[armijos, 2015] design of a discrete-event model simulation for a brts in ecuador

7/25/2019 [Armijos, 2015] Design of a Discrete-Event Model Simulation for a BRTS in Ecuador

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Design of a Discrete-Event Simulation Model for a

Bus Rapid Transit System (BRTS) in Guayaquil Ecuador

By:

0

!"#$%T& '! EGEERG "D T*E E+R'MET


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BENIGNO ALFREDO ARMIJOS DE LA CRUZ

ADissertation submittedin partial fulfillment of the degree of

Master of Science (M.Sc.) Transportation Planning and Engineering

October 2015

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SUMMARY

Governments from developing countries face a shortage of public funds formobility and accessibility projects and, more than ever, must take the most of

available budget to guarantee a good public transport service. BRT system, as apublic transport netork, has demonstrated in recent decades to be a pro!tablealternative for boosting an environment"friendly mobility at a lo cost ith ahigh attractiveness for non"motorised users. #evertheless, it is not the $panacea%or $magic and% that can resolve e&isting urban transport issues by itself in bothdeveloped and emerging economies. 'ctually, it has its on operative, urban,demographic, cultural and political constraints that have produced a groingbody of research in order to address possible orkarounds for improving thedeclining image of this highay transit system in some cities, especially from(atin 'merica. Bearing in mind the cumbersome situation above"mentioned, it iscrucial for transport planners and policy makers to study innovative methods

concerned ith analysis and policy formulation oriented to the continuousimprovement of BRT systems.

)ince 1*++, the use of computer modelling and simulation tools for solvingproblems of transport planning and engineering have gained popularity oing toits bene!ts such as study a comple& system at several dierent levels ofabstraction contemplating the essential elements of a real"orld systembehaviour, evaluate eects of particular layout decisions during the design phasediminishing the overall cost of building a transport system and generate asuitable and accurate solutions enabling a rapid prototyping of the scenarios tobe modelled. ' discrete"event modelling and simulation based on an object"

oriented approach is considered for the development of this research as a robustmethod consisting in segregating a comple& system on a group of processese&ecuted simultaneously at separate-discrete points of time. Both supply buses,corridors, stations, tra/c lights and demand factors passenger pertaining to aBRT system are synchronied and represented as object instances in a 23-43animation for veri!cation and communication purposes. 5inally, a set ofassumptions and constraints are declared in order to support the outcomes,looking at the hole mass transit netork.

6n order to design and deploy the aforementioned model, )imio as a microscopictransport modelling and simulation softare is used in this dissertation that

enables the design and interactive relationship beteen passengers, bus stops,vehicles, corridors and tra/c lights as part of a traditional BRT system. 7necorridor of 8etrovia BRT system hose operation is performed in Guaya9uil,:cuador is the case study selected ith the aim of understanding the key factorsaecting passenger density and bus service reliability in this type of publictransport system as ell as modelling its e&isting infrastructure through thesoftare package aforementioned. ;articularly, the eects of changing a bus


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potential solutions are provided to the BRT operating company based on bestpractices deployed in similar public transport systems across other countries in)outh 'merica hich has been analysed regularly by the scienti!c communitydedicated to !nd innovative alternatives for enhancing passenger capacity andservice reliability of mass rapid transit systems around the globe.

ACKNOWLEDGEMENTS

5irst of all, 6 ould like to say thanks to God and =irgin 8ary for all the blessingsreceived from them during the course of my postgraduate studies at >niversityof )outhampton, particularly throughout the vast challenges associated ith thedevelopment of this comple& dissertation as ell as the groth of my faith andpersonal resilience in both countries >nited ?ingdom and :cuador.

5urthermore, 6 e&press my heartfelt gratitude to my parents Benigno 'rmijos and(uisa 3e (a @ru ho have alays been my cornerstone for overcoming theforemost adversities of my life. 6n addition, my sincere thankfulness to my holefamily for their tenderness and far"reaching prayers as ell as all my dear friendsand colleagues for their unconditional support and ords of encouragement.

Besides, a special consideration goes to my personal tutor and supervisor 3r.Ben Aaterson for all his valuable guidance, shared knoledge andcomprehensive support given for the e&ecution of this research as ell as theacademic opportunities oered as a )tudent Representative ith the aim of

increasing my professional skills and ellbeing in the 5aculty of :ngineering andthe :nvironment.

'dditionally, my sincerest thanks to (eopoldo 5al9ue and ;edro @ordova horepresent 8etrovia 5oundation for the unrestricted accessibility to bus terminalsand stations during my investigation as ell as all the information providedabout the implementation and current operation of Bus Rapid Transit BRT)ystems in Guaya9uil, :cuador.

(ikeise, my e&ceptional acknoledgement to :dard Ailliams >niversity of8ichigan"3earborn, 3avid )turrock )imio ((@ and erey )mith 'uburn

>niversity for providing me a valuable insight for using an academic version of)imio softare package and all their mentorship about advance modelling andsimulation as ell as its potential applications in public transport systems.

8oreover, 6 e&press my sincere thanks to ;ablo 8edina and (uis 8ontesdeoca fortheir aid and brotherhood during our eorts oriented to achieve our commongoals in >nited ?ingdom. 6n the same vein, my special thanks to 'ondoseer 'too,

Tafta #ugraha, (ingao Can, )halini )harma and 8arissa Aang for all themoments and memories that e shared during our postgraduate studies.

5inally, a noteorthy gratefulness to the ;resident of :cuador Rafael @orrea ho

provided me the opportunity to achieve my fondest dream of pursuing apostgraduate degree in a prestigious foreign university through a scholarship

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aarded by the )ecretary of Cigher :ducation, )cience, Technology and6nnovation ):#:)@DT. This is just the beginning for the enhancement of ourproductive matri&.

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TABLE OF CONTENTS

)>88'RD...............................................................................................................1

'@?#7A(:3G:8:#T).......................................................................................... 2(6)T 75 56G>R:)....................................................................................................+

(6)T 75 T'B(:)......................................................................................................F

@hapter 1...............................................................................................................

6ntroduction...........................................................................................................

1.1 Background...............................................................................................

1.2 ;roblem )tatement and 8otivation...........................................................

1.4 Research 7bjectives..................................................................................*

1.E Research )igni!cance...............................................................................*@hapter 2.............................................................................................................10

(iterature Revie.................................................................................................10

2.1 The 6mpact of Bus Rapid Transit on ;ublic Transport...............................10

2.1.1 Bus Rapid Transit )ystemsH 3e!nition and 3esign :lements............10

2.1.2 Bus Rapid Transit )tatistics in the Aorld and (atin 'merica.............11

2.1.4 Bene!ts and 3rabacks of Bus Rapid Transit )ystems.....................12

2.2 @hallenges associated ith @apacity and )ervice Reliability of BRT)ystems............................................................................................................14

2.2.1 7vercroding of BRT )tations and 7verloading of =ehicles..............14

2.2.2 3elays in BRT )tations and Terminals...............................................1E

2.4 8odelling and )imulation of Bus Rapid Transit )ystems.........................1E

2.4.1 3iscrete":vent )imulationH 'dvantages and 3rabacks...................1E

2.4.2 Iueueing )ystemsH Theoretical 5rameork and ;erformance8easures.......................................................................................................1+

2.4.4 )imio as 'lternative to 8odel and )imulate Bus Rapid Transit)ystems.........................................................................................................1F

@hapter 4.............................................................................................................1

Research 8ethodology.........................................................................................1

4.1. )ite )tudy................................................................................................1

4.2 3ata @ollection )cheme..........................................................................1

4.4 8odel 3evelopment ith )imio...............................................................1*

4.4.1 8odelling ;assenger and )tation ;arameters...................................1*

4.4.2 8odelling Bus and @orridor ;arameters............................................20

4.E Research :&pected 7utcomes.................................................................21

4.+ Research 'ssumptions and @onstraints..................................................24

@hapter E.............................................................................................................2+

+


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3ata 'nalysis and 8odel @alibration...................................................................2+

E.1 Background.............................................................................................2+

E.2 3ata 'nalysis..........................................................................................2F

