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ABSTRACT
Now-a-days, lots of accidents happen on highways due to increase in traffic and also due to rash driving of the drivers. To address this issue, we propose an Intelligent Traffic Control System that provides an alternate path for the vehicles in case of a road accident. We design a re-routing algorithm that ensures real time update of the driver’s routes upon detection of any abnormal incidents. Our ultimate goal is to develop a model that will update a vehicle’s route when an accident occurs. Moreover the system reduces the traffic jam caused by unexpected road accidents. Bypasses the blocked road due to an accident, by updating their predefined static routes during simulation runtime. Reduces the commuters travel time.
INTRODUCTION
1.1 INTERNET OF THINGS:
The Internet of Things (IoT) is the network of physical objects, devices, vehicles, buildings and other items embedded with electronics, software, sensors, and network connectivity that enables these objects to collect and exchange data. The Internet of Things allows objects to be sensed and controlled remotely across existing network infrastructure, creating opportunities for more direct integration of the physical world into computer-based systems, and resulting in improved efficiency, accuracy and economic benefit. When IoT is augmented with sensors and actuators, the technology becomes an instance of the more general class of cyber-physical systems, which also encompasses technologies such as smart grids, smart homes, intelligent transportation and smart cities. Each thing is uniquely identifiable through its embedded computing system but is able to interoperate within the existing Internet infrastructure. Experts estimate that the IoT will consist of almost 50 billion objects by 2020.
APPLICATIONS:
ENVIRONMENTAL MONITORING:
Environmental monitoring applications of the IoT typically use sensors to assist in environmental protectionby monitoring air orwater quality, atmospheric or soil conditions, and can even include areas like monitoring the movements of wildlife and their habitats. Development of resource constrained devices connected to the Internet also means that other applications like earthquake or tsunami early-warning systems can also be used by emergency services to provide more effective aid. IoT devices in this application typically span a large geographic area and can also be mobile. It has been argued that the standardization IoT brings to wireless sensing will revolutionize this area.
INFRASTRUCTURE MANAGEMENT:
Monitoring and controlling operations of urban and rural infrastructures like bridges, railway tracks, on and offshore wind-farms is a key application of the IoT. The IoT infrastructure can be used for monitoring any events or changes in structural conditions that can compromise safety and increase risk. It can also be used for scheduling repair and maintenance activities in an efficient manner, by coordinating tasks between different service providers and users of these facilities. IoT devices can also be used to control critical infrastructure like bridges to provide access to ships. Usage of IoT devices for monitoring and operating infrastructure is likely to improve incident management and emergency response coordination, and quality of service, up-times and reduce costs of operation in all infrastructure related areas. Even areas such as waste management can benefit from automation and optimization that could be brought in by the IoT.
MANUFACTURING:
The IoT intelligent systems enable rapid manufacturing of new products, dynamic response to product demands, and real-time optimization of manufacturing production and supply chain networks, by networking machinery, sensors and control systems together. Smart industrial management systems can also be integrated with the Smart Grid, thereby enabling real-time energy optimization. Measurements, automated controls, plant optimization, health and safety management, and other functions are provided by a large number of networked sensors.
ENERGY MANAGEMENT:
Integration of sensing and actuation systems, connected to the Internet, is likely to optimize energy consumption as a whole.It is expected that IoT devices will be integrated into all forms of energy consuming devices and be able to communicate with the utility supply company in order to effectively balance power generation and energy usage. In fact, a few systems that allow remote control of electric outlets are already available in the market, e.g., Belkin's WeMo,Ambery Remote Power Switch, Budderfly,Telkonet's EcoGuard,WhizNets Inc.,etc.
MEDICAL AND HEALTHCARE SYSTEMS:
IoT devices can be used to enable remote health monitoring and emergency notification systems. These health monitoring devices can range from blood pressure and heart rate monitors to advanced devices capable of monitoring specialized implants, such as pacemakers or advanced hearing aids.Other consumer devices to encourage healthy living, such as, connected scales or wearable heart monitors, are also a possibility with the IoT.
BUILDING AND HOME AUTOMATION:
IoT devices can be used to monitor and control the mechanical, electrical and electronic systems used in various types of buildings in home automation and building automation systems.
