RESEARCH ABOUT ALLEVIATING

TRAFFIC CONGESTION IN URBAN

TRANSPORTATIONSUSING GIS

Made by-Aziz ZenaNaina GrewalAmey Sonawane

Outlier

Abstract Introduction Motivation Problem Definition Methodologies Conclusion Future Work References

Abstract

With the high speed of development in national economy and quickening of the urbanization process follow big increase in urban traffic which create imbalance of supply and demand of transportation system of traffic

Number of private car was increase which cause high traffic volume , increase number of accident and traffic jam.

Introduction

Traffic jam is one of the many hazards, people face everyday.

High traffic volume, construction, accidents, unexpected emergencies, events and visual obstructions are some main causes of traffic congestion

A lot of methods are present to solve this problem .

Motivation

Traffic congestion is one of the problems which everyone faces in his/her daily routine and people get stuck in Traffic almost everyday and waste their valuable time. Sometimes there is a case of emergency and you are stuck in Traffic.

When you are stuck in a Traffic Jam you will be inhaling all the gases coming out of the vehicles stuck there with engines running.

Problem Definition

After the big development in economy field number of private car was increase, traffic jams get more severe, traffic accidents become more frequently and traffic environment worsens. Which bring a huge pressure to urban traffic .

METHODOLOGIES

Road Pricing

Investigate a novel road pricing model to prevent and reduce the traffic congestion in urban areas

The road prices are changed dynamically according to both the traffic densities and popularities of the roads

road density, road capacity, the average speed of vehicle in the road, the destination parameter, vehicle type these parameters indicate the road selection criterions for a driver .

So the road with high density turn to have high toll fee.

Driver at a junction tends to follow a road having a low price, the traffic congestion at the roads with high traffic density is prevented and reduced because of the higher price of these roads.

Advantages/Disadvantages

It will need a dynamic road pricing model because it is impossible to pricing the entirely road on the route preference of vehicle users

The pricing is instantaneous two vehicles that consecutively enter a road can be charged differently

The users have the pricing information of only the next alternative roads when they arrive at a junction.

 

Shortest Path Algorithm

It addressed the problem of finding the shortest path under traffic jams in road network

Define two key concepts

-Road network

-Speed pattern Present improved algorithm by storage discard

routes and classify them during the query process to get more spare routes.

Advantages/Disadvantages

The test results show that the incremental algorithm is reliable and highly effective for Optimum path planning

The performance of algorithm was improved .

Method Based on VANET

They propose a new method through each vehicle independently performs the following actions.

Each vehicle periodically broadcasts a request message through a vehicle network (VANET) to obtain information of vehicles in the limited area around the vehicle

Each receiving side vehicle replies a response which includes information about the vehicle (vehicle ID, velocity of the vehicle, roadway segment ID in which the vehicle exists.

The sending side vehicle receives response from the receiving side vehicles and evaluates each roadway segments based on the responses.

The sending side vehicle calculates a route for a destination of the sending side vehicle

Function to calculate the congestion degree where degree’s value becomes zero when congestion levels of all areas are equal and degree’s value becomes high when congestion levels of neighboring areas are different. At first the whole driving field is divided into some areas. The traffic congestion degree is assigned to each area at each moment.

Advantages/Disadvantages

Each vehicle will need a wireless function in order to form a network

Each vehicle need GPS or function showing ID of road way segment plus each vehicle need a map road information in order to show a receiving side vehicle position to the road map

If each vehicle obtains traffic information of region which is small, many vehicles would fail to find non-congested area. So existing congested traffic flows are not solved efficiently.

If each vehicle obtains traffic information of region which is too wide, many vehicles would head to a non-congested area. So new congested traffic flows would happen at the non-congested area.

Traffic Management based on GIS

GIS is Geographical Information Systems. computer system designed to capture, store, manipulate, analyze, manage, and present all types of geographical data. The acronym GIS is sometimes used for geographical information science or geospatial information studies to refer to the academic discipline or career of working with geographic information systems and is a large domain within the broader academic discipline of Geoinformatics.

Advantages of GIS

Analyzing large quantities of data, such as statistics for individuals or buildings, across a geographic area.

Analyzing several different kinds of data across an area and understanding how they relate to one another e.g. property types, employment patterns and property values for a given neighbourhood .

Analyzing changes to data over time and visualising the results to allow their ready comparison; projections of future scenarios can be incorporated in the same way, although using any model to predict the future is high-risk.

Visualizing the results of analysis, to allow even non-expert users to understand them easily

Making it easier to spot errors and anomalies, smoothing out the effects of micro-scale phenomena and creating the most accurate possible picture of what’s at work.

Disadvantages of GIS

Its technical nature can make results appear more reliable than they are; poor operators can hide assumptions and errors in a composite results, while users can be ‘blinded with science’ and not apply their usual standards of questioning to what they are being told.

The results of a GI analysis can only ever be as accurate as the data which underlies them, and should only ever be reported at the finest spatial scale of any dataset used.

The availability of data at the required scale at a reasonable cost is a universal issue.