E.2.1 ;assenger 3aily 3emand during ;eak ;eriods..................................2F

E.2.2 ;assenger 3aily Boarding and 'lighting @ounts during ;eak ;eriods2

E.2.4 ;assenger 3aily Travel 'ctivity during ;eak ;eriods.........................2J

E.2.E Bus 3aily ourney Time among BRT )tations....................................2*

E.2.+ Bus 3aily (oad 5actors and 3ell Time during ;eak ;eriods.............41

E.2.F Bus 3aily Ceaday Time during ;eak ;eriods..................................42

E.4 8odel @alibration...................................................................................4E

E.4.1 ;assenger 6nter"'rrival Times............................................................4E

E.4.2 ;assenger 7rigin"3estination 8atri&.................................................4+

E.4.4 Bus Ride @apacity, Transport and Travel (ogic..................................4F

E.4.E Bus (anes and Tra/c (ights @ycle Timing........................................4

@hapter +.............................................................................................................4*

Result 'nalysis and 3iscussion............................................................................4*

+.1 )imulation 7utcomes..............................................................................4*

+.2 3iscussion...............................................................................................E0

+.4 @onclusions.............................................................................................E1

+.E Recommendations..................................................................................E1

+.+ 5uture Research......................................................................................E2

'ppendi& '. 7rigin"3estination 8atri& for '8 ;eak ;eriod " Dear 201+................E4

'ppendi& B. 7rigin"3estination 8atri& for 6nter ;eak ;eriod " Dear 201+.............EE

'ppendi& @. 7rigin"3estination 8atri& for ;8 ;eak ;eriod " Dear 201+................E+

'ppendi& 3. 'dd"7n ;rocess and )teps for =ehicle 8ovements in )imio............EF

'ppendi& :. Template for Boarding and 'lighting @ounts inside BRT =ehiclesduring ;eak ;eriods.............................................................................................E

'ppendi& 5. Template for ;assenger 3emand 'ssessment for ;eak ;eriod and

Aeek 3ay............................................................................................................EJ'ppendi& G. ;ictures of 8etrovia Bus Rapid Transit )ystem Terminals and 7ther5acilities...............................................................................................................E*

Reference (ist...................................................................................................... +0

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LIST OF FIGURES

5igure 1.2.1. ?ey @omponents of 8etrovia BRT )ystem in Guaya9uil, :cuador.....J

5igure 1.2.2. @urrent BRT @orridors managed by 8etrovia 5oundation 8etrovia,

2014..................................................................................................................... J

5igure 2.1.1 .Basic :lements of Bus Rapid Transit )ystems @T', 2014..............10

5igure 2.1.2 .Bus Rapid Transit Aorld ;anorama >6T;, 201+.............................11

5igure 2.1.4. @hallenges of 8etrovia BRT )ystem in Guaya9uil, :cuador 8etrovia,

201+................................................................................................................... 12

5igure 4.1.1. Blueprint of :&isting BRT @orridors in Guaya9uil, :cuador 8etrovia,

201+................................................................................................................... 1

5igure 4.2.1. 3ata Gathering 8ethod for 8etrovia BRT @orridor in Guaya9uil,

:cuador............................................................................................................... 1J

5igure 4.4.1. @omparison among 8etrovia BRT )helter and 43 =ie of a BRT

)tation in )imio....................................................................................................20

5igure 4.4.2. @omparison among 8etrovia BRT Bus 5leet and 43 ;erspective of a

BRT Bus )imio......................................................................................................21

5igure 4.E.1. Tally and 7utput )tatistics associated ith 8etrobastion BRT

@orridor in )imio..................................................................................................22

5igure 4.+.1. (ayout of Bus 5leet 3esigns 3ouble 3ecker vs. Bendy Bus in )imio

............................................................................................................................ 2E

5igure E.1.1. Blueprint of 8etrobastiKn BRT @orridor 8etrovia, 201+................2+

5igure E.2.1. 8etrobastion ;assenger 3aily 3emand during ;eak ;eriods...........2F

5igure E.2.2. 8etrobastion ;assenger 3aily Boarding and 'lighting @ounts during

;eak ;eriods........................................................................................................ 2

5igure E.2.4. 8etrobastion ;assenger 3aily Travel 'ctivity during ;eak ;eriods..2J


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5igure E.2.E. 8etrobastion ;assenger 3aily Travel 'ctivity during ;eak ;eriods..40

5igure E.2.+. 8etrobastion Bus 3aily 5actors and 3ell Time during ;eak ;eriods

............................................................................................................................ 41

5igure E.2.F. 8etrobastion Bus Ceaday Time during ;eak ;eriods....................44

5igure E.4.1. ;assenger 6nter"'rrival Time ;robability 3istributions in )imio.......4+

5igure E.4.2. 3evelopment of ;assenger 7rigin"3estination 8atri& 738 in )imio

............................................................................................................................ 4F

5igure E.4.4 .43 )imulation of a BRT )ystem in )imio based on a 3ouble 3ecker

Bus 5leet.............................................................................................................. 4

5igure E.4.E. 8odelling Tra/c (ights and its association to Bus (anes in )imio. .4J

5igure +.1.1. )imulation 7utcomes about 3ouble 3ecker =ehicle ;roductivity in

)imio....................................................................................................................4*

5igure +.+.1. 8odelling of Bus 5leet 8i&ture 7perations over 7verloaded BRT

)ystems...............................................................................................................E2

LIST OF TABLES

Table 2.1.1. :&amples of )imulation 'pproaches applied on BRT )ystems..........1F

Table 4.4.1 )imio )uite )tandard (ibrary 7bjects ?elton et.al, 201E.................1*

Table 4.E.1 ?ey ;erformance 6ndicators for 8etrobastion BRT @orridor...............21

Table E.4.1. )et of ;roperties and =alues for a )ource 7bject in )imio................4+

Table E.4.2. )et of ;roperties and =alues for an :ntity 7bject in )imio................4+

Table E.4.4. )et of ;roperties and =alues for a =ehicle 7bject in )imio................4

Table E.4.E. )et of ;roperties and =alues for a ;ath 7bject in )imio....................4J

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Chapter 1

Introdut!on

1"1 Ba#$round

Road transport has been seriously responsible for negative e&ternalitiespositioned in most of the cities such as road safety, tra/c accidents, urbangridlock and oil dependence. 's a result, the necessity for implementinginnovative and aordable public transport alternatives has been a critical issueduring recent decades, especially in developing countries here sustainablemobility for loer class and defenceless societies is re9uired as part of theimperative conditions for achieving the ne global )ustainable 3evelopmentGoals. The groth of Bus Rapid Transit BRT systems have alloed to deploy anoutstanding alternative in urban areas ith the aim of increasing public transport

patronage and oering better levels of sustainable mobility and accessibility. 'tthe moment, more than 42,EJ*,E+F passengers per day in 1*+ cities across theorld are gradually itnessing economic, environmental, social and culturalbene!ts of this highay transit mode ith a greater presence in (atin 'mericaand 'sia.

#onetheless, a signi!cant number of issues i.e. passenger capacity and servicereliability are part of the heavy public criticism often produced by its regularnon"motorised users, giving as a conse9uence an untrustorthy mass transitsystem, unrestrained groth of private motor vehicles and other adverseimpacts on local and national macroeconomic indicators. Therefore, an

e&haustive ork developed by tra/c engineers, transport planners andinfrastructure designers has been evidenced in the interest of boosting theperformance and credibility of this people"oriented transport system. >sing thecase of Guaya9uil, :cuador, this research provides a orth knoledge of thecommon issues saturating passenger capacity and service reliability on BRTsystems through an object"oriented modelling-simulation approach, consideringits e&isting supply and demand variables aecting its current operationperformance. 5urthermore, alternative solutions speci!cally associated ith bus


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Runn!n$ Wa+,

Bu,

Stat!on,

Bu,F(eet

Ser*!e,

RouteStrut

ure

FareCo((et

!onInte((!$ent

Tran,portat!o

nS+,te

),

8etrovia BRT system as introduced in uly 200F ith the aim of increasingmobility for the majority of citiens, diminishing transportation costs andboosting competitiveness across the city. 's Cidalgo and Graftieau& 200 pointout, 8etrovia system is a Nfeeder-trunk system, with longitudinally segregatedbusways for trunk services, large integration stations for feeder services,

enclosed stations with level boarding and prepayment and advanced control andticketing schemesO. This public transport mode supports the >rban 3evelopment;lan of Guaya9uil, providing massive transportation to and from the mayorresidential and ork areas.