TRANSPORTATION:
The IoT can assist in integration of communications, control, and information processing across various transportation systems. Application of the IoT extends to all aspects of transportation systems, i.e. the vehicle, the infrastructure, and the driver or user. Dynamic interaction between these components of a transport system enables inter and intra vehicular communication, smart traffic control, smart parking, electronic toll collection systems, logistic and fleet management, vehicle control, and safety and road assistance
1.2OVERVIEW OF THE PROJECT:
Recently, the increasing road traffic congestion hasattracted a lot of attention from the research community aimingat proposing innovative solutions to reduce the huge economicloss incurred by this problem. To overcome this issue, we propose a Smart Traffic Control System that enables real-time vehicles re-routing, to bypass the blocked road due toan incident, by updating their predefined static routes duringsimulation runtime. The proposed system uses Re-routing mechanism to dynamically update vehicle’s route thereby reducing the commuters travel time, in case ofan accident.
CHAPTER 2
LITERATURE REVIEW
[1] Recent Development and Applications of SUMO - Simulation of Urban Mobility - Daniel Krajzewicz, Jakob Erdmann, Michael Behrisch, and Laura Bieker - International Journal on Advances in Systems and Measurements, vol 5 no 3 & 4, year 2012.
SUMO is an open source traffic simulation package including the simulation application itself as well as supporting tools, mainly for network import and demand modeling. SUMO helps to investigate a large variety of research topics, mainly in the context of traffic management and vehicular communications. The number of projects and the different scales (from single junction traffic light control to whole city simulation) present the capabilities of the simulation suite. Together with its import tools for networks and demand and recently added features such as emission modeling and the powerful TraCI interface, SUMO aims to stay one of the most popular simulation platforms not only in the field of vehicular communication. In the future,online rerouting of persons will be addressed. Routing across trips must be undertaken before the start of the simulation.
[2] A TraCI: An Interface for Coupling Road Traffic and Network Simulators-Wegner,M. Piorkowski, M. Raya, H. Hellbruck, S. Fischer and J.P. Hubaux - CNS, Canada, 2008
Vehicular Ad-Hoc Networks (VANETs) enable communication among vehicles as well as between vehicles and roadside infrastructures. Currently available software tools for VANET research still lack the ability to assess the usability of vehicular applications. In this article, they presented Traffic Control Interface (TraCI), a technique for interlinking road traffic and network simulators. It permits us to control the behavior of vehicles during simulation runtime, and consequently to better understand the influence of VANET applications on traffic patterns.In contrast to the existing approaches, i.e., generating mobility traces that are fed to a network simulator as static input files, the online coupling allows the adaptation of drivers' behavior during simulation runtime. This technique is not limited to a special traffic simulator or to a special network simulator. They introduced a general framework for controlling the mobility which is adaptable towards other research areas. They described the basic concept, design decisions and the message format of this open-source architecture. They provided implementations for non-commercial traffic and network simulators namely SUMO and ns2, respectively. This coupling enables for the first time systematic evaluations of VANET applications in realistic settings.
[3] Proactive vehicle re-routing strategies for congestion avoidance - J. Pan, M. Khan, I. Popa, K. Zeitouni and C. Borcea.
Traffic congestion causes driver frustration and costs billions of dollars annually in lost time and fuel consumption. This paper presents three traffic re-routing strategies designed to be incorporated in a cost-effective and easily deployable vehicular traffic guidance system that reduces the effect of traffic congestions. This system collects real-time traffic data from vehicles and road-side sensors and computes proactive, individually-tailored re-routing guidance which is pushed to vehicles when signs of congestion are observed on their route. Extensive simulation results over two urban road networks show that all three strategies, namely multipath load balancing considering future vehicle positions (EBkSP), random multipath load balancing (RkSP), and dynamic shortest path (DSP), significantly decrease the average travel time. EBkSP is the best, with as much as 104% improvement compared to the “no rerouting” baseline. Additionally, it lowers with 34% the re-routing frequency compared to the other strategies. This article presented three strategies for vehicular traffic re-routing that show very promising results compared to the “no re-routing” case. The EBkSP strategy balances best the trade-offs between low average travel time and low overhead along several parameters.The results also show that significant benefits can be achieved even with low compliance rate and moderate penetration rate. The experiments demonstrated how the performance can be tuned by varying parameters such as re-routing period, number of alternative paths, and density threshold. As future work, we plan to design an adaptive approach for vehicle selection that considers additional parameters such as road segment length, measured compliance rate, and estimated penetration rate. Moreover, we will investigate a hybrid architecture that off-loads parts of the computation and decision process in the network and uses V2V communication to better balance the need for privacy, scalability, and low overhead with the main goal of low average travel time.
