which are extracted from the streets video cameras. In addition, the observations which are pointed out to the proposed approach show a high flexibility to learn all the street cases using a few training images, thus the adaptation to any intersection can be done quickly.

The large number of vehicles in big cities has become a serious problem in adjusting the timing of traffic signal at intersections of road networks. Traffic light control is an important factor in road traffic system. In this paper, optimization of traffic signal timing at signalized intersections on road network is presented. The movement of vehicles on road network is modeled by cellular automata while the fuzzy inference system is used to obtain optimal traffic signal timing. The performance of fuzzy inference system is determined by the delay time average at signalized intersections. Delay time is addition time required by a vehicle to pass through signalized intersection compared to road without intersection. Cellular automata and fuzzy inference system are running together through computer simulation program. The optimal signal timing from the simulation is compared to fixed time or static scheme which is obtained from historical data of six signalized intersections in Bandung...

The traffic in urban areas is primarily controlled by traffic lights, contributing to the excessive, if not properly installed, long waiting times for vehicles. The condition is compounded by the increasing number of road accidents involving pedestrians in cities across the world. Thus, this work presents an integrated tripartite module for an intelligent traffic light system. This system has enough ingredients for success that can solve the above challenges. The proposed system has three modules: the intelligent visual monitoring module, intelligent traffic light control module, and the intelligent recommendation module for emergency vehicles. The monitor module is a visual module capable of identifying the conditions of traffic in the streets. The intelligent traffic light control module configures many intersections in a city to improve the flow of vehicles. Finally, the intelligent recommendation module for emergency vehicles offers an optimal path for emergency vehicles. The evaluation of the proposed system has been carried out in Al-Sader city/Bagdad/Iraq. The intelligent recommendation module for the emergency vehicles module shows that the optimization rate average for the optimal path was in range 67.13% to 92%, where the intelligent traffic light control module shows that the optimization ratio was in range 86% to 91.8%.

Traffic in urban areas is mainly regularized by traffic lights, which may lead to the unnecessary long waiting times for vehicles if not efficiently configured. This inefficient configuration is unfortunately still the case in a lot of urban areas where most of the traffic lights are based on a 'fixed cycle' protocol. This paper aims to design an intelligent controller of an intersection in a specific city using associative memory with multi-connect architecture via using this structure of neural network the intelligent controller can adapt to all street cases, which may be faced during its work. Not like other controllers, this work uses small associative memory. It will learn all street traffic conditions. The controller uses virtual data about the traffic condition of each street in the intersection. Thus, in an image processing module this video camera will provide visual information. This information will be processed to extract data about the traffic jam. This data will be represented in a look-up table, then smart decisions are taken when the intersection management determines the street case of each street at the intersection based on this look-up table.

Although Hopfield neural network is one of the most commonly used neural network models for auto-association and optimization tasks, it has several limitations. For example, it is well known that Hopfield neural networks has limited stored patterns, local minimum problems, limited noise ratio, retrieve reverse value of pattern, and shifting and scaling problems. This research will propose multiconnect architecture (MCA) associative memory to improve the Hopfield neural network by modifying the net architecture, learning and convergence processes. This modification is to increase the performance of associative memory neural network by avoiding most of the Hopfield neural network limitations. In general, MCA is a single layer neural network uses auto-association tasks and working in two phases, that is learning and convergence phases. MCA was developed based on two principles. First, the smallest net size will be used rather than depending on the pattern size. Second, the learning process will be performed to the limited parts of the pattern only to avoid learning similar parts several times. The experiments performed show promising results when MCA shows high efficiency associative memory by avoiding most of the Hopfield net limitations. The results proved that the MCA net can learn and recognize unlimited patterns in varying size with acceptable percentage noise rate in comparison to the traditional Hopfield neural network.

I praise Allah the Almighty for granting me this opportunity and allowing me to complete this thesis. My initial and final thanks always go to Him. I would like to sincerely thank my supervisor, Dr. Mohamed Moustafa, for his guidance and support throughout the thesis period. This thesis would not have been in its current form if it were not for his help and supervision. I would also like to express my gratitude to the thesis committee, Dr. Mohamed Elkharashy and Dr. Amr Goneid for their insightful comments and encouragement. Moreover, I must express my very profound gratitude to my beloved parents for their trust in me, their endless support, love, encouragement, and their sacrifice throughout their life for me to reach to this stage. My gratitude also goes to my helpful sisters and my compassionate grandmother. The most special thanks goes to my partner and best friend, my husband. I am truly indebted to him for his continuous encouragement, unconditional support, and love.

