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2021-02-08 12:25
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2021年2月8日发(作者:rea)




湖南科技大学





智能控制理论论文







姓名:


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学院:


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班级:


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Intelligent Traffic Signal Control Using Wireless SensorNe


tworks



Abstract


The growing vehicle population in all developing and developed countries calls for a


major change in the existing traffic signaling systems. The most widely used automated


system uses simple timer based operation which is inefficient for non-uniform traffic. Adv


anced automated systems in testing use image processing techniques or advanced com


munication systems in vehicles to communicate with signals and ask for routing. This mig


ht not be implementable in developing countries as they prove to be complex and expens


ive. The concept proposed in this paper involves use of wireless sensor networks to sens


e presence of traffic near junctions and hence route the traffic based on traffic density in t


he desired direction. This system does not require any system in vehicles so can be impl


emented in any traffic system easily. This system uses wireless sensor networks technol


ogy to sense vehicles and a microcontroller based routing algorithm for traffic managem


ent.




Keywords:Intelligent traffic signals, intelligent routing, smart signals, wireless sensor


networks.












INTRODUCTION


The traffic density is escalating at an alarming rate in developing countries which c


alls for the need of intelligent traffic signals to replace the conventional manual and timer


based systems. Experimental systems in existence involve image processing based dens


ity identification for routing of traffic which might be inefficient in situations like fog, rain or


dust. The other conceptual system which is based on interaction of vehicles with traffic si


gnals and each other require hardware modification on each vehicle and cannot be practi


cally implemented in countries like India which have almost 100 million vehicles on road


[1]. The system proposed here involves localized traffic routing for each intersection base


d on wireless sensor networks. The proposed system has a central controller at every jun


ction which receives data from tiny wireless sensor nodes placed on the road. The senso


r nodes


have sensors that can detect the presence of vehicle and the transmitter wirelessly trans


mits the traffic density to the central controller. The controller makes use of the proposed


algorithm to find ways to regulate traffic efficiently.



THE NEED FOR AN ALTERNATE SYSTEM



The most prevalent traffic signaling system in developing countries is the timer


based system. This system involves a predefined time setting for each road at an int


ersection. While this might prove effective for light traffic, heavy traffic requires an adaptiv


e system that will work based on the density of traffic on each road. The first system prop


osed for adaptive signaling was based on digital image processing techniques. This syste


m works based on the captured visual input from the roads and processing them to find w


hich road has dense traffic. This system fails during environmental interaction like rain or


fog. Also this system in testing does not prove efficient. The advanced system in testing a


t Pittsburgh [2] involves signals communicating with each other and also with the vehicles


. The proposed system does not require a network between signals and vehicles and is a


standalone system at each intersection.



THE PROPOSED SYSTEM


This paper presents the concept of intelligent traffic routing using wireless sensor


networks. The primary elements of this system are the sensor nodes or motes consi


sting of sensors and a transmitter. The sensors interact with the physical environment wh


ile the transmitter pages the


sensor’s


data to the central controller. This system involves t


he 4 x 2 array of sensor nodes in each road. This signifies 4 levels of traffic and 2 lanes i


n each road. The sensors are ultrasonic or IR based optical sensors which transmits stat


us based on presence of vehicle near it. The sensor nodes transmit at specified time inter


vals via ZigBee protocol to the central controller placed at every intersection. The controll


er receives the signal and computes which road and which lane has to be given green sig


nal based on the density of traffic. The controller makes use of the discussed algorithm to


perform the intelligent traffic routing.


COMPONENTS INVOLVED IN THE SYSTEM




The proposed system involves wireless sensor networks which are comprised of t


hree basic components: the sensor nodes or motes, power source and a central controlle


r. The motes in turn are comprised of Sensors and transceiver module. The sensors sens


e the vehicles at intersections and transceiver transmit the


sensor’s


data to the central co


ntroller through a wireless medium. The Power source provides the power needed for the


sensor nodes and is mostly regenerative. The central controller performs all the computa


tions for the sensor networks. The controller receives the input from all sensors and proc


esses simultaneously to make the required decisions.


s


Sensors are hardware devices that produce a measurable response to a change in


a physical condition like temperature or pressure. Sensors measure physical data of the


parameter to be monitored. The continual analog signal produced by the sensors is digiti


zed by an analog-to- digital converter and sent to controllers for further processing. A sen


sor node should be small in size, consume extremely low energy, operate in high volumet


ric densities, be autonomous and operate unattended, and be adaptive to the environme


nt. As wireless sensor nodes are typically very small electronic devices, they can only be


equipped with a limited power source of less than 0.5-2 ampere-hour and 1.2-3.7 volts. S


ensors are classified into three categories: passive Omni-directional sensors; passive nar


row-beam sensors; and active sensors [3].


The sensors are implemented in this system placed beneath the roads in an intersec


tion or on the lane dividers on each road. The sensors are active obstacle detectors that


detect the presence of vehicles in their vicinity. The sensors are set in four levels on each


road signifying four levels of traffic from starting from the STOP line. The fourth level indi


cates high density traffic and signifies higher priority for the road to the controller. The se


nsors required for obstacle detection can be either ultrasonic or Infrared LASER based s


ensors for better higher efficiency.