Figure 1.2.1. Key Components of etrovia !"# $ystem in %uaya&uil, 'cuador

8etrovia BRT system is projected to progressively renovate all transit servicesacross the city as a ell"designed netork of seven integrated corridors, feederand complementary routes to be completely implemented before year 2020,acknoledged as an e&ceptional advancement for achieving a sustainablemobility across the city =on Buchald, 200. @urrently, its transport demand isabout 410,000 passengers per day ith a peak load around 1+,000 passengersper hour per direction and a peak fre9uency nearby 40 articulated buses perhour distributed in E+ km. across 4 corridors ith more than *0 bus stops,

spaced by an average distance of circa E00 m. among stations of the holenetork. Global BRT3ata, 201+. 6n relationship ith the layout of busays ande&pressays of the netork, these travel ays have dissimilar treatmentsaccording to the urban design and roaday conditions around the city. Busaysare located on the median, but there are other situated on the left hand side orin the central lane. (ane separation is provided through a concrete barrier alongthe road, plastic bumps. @onversely, e&pressays are just marked from side toside ith horiontal painted signs, including eaving sections for guiding tra/cfrom the median lane toards the curbside lane. BRT corridors currentlyoperating in an operating speed of 21 km-h are described in 5igure 1.2.

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Figure 1.2.2. Current !"# Corridors managed by etrovia Foundation (etrovia, 2)1*+

#otithstanding, several concerns have emerged during the last years ofoperations such as bus overloading load factors superior to J0L, overcrodingat bus stations density of roughly F pa&-m2, e&cessive aiting times beteen20"40 minutes during peak periods, heightened boarding-alighting times,degradation of technical and physical netork infrastructure i.e. scarcity ofsynchroniation in tra/c lights, intolerable levels of insecurity inside stations,disrespect among commuters as ell as high fre9uency of accidents and injuriesclose to bus doors. Bearing in mind these operational setbacks, passengerdiscomfort accompanied by multiple rubbished claims about 9uality serviceprovided by 8etrovia system has been a relapsing topic pending to be resolvedby the 8unicipality of Guaya9uil. Therefore, the outcomes of this research ill be

considered ith the purpose of !nding novel solutions to be considered astransport policies that could diminish the operational risks associated ithpassenger capacity overcroding of vehicles and stations and service reliabilitytravel times, in"vehicle times, aiting times, and headay times.

1"- Re,earh O'.et!*e,

The overarching objective of this research is to improve the decision"makingprocess of potential solutions to be adopted as transport policies on theperformance of BRT systems through a discrete"event modelling and simulation

based on an object"oriented approach. 8oreover, this study seeks toH 6dentify the key supply and demand variables in


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1"/ Re,earh S!$n!0ane

This research provides a far"reaching impact on the state"of"the"art discrete"event modelling and simulation of BRT systems, hich could be consideredduring process improvement-reengineering projects associated ith mass transit

systems, taking into account the milestones detailed beloH 3esign an innovative solution frameork for modelling BRT systems

netorks, including a set of methods for calibration, veri!cation, validationand data"drive presentation of inputs-outputs during an end"to"endsimulation. The model identi!es the substantial eect of supply and demandfactors on an entire BRT corridor in (atin 'merica.

6llustrate the value of 23-43 compelling animation of dynamic objects for a

better understanding of mass transit systems. )imulation models are oftenlarge and di/cult, being challenging to manage its comple&ity and

comprehend both the details and the Nbig pictureO of transport projects. Thishelps to the aareness of the key drivers in


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Chapter %

L!terature Re*!e

%"1 The I)pat o2 Bu, Rap!d Tran,!t on &u'(! Tran,port

%"1"1 Bu, Rap!d Tran,!t S+,te),3 De0n!t!on and De,!$n E(e)ent,

6n recent years, there has been an increasing interest in the deployment of BusRapid Transit BRT systems as a potential green mode of urban transport ;aget")eekins, 201+. #onetheless, to date there has been little agreement on thestandardiation of the de!nition for this Nmetro"levelO bus system amongtransport planners and policy"makers Boncompte and Galilea, 2014P 8ishraet.al, 2014. 3raing on an e&tensive range of perspectives, BRT systems isfre9uently conceptualised as an integrated bus"based transit mode ith aseparate infrastructure composed by a set of e9uipment, amenities and utilitiesthat holistically rivalie in a cost"eective ay ith urban rail transport, enablinggreater commercial speeds, service reliability and passenger capacity (evinsonet.al, 2004P =uchic, 200P Aright and Cook, 200P @ervero, 2014. 'lbeit thereare criticism and scepticism about the sustainability and resilience of itsinfrastructure in developing countries Cidalgo and Gutierre, 2014 as ell as itsoperational performance 8uQo et.al, 2014P =ele et.al, 201EP, generally thispublic transport alternative is ell"recognied as a high 9uality service, bus"based transit system ith a lo cost of implementation and deployment #ilesand erram, 2010P Batarce et.al, 201+.

Figure 2.1.1 .!asic 'lements of !us "apid #ransit $ystems

3iscussions about standard features that normally outline a BRT system basicallycorrespond to dedicated right"of"ay, busay alignment, o"board farecollection, intersection treatments and platform"level boarding, as is shon in5igure 2.1.1 associated ith the case study. #onetheless, in a detailede&amination developed by )corcia 2010, (evinson et.al 2004 and (indau et.al

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2012, the key physical and operational elements that have a signi!cant impacton its systemic e/ciency are particularly tra/c signal timing, spacing beteenbus stations, location of the bus stops in regard ith tra/c signals, interfacebeteen buses and stations, length of vehicles, number of berths at stations,vehicle load factors, travel speeds and pre"planned


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Figure 2.1.2 .!us "apid #ransit orld anorama (/#, 2)1*+

;articularly, (atin 'merica, ith 1*.4 million passengers per day representsroughly F2L of all BRT ridership in 12 countries, being spread mainly in Brailith F0L of its ridership idespread in 44 cities, 8&ico ith *L of its patronage

deployed in 10 cities, @olombia ith 1+L of its demand distributed in F cities,'rgentina ith EL of its ridership dispersed in 4 cities and !nally :cuador ithFL of its patronage scattered in 2 cities. 'n outline of the most successful andfamous of BRT implementations across (atin 'merica are 6ntegrated Transit#etork R6T in @uritiba Brail, Transmilenio in Bogota @olombia,

Transantiago in )antiago, @hile and 8etrobus"I in Iuito :cuador. 8ost of themare categoried as gold"standard and silver"standard as systems that achieve anoutstanding performance ith an un9uestionably compliance to internationalbest practices 6T3;, 201+. 6n a large longitudinal analysis developed by:8B'RI 201+ associated ith the BRT industry in :cuador, Guaya9uil andIuito account for about 1.2 million passengers per day, being concentrated most

of the national ridership in the latter city 2JL and 2L respectively.

%"1"- Bene0t, and Dra'a#, o2 Bu, Rap!d Tran,!t S+,te),

' large and groing body of literature has investigated the strengths andeaknesses of BRT systems, analysing them from dissimilar perspectives such asavailability, accessibility, aordability, reliability, safety and security )org,2011. )ome authors e.g. @arrigan et.al, 2014 identify conclusive impactsassociated ith the implementation of this mass transit system such as journeytime savings, road congestion reductions, non"motorised users% groth andbettered urban surroundings. (ikeise, Censher et.al 201E observesH NBRTsystems can be delivered at a fraction of the cost of a rail based system,beteen four to tenty times less than a light rail transit system and beteen

1+


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ten to 100 times less than a metro system for the e9uivalent level of service incontrast to vehicle capacity per hourO. @orrespondingly, in a study conducted byAUhrnschimmel et.al 200J found that e&posure to air pollutants and othervolatile organic compounds during commuting could be eectually decreased byBRT systems in contrast ith other conventional transport modes i.e. minibuses

and buses. 6n general, therefore, it seems that positive e&ternalities associatedith the employment of this public transport system are numerous ith thepurpose of fostering a sustainable urban mobility.