The large number of vehicles in big cities has become a serious problem in adjusting the timing of traffic signal at intersections of road networks. Traffic light control is an important factor in road traffic system. In this paper, optimization of traffic signal timing at signalized intersections on road network is presented. The movement of vehicles on road network is modeled by cellular automata while the fuzzy inference system is used to obtain optimal traffic signal timing. The performance of fuzzy inference system is determined by the delay time average at signalized intersections. Delay time is addition time required by a vehicle to pass through signalized intersection compared to road without intersection. Cellular automata and fuzzy inference system are running together through computer simulation program. The optimal signal timing from the simulation is compared to fixed time or static scheme which is obtained from historical data of six signalized intersections in Bandung...

The traffic in urban areas is primarily controlled by traffic lights, contributing to the excessive, if not properly installed, long waiting times for vehicles. The condition is compounded by the increasing number of road accidents involving pedestrians in cities across the world. Thus, this work presents an integrated tripartite module for an intelligent traffic light system. This system has enough ingredients for success that can solve the above challenges. The proposed system has three modules: the intelligent visual monitoring module, intelligent traffic light control module, and the intelligent recommendation module for emergency vehicles. The monitor module is a visual module capable of identifying the conditions of traffic in the streets. The intelligent traffic light control module configures many intersections in a city to improve the flow of vehicles. Finally, the intelligent recommendation module for emergency vehicles offers an optimal path for emergency vehicles. The evaluation of the proposed system has been carried out in Al-Sader city/Bagdad/Iraq. The intelligent recommendation module for the emergency vehicles module shows that the optimization rate average for the optimal path was in range 67.13% to 92%, where the intelligent traffic light control module shows that the optimization ratio was in range 86% to 91.8%.

The large number of vehicles in big cities has become a serious problem in adjusting the timing of traffic signal at intersections of road networks. Traffic light control is an important factor in road traffic system. In this paper, optimization of traffic signal timing at signalized intersections on road network is presented. The movement of vehicles on road network is modeled by cellular automata while the fuzzy inference system is used to obtain optimal traffic signal timing. The performance of fuzzy inference system is determined by the delay time average at signalized intersections. Delay time is addition time required by a vehicle to pass through signalized intersection compared to road without intersection. Cellular automata and fuzzy inference system are running together through computer simulation program. The optimal signal timing from the simulation is compared to fixed time or static scheme which is obtained from historical data of six signalized intersections in Bandung...

A considerable amount of work has been done on the problem of modeling and controlling traffic junctions. Although the description of the events occurring in any individual intersection in the linked or disjoint system can be useful in modeling simple traffic networks the major problem in cities concerns sets of intersections (not individual ones). An adaptive traffic controller using fuzzy logic is presented which takes into consideration the situation of its neighbors. The controller gives preference to less fuel consuming vehicles, public transport vehicles and high priority vehicles. A traffic grid system is designed using Simevents Toolbox of Simulink with provisions for turning and random class of vehicles. The results obtained from the implementation of this fuzzy logic controller are tabulated against those corresponding to a conventional cyclic fixed time controller, vehicle actuated controller and a basic fuzzy logic controller (designed for isolated junction). Simulations...

Most recent studies have focused on using modern intelligent techniques especially those developed InDoor surveillance systems. Such techniques have been built depending on modern Artificial intelligence-based modules. Those modules act like a human brain, they learn and recognize what they learned. The importance of developing such systems came after the requests of customers and establishments to defend their properties and avoid Intruders' damages. This would be provided by an intelligent module that ensure the correct alarm for correct non-secured state, Thus, an Indoor surveillance module depending on Multi-Connect Architecture Associative Memory (MMCA) has been proposed. This proposed system can be trained for more than to shoot. Thus the module can recognize more than one true state that might be secured or non-secured states in real-time. The current study found an accepted accuracy level between (62.778.8%) at first training cycle with two images. While the final result were between (97-100%) at the fifth training cycle with (10) images. It considered a high performance and very excellent results.

The unnecessary long cross road waiting times for pedestrians is due to the inefficient traffic light system configuration and has led to the increasing number of traffic accidents involving pedestrians. This research designs, develops, constructs and tests the pedestrian monitoring approach to elevate the efficiency of traffic lights system configuration. This approach consists of two phases which are the learning and monitoring phase. The monitoring phase involves the use of a single video camera to each street in the intersection. The experiments have shown an increase in the traffic light system configuration as well as the rapid adaptability of the proposed monitoring approach to the environment.

A traffic light control module base on PSO algorithm has been presented to find the optimal set of adjacent streets that are the candidate to take the green period time providing the best vehicles flow. In our previous work a visual traffic light monitoring module has been presented. This module able to determine the traffic conditions (crowded, normal and empty). The proposed control module should be able to integrate with the previous monitoring module to develop a new complete intelligent traffic light system. Promising results have been obtained via applying the proposed traffic light controller module. The controller module shows its ability to select a set of streets. The green period time will be given to these selected streets to achieve the optimal vehicle flow through the traffic light's intersections. The results show that the proposed control module improving the flow ratio about 85% to 96% with a different number of traffic lights.

