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Design and Simulation of an Artificially Intelligent VANET for Solving Traffic Congestion Ali Ghazy, Tarik Ozkul
Abstract— Traffic congestion has been plaguing motorists for years, and it progressively continues to get worse as the population continues to increase, resulting in an increase in the number of vehicles on the road. There are many factors that contribute to traffic congestion, however; there is one that plays a major role in giving rise to a phenomenon called “Traffic Waves”, and that is driver behavior. Traffic waves also called “stop waves” or “traffic shocks”, and they are travelling disturbances in the distribution of cars on a highway, which seems to appear without any reason, propagating backwards and severely slowing traffic flow on roads. The proposed research aims at reducing/eliminating traffic waves by integrating Artificial Intelligence, and Vehicular Ad-hoc Network (VANET) to create a driver aid that helps in combating traffic congestion as well as embedding safety awareness by dynamically rerouting traffic I.
cluster of several vehicles was forced to stop completely for a moment. B. Conditions that lead to Traffic Congestion Conditions that lead to traffic congestion can be explained using the Fundamental diagram of traffic flow [2] shown in Figure 1. The Fundamental Diagram of Traffic Flow is of major importance for the design of road and traffic management systems as it explains under what conditions traffic congestion occurs.
INTRODUCTION
A. Traffic Congestion and Traffic Waves Traffic congestion is quickly becoming an enormous problem especially in developed and developing countries. It is a phenomenon that is experienced by many motorists especially during rush hour periods. People often find themselves stuck in traffic for as long as several hours. Traffic congestion can occur either naturally due to external factors such as road maintenance, rush hours etc., or indirectly created due to bad driving behavior and not following the rules of the road. This type of traffic congestion is referred to as the “Traffic Wave Phenomena”. Traffic Waves are travelling disturbances in the distribution of cars on a highway, which seems to appear without any reason, and propagate backwards, severely slowing traffic flow on roads. According to researchers from Japan [1] the main cause of traffic waves is “Human Error”. In an experiment which was conducted, 22 vehicles were placed in a 230m single lane circuit, and the drivers of the vehicles were asked to drive at a constant speed of 30 km/h. Due to human error, the drivers were not able to maintain this constant speed and before long small fluctuations appeared in the distances between cars, breaking down the free flow, until finally a Fig. 1 Fundamental diagram of traffic flow. A. Ghazy is a graduate student at the American University of Sharjah, U.A.E., Majoring in Mechatronics Engineering. (Tel.+97150-5621762 email:
[email protected]). T. Ozkul is with Computer Science and Engineering Department of American University of Sharjah, Sharjah UAE. (e-mail:
[email protected]).
According to Fig. 1, traffic flow is governed by the following equation:
MASAUM Journal of Basic and Applied Sciences Vol.1, No. 2 September 2009 (1) Where Q is the traffic flux measured in cars/h, V is the vehicle velocity measured in km/h, and D is the traffic density measured in cars/km. From Figure 1 and equation 1 the following statements can be made [2]: • There is a relationship between traffic density and vehicle velocity, and it is such that the more vehicles on the road, the slower their velocity will be. • To prevent congestion and maintain stable traffic flow, the number of vehicles entering a road must be less than or equal to the number of vehicles leaving the road. • At critical traffic density and at critical vehicle velocity, the state of traffic flow will change from stable to unstable. • If one vehicle brakes during the unstable state, the whole traffic regime will collapse. C. Effects of Traffic Congestion Traffic congestion has numerous unfavorable effects especially when it comes to the health of the community, the environment and the economy. Studies conducted in the U.K. [3] and U.S.A [4] indicates that longer commutes to work affect us both physically and mentally. Longer commutes mean longer exposure to exhaust emissions from neighboring vehicles which contain harmful substances such as fine particles, Nitrogen Dioxide and ultrafine particles. As a result, longer exposure increases the chances of acquiring cancer as well as heart disease, and in case of children the development of asthma. Similarly, studies also show that the longer commutes and delays on the roads result in people being overwhelmed with time demands, getting stress, and suffering from road rage. D. Problem Statement Based on what was mentioned so far, it is clear that traffic congestion is a serious problem which is not to be taken lightly. There are two ways to combating traffic congestion and these are by changing road infrastructure to cater to the demands of the road, or by developing traffic management systems. Changing the road infrastructure is not an easy process to do as it is very expensive and sometimes it is not possible in the first place, whether it is increasing the number of lanes of the road, building tunnels or bridges. Also, changing the road infrastructure may improve traffic in one area but make things worse in another. Therefore, the other solution would be to develop traffic management systems and driver aids to regulate traffic. Although this solution seems very appropriate, implementing and performing real-tests it is very expensive and impractical. Therefore, scientists and researchers turn to simulation as a tool for observing the effects of infrastructural changes before actually performing any real infrastructure implementations. Modern network communication tools provide an efficient and inexpensive way of communication between vehicles and drivers within certain proximity. If these networking tools can be used in a way to help driver with a driver aid that manages
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to distribute traffic efficiently in a road network, this would greatly reduce traffic congestion, and possibly eliminate traffic waves.