Figure 2.1.0. Challenges of etrovia !"# $ystem in %uaya&uil, 'cuador (etrovia, 2)1*+

@onversely, Rivi and )clar 201E and (indau et.al 201E reported a set ofremaining issues associated ith planning and e&ecuting BRT projects, indeveloped and emerging economies, for e&ample, hasty implementations,constrained operational budgets, advertising of mobility substitutes, untimelyorsening of facilities, delayed fare integration, discontinuous politicalcommitment, insu/cient legislation and regulation, scarcity of communityinvolvement, as ell as perception of BRT systems as a loer 9uality transport

method. )imilarly, 8uQo and Cidalgo 2014 revealed technical drabacks thathave in common ell"knon BRT systems in )outh 'merica such as Transantiago@hile and Transmilenio @olombia. Risky


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%"% Cha((en$e, a,,o!ated !th Capa!t+ and Ser*!e Re(!a'!(!t+ o2 BRTS+,te),

%"%"1 O*errod!n$ o2 BRT Stat!on, and O*er(oad!n$ o2 4eh!(e,

;assenger capacity on BRT systems generally is de!ned as the ma&imum

9uantity of passengers that can be transported per direction per hour, hichdepends principally on the physical dimensions of bus stops to supportpassenger demand inside bus stops and bus density through available busberths. BRT stations are a key driver in providing satisfactory capacity along aBRT line. They are also a critical factor in achieving higher levels of identity andimage as a public transport mode. 6n some cities across (atin 'merica,overcroding is the most fre9uent criticism re


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any other time whereby a bus has its doors open without passenger service.53ell time may cover a great proportion of total travel time, hich shos theimportance of boarding and alighting processes on BRT operation. 6nstability ofboarding and alighting times could have e&pected but une&plored conse9uenceson bus interval volatility, ith groing aiting times. 'long ith urban gridlock

and demand variability, dell time deviation declines the likelihood of servicetrustorthiness. Ahilst bus fre9uency is high and-or dell times are lengthy,vehicles could arrive at bus stations hereas previous vehicles are using allavailable berths for loading-unloading passengers, generating bottleneckscharacteried by 9ueueing delays into the BRT system.

6n an investigation into engineering of bus stops, Tirachini 201E reported thaton BRT services ith a !&ed"stopping scheme, commercial speed, bus fre9uencyand dell time are strategic factors in


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3:) techni9ue is advantageous because of it uses graphical user interface andanimation ith the aim of representing entities and events that occur in realsystems, providing at the same time a reliable understanding to the modeller.8oreover, this approach delivers a set of statistical and analytical methods e.g.con!dence intervals for simulation outcomes displayed in tabular or graphical

forms hile the simulation is running. Besides, more comple& logic, delays orinteractions and constraints can be added using this approach ith the aim ofmeeting speci!c demands. @onversely, there are some disadvantages associatedith 3:) models. 5or instance, this alternative does not provide an impact to thereal variability and only support measures of central tendency. 6n addition, 3:) isnot suitable for modelling human behaviour because its key purpose is to focuson the processes and events related to the system. 5urthermore, simulationresults need intelligent analysis by people ith some knoledge of statistics, asit is easy to dra disastrously rong conclusions. 8oreover, 3:) approach isusually criticised by its signi!cant time consumption to get statisticallysigni!cant results as ell as to perform sensitivity analyses.

%"-"% 6ueue!n$ S+,te),3 Theoret!a( Fra)eor# and &er2or)aneMea,ure,

Iueueing theory is a mathematical method oriented to study aiting lines or9ueues. ?elton et al. 201E de!nes a 9ueueing system as one in hich entitiesi.e. customers and passengers arrive, get served either at a single station or atseveral stations in turn, might have to ait in one or more 9ueues for service,and then may leave. Iueueing systems typically have to states of behaviour,short"term or transient folloed by long"term or steady"state behaviour. Iueuingmodels have found idespread use in the analysis of service facilities, production

and many other situations here congestion or competition for scarce resourcesmay occur. The three basic elements of a 9ueuing process are arrivalscharacteristics sie of the calling population, interarrival distribution andbehaviour of the arrival, the aiting line features length of a 9ueue and 9ueuediscipline and service facilities con!guration of the service system and servicetime distribution. 5urther interesting performance measures of 9ueueing modelsare time in 9ueue time that an entity spends aiting in line, time in systemtime in 9ueue plus the time in service, 9ueue length number of entities in9ueue, number in system number of entities in 9ueue plus in service andutiliation of a server time"average number of individual servers in the groupho are busy, divided by the total number of servers in the group.

8Uller 201E points out the main bene!ts of 9ueueing theory, regarding ithproviding models that are capable of determining arrival pattern of customers ormost appropriate number of service stations. 5urthermore, 9ueueing models arehelpful in creating balance beteen the to opportunity costs for optimiation ofaiting costs and service costs. Besides, this mathematical approach contributesith a better understanding of aiting lines so as to develop ade9uate serviceith tolerable aiting. @ontrariise, Rahman 2014 provides in"depth analysis ofthe major limitations of 9ueueing theory, concluding that these models are verycomple& and di/cult to be understood oing to the level of uncertainty e&istingin almost all 9ueueing circumstances. 6n addition, 9ueue discipline may also

impose certain limitations e.g. if the assumption of $!rst come, !rst served% isnot a true one, 9ueueing analysis become more comple&. (ikeise, the

1*


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observed patterns of service times and interarrival times cannot be !tted in themathematical distributions of usually assumed in 9ueuing models. 8oreover, inmulti"server 9ueueing systems, the departure from one 9ueue often forms thearrival of another, making the analysis more challenging.

%"-"- S!)!o a, A(ternat!*e to Mode( and S!)u(ate Bu, Rap!d Tran,!tS+,te),

The development of BRT modelling and simulation softare has signi!cantlyassisted the process of transport supply and demand predictions. #evertheless,from the author%s perspective, there is no a standard suite that ill be inherentlycorrect. 5urthermore, in an analysis of the current softare packages available inBRT market, Aright and Cook 200 reported that although the e&istence ofstrongest and ell"knon modelling and simulation softare such as ;aramics,=issim and 'imsun as can be seen in Table 2.1.1. these softare packages arecharacteried by their e&pensive sunk costs as ell as the lack of capabilities for

a robust BRT netork analysis. Therefore, the importance of !nding an accessibleand lo"cost 3:) tool that let the modeller to emulate a real mass transit systemin a fast and cost"eective form. Cence, )imio is considered as the solution forthis research project.

Author Ma!n O'.et!*e BRT Ca,e So2tare

Gunaan7%81/9

3iscuss about the potential use ofdiscrete event simulation frameork tomodel the dynamics of a BRT system.

Transakarta,6ndonesia

)imulink8athAork

s

Ra. et"a(

7%81/9

;rovide a 9uantitative performanceframeork, considering variables such as

speed, travel time, delay and capacityfactors.

3elhi BRT),

6ndia

=issim

;T=

Yan$ et"a(7%81-9

@ompare to priority strategies andintegrate them ith e&clusive bus lane tosee ho they ould impact publictransport e/ciency.

Dingtan,@hina

=issim;T=

W!danapath!rana$e

et"a( 7%81-9

'nalyse the relationship beteen busstation capacity and BRT line buscapacity for a speci!ed range ofcontrolled scenarios of dell timefeatures

BrisbaneBusay,'ustralia

'imsunT))

Anora et"a(7%81%9

;ropose a ne microsimulation

frameork called TR'#)68T consideringpassenger, vehicles, bus stops, tra/ccontrols and other con!gurations.

:>R Rome6taly

'renaRockare

S!dd!:ueet"a(

7%88;9

3evelop a microscopic simulationapproach in order to perform a capacityanalysis on BRT corridors for handlinghigh volumes of transit buses.