B. Motes


A mote, also known as a sensor node is a node in a wireless sensor network that i


s capable of performing some processing, gathering sensory information and communica


ting with other connected nodes in the network. The main components of a sensor node


are a microcontroller, transceiver, external memory, power source and one or more sens


ors [3].



C. Need for Motes


The primary responsibility of a Mote is to collect information from the various distrib


uted sensors in any area and to transmit the collected information to the central controller


for processing. Any type of sensors can be incorporated with these Motes based on the r


equirements. It is a completely new paradigm for distributed sensing and it opens up a fa


scinating new way to look at sensor networks.


D. Advantages of Motes


?


The core of a mote is a small, low-cost, low-power controller.


?


The controller monitors one or more sensors. It is easy to interface all sorts of


sensors, including sensors for temperature, light, sound, position, acceleration, vibrat


ion, stress, weight, pressure, humidity, etc. with the mote.


?


The controller connects to the central controller with a radio link. The most comm


on radio links allow a mote to transmit at a distance of about 3 to 61 meters. Power cons


umption, size and cost are the barriers to longer distances. Since a fundamental concept


with motes is tiny size and associated tiny cost, small and low-power radios are normal.


?


As motes shrink in size and power consumption, it is possible to imagine solar


power or even something exotic like vibration power to keep them running. It is hard


to imagine something as small and innocuous as a mote sparking a revolution, but that's


exactly what they have done.


?


Motes are also easy to program, either by using serial or Ethernet cable to conne


ct


to the programming board or by using Over the Air Programming (OTAP).



E. Transceivers


Sensor nodes often make use of ISM band, which gives free radio, spectrum


allocation and global availability. The possible choices of wireless transmission medi


a are radio frequency (RF), optical communication and infrared. Lasers require less ener


gy, but need line-of- sight for communication and are sensitive to atmospheric conditions.


Infrared, like lasers, needs no antenna but it is limited in its broadcasting capacity. Radio


frequency- based communication is the most relevant that fits most of


the WSN applications. WSNs tend to use license-free communication frequencies: 173, 4


33, 868, and 915 MHz; and 2.4 GHz. The functionality of bothtransmitter and receiver are


combined into a single deviceknown as a transceiver [3].


To bring about uniqueness in transmitting and receiving toany particular device vari


ous protocols/algorithms are devised. The Motes are often are often provided with powerf


ul transmitters and receivers collectively known as transceivers for better long range oper


ation and also toachieve better quality of transmission/reception in any environmental co


nditions.



F. Power Source


The sensor node consumes power for sensing, communicating and data


processing. More energy is required for data communication than any other process.


Power is stored either in batteries or capacitors. Batteries, both rechargeable and non-re


chargeable, are the main source of power supply for sensor nodes. Current sensors are


able to renew their energy from solar sources, temperature differences, or vibration. Two


power saving policies used are Dynamic Power Management (DPM) and Dynamic Voltag


e Scaling (DVS). DPM conserves power by shutting down parts of the sensor node which


are not currently used or active. A DVS scheme varies the power levels within the senso


r node depending on the non-deterministic workload. By varying the voltage along with th


e frequency, it is possible to obtain quadratic reduction in power consumption.


G. Tmote Sky


Tmote Sky is an ultra low power wireless module for use in sensor networks,


monitoring applications, and rapid application prototyping. Tmote Sky leverages indu


stry standards like USB and IEEE802.15.4 to interoperate seamlessly with other devices.


By using industry standards, integrating humidity, temperature, and light sensors, and pr


oviding flexible interconnection with peripherals, Tmote Sky enables a wide range of mes


h network applications [4]. The TMote is one of the most commonly used motes in wirele


ss sensor technology. Any type of sensor can be used in combination with this type of mo


te.


Tmote Sky features the Chipcon CC2420 radio for wireless communications. The


CC2420 is an IEEE 802.15.4 compliant radio providing the PHY and some MAC function


s [5]. With sensitivity exceeding the IEEE 802.15.4 specification and low power operation,


the CC2420 provides reliable wireless communication. The CC2420 is highly configurabl


e for many applications with the default radio settings providing IEEE 802.15.4 complianc


e. ZigBee specifications can be implemented using the built-in wireless transmitter in the


Tmote Sky.


H. Tmote Key Features


?


250kbps 2.4GHz IEEE 802.15.4 Chipcon Wireless Transceiver


?


Interoperability with other IEEE 802.15.4 devices.


?


8MHz Texas Instruments MSP430 microcontroller (10k RAM, 48k Flash Memory)


?


Integrated ADC, DAC, Supply Voltage Supervisor, and DMA Controller


?


Integrate


d onboard antenna with 50m range indoors / 125m range outdoors


?


Integrated Humidity


, Temperature, and Light sensors


?


Ultra low current consumption


?


Fast wakeup from


sleep


(<6μs)



?


Hardware link-layer encryption and authentication


?


Programming and data collec


tion via USB


?


16-pin expansion support and optional SMA antenna connector


?


TinyOS support : mesh networking and communication implementation


?


Compli


es with FCC Part 15 and Industry Canada regulations


?


Environmentally friendly



compl


ies with RoHS regulations [4].