II. VANETS AND ROAD TRAFFIC SIMULATORS Wireless Cellular Networks have been used since the 1980’s and have evolved from 1st generation to 3rd generation wireless systems and beyond. These wireless systems can only operate with the help of support structures such as access points, as they are what allow the communication between the different nodes on the network. The problem is that presence of such support structures limits the adaptability of the wireless systems, since a wireless system not within the range of the support structure would not be able to communicate with other nodes. Recent advancements in wireless technology gave birth to a new type of wireless system known as “Mobile Ad-Hoc Networks” [5] or MANET for short. Mobile nodes connected to MANETs are able to communicate in the absence of the supporting structure, as well as have the added benefit of remaining connected while moving and organizing themselves in any arbitrary fashion. Vehicle Ad-Hoc Networks [6] or VANETs for short are a form of Mobile Ad-Hoc Networks with the difference being that the mobile nodes are vehicles, which can communicate with other nearby vehicles and roadside equipment. Simulation has become an integral part of testing system models and predicting how systems will react to particular events. The reason for this is that, for large scale projects involving VANETs and MANETs, it is much cheaper to implement and much easier to modify test parameters than perform a live testing. On the other hand, results obtained from live testing are much more accurate since not every single detail can be tested with simulation. There may also be external factors that affect the performance of sensors such as temperature, humidity, electromagnetic interference, etc. A reliable simulator however should still give reliable results. A. Road Traffic Simulators With regard to traffic congestion, several open source simulators have been developed under the general user license (GNU) to aid scientists and researchers. Such simulators include “SUMO” (Simulation of Urban Mobility) [7], which is a microscopic road traffic simulation package designed to handle large road networks, and the browser based “Dynamic Traffic Simulation” [8], which shows how traffic congestion and traffic waves are created in different scenarios depending on the simulation criteria selected. Until recently however, these road traffic simulators did not have any means of simulating VANETs as there weren’t any simulators that coupled VANET and road traffic simulators. This made it difficult to acquire reliable test data or accurate simulation data regarding the effectiveness of VANETS.
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Some of the favored network simulators for research purposes include ns-2 [9], which is a discrete event simulator and is popular for its extensibility, OMNeT++ [10], another discrete event simulator, and Swans [11], a java based scalable ad-hoc network simulator. Using network simulators greatly helps in predicting the outcome of large scale experiments that would have otherwise been too expensive or impractical to conduct. Despite the advantages of network traffic simulators, another problem presented itself. In order to properly simulate VANETs, there are two major pre-requisites that need to be satisfied. First, detailed network simulation of all the communication protocol layers is necessary, and secondly, realistic simulation of the vehicle’s mobility is required to estimate the position and movement of the involved components. According to [12], these network simulators are not properly equipped for simulating the mobility of moving vehicles as they lack proper mobility models. Fig. 2 Dynamic Traffic Simulation
B. Vehicle Ad-hoc Network (VANET) Simulators During the past decade, there has been a growing interest in Mobile Ad-hoc Networks (MANETs) among researchers. Vehicle Ad-Hoc Networks (VANETs), usually describes as road side assistance devices, are a form of MANET and aim for the safety and comfort of the passenger. In order for the vehicles to communicate, they are equipped with a VANET device, which is typically a type of wireless communication device, to become a node in the Ad-hoc network. Performing real-time VANET experiments however are impractical as the cost of the experiment would be too high, and the experiment would need to be conducted on a large scale multiple times in order for the data to have any real significance. As a result, Wireless Sensor Network Simulators were needed tools for conducting such experiments.