[4] A Comparative Study if Vehicles’ Routing Algorithms for Route Planning in Smart Cites - V. Ngoc Nha, S. Djahel and J. Murphy
Vehicle routing problem (VRP) is a generic name referring to optimization problems in transportation, distribution and logistics industry. They mainly focus on serving a number of customers by a number of vehicles. Route planning techniques is one of the main tasks of VRP which aims to find an optimal route from a starting point to a destination on a road map. As road traffic conditions may change during the car journey, the optimal route should be re-evaluated as soon as an update in traffic conditions is available. Choosing an appropriate route planning algorithm among the existing algorithms in the literature to apply it in real road networks is an important task for any transportation application.For the purpose of comparison, the behavior of these algorithms were simulated during runtime using Simulation of Urban Mobility (SUMO) package and TraCI. Dijkstra, the most well-known shortest path algorithm, was the first algorithm to be implemented in SUMO. Upon reception of any traffic conditions update that affects the current optimal route of a car, TRACI was used to re-apply the algorithm and change this cars route accordingly. In the near future, target is to simulate other algorithms and compare their performance based on the quality of the obtained best route.
[5] Timing Optimization and Control for Smart Traffic-Pei-Chi Hsieh, You-Ren Chen, Wen-Hao Wu, Pao-Ann Hsiung – 2014 IEEE.
Currently, traffic signals in most countries have preset time periods for the red/yellow/green signals. Different roads in a city are configured with different time periods based on type and location of roads and the peak or off-peak hours. Such preset timings are inadequate in coping with unexpected situations such as a traffic accident or other natural disasters or specific unplanned events. Because the traffic control system is generally not adaptive enough, traffic congestion occurs often in most metropolitan cities. A smart traffic optimization system was proposed that can adjust the setting of traffic signals in real time based on sensor and camera inputs. In this work, microscopic simulator with heuristic optimization, as well as, an embedded system prototype for traffic optimization was developed. A genetic algorithm based optimization method was employed. They employed the degree of traffic congestion as a factor to adjust the traffic signal time now. In the near future, some sensors can be added to monitor the environment so that the sensed environment data can also be used for traffic control optimization. Another important development could be the inclusion of a guiding system that can suggest some alternative paths for user to avoid congested roads. Besides vehicle monitoring, the number of waiting pedestrians and the average arriving pedestrians in an intersection can be monitored.
CHAPTER 3
SYSTEM ANALYSIS:
3.1 PROBLEM DEFINITION:
Now-a-days, lots of accidents happen on highways due to increase in traffic and also due to rash driving of the drivers. Currently, traffic signals in most countries have preset time periods for the red/yellow/green signals. Different roads in a city are configured with different time periods based on type and location of roads and the peak or off-peak hours. Such preset timings are inadequate in coping with unexpected situations such as a traffic accident or other natural disasters or specific unplanned events. Because the traffic control system is generally not adaptive enough, traffic congestion occurs often in most metropolitan cities.
3.2 PROPOSED SYSTEM:
We propose a smart traffic control system that provides real time rerouting of vehicles inorder to bypass the blocked road in case of an accident by updating their predefined static routes during simulation.Our system ensures real time update of driver’s routes upon detection of any abnormal accident inorder to avoid the delay incurred by the occurrence of random accidents on the roads.
3.2.1 Advantages:
The proposed system automatically diverts vehicle’s route, in case of any accident, thereby reducing commuters travel time and congestion level. Our system is useful for the emergency services to provide quick accident relief. Moreover, it enhances travel experiences by providingmore efficient and secure path to reach the destination.
The system architecture for our proposed system is shown in Fig 3.2.2.1. Considering our system as a Smart Intelligent Traffic System, to show the automatic diversion, we are simulating the road network using a traffic simulator called SUMO (Simulation of Urban MObility). Re-routing occurs only in case of an accident, therefore simulating an accident is our next module.
An incident refers to anything that stops the traffic flow.Examples in the real world could be a collision, road works,bad weather conditions causing very slow speeds, or justsheer volume of traffic. There are a few options in SUMOto simulate such event. Stopping a car for a fixed period oftime, by defining a point along its route when it should haltand for how long. Manipulating traffic lights so that one staysred for a longer period of time than the regular phase duration.Or setting the speed limit on a lane so low that the traffic ispractically not moving. We decided to employ the first optionsince stopping a car was the easiest of the three to implementand therefore easy to port from one scenario to another. Inorder to stop a car on a given edge we first need to get theedge and lane IDs. Then, we look up the edge list and includethe following code in the vehicle definition that we want tostop to simulate a traffic jam.