GIS based Systems

Real Time GIS with MapInfo and SCOOT. Traffic Congestion Information Promulgating System. GIS based Intelligent Traffic System. GIS based Transport Decision Support System. Traffic Map Editor Using GPS with Shortest Path Algorithm. Traffic Incident Information Management System.

Real Time GIS with MapInfo and SCOOT.

This system facilitates the use of geographical data in the context of time-varying information and integrates traffic data as a new component of GIS.

The GIS database integrates historical and current traffic states within appropriate network components.

Traffic data are overlaid on urban maps or geographical reference. The application takes advantages of existing software such as the GIS MapInfo and traffic management system SCOOT.

Traffic Congestion Information Promulgating System

This system has been develop by using VB6.0 and MapX5.0. This system can intuitively and visualization provides real-time traffic congestion information for traffic managers and users, e.g. the queue lengths at intersections.

This system can realize the functions of dynamic management and analyze of the traffic congestion information, which can display the spatial places on the map vividly.

GIS based Intelligent Traffic System

This is a software that integrates control, management and decision-making.

It is designed and developed for the modernized traffic command center.

It works by utilizing the advanced information process technology, navigation technology, wireless communication technology, automatic control technology, image analysis technology and computer network technology.

GIS based Transport Decision Support System.

This system as the functions of designing traffic networks on digital maps and doing traffic equilibrium analysis as well as a novel function to integrate local detailed structures of intersections into global networks. The latter is particularly useful for the analysis of large traffic networks.

To minimize the time of map showing o editing, this system is using bucket-based method, which separates the data as rectangular unit(bucket) to index and describes by layer.

Traffic Map Editor Using GPS with Shortest Path Algorithm

There are two different applications in this system which we are using to solve the problem.

One to take data from the real traffic and the other one to find the optimum route or a daily driver.

All this data are also taken as input for the central distributed applications that manages to analyze them and to offer a starting point in predicting traffic congestions and solve them by using the already existing resources in the infrastructures.

Traffic Incident Information Management System

GIS-T and traffic information platform is the base of TIIMS. It also provides traffic incident information and geographical information.

This system platform is MapInfo as MapInfo has many functions such as strong graph handling, statistics analysis, query and option, and can visit various databases such as SQL, Oracle, Sybase and Informix.

Thus TIIMS can provide multiple input types, and save, query, analyze and show incident information.

Real Time Route planning in road networks

A route planning system subsystem is responsible for determining optimum route between user specified origin and destination.

Much of the capability and functionality of route planning derived from database which has specially designed to facilitate path planning problem.

Provides In-vehicle route guidance. Criteria for optimum route planning

-Minimum time.

-Minimum distance.

-Minimum Turns(i.e. Maneuvers or instructions).

-Avoiding or encouraging freeways.

For In-vehicle guidance, two scenarios considered “Strategic” route planning and “Tactical” route planning.

Three major components of route planning subsystem to solve problem.

-A database with sufficient breadth of information.

-A modified A* graph search procedure for searching road network to determine optimum route.

-A database structure and interface which enables A* algorithm to efficiently access all necessary data.

Method used for route planning-

A* graph technique introduced by Nisson which guarantee to find optimum route through graph, performs directed breadth first search from source to destination.

Terms used in this algorithm

-Node

-Segment

-Path

-Every node in search path has a cost ‘f’ and defined as

f(n) = g(n) + h(n)

The term g(n) is ‘known’ cost get from source to node n and h(n) is heuristic estimate(intelligent guess) of the cost of path from node n to the destination.

Optimization criteria:-

-The ‘g’ cost is know cost from source to the current node.

-The ‘g’ cost includes weighted combination of following

-Segment Speed limit, Segment distance, Traffic signals and stop time delays, Type of segment( freeway, artery, ramp, street etc), turning delays at an intersection.

Advantages of the paper

A* algorithm considered to be good for finding optimum path when In-vehicle guidance, so In future it can be used for to find optimum path when traffic jam occurs.

Avoiding Traffic Jam using Ant colony Optimization

Ant colony optimization is meta heuristic based on colony of artificial ants which work co-operatively, building solutions by moving on the problem graph and by communicating through artificial pheromone trails mimicking real ants.

It is multi agent multi heuristic technique where artificial ants built better solution by communication through artificial pheromone imitating real ants.

ACO algorithms are construction algorithm where every ant constructs a solution to the problem by travelling on a construction graph in each iteration.

DSATJ(Dynamic System for avoiding traffic jams)

Which aims at choosing an alternative optimum path to avoid traffic jams and then resuming that same path again when the traffic is regulated.

Traffic jam is detected through pheromone values on edges which are updated according to goodness of solution on the optimal tours only.

DSATJ cont.

Approach : A weighted connected graph is taken as input. Nodes represents different places and weighted edges

represents distance between places. Edge has two types of information one is ‘physical distance’

and other ‘artificial pheromone trail information’, both the information are combined to select next node travel by ant.

Goal is to travel optimal path.

Metrics used in DSATJ

Physical distance:-

-actual distance between two nodes Node Selection:-

-To ensure exploration of maximum, paths a random function is used in addition to probability function.