Transitay,@anada

#:T)68

4anderWer2t

7%88


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)imio employs an object"oriented modelling approach, hereby models are builtthrough the employment of objects that represent the physical elements of realsystems. 6t is feasible to build customised models using the objects provided inthe )tandard 7bject (ibrary, hich is general purpose set of objects that comesstandard ith )imio. 5urthermore, the model logic and animation are built in a

single step in this softare, making the modelling process very intuitive andconsidering animation useful to rep 43 Aarehouse, a library of graphic symbols for animating 43 objects.)imio also oers to basic modes for e&ecuting modelsH the interactive and thee&perimental modes. 6n the !rst it is possible to atch the animated modele&ecute, hich is useful for building and validating the model. >p to no, thereare not many studies that use )imio for modelling BRT systems. #evertheless, itis possible to !nd some studies that used this tool for other types oftransportation problems. Therefore, the potential of representing the 8etrovia

BRT )ystem based on 3:) and 778 approaches is highly challenging but eye"catching for the scienti!c community interested in knoing more aboutmodelling and simulation of BRT systems in (atin 'merica.

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Chapter -

Re,earh Methodo(o$+

-"1" S!te Stud+

Figure 0.1.1. !lueprint of '6isting !"# Corridors in %uaya&uil, 'cuador (etrovia, 2)1*+

Aith the aim of assessing passenger capacity and service reliability on 8etroviaBRT )ystem, one of the three e&isting BRT trunks is compulsory to be identi!ed,selected and analysed. Cence, considering information availability for thistransport mode as ell as other technical features e.g. accessibility on stations,security inside vehicles and safety throughout the system, the trunk=Metro'a,t!>n? hich cover the route =Ba,t!on &opu(ar 5 Centro? and=Centro 5 Ba,t!on &opu(ar?is chosen as a representative sample for analysingand modelling of the day"to"day operation and throughput related to the BRTsystem in Guaya9uil. Aorking since 200J, this corridor has a current daily

demand of roughly 10,000 passengers per day ith a peak load around +,20passenger per hour per direction ith 2E bus stations distributed in almost 1F.+0kilometres of segregated bus lanes restricted of overtaking manoeuvres as canbe described in 5igure 4.2.1. Regarding to vehicle


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-"% Data Co((et!on She)e

@entral to being able to model the BRT system accurately is an assessment ofthe boarding and alighting stops of current users. 's this data is not availableautomatically from the operating company it is necessary to undertake a survey

hich can then be scaled up to provide an overall 7rigin"3estination 8atri&738 as ell as other key performance indicators associated ith the BRTcorridor in study. 's can be seen in 5igure 4.4.2, data gathering is based on aregular visual observation of the BRT system in Guaya9uil, primarily travelinginside of a sample of articulated buses and visiting some speci!c bus sheltersassociated ith the BRT corridor during 10 typical ork days from 8onday to5riday e&cluding eekends for the folloing peak periodsH

'8 ;eak 0FH00 '8 " 0*H00 '8

6nter ;eak 12H00 ;8 " 02H00 ;8

;8 ;eak 0+H00 ;8 " 0JH00 ;8

Figure 0.2.1. 8ata %athering ethod for etrovia !"# Corridor in %uaya&uil, 'cuador

3uring the bus trip across the dierent shelters of the corridor, arrival anddeparture time as ell as number of alighting and boarding passengers has beenregistered by each bus stop. ' sample of passengers boarding the bus has alsobeen noted to identify hich corresponding stops they alight at. 5inally, in thedestination terminal the surveyor has recorded the number of passengersremaining and arrival time of the bus. 't the end of the survey, every data sheethas been consolidated per peak period per day in order to proceed ith the datacleansing stage as ell as other methods ith the aim of obtaining the baselineof measurements pertaining to passenger service times of the BRT corridorselected. )ome additional observations has been provided by the operatingcompany associated ith the physical characteristics of each BRT station i.e.bus stop spacing, bus


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in order to validate the accuracy of the information gathered inside the BRTvehicles.

-"- Mode( De*e(op)ent !th S!)!o

7nce the netork acknoledgment and data gathering stages have beendeclared, it is necessary to specify hich factors of the BRT system can berepresented through a softare package. )imio has been selected as a tool forperforming a microscopic simulation of the BRT netork, considering theadvantages of its object"oriented modelling approach. 's can be described in

Table 4.4.1, there are key objects from the )imio standard library objects to beconsidered in order to design the BRT system related to passengers, vehicles,stations, tra/c lights, bus lanes, etc.

O'.et De,r!pt!onBRT

E(e)entRepresents a dynamic object in the modelthat can follo a ork


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load entities into vehicles approaching the server. @onversely, the centralpurpose of each basic node is unload entities from vehicles and transfer them tosinks representing in that ay the departure of passengers from BRT stations.8oreover, each server has a de!ned process logic composed by a !&ed capacityfor processing entities, ranking rules for serving entities based on 9ueueing

principles e.g. 5657, (657, etc. as ell as a set of input buer, output buer andprocessing stations for controlling 9ueues produced by the arrival and departurepatterns of passengers on BRT stops. 5inally, transfer and basic nodes close tothe server are connected ith a single path in order to control the number ofvehicles that are able to park close the server for performing pickups-drop"os ofentities acting as berths in a real BRT station.

Figure 0.0.1. Comparison among etrovia !"# $helter and 08


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resource and reliability properties allo the modeller to determine orkschedules, daily patterns, selection rules and o shift actions for each one of thevehicles to be employed in both upstream and donstream lanes of the BRTnetork. 6n addition, as can be vieed in 5igure 4.4.2, bus lanes can also berepresented in )imio as paths that are basically used for controlling overtaking

manoeuvres, entry ranking rules based on 9ueueing methods e.g. 5657, (657,etc., traveller capacity, speed limits as ell as logical lengths beteen serversas the current spacing among BRT stations ith the purpose of determining real

journey times along the BRT netork. 6n addition, paths can also support themanagement of tra/c lights modelled in )imio as resources through add"onprocess triggers once a vehicle has approached the end of the path close to thene&t server.

Figure 0.0.2. Comparison among etrovia !"# !us Fleet and 08 erspective of a !"#

!us $imio

-"/ Re,earh E@peted Outo)e,

Aith the aim to analyse the operational capacity and service trustorthiness of8etrobastion BRT corridor, a set of indicators are summarised in Table 4.E.1.hich are the foundation for justifying the use of a discrete"event modelling andsimulation approach in this research project, considering the currentinfrastructure and nameplate capacity of 8etrovia BRT system in Guaya9uil,:cuador.

Ind!ator De0n!t!on Cate$or+

2F


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LoadFator

'verage percentage of oered bus capacity utilied bypassengers during peak periods

;assenger3ensity

ourne+T!)e

'verage time considering bus dell time at origin stopplus in"vehicle travel time beteen the origin anddestination stops.

)erviceReliability

Wa!t!n$

T!)e

'verage time beteen the passenger arrival and ne&t

bus departure in a speci!c bus stop.

)ervice

Reliabilityeada+

T!)e'verage time beteen the passing of the front ends ofsuccessive bus units moving along the same direction.

)erviceReliability

#able 0.;.1 Key erformance /ndicators for etrobastion !"# Corridor

6n the same vein, formulas for obtaining the aforementioned indicators aredetailed belo, hich are also de!ned in )imio as part of the set of output, stateand tally user"de!ned statistics as is illustrated in 5igure 4.E.1. to be obtainedafter running the underlying simulation of 8etrobastion BRT @orridor. Theseoutcomes ill enable a comprehensive assessment about the eects of changinga bus +

ourne+ T!)e3 5actors involved for determining the total travel time of a tripare stated in :9uation 4.E.E. ;assengers boarding at the earliest portion of

2


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the loop route ill have the longest journey time to arrive at the transferterminal.

'&uation 0.;.2. !us ?ourney #ime for a !"# $ystem (right and =ook, 2))>+

Wa!t!n$ T!)e3 5actors involved for estimating the average aiting time are

announced in :9uation 4.E.+, bearing in mind the fre9uency of buses per hourde!ned for the operation of the BRT corridor to be analysed.