I. ZigBee Wireless Technology


ZigBee is a specification for a suite of high level communication protocols using


small, low-power digital radios based on an IEEE 802.15.4 standard for personal ar


ea networks [6] [7]. ZigBee devices are often used in mesh network form to transmit data


over longer distances, passing data through intermediate devices to reach more distant o


allows ZigBee networks to be formed ad-hoc, with no centralized control or high


-power transmitter/receiver able to reach all of the devices. Any ZigBee device can be tas


ked with running the network. ZigBee is targeted at applications that require a low data ra


te, long battery life, and secure networking. ZigBee has a defined rate of 250kbps, best s


uited for periodic or intermittent data or a single signal transmission


from a sensor or input device. Applications include wireless light switches, electrical


meters with in-home- displays, traffic management systems, and other consumer and ind


ustrial equipment that requires short-range wireless transfer of data at relatively low rates


. The technology defined by the ZigBee specification is intended to be simpler and less e


xpensive than other WPANs, such as Bluetooth.


J. Types of ZigBee Devices ZigBee devices are of three types:


?


ZigBee Coordinator (ZC): The most capable device, the Coordinator forms the


root of the network tree and might bridge to other networks. There is exactly one Zig


Bee Coordinator in each network since it is the device that started the network originally.


It stores information about the network, including acting as the Trust Center & repository


for security keys. The ZigBee Coordinator the central controller is in this system.


?


ZigBee Router (ZR): In addition to running an application function, a device


can act as an intermediate router, passing on data from other devices.


?


ZigBee End Device (ZED): It contains just enough functionality to talk to the


parent node. It cannot relay data from other devices. This relationship allows the no


de to be asleep a significant amount of the time thereby giving long battery life. A ZED re


quires the least amount of memory, and therefore can be less expensive to manufacture t


han a ZR or ZC.


K. ZigBee Protocols


The protocols build on recent algorithmic research to automatically construct a low-s


peed ad-hoc network of nodes. In most large network instances, the network will be a clu


ster of clusters. It can also form a mesh or a single cluster. The current ZigBee protocols


support beacon and non-beacon enabled networks. In non-beacon- enabled networks, an


un-slotted CSMA/CA channel access mechanism is used. In this type of network, ZigBee


Routers typically have their receivers continuously active, requiring a more robust power


supply. However, this allows for heterogeneous networks in which some devices receive


continuously, while others only transmit when an external stimulus is detected. In beacon


-enabled networks, the special network nodes called ZigBee Routers transmit periodic be


acons to confirm their presence to other network nodes. Nodes may sleep between beac


ons, thus lowering their duty cycle and extending their battery life. Beacon intervals depe


nd on data rate; they may range from 15.36ms to 251.65824s at 250 kbps. In general, th


e ZigBee protocols minimize the time the radio is on, so as to reduce power use. In beac


oning networks, nodes only need to be active while a beacon is being transmitted. In non


-beacon-enabled networks, power consumption is decidedly asymmetrical: some devices


are always active, while others spend most of their time sleeping.


V. PROPOSED ALGORITHM A. Basic Algorithm


Consider a left side driving system (followed in UK, Australia, India, Malaysia and 72


other countries). This system can be modified for right side driving system (USA, Canada


, UAE, Russia etc.) quite easily. Also consider a junction of four roads numbered as node


1, 2, 3 and 4 respectively. Traffic flows from each node to three other nodes with varied


densities. Consider road 1 now given green signal in all directions.



1)



Free left turn for all roads (free right for right side driving system).


2)



Check densities at all other nodes and retrieve data from strip sensors.


3)



Compare the data and compute the highest density.


4)



Allow the node with highest density for 60sec.


5)



Allowed node waits for 1 time slot for its turn again and the process is repeated f


rom




step 3.


B. Advanced Algorithm


Assume road three is currently given green to all directions. All left turns are always f


ree. No signals/sensors for left lane. Each road is given a time slot of maximum 60 secon


ds at a time. This time can be varied depending on the situation of implementation. Consi


der 4 levels of sensors Ax, Bx, Cx, Dx with A having highest priority and x representing ro


ads 1 to 4. Also consider 3 lanes of traffic: Left (L), Middle (M) and Right(R) correspondin


g to the direction of traffic. Since left


turn is free, Left lanes do not require sensors. So sensors form 4x2 arrays with 4 levels of


traffic and 2 lanes and are named MAx, RAx, MBx, RBx and so on and totally 32 sensor


s are following flow represents the sequence of operation done by the sign


al.


1) Each sensor transmits the status periodically to the controller. 2) Controller recei


ves the signals and computes the following


3) The sensors Ax from each road having highest priority are compared. 4) If a sin


gle road has traffic till Ax, it is given green signal in the next time slot. 5) If multiple road


s have traffic till Ax, the road waiting for the longest duration is given the green.


6) Once a road is given green, its waiting time is reset and its sensor status is negle


cted for that time slot


7) If traffic in middle lane, green is given for straight direction, based on traffic, either


right side neighbor is given green for right direction, of opposite road is give green for str


aight direction.


8) If traffic in right lane, green is given for right, and based on traffic, left side neighb


or is given green for straight or opposite is given green for right.


9) Similar smart decisions are incorporated in the signal based on traffic density and


directional traffic can be controlled.