-Probability function of ant ‘k’ at node ‘i’ need to travel to node ‘j’ at time ‘t’ is given by,

Where ‘τij(t)’ is intensity of pheromone trail on edge (i,j) at time ‘t’.N is number of nodestabu(k) is dynamically growing vector containing the nodes already visited by ant(k)allowed(k)={ N- tabu(k)}, ἠij(visibility factor)= 1/dij.( dij distance between nodes I and j)α,β are the parameters that control the relative importance of pheromone trail vs visibility.

Advantages/Disadvantages

Experiments shows that distance increases when there is traffic jams as compare to situation when there is no traffic jams or traffic is normalized.

This approach successively computes alternative optimum path to avoid traffic jams.

ACO for solving travelling salesman problem

MMAS-MDS algorithm( Max/ Min Ant system extended by multidimensional scaling).

- This algorithm for solving travelling salesman problem more effectively in congested traffic network.

MMAS has several characteristic which make it an excellent ACO method.

1)The range of pheromone trail on each edge is limited to an interval [τmin,τmax]

2)It exploits the best solution by only allowing the iteration-best ant or best-so-far ant to add pheromone.

3) Re- initialization of the pheromone trail occurs occasionally to provide higher exploration of solution.

MMAS-MDS Algorithm

MDS is a methodology that takes the proximity measurements of object pairs as inputs and represent configuration of points as distance in m-dimensional space.

Though MMAS perform well to solve TSP in Euclidean distance but it will not provide optimal solution.

The main reason is that heuristic information in the ant tour construction only concerns the distance in the neighborhood of city i regardless of global guides.

So time-space configuration constructed by MDS provides good and effective global guide.

If there are n cities, then all the time relationships of n(n-1)/2 pairs of cities are involved in MDS computation.

Advantages/Disadvantages

MMAS-MDS has advantage that not only considers the promising paths with local heuristic information but also considers global promising routes.

Experiments shows that optimum path found by MMAS-MDS algorithm is much better than MMAS algorithm in congested transportation system.

Conclusion

Several methods have been implemented in order to avoid traffic congestion but it can’t be completely avoided as the number of vehicles and population in every metropolitan cities is increasing multiple times.

A powerful platform –Geographic Information System has been introduced that take full account of spatial characteristics of traffic information.

The system can promulgate and forecast the real time operating status of urban traffic and analyze the trend of regional traffic safety.

Conclusion Cont.

It improves the efficiency of transportation sector so that traffic resources are fully utilized.

Various congestion activities are also used such as redesigning traffic signal timing to improve progressive traffic movements along road ways, road widening projects to be implemented to improve the service.

Constructing turning lanes at critical intersections to separate turning and through traffic.

Future Work

Road pricing is efficient and environmentally beneficial available tool for congested cities.

However, road pricing cannot by itself deal with the transport problems. It must be seen as a part of comprehensive policy package, which includes substantial improvements to public transport and other alternative modes, environmental enhancements, and in the long term, new approaches to land use planning and more usages of intelligent transport systems.

Future Work Cont.

The simulation results of the road pricing method shows that the pricing based traffic congestion algorithm proposed in this paper homogenizes the traffic densities over the entire traffic network and traffic congestion can be prevented.

Lately VANET method has also been used for alleviating traffic congestion in urban transportations. Simulation results showed that this method is effective in terms of velocity and trip time of vehicles in environment that traffic varies temporally and spatially .

The method should be improved by controlling how much each vehicle collects information on traffic congestion.

References

C.Claramunt,E.Peytchev and A.Bargiela “A real time GIS for the analysis of a traffic System”.

Jingang Bao,Jiangqian Ying. “Development of Traffic Analysis System using GIS”.

Shao Fei, Deng Wei ,Zhang Bing. “Traffic information Mnaggemnt and promulgationg system based on GIS”.

Guiyan Jiang,Mingchen Gu, Guohua Han and Xianping Yang “Traffic Incident Information Mangement Systems based on GIS-T”.

Bing Zhang,Wei Deng and Ling Mao.”Traffic congestion Information Promulgating System based on GIS-T”.

Fahri Soylemezgiller,Murat Kuscu and Deniz Kilinc.”A traffic Congestion avoidance algorithm with dynamic road pricing for smart cities “.

References

Emilian Necula,Raluca Necula and Adrian Iftene.”A gis integrated solution for traffic management”.

Zhiheng Li,Kezhu SONG,and Zhiganag QIU”Integrated Traffic Management Platform Design Based on GIS-T”.

Xiao Juan,Ye Fing .“A study on the Framework of GIS-based Basic Traffic Management System”.

Tu Shengwu.“Design of road traffic safety evaluation system based on GIS-T.”

Xiayu Song ,Lanyang Yu,Huanliang Sun.“An incremental query algorithm for optimal path queries under traffic jams”.

Mitsuhisa Kimura, Yousuke Taoda,Yoshiaki Kakuda and Tadishi Dohi.“A novel based method on VANET for alleviating Traffic Congestion in urban transportations”