RAPID TRANSIT SYSTEM

INTEGRATED QUALITY FUNCTION DEPLOYMENT (QFD) MODEL FOR SUSTAINABALE URBAN MASS RAPID TRANSIT SYSTEMGuided By:

(Dr. Debasis Sarkar, Associate Professor & Head)

Presented By:

(Nirali Shukla, 13MCL018)

1NEED FOR STUDYTransport is a primary public good and its sustainability will be determined by the policies and investments made by national governments.

Without sustainable transportation, children cannot go safely to school, goods cannot efficiently reach markets, and harmful emissions will exacerbate climate change.

Despite this, significant work remains to ensure the momentum for sustainable transport emerging translates into real change.

The current model for transportation development one of mismanaged motorization is highly unsustainable.

Rapid growth in automobile travel and concomitant growth in fossil fuel use, traffic congestion, air pollution, greenhouse gases, and traffic fatalities is degrading the quality of life for people around the world.

A global shift to a sustainable transport paradigm is desperately needed.

This new archetype must become a central part of economic, social, and environmental agendas. OBJECTIVES

To develop a Quality Function Deployment (OFD) for selection of a sustainable Mass Rapid Transit System model for Ahmedabad-Gandhinagar city.

To carry out technical and economic feasibility analysis for launching a sustainable mode of Mass Rapid Transit System for Ahmedabad-Gandhinagar city.

To propose a suitable mode of MRTS model using integrated QFD-ANP and QFD-Fuzzy approach.

SCOPE OF STUDY

The scope of the work will be limited to Mass Rapid transit Project in Ahmedabad-Gandhinagar region.RESEARCH METODLOGYLITERATURE REVIEWThe literature for past two decades by eminent researchers in the area of management was reviewed from published papers of journals to find out the research gap.

DATA COLLECTIONData shall be collected from AUDA, GUDA, JNNURM and organizations involved in its operation.

DATA ANALYSISAnalysis shall be done for the data acquired with the help of suitable software such as SUPER-DECESIONS, QFD-Excel Add-In, SPSS, etc.

CONCLUSIONBased on the results of above analysis, a model for sustainable transportation will be developed which will eliminate the hindrances faced in the current transportation system.RESEARCH PLAN

LITERATURE REVIEWRapid transit is a type of high-capacity public transport generally found in urban areas.

Unlike buses, trams or light rail, rapid transit systems operate on an exclusive right-of-way.

The world's largest rapid transit system by both length of track and number of stations is the New York City Subway.

Rapid transit is used in cities, agglomerations, and metropolitan areas to transport large numbers of people often short distances at high frequency.SOURCE: http://en.wikipedia.org/wiki/Rapid_transitBanister (2007) stated that it has often been said that transport planning is at a crisis point and that it underestimates the key challenges facing urban planners.

Broad coalitions should reformed to include specialists, researchers, academics, practitioners, policy makers and activists in the related areas of transport, land use, urban affairs, environment, public health, ecology, engineering, green modes and public transport.

Chanas and Kuchta (1996) stated that among many specific linear programming problems, the one called transportation problem is very popular.

Therefore it seems that the ability to define and to determine the optimal solution of the transportation problem with fuzzy cost coefficients may be important.

A fuzzy number is a convex fuzzy set, characterized by a given interval of real numbers, each with a grade of membership between 0 and 1. WORLDS UNSUCCESSFULL RAPID TRANSIT SYSTEMLos Angeles red line subway system.

New Orleans transit system.

Minneapolis Metro Service.

Boston Red & Green subway lines.

Toronto Transit Commission (TTC)

Delhi Bus Rapid transit system.

Atlantas MARTA System.

Melbourne Metro Service.

SOURCE: http://jalopnik.com/the-worlds-ten-worst-public-transit-systems-657407658Fouracre et al. (2003) concluded that an ideal decision making process that would allowed considerations of the intangible benefits of MRT options, while screening out the projects that would cause severe financial hardships. MULTI-CRITERIA DECISION MAKING TOOLSAnalytic Hierarchy Process (AHP)AHP is a theory of measurement through pairwise comparisons and relies on the judgments of experts to derive priority scales.

Fuzzy TheoryFuzzy set theory is an extension of classical set theory that allows solving a lot of problems related to dealing the imprecise and uncertain data.

Data Envelopment Analysis (DEA)DEA uses a linear programming technique to measure the relative efficiencies of alternatives.SOURCE: Velasquez and Hester (2013) An Analysis of Multi-Criteria Decision Making Methods

10Simple Additive Weighting (SAW)SAW is a value function is established based on a simple addition of scores that represent the goal achievement under each criterion, multiplied by the particular weights.

TOPSISTOPSIS is an approach to identify an alternative which is closest to the ideal solution and farthest to the negative ideal solution in a multi-dimensional computing space.

Quality Function Deployment(QFD)QFD is a method to transform qualitative user demands into quantitative parameters, to deploy the functions forming quality, and to deploy methods for achieving the design quality into subsystems and component parts, and ultimately to specific elements of a process.SOURCE: Velasquez and Hester (2013) An Analysis of Multi-Criteria Decision Making Methods

FLOW CHART OF QFD

SOURCE: Govers (1996). What and how about Quality Function Deployment (QFD).

INTEGRATED AHPSW. Ho (2008) stated in his research that integrated AHPs are better than the stand-alone AHP, and also aids the researchers and decision makers in applying the integrated AHPs effectively.