C. Implementation and Restrictions


This system can be implemented by just placing the sensor nodes beneath the road


or on lane divider and interfacing the central controller to the existing signal lights and co


nnecting the sensor nodes to the controller via the proposed wireless protocol. The only r


estriction for implementing the system is taking the pedestrians into consideration. This h


as to be visualized for junctions with heavy traffic such as highway intersections and amo


unt of pedestrians is very less. Also major intersections have underground or overhead fo


otpaths to avoid interaction of pedestrians with heavy traffic.



ACKNOWLEDGMENT


The Authors would like to take this opportunity to thank Ms. P. Sasikala, Assistant Pr


ofessor, ECE department, Sri Venkateswara College of Engineering, Sriperumbudur, wh


o gave the basic insight into the field of Wireless Sensor Networks. We also thank Mrs. G


. Padmavathi, Associate Professor, ECE department, Sri Venkateswara College of Engin


eering, Sriperumbudur, who with her expertise in the field of networks advised and guide


d on practicality of the concept and provided helpful ideas for future modifications. We als


o express our gratitude to Dr. S. Ganesh Vaidyanathan, Head of the department of ECE,


Sri Venkateswara College of Engineering, Sriperumbudur, who supports us for every inn


ovative project and encourages us


“think



beyond”


for better use of technology. And finall


y we express our heart filled gratitude to Sri Venkateswara College of Engineering, which


has been the knowledge house for our education and introduced us to the field of Engine


ering and supports us for working on various academic projects.



Adaptive urban traffic control


Adaptive signal control systems must have a capability to optimise the traffic flow by


adjusting the traffic signals based on current traffic. All used traffic signal control methods


are based on feed-back algorithms using traffic demand data -varying from years to a co


uple of minutes - in the past. Current adaptive systems often operate on the basis of ada


ptive green phases and flexible co-ordination in (sub)networks based on measured traffic


conditions (e.g., UTOPIA-spot,SCOOT). These methods are still not optimal where traffic


demand changes rapidly within a short time interval. The basic premise is that existing si


gnal plan generation tools make rational decisions about signal plans under varying condi


tions; but almost none of the current available tools behave pro-actively or have meta-rul


es that may change behaviour of the controller incorporated into the system. The next log


ical step for traffic control is the inclusion of these meta- rules and pro active and goal-orie


nted behaviour. The key aspects of improved control, for which contributions from artificia


l intelligence and artificial intelligent agents can be expected, include the capability of dea


ling with conflicting objectives; the capability of making pro-active decisions on the basis


of temporal analysis; the ability of managing, learning, self adjusting and responding to n


on-recurrent and unexpected events (Ambrosino et al.., 1994).


What are intelligent agents


Agent technology is a new concept within the artificial intelligence (AI). The agent pa


radigm in AI is based upon the notion of reactive, autonomous, internally-motivated entiti


es that inhabit dynamic, not necessarily fully predictable environments (Weiss, 1999). Aut


onomy is the ability to function as an independent unit over an extended period of time, p


erforming a variety of actions necessary to achieve pre-designated objectives while respo


nding to stimuli produced by integrally contained sensors (Ziegler, 1990). Multi-Agent Sys


tems can be characterised by the interaction of many agents trying to solve a variety of pr


oblems in a co-operative fashion. Besides AI, intelligent agents should have some additio


nal attributes to solve problems by itself in real- time; understand information; have goals


and intentions; draw distinctions between situations; generalise; synthesise new concept


s and / or ideas; model the world they operate in and plan and predict consequences of a


ctions and evaluate alternatives. The problem solving component of an intelligent agent c


an be a rule-based system but can also be a neural network or a fuzzy expert system. It


may be obvious that finding a feasible solution is a necessity for an agent. Often local opt


ima in decentralised systems, are not the global optimum. This problem is not easily solv


ed. The solution has to be found by tailoring the interaction mechanism or to have a supe


rvising agent co-ordinating the optimisation process of the other agents.


Intelligent agents in UTC,a helpful paradigm


Agent technology is applicable in different fields within UTC. The ones most importa


nt mentioning are: information agents, agents for traffic simulation and traffic control. Curr


ently, most applications of intelligent agents are information agents. They collect informati


on via a network. With special designed agents user specific information can be provided


. In urban traffic these intelligent agents are useable in delivering information about weath


er, traffic jams, public transport, route closures, best routes, etc. to the user via a Person


al Travel Assistant. Agent technology can also be used for aggregating data for further di


stribution. Agents and multi agent systems are capable of simulating complex systems fo


r traffic simulation. These systems often use one agent for every traffic participant (in a si


milar way as object oriented programs often use objects). The application of agents in (Ur


ban) Traffic Control is the one that has our prime interest. Here we ultimately want to use


agents for pro- active traffic light control with on-line optimisation. Signal plans then will be


determined based on predicted and measured detector data and will be tuned with adjoi


ning agents. The most promising aspects of agent technology, the flexibility and pro-activ


e behaviour, give UTC the possibility of better anticipation of traffic. Current UTC is not th


at flexible, it is unable to adjust itself if situations change and can't handle un-programme


d situations. Agent technology can also be implemented on several different control layer


s. This gives the advantage of being close to current UTC while leaving considerable free


dom at the lower (intersection) level.


Designing agent based urban traffic control systems


The ideal system that we strive for is a traffic control system that is based on actuate


d traffic controllers and is able to pro actively handle traffic situations and handling the diff


erent, sometimes conflicting, aims of traffic controllers. The proposed use of the concept


of agents in this research is experimental.