< stoplane= "0/0to1/0_0" endPos= "10"duration= "200"/>
This code stops the vehicle on the specified lane at 10meters for 200 seconds duration.
Then, re-routing mechanism is applied to the simulation output with the help of an interface called Traffic Control Interface (TraCI). TraCI retrieves values from simulated objects and manipulates their behaviour on-line. It uses client-server architecture. It establishes connection with SUMO using the port number 8873.The TraCI API in python is used to alter the state ofvehicles during simulation runtime. The vehicles to be re-routed do not have to be defined beforehand, but only theroad segment in which the accident will occur and the triggerroads surrounding it.
SYSTEM IMPLEMENTATION:
4.1 SYSTEM REQUIREMENTS:
4.1.1 HARDWARE REQUIREMENTS
• 4GB RAM
• Any Intel or AMD x86 processor
4.1.2 SOFTWARE REQUIREMENTS
• SUMO (Simulation of Urban Mobility)
4.2 SIMULATION TOOL:
SUMO (“SIMULATION OF URBAN MOBILITY”):
SUMO is a microscopic, multi-modal, space-continuous and time-discrete traffic flow simulation platform. The implementation of SUMO started in 2001, with a first open source release in 2002. There were two reasons for making the work available as open source under the gnu public license (GPL). The first was the wish to support the traffic simulation community with a free tool into which own algorithms can be implemented. Many other open source traffic simulations were available, but being implemented within a student thesis, they got unsupported afterwards.
A major drawback besides reinvention of the wheelis the non-existing comparability of the implemented models or algorithms, and a common simulation platform is assumed to be of benefit here. The second reason for making the simulation open source was the wish to gain support from other institutions. Within the past ten years, SUMO has evolved into a full featured suite of traffic modeling utilities including a road network importer capable of reading different source formats, demand generation and routing utilities, which use a high variety of input sources, a high performance simulation usable for single junctions as well as whole cities including a “remote control” interface to adapt the simulation online and a large number of additional tools and scripts. The major part of the development is undertaken by the Institute of Transportation Systems at the German Aerospace Center (DLR).
THE SUMO SUITE :
SUMO is not only a traffic simulator, but rather a suite of applications, which help to prepare and to perform the simulation of a traffic scenario. As the simulation application “sumo”, which is included in the suite, uses own formats for road networks and traffic demand, both have to be imported or generated from existing sources of different kind. Having the simulation of large-scale areas as the major application for sumo in mind, much effort has been put into the design and implementation of heuristics which determine missing, but needed attributes. In the following, the applications included in the suite are presented, dividing them by their purpose: network generation, demand generation, and simulation.
ROAD NETWORK GENERATION:
SUMO road networks represent real-world networks as graphs, where nodes are intersections, and roads are represented by edges. Intersections consist of a position, a shape, and right-of-way rules, which may be overwritten by a traffic light. Edges are unidirectional connections between two nodes and contain a fixed number of lanes. A lane contains geometry, the information about vehicle classes allowed on it, and the maximum allowed speed. Therefore, changes in the number of lanes along a road are represented using multiple edges. Such a view on road networks is common; though some other approaches, such as Vissim’s network format or the OpenDRIVE format, exist. Besides this basic view on a road network, SUMO road networks include traffic light plans, and connections between lanes across an intersections describing which lanes can be used to reach a subsequent lane.
SUMO road networks can be either generated using an application named “netgenerate” or by importing a digital road map using “netconvert”. netgenerate builds three different kinds of abstract road networks: “manhattan” - like grid networks, circular “spider-net” networks, and random networks. Each of the generation algorithms has a set of options, which allow adjusting the network’s properties. The road network importer netconvert converts networks from other traffic simulators such as VISUM, Vissim, or MATSim. It also reads other common digital road network formats, such as shapefiles or OpenStreetMap. Besides these formats, netconvert is also capable to read less known formats, such as OpenDRIVE or the RoboCup network format.