'&uation 0.;.0. assenger aiting #ime for a !"# $ystem (9iu and $inha, 2))>+

eada+ T!)e3 :9uation 4.E.F determines the time beteen the passing ofthe front ends of successive bus units moving along the same corridor in thesame direction. The longer the headay, the more inconvenient transitservice becomes

'&uation 0.;.;. !us =eadway #ime for a !"# $ystem ($aberi et.al, 2)10+

-"< Re,earh A,,u)pt!on, and Con,tra!nt,

The cornerstone of this discrete"event model is de!ned by 9ueueing theoryprinciples, based on the foremost statement that each bus station of the BRTcorridor e&amined follos a stochastic passenger arrival and departure rate,hich is de!ned traditionally as an 8-8-1 9ueueing system. Aith the intention ofrepresenting the foremost characteristics of a BRT system i.e. passengers,

stations, vehicles, tra/c lights, etc. other assumptions are also described belo,bearing in mind its impact on the metrics to be obtained through this simulation.

2J


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A,,u)pt!on, a,,o!ated !th &a,,en$er Beha*!our3

;assenger inter"arrivals are attended on a 5irst 6n 5irst 7ut 5657 ranking ruleand every passenger arrival aits to be served irrespective of the length ofthe 9ueue inside-outside the BRT station.

;assenger inter"arrivals are independent of preceding arrivals, hoever thepassenger arrival rate does not change along the time.

;assenger inter"arrivals are described by a ;oisson probability distribution and

it is derived from an in!nite or very large passenger population. ;assenger departure rate also contrasts from one passenger to the

succeeding and is autonomous of one another, but their e&pected mean isknon

;assenger aiting time in a 9ueue and the aiting line e&perienced by a

particular passenger are treated as stochastic variables ;assenger departure rate is greater than the passenger arrival rate and the

aiting space available in BRT stations for passengers in the 9ueue is in!nite. ;assenger arrival and departure behaviour on BRT stations are considered

Vstationary processesV, in other ords, both have e9ual probabilitydistributions among every day of operation.

A,,u)pt!on, a,,o!ated !th 4eh!(e Operat!on3

BRT vehicles are served in both bus stops and tra/c lights on a 5irst 6n 5irst7ut 5657 ranking rule and every bus aits to be served irrespective of thelength of the BRT berth-lane 9ueue.

BRT vehicles circulate at a constant commercial speed during journeys among

bus stops ith a !&ed route and narroed by just one roundtrip per peakperiod. 8ost of the pickups and drop"os of passengers occur at the latter BRT stops

of the northbound-southbound streams. Boarding and alighting times per bus stop are in


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Figure 0.*.1. 9ayout of !us Fleet 8esigns (8ouble 8ecker vs. !endy !us+ in $imio

6t is necessary to mention that part of a traditional analysis based on theperformance of BRT systems is to 9uantify the eects of the number of bus doorsover service reliability of the netork. 's it as described in earlier sections,vehicles in )imio provide several advantages for modelling real transportsystems. #onetheless, this type of objects has only con!gured one ride stationone door as part of its native functionality, hich is not compatible ith realBRT bus


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Chapter /

Data Ana(+,!, and Mode( Ca(!'rat!on

/"1 Ba#$round

6n order perform a standard e&ploratory data analysis of the current performanceof 8etrobastion BRT trunk, considering its northbound and southbound streams,a layout of the corridor as ell as acronyms for each one of its bus stops andterminals is detailed in 5igure E.1.1. 's it as e&plained in previous chapters, thisBRT corridor has roughly 22 bus stations and 2 terminals, being classi!ed eachone by a speci!c type of one i.e. leisure, ork, education, shopping andinterchange. 5rom the northbound stream, trip generation begin ith TerminalBastion TB interchange and !nish ith Biblioteca 8unicipal B8 terminal.@onversely, from the southbound stream, trip generation begin ith Biblioteca

8unicipal B8 terminal and end ith Terminal Bastion TB terminal. Based onthe observations and data gathering performed across this corridor, bothstreams have an average total travel time of roughly 4J minutes and EE secondsith a ma&imum journey time of appro&imately E+ minutes and + seconds.'mong bus stops, there is an average travel time of 1 minute and EJ seconds,being the minimum and ma&imum journey time of E seconds and 4 minutesith 22 seconds respectively.

Figure ;.1.1. !lueprint of etrobasti@n !"# Corridor (etrovia, 2)1*+

:ach eather"protected bus station has an average length of 40 metres, idth ofF metres and height of + metres ith a ma&imum capacity of 200 passengers.'longside each bus shelter, there is a sub"stop ith just one docking bay herebuses can let passengers board and alight as ell as dedicated passing lanesamong bus stations. The majority of bus stops have turnstile"controlled o"boardfare collection machines hereby each passenger should use its travel card for

entering inside of the bus stop. 'll the stations have sliding doors ith real"timeand up"to"date static passenger information. 7utside of bus stops, there are safe

41


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and signalied pedestrian crossalks, enabling the integration to ith otherpublic transport systems i.e. ta&i services and conventional buses. @onversely,each BRT vehicle has an average length of 1J m., idth and height of 4 metresith a ma&imum capacity of 1F0 passengers. 6n addition, all vehicles operates ata minimum average commercial speed 20 km-h ith E ide doors for speeding

loading-unloading of passengers and platforms for reducing the gap among thebus berth and boarding station.

/"% Data Ana(+,!,

/"%"1 &a,,en$er Da!(+ De)and dur!n$ &ea# &er!od,

Figure ;.2.1. etrobastion assenger 8aily 8emand during eak eriods

'ccording to the information collected during 2 eeks at bus stops in peakperiods, the 8etrobastiKn passenger daily demand bounded to peak periods,considering northbound and southbound streams is detailed in 5igure E.2.1. 6tcan be inferred that passenger density in this corridor is greater during Tuesdays,

Aednesdays and 5ridays because of at the beginning of the orkeek inGuaya9uil city there is a signi!cant number of passengers on 8ondays thatprefer the usage of other transport modes i.e. ta&i, conventional buses ith theaim of arriving timely to their destinations. 6n the same vein, during eekendsthe use of this transport system is lo, especially on )undays. 3uring the '8;eak periods, it can be vieed that the bus stops that contribute ith higherpassenger inter"arrival rates are ;ar9ue @alifornia @( and >niversidad deGuaya9uil >G due to their commercial and education activities ith a highpassenger production for this public transport system, reaching occasionally ama&imum average demand of almost +,000 pphpd, giving as a resultovercroding conditions inside of the bus shelters and producing higher load

factors toards the forthcoming bus stations. @onversely, some bus stationssuch as (as 8onjas 8 and 5erroviaria 5= contribute ith just 2L of the hole

42


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passenger demand generated across the BRT corridorP this situation is becauseof there is are signi!cant levels of tra/c congestion across these bus stops ithdesynchronied tra/c lights close to crossalks that not allo an easyaccessibility to this mass transit system.

7n the other hand, during the 6nter"peak period, it can be seen that no dailypassenger demand is almost evenly spread among the bus stations located atthe middle of the BRT corridor. 7nce again, >niversidad de Guaya9uil >G and;ar9ue @alifornia are bus stops that produce higher passenger inter"arrival ratestaking into account that beteen 12H00 ;8 and 02H00 ;8 most of the businessand higher education activities are performed near to those stations, but thema&imum average demand achieved is beteen 4,000 and 4,+00 pphpd. 3uringthe studies it as observed that inside of these stations there is a greater degreeof overcroding, causing some problems at the moment of boarding-alightingto-from vehicles approaching the bus berth. 6n the same vein, during ;8 ;eakperiods, it can be realised the same demand patterns in comparison ith 6nter"

peak periods achieving almost the same levels of passenger trips, hoever,there is an slight groth of passenger among @entro de 'rte @', @olegio 2J de8ayo =8 and >niversidad @atolica >@ stations due to leisure college andhigher education activities increase beteen 0+H00 ;8 and 0JH00 ;8 nearbythese ones. (ikeise, level of passenger demand drops dramatically among>niversidad @atolica >@ and >niversidad de Guaya9uil >G stations, inconse9uence of the lack of trips in 5erroviaria 5= bus stop possibly as a resultof being located in an isolated place surrounded by residential areas ithout thesame bene!ts of accessibility to commercial ones that other bus stops haveclose to the @B3.