Assumptions and considerations on agent based urban traffic control


There are three aspects where agent based traffic control and -management can im


prove current state of the art UTC systems:


- Adaptability. Intelligent agents are able to adapt its behaviour and can learn from e


arlier situations.


- Communication. Communication makes it possible for agents to co-operate and tun


e signal plans.


- Pro- active behaviour. Due to the pro active behaviour traffic control systems are abl


e to plan ahead.


To be acceptable as replacement unit for current traffic control units, the system sho


uld perform the same or better than current systems. The agent based UTC will require o


n-line and pro- active reaction on changing traffic patterns. An agent based UTC should b


e demand responsive as well as adaptive during all stages and times. New methods for tr


affic control and traffic prediction should be developed as current ones do not suffice and


cannot be used in agent technology. The adaptability can also be divided in several differ


ent time scales where the system may need to handle in a different way (Rogier, 1999):


- gradual changes due to changing traffic volumes over a longer period of time, - abr


upt changes due to changing traffic volumes over a longer period of time,


- abrupt, temporal, changes due to changing traffic volumes over a short period of ti


me,


- abrupt, temporal, changes due to prioritised traffic over a short period of time


One way of handling the balance between performance and complexity is the use of


a hierarchical system layout. We propose a hierarchy of agents where every agent is res


ponsible for its own optimal solution, but may not only be influenced by adjoining agents


but also via higher level agents. These agents have the task of solving conflicts between l


ower level agents that they can't solve. This represents current traffic control implementat


ions and idea's. One final aspect to be mentioned is the robustness of agent based syste


ms (if all communication fails the agent runs on, if the agent fails a fixed program can be


executed.


To be able to keep our first urban traffic control model as simple as possible we have


made the following assumptions: we limit ourselves to inner city traffic control (road seg


ments, intersections, corridors), we handle only controlled intersections with detectors (int


ensity and speed) at all road segments, we only handle cars and we use simple rule base


s for knowledge representation.


Types of agents in urban intersection control


As we divide the system in several, recognisable, parts we define the following 4 typ


es of agents:- Roads are represented by special road segment agents (RSA),


- Controlled intersections are represented by intersection agents (ITSA), - For specifi


c, defined, areas there is an area agent (higher level),


- For specific routes there can be route agents, that spans several adjoining road se


gments (higher level).


We have not chosen for one agent per signal. This may result in a more simple soluti


on but available traffic control programs do not fit in that kind of agent. We deliberately ch


oose a more complex agent to be able to use standard traffic control design algorithms a


nd programs. The idea still is the optimisation on a local level (intersection), but with local


and global control. Therefor we use area agents and route agents. All communication ta


kes place between neighbouring agents and upper and lower level ones.


Design of our agent based system


The essence of a, demand responsive and pro-active agent based UTC consists of s


everal ITSA's (InTerSection Agent).,some authority agents (area and route agents) and o


ptional Road Segment Agents (RSA). The ITSA makes decisions on how to control its int


ersection based on its goals, capability, knowledge, perception and data. When necessar


y an agent can request for additional information or receive other goals or orders from its


authority agent(s).


For a specific ITSA, implemented to serve as an urban traffic control agent, the follo


wing actions are incorporated (Roozemond, 1998):


- data collection / distribution (via RSA - information on the current state of traffic; fro


m / to other ITSA's - on other adjoining signalised intersections);


- analysis (with an accurate model of the surrounds and knowing the traffic and traffi


c control rules define current trend; detect current traffic problems);


- calculation (calculate the next, optimal, cycle mathematically correct);


- decision making (with other agent deciding what to use for next cycle; handle curre


nt traffic problems);


- control (operate the signals according to cycle plan).


In figure 1 a more specific example of a simplified, agent based, UTC system is give


n. Here we have a route agent controlling several intersection agents, which in turn mana


ge their intersection controls helped by RSA's. The ITSA is the agent that controls and op


erates one specific intersection of which it is completely informed. All ITSA's have direct c


ommunication with neighbouring ITSA's, RSA's and all its traffic lights. Here we use the a


gent technology to implement a distributed planning algorithm. The route


agents’


tasks ar


e controlling, co-ordinating and leading the


ITSA’s


towards a more global optimum. Using


all available information the ITSA (re)calculates the next, most optimal, states and contro


l strategy and operates the traffic signals accordingly. The ITSA can directly influence the


control strategy of their intersection(s) and is able to get insight into on-coming traffic


The internals of the ITSA model


Traffic dependent intersection control normally works in a fast loop. The detector


data is fed into the control algorithm. Based upon predetermined rules a control strategy i


s chosen and the signals are operated accordingly. In this research we suggest the introd


uction of an extra, slow, loop where rules and parameters of a prediction- model can be c


hanged by a higher order meta-model.