The five tools that commonly combined with the AHP include mathematical programming, quality function deployment (QFD), meta-heuristics, SWOT analysis, and data envelopment analysis (DEA).

Chuang (2001) applied the combined AHPQFD approach to deal with the facility location problem.

Yang and Kuo (2003) proposed the combined AHPDEA approach to solve the facility layout design problem.

W. Ho (2008) concluded that the most popular tool integrated with AHP is mathematical programming followed by QFD. SOURCE: Ho (2008). Integrated analytic hierarchy process and its application- A literature review.SELECTION CRITERIA

SOURCE: Tuzkaya and Onut (2008). A fuzzy analytic network process based approach to transportation-mode selection between Turkey and Germany: A case study.

RESEARCH GAPFrom the literature survey done for past two decades through the research done by eminent researchers, it is found that the research has been limitedly applied to only few phases of any transportation project.

Very nominal work has been done in transportation field using Multi-Criteria decision making tools.

Only the efficiency and the performance have been analyzed for mass rapid transit systems using MCDM tools. Also, there is no proper decision making model for mass rapid transit systems.

Thus, the present research aims to develop a decision support system for choosing a correct and sustainable mode of transportation for a particular city using MCDM techniques.

AHMEDABAD CITY - OVERVIEWThe city of Ahmedabad, largest among all cities of Gujarat state, accommodating about 5 million people, has a registered vehicular strength of 1.4 Million.

The rate of growth of vehicles has been about 9 to 10% per annum.

Public transport situation has deteriorated rapidly over the past decade.

The end result is visible in terms of increasing congestion on the city streets and the worsening of air quality.

The Gandhinagar city is linked with Ahmedabad on the south by a direct road, having a straight alignment and a right of way 91.44m.. wide between the proposed Capital and the railway station of Khodiar about ten kilometers (six miles) to the west and having a total population of 395,891.The present initiative is to develop Mass Rapid Transit System recognition of the fact that no single mode Will completely serve the mobility needs between the city, and the bus system, both in its basic Form and rapid transit form, makes it a critical and major component in an integrated Transit system of any Twin city.

TRAFFIC DEMAND FORECASTGovernment of Gujarat have forecasted the number of commuters that will be travelling from Ahmedabad towards Gandhinagar for the year 2023 which is shown in following table:SectionLength (Km)Number of Passengers (millions)1: Changodar Vishala RTO - Sabarmati Akshardham45.101.3272: Kalupur RTO Thaltej9.8450.6283: Vishala RTO12.500.2254: Sarkhej Indroda Circle (NH8C)31.850.818Total99.2952.998Source : MEGA

ALTERNATIVES OF MASS RAPID TRANSIT SYSTEMSFollowing are the feasible alternatives which can be applied to the current study as per the guidelines of urban transportation as well as the traffic demand forecast as shown in the last slide.DATA FOR QFD-ANP ANALYSISFor the QFD analysis following surveys were done.

PCMHOQ 1 CONSTRUCTION PHASEHOQ 2 OPERATION PHASEHOQ 3 MAINTENANCE PHASEHOQ 4 RISK ASSESSMENTProximityRight of WayImpact on TrafficCleanlinesssafety and securityFrequencyConstruction Cost/kmPT integrationVehicle failureEnvironmental ImpactsEconomyIntegration of Technologies and CommunicationPractical CapacityBreakdwon timePublic Sector affordabilitySafetyComplexity of ConstructionOperating Speedmaintainance of vehicleSocial RiskComfortScale of ChallengeLevel of Service (In terms of travel comfort)Maintanance of routeTechnical RiskTravel TimeGrade ChangeLevel of SeggregationMonitoring of Signalling and CommunicationPolitcal RiskWaiting TimeIncremental DevelopmentSignalling (Installation/ Complexity/ Failure)Maintanance Cost/kmProbability of burglaryRide SharingCorridor SizeInstallation of Ticketing CountersRoad accidentsMinimum HeadwayCapacity flexibilityReliabilityPlatform LevelRoute FlexibilityAccountability in late arrivalStation SpacingOperating Cost/kmStable arrivalPassenger/ CarReliability on SchedulesCars/TrainAdaptibilityTrain LengthBarriersTable: Parameters incorporated in House Of Quality - QFDDATA FOR ANP ANALYSIS

IMPORTANT PARAMETERS EXTRACTED

DATA ANALYSIS - ANP

DEPENDENCIES OF SUB-CLUSTERS

PAIR-WISE COMPARISON

OUTPUT OF ANALYSIS

DEPENDENCIES OF COST CLUSTER

OUTPUT BASED ON MICRO-SCOPIC ANALYSIS

CONCLUSIONSQFD-ANP approach can be used for deciding any public transit system for a particular city in its Planning phase.

Commuters requirements are also a part of the model which makes the selection peoples preferable choice.

Experts rating and weightages plays a major role as more precise and realistic the weighatges are entered the output obtained will be more definite.

The planners can develop customized QFD-ANP model on the basis of their need and get the results which helps them in making up the decision in selection of the Transit Mode.

Only the thumb rule of population should not be considered while proposing a particular mode as more uncertainties are ought to be involved in it.

Thus, detail analysis using the Multi-Criteria Decision Making tools helps in gaining a clear vision.

Chances of failure of the mode reduce if a detailed analysis is run in these models. Also, the model can be developed for a specific analysis.

Customized model relating to only one or few parameters can be developed for the analysis.