This paper presents an application of diverse soft-computing techniques to adaptive traffic light controls. The proposed methodology consists of two main phases: off-line and on-line. First, clustering techniques and optimization methods are used at the off-line stage to discover the prototypes which characterize the traffic mobility patterns at an intersection. After this process an optimum timing plan is decided for each mobility pattern detected. In the on-line phase, a prediction model is then constructed on the basis of the prototypes found. Fuzzy Logic based techniques are used to formally represent the prototypes in the prediction model and these prototypes are parametrically defined through frameworks. During the on-line phase an intelligent transportation system, by using the prediction model, matches the current traffic conditions to the mobility patterns detected at the off-line stage in order to identify the most suitable one to be used. The use of these techniques supposes a substantial contribution to the significance of the prediction model, making it robust in the face of anomalous mobility patterns, and efficient from the point of view of real-time computation.

The paper presents a dual approach for modeling urban traffic. The modeling goal is to eliminate congestion by controlling traffic lights cycle structure and duration. The control problem has to be solved using both analytical methods, and simulation, with continuous models implemented in Matlab/Simulink. Analytical methods are based on the Hybrid Petri Net approach. In the paper are presented both kinds of models and they are evaluated using the same input data. It results that the two modeling approaches are complementary and, by their similar behavior, they reciprocally validate.

The paper presents respectively a modeling and a control approach of urban traffic networks, integrated into a software application. These approaches are based on an agentoriented methodology and have as a goal the possibility of simulating, evaluating and optimizing the traffic in crowded areas, as large cities, using both off line scenario policies and fine tuning scenario control.

This paper presents an application of diverse soft-computing techniques to adaptive traffic light controls. The proposed methodology consists of two main phases: off-line and on-line. First, clustering techniques and optimization methods are used at the off-line stage to discover the prototypes which characterize the traffic mobility patterns at an intersection. After this process an optimum timing plan is decided for each mobility pattern detected. In the on-line phase, a prediction model is then constructed on the basis of the prototypes found. Fuzzy Logic based techniques are used to formally represent the prototypes in the prediction model and these prototypes are parametrically defined through frameworks. During the on-line phase an intelligent transportation system, by using the prediction model, matches the current traffic conditions to the mobility patterns detected at the off-line stage in order to identify the most suitable one to be used. The use of these techniques supposes a substantial contribution to the significance of the prediction model, making it robust in the face of anomalous mobility patterns, and efficient from the point of view of real-time computation.

This paper presents an application of diverse soft-computing techniques to adaptive traffic light controls. The proposed methodology consists of two main phases: off-line and on-line. First, clustering techniques and optimization methods are used at the off-line stage to discover the prototypes which characterize the traffic mobility patterns at an intersection. After this process an optimum timing plan is decided for each mobility pattern detected. In the on-line phase, a prediction model is then constructed on the basis of the prototypes found. Fuzzy Logic based techniques are used to formally represent the prototypes in the prediction model and these prototypes are parametrically defined through frameworks. During the on-line phase an intelligent transportation system, by using the prediction model, matches the current traffic conditions to the mobility patterns detected at the off-line stage in order to identify the most suitable one to be used. The use of these techniques supposes a substantial contribution to the significance of the prediction model, making it robust in the face of anomalous mobility patterns, and efficient from the point of view of real-time computation.

The traffic in urban areas is mainly regularized by traffic lights, which may contribute to the unnecessary long waiting times for vehicles if not efficiently configured. This inefficient configuration is unfortunately still the case in a lot of urban areas, where most of the traffic lights are based on a 'fixed cycle' protocol. To improve the traffic light configuration, this paper proposed monitoring system to be as an additional component (or additional subsystem) to the intelligent traffic light system, this component will be able to determine three street cases (empty street case, normal street case and crowded street case) by using small associative memory. The proposed monitoring system is working in two phases: training phase and recognition phase. The experiments presented promising results when the proposed approach was applied by using a program to monitor one intersection in Penang Island in Malaysia. The program could determine all street cases with different weather conditions depending on the stream of images, which are extracted from the streets video cameras. In addition, the observations which are pointed out to the proposed approach show a high flexibility to learn all the street cases using a few training images, thus the adaptation to any intersection can be done quickly.