ITSA model


The internals of an ITSA consists of several agents. For a better overview of the inter


nal ITSA model-agents and agent based functions see figure 2. Data collection is partly p


laced at the RSA's and partly placed in the ITSA's. The needed data is collected from diff


erent sources, but mainly via detectors. The data is stored locally and may be transmitted


to other agents. The actual operation of the traffic signals is left to an ITSA-controller age


nt. The central part of the ITSA, acts as a control strategy agent. That agent can operate


several control strategies, such as anti-blocking and public transport priority strategies. T


he control strategy agent uses the estimates of the prediction model agent which estimat


es the states in the near future. The ITSA-prediction model agent estimates the states in t


he near future. The prediction model agent gets its data related to intersection and road s


egments - as an agent that


‘knows’


the forecasting equations, actual traffic conditions an


d constraints - and future traffic situations can be calculated by way of an inference engin


e and


it’s


knowledge and data base. On-line optimisation only works if there is sufficient q


uality in traffic predictions, a good choice is made regarding the performance indicators a


nd an effective way is found to handle one-time occurrences (Rogier, 1999).


Prediction model


We hope to include pro-activeness via specific prediction model agents with a task of


predicting future traffic conditions. The prediction models are extremely important for the


development of pro active traffic control. The proposed ITSA- prediction model agent esti


mates the states of the traffic in the near future via its own prediction model. The predicti


on meta-model compares the accuracy of the predictions with current traffic and will adju


st the prediction parameters if the predictions were insufficient or not accurate. The predi


ction model agent is fed by several inputs: vehicle detection system, relevant road conditi


ons, control strategies, important data on this intersection and its traffic condition, commu


nication with


ITSA’s


of nearby intersections and higher level agents. The agent itself has


a rule-base, forecasting equations, knows constraints regarding specific intersections and


gets insight into current (traffic) conditions. With these data future traffic situations shoul


d be calculated by its internal traffic forecasting model. The predicted forecast is valid for


a limited time. Research has shown that models using historic, up-stream and current link


traffic give the best results (Hobeika & Kim, 1994).


Control strategy model


The prediction of the prediction model is used in the control strategy planning phase.


We have also included a performance indicating agent, necessary to update the


control parameters in the slower loop. The control strategy agent uses the estimates of th


e prediction model agent to calculate the most optimal control strategy to pro-act on the f


orecasts of the prediction model agent, checks with other adjoining agents its proposed tr


affic control schema and then plans the signal control strategy The communication sche


ma is based on direct agent to agent communication via a network link. The needed nego


tiation finds place via a direct link and should take the global perspective into consideratio


n. Specific negotiation rules still have to be developed. Some traffic regulation rules and


data has to be fed into the system initially. Data on average flow on the links is gained by


the system during run-time. In the near future computer based programs will be able to d


o, parts of, these kind of calculus automatically. For real-time control the same basic com


puter programs, with some artificial knowledge, will be used. Detectors are needed to giv


e information about queues and number of vehicles. The arrival times can also be given


by the RSA so that green on demand is automatically covered.


Intersection Traffic Channelization Characteristics Based on Harmonious


Traffic


In(2005), we put


forward that the strategic concept is to construct harmonious traffic, of which two


basic characteristics are humanism and environmentalism and three critical


objectives are safety, convenience and environmentalism are as follows.


1) Service pattern which fully protects the weak transportation community


In view of released traffic energy risk degree of different traffic modes, the


pedestrians and bicyclists are classified as weak transportation community and the


service level gradually increases according to the rank of private car,public


transportation, bicyclist and pedestrian respectively.


2) Safety


With the aid of traffic island and legible traffic signs, the distinct distinction of


temporal and spatial rights among pedestrians, bicyclists and motors can be achieved


to enhance traffic safety.


3) People oriented


The design of traffic islands and non- barrier facility and so on should meet the


demand of human behavior, and pay attention to the demand of disable group.



4) The Optimization of capacity


It is effective to optimize intersection capacity by reducing conflicting area,


advancing the stop line, reasonably arranging the entry and exit approaches.


5) Environment friendly


Traffic channelization should be connected with landscape design and the


humanity design to improve the environmental benefit of intersections.


3 A Case Study of Chengdu


3.1 Analysis on intersection and its traffic demand characteristics of Chengdu


Table1. Related characteristics of arterial road in Chengdu


level of


road


Road


Restriction


Line(m)

?


?



Arterial


road


40~60


3000~


4500


1000~2000


3000~5000


2500~450


0


2000~300


0


Area


(m


?


)


Volume of


Bicycle


Volume


of


Conflict


Area


(m2)


pedestrian ( p/h)


Volume (b/h)


vehicles


two-way


?



two-way


?



(pcu/h)


Walking and bicycling accounts for a large proportion in travel behavior, which


respectively is 27.2% and 36.0%. The non-motor traffic volume on Arterial road


reaches to 5000 bicycles each peak hour and the acreage in most arterial intersections


is above 2500 m


?


. Though the commodious intersection space resources accommodate


theinterchanging


demand


between


pedestrians


and


bicycles


or


motors,


the


lack


of


reasonable trafficchannelization, the low utility of space resources,and the serious conflict


greatly restrict the intersection capacity(table.1).


Fig.1 Temporal and spatial separation of motor and non-motorized traffic




3.2 Temporal and spatial separation of motor and non- motor


According to the commodious space resources and the serious traffic conflict,


temporal and spatial separation of motor and non-motor should be conducted at


intersections to classify the road rights of pedestrians, bicycles and motors in time


and space. With regard to the spatial road rights the motors, pedestrians and bicycles


should be restricted to travel in different areas and bicycles travel anticlockwise


while the pedestrians get a bidirectional crossing (fig.1).