Additionally, netconvert reads a native, SUMO-specific, XML-representation of a road network graph referred to as “plain” XML, which allows the highest degree of control for describing a road network for SUMO. This XML representation is broken into five file types, each for description of nodes, edges, optionally edge types, connections, and traffic light plans. Edge types name sets of default edge attributes, which can be referenced by the later loaded edges. Nodes describe the intersections, edges the road segments. Connections describe which lanes incoming into an intersection are connected to which outgoing lanes. The simulation network created by netconvert contains heuristically computed values wherever the inputs are incomplete as well as derived values such as the exact geometry at junctions. It is also possible to convert a simulation network back into the “plain” format.
Multiple input formats can be loaded at the same time and are automatically merged. Since the “plain” format allows specifying the removal of network elements and the adaption of single edge and lane parameters, it can be used for a wide range of network modifications. To support such modifications SUMO additionally provides the python tool, which computes the (human-readable) difference D between two networks A and B. Loading A and D with netconvert reproduces B. Most of the available digital road networks are originally meant to be used for routing purposes.
As such, they often lack the grade of detail needed by microscopic road traffic simulations: the number of lanes, especially in front of intersections, information about which lanes approach which consecutive ones, traffic light positions and plans, etc., are missing. Sharing the same library for preparing generated/imported road networks, both, netgenerate and netconvert, try to determine missing values using heuristics.
Even with the given functionality, it should be stated that preparing a real-world network for a microscopic simulation is still a time-consuming task, as the real-world topology of more complicated intersections often has to be improved manually. A new tool named “netedit” allows editing road networks graphically. This is in many cases simpler and faster than preparing XML input files. It also combines the otherwise separate steps of network generation and inspection with netconvert and the simulation GUI. netedit is not yet available for public use.
SIMULATION:
The application “sumo” performs a time-discrete simulation. The default step length is 1s, but may be chosen to be lower, down to 1ms. Internally, time is represented in microseconds, stored as integer values. The maximum duration of a scenario is so bound to 49 days. The simulation model is space-continuous and internally, each vehicle’s position is described by the lane the vehicle is on and the distance from the beginning of this lane. When moving through the network, each vehicle’s speed is computed using a so-called car-following model. Car-following models usually computes an investigated vehicle’s speed by looking at this vehicle’s speed, its distance to the leading vehicle (leader), and the leader’s speed.
The computation of lane changing is done using a model developed during the implementation of SUMO. Two versions of the traffic simulation exist. The application “sumo” is a pure command line application for efficient batch simulation. The application “sumo-gui” offers a graphical user interface (GUI) rendering the simulation network and vehicles using openGL. The visualization can be customized in many ways, i.e., to visualize speeds, waiting times and to track individual vehicles. Additional graphical elements – points-of-interest (POIs), polygons, and image decals – allow to improve a scenario’s visual appearance. The GUI also offers some possibilities to interact with the scenario, e.g. by switching between prepared traffic signal programs, changing reroute following grades, etc. Besides conventional traffic measures, SUMO was extended by a noise emission and a pollutant emission / fuel consumption model. All output files generated by SUMO are in XML-format.
4.3 RE-ROUTING ALGORITHM:
4.3.1 Principle:
There were two options to reroute vehicles in SUMO. Thefirst one consists of using statically defined routing mechanismprovided by SUMO. This static method is deployed by addinga re-routing file to SUMOs configuration. All vehicles thatneed to be re-routed have to be listed in this file prior tosimulation runtime. This method was tested using very smallscale scenarios in which it worked well. However, since allthe vehicles had to be defined prior to runtime it was notfeasible to use this approach; the main reason being that inother scenarios there would be thousands of cars. This staticmethod could be made dynamic by writing a script perhapsbut the second re-routing option seemed more appropriate tosimulate realistic road traffic and random accidents.
The trigger mechanism was used to make our solution less complex as it consists of adding the road segments connectedto the accidents lane to a list, and then comparing each vehiclescurrent lane ID to each trigger to identify the vehicles thatneed to be re-routed. The alternative to this approach was toonly reroute the vehicles that have the accident lane in theirroute definition. In order to deploy this alternative approach,the routes of all the vehicles have been written in a list, then the vehicles forwhich the route contains the accident lane have been identifiedby running a simple check.
4.3.2 Mechanism:
Step 1: Add the road segments connected to the accident lane to a list.
Step 2: Compare each vehicles current lane ID with that of the list.
Step 3: Identify the vehicles that need to be re-routed.
Step 4: Identify the destination lane ID of those vehicles from its route.
Step 5: Find the alternate path, to reach that destination.
Step 6: Assign that alternate route, to the vehicle.