Although Hopfield neural network is one of the most commonly used neural network models for auto-association and optimization tasks, it has several limitations. For example, it is well known that Hopfield neural networks has limited stored patterns, local minimum problems, limited noise ratio, retrieve reverse value of pattern, and shifting and scaling problems. This research will propose multiconnect architecture (MCA) associative memory to improve the Hopfield neural network by modifying the net architecture, learning and convergence processes. This modification is to increase the performance of associative memory neural network by avoiding most of the Hopfield neural network limitations. In general, MCA is a single layer neural network uses auto-association tasks and working in two phases, that is learning and convergence phases. MCA was developed based on two principles. First, the smallest net size will be used rather than depending on the pattern size. Second, the learning process will be performed to the limited parts of the pattern only to avoid learning similar parts several times. The experiments performed show promising results when MCA shows high efficiency associative memory by avoiding most of the Hopfield net limitations. The results proved that the MCA net can learn and recognize unlimited patterns in varying size with acceptable percentage noise rate in comparison to the traditional Hopfield neural network.

—Although Hopfield neural network is one of the most commonly used neural network models for auto-association and optimization tasks, it has several limitations. For example, it is well known that Hopfield neural networks has limited stored patterns, local minimum problems, limited noise ratio, retrieve reverse value of pattern, and shifting and scaling problems. This research will propose multi-connect architecture (MCA) associative memory to improve the Hopfield neural network by modifying the net architecture, learning and convergence processes. This modification is to increase the performance of associative memory neural network by avoiding most of the Hopfield neural network limitations. In general, MCA is a single layer neural network uses auto-association tasks and working in two phases, that is learning and convergence phases. MCA was developed based on two principles. First, the smallest net size will be used rather than depending on the pattern size. Second, the learning process will be performed to the limited parts of the pattern only to avoid learning similar parts several times. The experiments performed show promising results when MCA shows high efficiency associative memory by avoiding most of the Hopfield net limitations. The results proved that the MCA net can learn and recognize unlimited patterns in varying size with acceptable percentage noise rate in comparison to the traditional Hopfield neural network.

1. Introduction The main purpose of the pedestrian detection in video surveillance is for safety of the human in the world. As lots of people are killed in road accidents every day, there should be a system which can reduce the pedestrian accidents fatalities in the world. Another problem is the traffic control. Frequency of traffic accidents and traffic blocking has become a significant problem towards traffic control. So to manage traffic, efficient video surveillance system is necessary. Researches on intelligent transportation systems and algorithms for enhancing the accuracy have been proposed for improving road traffic's safety and decreases traffic blocking. Vehicle detection and human detection is one of the major problems today in the world. So for the detection purpose many different methodologies have been proposed up till now and also advanced research is going on for the betterment of the existing techniques and to get more accurate detection results. Current scenario has been improved by giving alarm to the pedestrian or the driver of the vehicle. This technique has been implemented in VOLVO S60 car. It is having automatic pedestrian detection with full automatic braking capability. So if the driver does not respond to the alarm then the brake will be applied automatically. Object detection algorithms vary according to the application. Human detection and vehicle detection both require different approaches to get better results. As the shape, height, width, area and length are different for human as well as for vehicle. So we have to develop a system such that it can detect and recognize the object as per the application. Detection requires basic stages as follows: Pre-processing, background removal, filtering, object detection and object tracking. There are several ways to detect pedestrian or human by the shape or pattern, skin color and movement of the object. Also the light condition and environment condition affect the detection result in case of skin and motion-based detection techniques. By using combination of two or more information, we can detect pedestrians more precisely. The motion based detection is helpful in case of traffic signals as the pedestrian are crossing the road. Most of pedestrian victims are at crossroads or at traffic signals. The rest of the paper is organized as follows, section II presents an overview to guide pedestrian away from dangerous streets and crossing points (traffic safety), Section III presents an overview to guide pedestrians in their dangerous situation and automatic braking, Section IV presents detailed literature survey in intelligence transport system, Section V presents problems and directions to initiate ideas for further research and Section VI summarize the work and quoted references are provided in the end of the paper.

The unnecessary long cross road waiting times for pedestrians is due to the inefficient traffic light system configuration and has led to the increasing number of traffic accidents involving pedestrians. This research designs, develops, constructs and tests the pedestrian monitoring approach to elevate the efficiency of traffic lights system configuration. This approach consists of two phases which are the learning and monitoring phase. The monitoring phase involves the use of a single video camera to each street in the intersection. The experiments have shown an increase in the traffic light system configuration as well as the rapid adaptability of the proposed monitoring approach to the environment.

Abstract: The paper presents the first results of a national budgeted “Intelligent Techniques for the Modeling, Analysis, and Optimization of Urban Traffic” research project concerning the modeling, evaluation and optimization of urban traffic. For the modeling phase a dual approach was used–using both Hybrid Petri Nets and Matlab/Simulink models. Models were realized starting from the same assumptions and ontology.