3.3 Placement of Entry and Exit lanes


The number of entry lanes should more than the exit. Methods that can be


adopted to add entry lanes include offsetting median pavement marking, enlarging


the entry approaches or removing the median closure while considering matching the


number of entry lanes with road capacity. The suggested proportion of the number of


entry lanes to that of lanes on road in signal direction is 1:1~2:1 (fig.2).



Fig.2 methods to add entry lanes



Table 2 Relationship between lanes on road (one way) and entry lanes at intersection


Roadways of road


?


one way


?



corresponding intersection entrance


roadways



3.4 Traffic Island Design


1)the location of traffic island


The location of traffic island should reasonably guide motor and non-motor


traffic flow, reduce the severity of conflict, ensure security and enhance the capacity.


The location of island is related to the placement of advanced right-turn lane. When


≥2



1


?


2


2



3


3



4


4



5


5



the angle of two crossing roads is less than 75


°


and there is enough space resources


for pedestrian refuge, advanced right-turn lane should be adopted, or else not(fig.3,


fig.4,tab.3).



Fig3 Location of traffic island










Fig4 location of traffic island


(without advanced right-turn lane)








(with advanced right-turn lane)



Tab.3 Parameters related to the location of island



Design


speed


km/h


?



Bicycle lane


width


corner


D1


?


m


?



>40


D1



D4


Safe


inward


offsetting


width


D3


?


m


?



Bicycle lane


width on


roadway


D4


?


m


?



Vehicle


lane


width at


corner


D5


?


m


?



(

L


?


/


R



0.1


V


at


offsetting


width


D2


?


m


?



0.4


R




2


/


2


R




3.5



L



/2


/


R


)2


+< /p>


2.8


(


L


?


/


R



0.1


V



40


D1



D4


0.2


R




2


/


2


R




3.0



L



/2


/


R


)2


+< /p>


2.8


?


footnote:


R’



the minimal turning radii


L’



vehicle width


R



turning radii of advanced right turn lane


L



vehicle length


2) The refuge area of traffic island


The refuge area of traffic island should be no less than 5.0m2. Pavement


markings can be adopted while the area is too limited. The area on safety island


should be large enough to accommodate the minimal need of pedestrians and the


bicyclists waiting in red time.



Model lists as follow:


A=(Q


p


·


T


p


·


A


p

< p>
+Q


b


·


T


b


·


A


b

< br>)S


a


·


S

l


/3600



















(1)


In the equation:A



The refuge area of traffic island (m


?


);


Q


p



Q


b



the arrival rate of pedestrians and bicyclists (person/hour,bicycles/hour);


T

p



T


b



the maximal red time that the pedestrian and bicyclist have waited for


(second);


A


p

< p>


A


b



the static refuge areafor one person and onebicyclist. They are respectively


0.6 m


?



and 1.6 m


?


;


S


a



safety coefficient 1.3;


S


l



the coefficient of servicelevel,0.85



congestion,1



relative congestion ,2




ge neral,3



relative comfort,5



comfort.


3



traffic island design


The contour of traffic island is the combination of beeline and circular curve.


The refuge area should be set inside traffic island for crossing pedestrians and


bicyclists. The end of traffic island should be observable and smooth enough to direct


vehicles. Considering the demand on comfort and convenience for pedestrian and



bicyclist entrancing and exiting traffic island



the refuge area should higher 3-5cm


than the ground, with a 1:10 conjunctive grade. Blind way should be set on the


refuge area and connected with pavement (fig.5, tab.4).






Fig.5 Traffic island design




Table.4 Traffic island traffic channelization design


longitudinal


width


L1(m)


?



Transverse


width



L2(m)


?



Entry


Width


L3(m)


?



Central


landscape


Entry


landscape


Exit


landscape


island(m)


?



grade of


traffic island


island(m)


?



island(m)


?



R11


R1


2


R21


R22


R31


R32


H


R

Max[10,Q


Max[10,Q


2

/


2


L


1


?


L


2


2



>8


>8


>1


>2


>1


>2

< br>3


1


1


/4+5 ]


4+5 ]


~5cm


:10


Footnote:Q


1


,Q


2



the maximal longitudinal and transverse bicycles waiting during a cycle


at peak hour


4) Landscape design of traffic island


The triangular area on traffic island can be utilized to design urban greening and


landscape, and the background and pattern design of refuge area can be connected


with the local historical culture.


4 Traffic Channelization Design of the Yu Daiqiao Roundabout Flyover


4.1 Traffic Channelization Design


The Yu Daiqiao intersection is located in the Luo Mashi CBD of Chengdu.


Before reconstruction the roundabout model restricted the capacity, traveling


conditions under flyover is bad and the conflict between bicyclists and pedestrians is


serious. The one-way traffic control in western and northern entry approaches


reduces the accessibility of the road and results in excess concentration of traffic flow.


The level of service of the intersection is difficult to accommodate the demand on


safety, efficiency, convenience and environmentalism. Based on the traffic demand


characteristic, the channeling measures mainly include (tab.5):


Table 5The Yu Daiqiao intersection traffic channelization design


Contrast


key points of traffic


channelization



Remove the roundabout flyover


for grade crossing.




Optimize


lanes


of


entry


and


After


exit.



Set traffic island to guarantee the


safe


and


convenientcrossing


of


pedestrians and bicyclists.