C. Mobility Modeling In [12], research was conducted on the evolution of mobility models, and how accurate they were at simulating vehicle mobility. There were different strategies for vehicle mobility modeling implemented during the last decade. These were, Random node movement, Real-world traces, Road traffic micro simulations, and finally bidirectional coupled simulations. With each new strategy, model complexity increased allowing for more accurate vehicle mobility modeling. Fig. 4 shows the different mobility modeling techniques that have been developed over the past decade.
Fig. 4. Mobility Modeling Techniques
Fig. 3 Java Based Swans Simulator
1) Random Node Movement Random node movement was the first strategy used when modeling MANETS, and although they were straightforward and easy to implement, they were not reliable for VANET
MASAUM Journal of Basic and Applied Sciences Vol.1, No. 2 September 2009 applications as it was imprecise due to lack of vehicle data, and it was potentially unstable. 2) Real World Traces To deal with the problem of not having vehicle mobility data, real world experiments were conducted and the vehicle mobility data was then acquired from GPS devices recorded and used to generate trace files. These files were then used to control node movement in the network simulation. While this approach produces the most realistic vehicle mobility data, it is a very difficult process to do as it is very expensive and time consuming. Additionally, as a result of it being pre-recorded data, only a small set of mobility parameters could be changed, thereby limiting the overall usefulness of the data. 3) Road Traffic Microsimulations The problem faced by the real world traces method was easily overcome by artificially generating the trace files used by the network simulators. Using this method, the realism of the node is constrained only by the mobility model used, and can be as simple as just collision free node movement to something as complex as the use of a fully featured mobility simulator. This technique has the advantage of providing simulations with realistic mobility traces while at the same time allowing mobility parameters to be freely adjusted to examine the effects on the simulation’s outcome. While this method has many benefits compared to its predecessors, there are still some aspects of VANETS that cannot be answered by this approach alone, as in many real world VANET scenarios both node mobility and network connectivity affect each other. 4) Bidirectionally Coupled Simulators In many real world VANET scenarios, we would like events that influence driver behavior such as road congestion information, accident reports, and hazard warnings to be transmitted over a VANET. In order to achieve this task, continuous two-way communication between both the network and road traffic simulators is required. Two such simulators have recently been developed, TraCI[13], an interface for coupling road traffic and network simulators, and TraNS [14],a simulator that integrates Sumo and Ns-2 simulators for the purpose of VANET research. According to [12] these simulators provide detailed information regarding the effects on (and of) network traffic, and at the same time does not add any extra overhead that may hinder real time simulation. With this approach, no trace files are generated as node mobility is calculated on-the-fly.
III. RESEARCH HYPOTHESIS Since this is still an ongoing research, in this section I will briefly mention the motivation about this project, as well as briefly give information about the Zigbee [15],[16] Standard. In this research we will be using the TraNS simulator to implement an Artificially Intelligent Zigbee VANET, and test the network to confirm that it does aid in reducing traffic congestion and traffic waves by dynamically rerouting traffic to promote free flow as well as allow congested areas to free
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up. A. Motivation The motivation for this project came from the following pretext: As a driver, our main goal is to reach our destination as quickly as possible. To achieve this goal, it is only natural to travel along the shortest path as the shorter the distance between the driver and his goal state, the shorter the commuting time will be. This is generally true when there are no constraints in road capacity. However, in reality roads have a certain capacity which they can manage before it starts to get congested. Similarly, there is no way to know beforehand if there are any accidents or road maintenance taking place before getting stuck in the jam Therefore the shortest path is not necessarily the most efficient time wise. Also, it is not possible for the driver to look ahead and get accurate road states due to the limited field of view when seated in the car. What if there was a way we could acquire road information similar to when looking at the road from a birds-eye view? This would give the driver a larger field of view and make it easier to make routing decisions to less congested roads in order to avoid traffic congestion. With the use of VANETS performing such a feat is possible. Before explaining how we plan to achieve this goal, we first need to explain the Zigbee standard, which is the VANET technology used in this research. B. Zigbee Standard ZigBee is a low-cost low-power, wireless mesh networking standard. The low cost allows the technology to be widely deployed in wireless control and monitoring applications, the low power-usage allows longer life with smaller batteries, and the mesh networking provides high reliability and larger range. It is a global standard based on the IEEE 802.15.4 standard, which was designed for low rate Wireless Personal Area Networks. Where Zigbee lacks in high speed data transmission, it makes up for in reliable data delivery, long battery life (a couple of months to a few years depending on how extensively they are used), and security features (authentication, encryption and integrity services). For simulation purposes, it is important to know about the types of Zigbee devices, supported topologies, and how communication takes place. There are 3 types of Zigbee Devices: • Zigbee Coordinator: At least one should be present in every Zigbee ad-hoc network. It maintains overall network knowledge, and is the most sophisticated of all the Zigbee devices. It also consumes the most power as it is always on. • Zigbee Router: Carries all 802.15.4 functionality and all features specified by the standard. • Zigbee End Device: Carries limited functionality to control cost and complexity. It is also usually the least active in order to preserve battery life. The three different network Topologies that are supported by the Zigbee standard are shown in Figure 5.