/"%"% &a,,en$er Da!(+ Board!n$ and A(!$ht!n$ Count, dur!n$ &ea#&er!od,

Figure ;.2.2. etrobastion assenger 8aily !oarding and 7lighting Counts during eak

44


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eriods

)imilarly, based on the results obtained from the 7rigin"3estination 8atri& 738per peak period produced by 8etrovia 5oundation in 2012 and projected to 201+through the 5urness method as a ell"knon trip distribution techni9ue 7rtWar

et al, 2011, 8etrobastion passenger daily boarding and alighting counts ise&plained in 5igure E.2.2 considering northbound and southbound streams andbounded to peak periods. 6t can be evidenced that there is a high"level of loadingand unloading of passengers in ;ar9ue @alifornia @( and >niversidad deGuaya9uil >G stations, particularly pickups rates are higher than drop"os ratesoing to these stations produce roughly the J0L of the total passenger demandin both streams. 6n opposition, alighting rates are greater than boarding rates inthe majority of the bus stations hich is normal in stops close to the end of theinbound and outbound streams of the BRT corridor. 8oreover, it is necessary tohighlight the attractiveness of the stations situated at the middle of bothstreams, most of them enabling accessibility to education areas e.g. @olegio 2J

de 8ayo =8 and =icente Rocafuerte =R bus stations, others related tocommercial ones close to the @B3 i.e. ;laa =ictoria ;= and 8ercado @entral8@ bus shelters and fe of them providing mobility toards business areasnear enough to the northbound stream i.e. Gallegos (ara G(, and 6nmaconsa6# bus stops. #onetheless, boarding and alighting counts are dissimilar henthe throughput is disaggregated by peak periods, focusing on the bus stops thatcontribute implicitly in the spread among these rates.

5or instance, it can observed that during '8 ;eak periods pickups rates aresuperior in stations close to interchanges, reaching a ma&imum level of almost+,000 passengers i.e. ;ar9ue @alifornia @( bus stop ho are spread across the

BRT netork, particularly in education areas and other ones close to the @B3. 6naddition, through the '8 ;eak chart is possible to perceive that alighting ratesare uniformly distributed across the middle stations of the BRT corridor, becauseof close to those ones there are fre9uent users such as students and orkersith regular schedules oriented to develop their daily scholastic and businesstrips. @onversely, during the 6nter ;eak period the boarding counts decreasearound +0L in some bus stations located close to the start point of thenorthbound stream, nonetheless this situation is dissimilar if boarding rates areassessed from stations nearby to the @B3. Besides, alighting counts remainssteady from Gallegos (ara G( to 8apasingue 8; bus stops ith an e&ceptionin 3olores )ucre 3) station. There are also noteorthy gaps beteen loading

and unloading rates in stops close to educative areas i.e. >niversidad deGuaya9uil >G as a conse9uence of university users that begin their activitiesin the morning should perform residential trips as part of their regular travelpatterns. 5inally, during ;8 ;eak periods it can be inferred that most of the tripsare produced from the start bus stops of the southbound stream toards itslatter bus stations hich basically represents the traditional travel pattern oftravelling from donton to suburbs.

4E


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/"%"- &a,,en$er Da!(+ Tra*e( At!*!t+ dur!n$ &ea# &er!od,

Figure ;.2.0. etrobastion assenger 8aily #ravel 7ctivity during eak eriods

(ikeise, 5igure E.2.4 presents the 8etrobastion passenger travel patternsduring peak periods based on the results obtained from the ne 7rigin"3estination 8atri& 738 by peak period and some surveys developed insideBRT stations. Ahat is interesting in this data is that most of the trips developed

in correspond roughly 41L to ork activities essentially moving toards thenorthbound stream as ell as 40L to education destinations hich isreasonable oing to there are several colleges and university users across thenetork of this public transport system. 5rom the pie chart above e can alsosee that the other to groups shopping 20L and others activities 14L areassociated ith events developed nearby the @B3. 6n addition, the top right ofthe chart provides the breakdon of travel activity by each bus station during '8;eak periods. 6t is apparent from this bar chart that appro&imately 4+L of thetrips are associated ith educative destinations ith a highest production of this

journeys originated from (u del Guayas (G, @erros de 8apasingue @8 andother stations close to the @B3 e.g. ;laa =ictoria ;= and 8ercado @entral8@ bus stops. 8oreover, over 2JL of those ho ere surveyed indicated thattheir trips correspond to business activities, being the Terminal Bastion TB itsbiggest contributor ith EEL of the total passenger demand generated. Then,almost 20L of the trips during early hours correspond to passengers demandoriented to ones near enough to the @B3, being 5ederacion del Guayas 5G,and >niversidad de Guaya9uil >G the top bus stops that produce this type oftrips. There are fe leisure journeys related to these category only FL of thetotal passenger demand.

'dditionally, from the bottom half of the chart about 6nter ;eak trips it can beinterpreted that the proportion of passenger ith labour destinations 41L isalmost the same in comparison ith those hose primary purpose are educativeactivities 40L, notithstanding the production of ork trips correspond to

4+


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those bus stops located close to the beginning of the northbound stream of theBRT corridor and trip generation associated ith scholastic purposes is derivedfrom the bus stops located in the middle of the northbound-southbound stream.)hopping and leisure activities have a lo demand pro!le 1*L and FLrespectively during this stage, but ith a highest trip generation rate in bus

stops close to the @B3. 5inally, from the bottom right of the chart about ;8 ;eakjourneys, it can be inferred that the rate of passengers ho have labourdestinations 44L is higher in contrast ith those users ith scholastic activities2*L being this relationship produced by the return of people from their jobslocated close the @B3 and travelling through the southbound stream at thisperiod. 6nterestingly, albeit the proportion of passengers hose destination isoriented to shopping areas is evenly distributed across the bus stops in bothstreams. #aturally, leisure and other type of trip attractions remain ith loerrates across the netork, bearing in mind that beteen 1JH00 and 20H00 most ofthe passengers surveyed return to their residential ones.

/"%"/ Bu, Da!(+ ourne+ T!)e a)on$ BRT Stat!on,

5igure E.2.E presents a summary of the journey times among the 2E BRT busstops, in reference to the measurements gathered inside BRT articulated busesduring 2 eeks in peak periods. 's can be vieed from the line chart, there is anaverage travel time amidst bus stops of 1 minute and EJ seconds, ith astandard deviation of E4 seconds caused by the delays on loading and unloadingof passengers, e&cessive aiting times during the eective red cycle time oftra/c lights alongside each bus station as ell as urban gridlocks on junctionsclose to the BRT netork. 6t can also be seen that the highest in"vehicle times isfound among the @olegio 2J de 8ayo =8 and (as 8onjas 8 bus stations ith

an e&pected travel time of due to an e&pected distance of around *+ metresbeteen those locations as ell as the presence of 2 bridges hereby BRTvehicles should circulate in mi&ed tra/c conditions ith private cars, ta&is,motorcycles and other motorised users. @orrespondingly, signi!cant average

journey times of at least 2 minutes can be observed at the beginning of thenorthbound stream close to the ;ar9ue @alifornia @( stop, amid 5uerteCuancavilca 5C and uan Tanca 8arengo T stations as ell as adjacent to>niversidad de Guaya9uil >G shelter, sometimes oing to the e&istence of atleast 4 tra/c lights giving more priority to other passenger car units rather thanusers of this mass transit system. By the same token, bus stations that are closeto the @B3 presents higher journey times occasionally caused by mi&ed tra/c

conditions and some tra/c jams derived by commercial and business activitiesduring peak periods.