Set greening islands at the ends


of


traffic


island


toensure


the


safety


of


pedestrians


and


the



bicyclists


as


wellas


beautifying


urban environment.




Divide


traveling


area


for



Before



traffic channelization design


pedestrians and the bicyclists.



Conduct


barrier


free


design


on


crosswalks


?




Set


storage


area


for


left


turn


vehicles



Set


legible


and


perfect


traffic


signs and markings



Set


isolation


pegs


to


reduce


conflicts


between


vehiclesand


the bicyclists.


4.2 Evaluation on traffic channelization effect


A


quantitative


evaluation


is


conducted


by


combining


the


theoreticalcalculation


and


traffic


micro-simulation


(table.6).


Besides


the


safety


and


convenienceof


pedestrian


crossing is largely enhanced, the landscaping benefit at intersection ispromoted, and the


road accessibility is improved through optimizing the organizationof traffic flow while the


business turnover around the area increases by 12 percents.


Table.6 Before & after effects of traffic channelization at intersection


Items


Lane


Capacit


y(pcu/h)


Function


Before


After


Before


After


Befor


e


Eastern


entry


approac


h


Left turn


Through


Right


turn


1






1


1


1


364






279


537


545


1.86






0.73


0.46


0.46


93






54


35


34


16






9


9


7


Saturati


on


After


Delay(s/c


ycle)


Before


After


Length(pcu


/cycle)


Before


After


Southern


Left turn


entry


approac


h


Western


entry


approac


h


Northern


Left turn


entry


approac


h


Through


Right


turn


Left turn


Through


Right


turn


Through


Right


turn




2


1


2


2


1




560


945


787


642


750




1.21


0.96


0.89


0.77


0.89




46


21


41


31


19




13


5


9


8


4


1


2


2


1


2


2


910


1201


1850


279


537


503


1.13


1.55


0.64


0.73


0.90


0.70


65


52


23


59


46


20


3


15


5


3


12


4






1


2




1








207


640


678








0.98


0.62


0.62








65


36


31








6


7


3


footnote:“Before, After” respectively represents before and after traffic channelization.



Typical urban traffic signal control system


With the development of computer and automatic control technology, and the consta


nt improvement of traffic flow theory, transportation organizations and optimization theory


continuously improve the technical level, the function of traffic control is enhanced more


and more advanced means of control, the formation of a number of high-level the effectiv


e urban road traffic control system.


Representative countries in the world most widely used and effective traffic control s


ystem the TRANSYT systems, SCATS and SCOOT system.


Domestic urban traffic signal control system status


The late start in China in the development and application of traffic signal control sys


tem, started in 1973 the development of single - point signal control of urban traffic by-wir


e system in


“Beijing


three Main Street


As the


versity has developed a set of the domestic intelligent traffic signal control system, due to


various reasons, a number of important functions, such as real-time adaptive timing did


not use. Tianjin University (1989-1991) research and development of urban traffic control


system, TICS (Traffic Intelligent Control system) successfully for the first time since learni


ng intelligent principle applied to the traffic signal control system. Jilin University in 2000-


2002, under the auspices of Professor Yang Zhaosheng research and development of ur


ban mixed traffic characteristics of adaptive signal control system and its software mixed


traffic adaptive signal control system, the system included bicycle traffic flow detection, c


onsider bicycle traffic during signal timing optimization program with practical engineering


applications , and achieved good results.


Fuzzy logic - based urban traffic control


Zadeh fuzzy set theory to solve the complex process uncertainty provides a concept


ual framework for those people only a rough approximation of the process of modeling an


d analysis provides a strong conceptual basis, which can decision-making within a certai


n range by artificial experience. Has a very complex characteristics of urban traffic control


, while the long- term practice has accumulated a lot of experience in manual control, whic


h is very suitable for the application of fuzzy control method.


Fuzzy control has become one of the intelligent automation control study of the most


active and fruitful fields and has attracted the attention of the world's scientists. A lot of fu


zzy technology products have been applied in industrial and civil. In 1986, for example, th


e fuzzy controller in Japan already become a commodity. In 1987, based on fuzzy control


Sendai subway opened, then, various home appliances fuzzy successively successfully


developed and put into the market.


The research of significance


1, The theoretical significance


In-depth study of the traffic signal control theory, control method of combining fuzzy


control and timing control, and programmable controllers used in industrial applicatio


ns mature, the design ideas achieve regional control reference significance.


The implementation of the system to maximize the use of the city existing transportat


ion facilities, improve the utilization of the road network, the effective control of urban traff


ic conditions, reduce traffic congestion, reduce environmental pollution, improve economi


c efficiency has important significance; while our smart ITS research and development of


the transportation system is also of great significance.


The basic parameters of the traffic signal control


Easy paper described in this section is a brief introduction to the basic terms of the tr


affic signal control.


Signal cycle: for directing traffic signal always step-by-step cycle change a loop by a


finite number of steps. Step and each step within a cycle is known as a signal cycle, repr


esented by C. If a loop n steps, each step by step length tl, t2,


?



?



?


, tn, then


C=t1+t2+···+tn


(2.1)


Phase : traffic control in order to avoid conflict between the Intersection on the traffic


flow in all directions, usually using the method of time-sharing traffic intersection on a one

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