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Fig. 5 Network topologies supported by the Zigbee Standard
From Figure 5 we notice an important point which is that End User nodes (Zigbee Devices) cannot communicate directly with each other. In order for them to communicate they have to go through either a Coordinator or Router. C. Design and Testing For this research, I will be using the TraNS bidirectionally coupled simulator in conjunction with TraCI in order to simulate the desired system model shown in fig. 6.
Fig. 6 System diagram.
Zigbee coordinator nodes will be distributed throughout the map, and will register the number of vehicles (which will be fitted with the zigbee end user nodes) in their cluster. This information will then be sent to all neighboring vehicles so that they know the road conditions before hand, allowing the driver to take more efficient routes. The TraNS framework uses the TraCI interface to exchange information between SUMO traffic simulator and the NS-2 network simulator. This relationship is illustrated in figure 7.
Fig. 7 TraNS Framework
Therefore, several steps need to be taken in order to make the simulation. Firstly, a suitable map will be required for the simulation. The SUMO simulator allows you to create a map manually by creating xml files, or randomly generating a map using one of the built in functions. Unfortunately building a sophisticated map manually requires a lot of time and is tedious, and while a randomly generated map is quicker to build it is not realistic. To overcome this problem another program called eWorld [17] can be used to obtain a suitable map. eWorld is a free program under the GPL license that can be used to download maps from “Openstreetmap” website, and converts it into a format that SUMO can read. This makes map generation easy and reliable. The map I am using for this research is the map of Dubai. Next, the Zigbee standard model needs to be created in NS2 in order to have a reliable simulation. Since Zigbee uses the IEEE 802.15.4 Standard, I am currently working on this stage. Previous work has been done to try and design the Zigbee IEEE 802.15.4 standard [18]. Finally, I will extend TraNS to alter the vehicle direction to simulate the driver changing direction, by issuing TraCI commands to SUMO depending on the network data received from NS-2. In order to test the effectiveness of the system, several scenarios will be played out. The first scenario is where none of the vehicles use te designed driver aid. The congestion level in roads will then be measured for different car densities. For the second scenario, the number of cars with driver aids will be increased to 40% of the total cars on the road, to see if it affects congestion level in roads, and the same test will take place the same car density values used in the first scenario. For the third scenario, all the cars will be installed with the driver aid and the traffic congestion levels will be calculated and be compared with the results of the precious scenarios to determine to overall effectiveness of the driver aid.
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IV. CONCLUSION Traffic congestion is a serious problem that continues getting worse as countries become more developed, and population continues to grow, however thanks to modern advances in technology such as Road Traffic and Network simulators, VANETS, researchers can come up with ways to combat congestion.
V. REFERENCES [1]
M. Glaskin, “Shockwave traffic jam recreated for first time”, NewScientist.com, March 2008. [Online] Available: http://www.newscientist.com/article/dn13402 [Accessed: 27- 2-2009]. [2] “Fundamental diagram of traffic flow”, [Online]. Available: http://en.wikipedia.org [Accessed: 27-2-2009] [3] Ontario Collage of Family Physicians, The Health Impacts of Urban Sprawl: Social and Mental Health, Vol. 4,Toronto Ontario, 2005. [4] Environmental Defense, All Choked Up: Heavy traffic, dirty air and the risk to New Yorkers, Environmental Defense, 2007. [5] “Mobile Ad-hoc Network”, [Online]. Available: http://en.wikipedia.org/wiki/MANET [6] “Vehicular Ad-hoc Network”, [Online]. Available: http://en.wikipedia.org/wiki/VANET [7] “SUMO - Simulation of Urban Mobility”, May 2007, [Online]. Available: http://sumo.sourceforge.net/ [Accessed:27-2-2009]. [8] “Microsimulation of road traffic”, [Online]. Available: http://www.traffic-simulation.de/ [Accessed: 27-2-2009]. [9] “The Network Simulator - ns-2”, [Online]. Available: http://www.isi.edu/nsnam/ns/ [Accessed: 27-2-2009]. [10] “OMNeT++ community site”, [Online]. Available: http://www.omnetpp.org/ [11] “SWANS: Scalable Wireless Ad-hoc Network Simulator”, [Online]. Available: http://jist.ece.cornell.edu/ [12] C. Sommer, F.Dressler, Progressing towards realistic mobility models in VANET simulations, Germany [13] C. Sommer, “SUMO Traffic Control Interface (TraCI) modules for OMNeT++”, January 2009. [Online]. Available: http://www7.informatik.uni-erlangen.de [Accessed: 27-2- 2009]. [14] “TRANS – Realistic Simulator for VANETS”,[Online]. Available: http://trans.epfl.ch/ [Accessed: 27-2-2009]. [15] Daintree Networks, Getting Started with Zigbee and IEEE 802.15.4, Daintree Networks Inc, 2008. [16] Zigbee Alliance Zigbee FAQ, Meshnetics, 2008. [Online]. Available: http://www.meshnetics.com [17] “eWorld”,[Online]. Available: http://eworld.sourceforge.net [Accessed: 27-2-2009] [18] J. Zheng and Myung J. Lee, "A comprehensive performance study of IEEE 802.15.4," Sensor Network Operations, IEEE Press, Wiley Interscience, Chapter 4, pp. 218-237, 2006. [19] Dr.Treiber, “Dr. Martin Treiber's Institute Page”, April 2008. [Online]. Available: http://www.mtreiber.de/ [Accessed: 27-2-2009]. [20] M. Piorkowski, M. Raya, A. L. Lugo, P. Papadimitratos, M. Grossglauser, and J.-P. Hubaux, “TraNS: Joint Traffic and Network Simulator,” in 13th ACM Annual International Conference on Mobile Computing and Networking (MobiCom’07), Montreal, Canada, September 2007. [21] C. Sommer, Z. Yao, R. German, and F. Dressler, “On the Need for Bidirectional Coupling of Road Traffic Microsimulation and Network Simulation,” in 9th ACM International Symposium on Mobile Ad Hoc Networking and Computing (ACM Mobihoc 2008): 1st ACM International Workshop on Mobility Models for Networking Research (MobilityModels’08). Hong Kong, China: ACM, May 2008. [22] I. Macaluso, A. Chella, Machine Consciousness in CiceRobot, a Museum Guide Robot, Palermo - Italy, 2006. [23] D. Gamez, “Progress in machine consciousness”, Consciousness and Cognition, vol. 17, p. 887, June 2007. [Abstract]. Available: http://www.ncbi.nlm.nih.gov/pubmed/17572107
Ali Ghazy has received his B.Sc in Computer Engineering from the American University of Sharjah, Sharjah city, UAE in 2005, and his M.S. Degree in Mechatronics Engineering from the American University of Sharjah, Sharjah city U.A.E. in 2009. He is currently working as a Lab Engineer at the Institute of Applied Technology. His research interests include Autonomous Systems, Soft Computing, Robotics and its applications, Mobile Sensor Networks. Tarik Ozkul has received his B.S in engineering degree in electrical engineering from Bogazici University, Istanbul, Turkey in 1981 and his M.S and PhD degrees in computer and electrical engineering department of Florida Institute of Technology, Fl, USA in 1984 and 1988 respectively. He has worked in industry in different capacities ranging from DESIGN ENGINEER to DIRECTOR of R&D designing and manufacturing microprocessor based equipment, medical and optical equipment. He has published a book and several journal articles all related to computer engineering. He has joined Computer Engineering Department of American University of Sharjah in 2001. He is currently working as ASSOCIATE PROFESSOR at American University of Sharjah. His research interest is in computer architecture, soft computing and engineering applications of soft computing. He has several patents and avid supporter of implementation of innovation in engineering education. Dr. Ozkul is a member of IEEE and ISA.