4F


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Figure ;.2.;. etrobastion assenger 8aily #ravel 7ctivity during eak eriods

'dditionally, as can be vieed at the bottom of the abovementioned chart, a?olmogorov")mirnov goodness"of"!t comparison is developed for the observed

journey times among BRT stations ith the aim of determining the likelihood ofachieving mean travel times up to 2 minutes as ell as beyond this threshold.Based on the traditional Gamma, (og (ogistic and (og"normal stochasticdistributions regularly used for modelling mathematically this service reliabilitymeasure in high"capacity public transport systems Aeifeng et al., 2014 it canbe concluded that the probability of reaching journey times less than 1 minute isappro&imately loer than 10L. 6n the same vein, there is a high likelihood ofalmost JL of getting average journey times beteen 1 minute and 2.+ minutes.5inally, there is a meaningful probability of roughly 40L of achieving averagetravel times beyond 2 minutes among BRT stations. Bearing in mind theseresults, it can be inferred that the longest the distance among bus stops and theabsence of bus priority measures, the highest the likelihood of accomplishing

4


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average journey times of more than 2 minutes ill be, hich is an outstandingissue hen a faster and reliable BRT systems is re9uired.

/"%"< Bu, Da!(+ Load Fator, and De(( T!)e dur!n$ &ea# &er!od,

Figure ;.2.*. etrobastion !us 8aily Factors and 8well #ime during eak eriods

6n the same vein, 5igure E.2.+ provides an overvie of the e&pected vehicle loadfactors and dell time by peak periods and BRT streams, hich are based on thedata collected at the moment of being inside of the articulated buses, studyingboarding and alighting patterns by each one of the E bus doors. 6t can beobserved that there are situations in hich the e&pected capacity of 1F0passengers per vehicle is e&ceeded, particularly in bus stops such as Gallegos(ara G( and @erros de 8apasingue @8 ith average load factors among 1.0+and 1.0 respectively travelling through the northbound stream during the '8;eak period. #evertheless, this issue is overcome after unloading passengersmassively approaching 5ederaciKn del Guayas 5G in hich overloading

conditions decrease by nearly 2EL because of most of the scholastic users alightbuses in that speci!c section of the corridor. 8oreover, the chart illustrates someof the main characteristics of travel patterns during 6nter ;eak and ;8 ;eak inthis corridor, resulting in less trips coming from the northbound stream toardsthe @B3. 6n the same ay, there are overcroding conditions in the southboundstream of 8etrobastion corridor, essentially in bus stations such as >niversidad@atolica >@ and @olegio 2J de 8ayo =8 ith average load factors beteen1.04 and 1.0 respectively travelling during the ;8 ;eak period.#otithstanding, the problem mentioned before is !&ed at the moment of unloadpassengers in ne&t bus stops such as >niversidad de Guaya9uil >G and @olegio=icente Rocafuerte =R due to their scholastic attractiveness. )imilarly, it can beconcluded that travel patterns during '8 ;eak and 6nter ;eak periods let vehiclesto operate at steady"state conditions in this stream of the BRT corridor.

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'dditionally, from the area chart described above it can be observed thatdelling times in the northbound stream are higher during '8 ;eak periods atBRT terminals because of there is an average aiting time of appro&imately *0seconds de!ned by 8etrovia 5oundation as a transport policy for loading

passengers until that BRT vehicles could achieve a passenger capacity of around4+L in both Terminal Bastion TB and Biblioteca 8unicipal B8 interchanges.Coever, there are higher boarding and alighting times beteen 20 and 40seconds in some bus stops such as Gallegos (ara G(, @erros de 8apasingue@8, @entro de 'rte @' and >niversidad @atolica >@ oing to there arehigher passenger densities aiting for BRT vehicles that are sometimes delayedby unban gridlocks at the middle of the stream as ell as by unsynchroniedsemaphores, generating also bus bunching conditions until 4 bendy busesarriving almost simultaneously at these stations. )imilarly, at 6nter ;eak periods,there are higher boarding rates in the aforementioned BRT terminals, producinga range of delling times amidst 2+ seconds and 40 seconds. 3uring this period

the ne bus stations ith the ma&imum delling times are @entro de 'rte @',@olegio 2J de 8ayo =8 and >niversidad de Guaya9uil >G, taking into accountthat beteen 12H00 ;8 and 02H00 ;8 most of the high schools and collegesgenerate a ell"knon demand of mass transit services. )imilar bus dellingconditions are also observed during ;8 ;eak periods across the northboundstream.

@ontrariise, analysing the southbound stream of 8etrobastion corridor, it canbe inferred a e&istence of higher delling rates during ;8 ;eak periods,fundamentally oing to a signi!cant passenger density on BRT stations close tothe @B3 and other located close to higher education institutions i.e. >niversidad

de Guaya9uil >G and >niversidad @atolica >@ bus stops in hich the sum ofboarding, alighting and doors open-close times surpass the threshold of 20seconds established by 8etrovia 5oundation, because of during 0FH00 ;8 and0JH00 ;8 these scholastic areas generate a massive demand of public transportsystems in combination ith comple& urban gridlocks caused by the end of theork period. @onversely, during 6nter ;eak periods, delling times can achieve ama&imum lapse of 4+ seconds, principally in @olegio 2J de 8ayo =8 bus stopdue to the ma&imum passenger capacity is usually overhelmed by an e&tremeamount of student users aiting outside of the station for using this transportmode for returning in most of the cases to their residential areas or other onesclose to the @B3 and bus interchanges. 5inally, the scenario is dissimilar during

'8 ;eak periods hereby delling rates are belo 20 seconds in the majority ofbus stations, including some congested stops e.g. @olegio 2J de 8ayo =8 and>niversidad de Guaya9uil >G. Taking into account these results it can bedetermined that the higher the passenger density in bus stations, the longest thedelling period ill be, even e&cluding some other negative e&ternalities causedby other motorised users close to the BRT netork.

/"%"; Bu, Da!(+ eada+ T!)e dur!n$ &ea# &er!od,

(astly, 5igure E.2.+ shos the statistical analysis of average headay timesmeasurements, hich ere basically collected inside of each bus shelter,observing the departure of the last articulated bus and the arrival of the ne&tbendy bus to the docking bay. 's can be seen in the aforementioned chart, a

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?olmogorov")mirnov ?) goodness"of"!t disaggregated by peak period isdeveloped based on the studies of ang et al. 2011, !tting the observed data to(og (ogistic and (og"normal continuous distributions recurrently adopted formodelling mathematically this service reliability metric in uninterrupted publictransport facilities. The data correspond to the measurements obtained at the

start points of the northbound and southbound streams such as Terminal BastionTB and Biblioteca 8unicipal B8 as ell as in a most congested bus stop i.e.@olegio 2J de 8ayo =8 hich is basically located at the middle of the BRTcorridor, pro&imate to the @B3. 6t is necessary to mention that 120 seconds isde!ned by 8etrovia 5oundation as the ma&imum headay time across the BRTnetork, theoretically controlled at the beginning of the northbound andsouthbound streams.

's can be identi!ed at the beginning of the northbound stream, during the '8;eak period most of the headay times are adjusted to a (og (ogisticdistribution, in hich the likelihood of obtaining average headay times belo

*0 seconds is around 2FL, hich is higher if this result is contrasted ith theprobability of achieving average headay times superior to 120 seconds, hoseresult is 11L. Ahat is interesting in this chart is the *0L of likelihood forobtaining average headay times amongst F0 and 120 seconds, hich iscoherent to the delling times obtained for this type of BRT terminal. (ikeise,for 6nter ;eak periods, the probability of getting average headay times belo1.+ minutes is almost EL, FL for achieving averaged headay times beteenF0 and 120 minutes and around 2EL for more than 2 minutes. @onversely,during ;8 ;eak periods the likelihood of reaching average headay times amidF0 and 120 seconds is no close to *0L and the probability for obtaining anaverage headay time upper to 120 seconds is almost 1FL, considering that

mi&ed tra/c conditions close to labour and commercial areas as ell as tra/clights cycle times could increase signi!cantly its variability.

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Figure ;.2.A. etrobastion !us =eadway #ime during eak eriods

6n the same ay, a probability analysis is developed for @olegio 2J de 8ayo =8as one of the most overcroded bus stations located at the centre of the BRTcorridor. Based on a (og"normal, it can be inferred that during the '8 ;eakperiods, the probability of observing average headay times belo F0 seconds isroughly 44L hich is almost the same ith the probability of achieving anaverage headay time upper than 120 seconds hich is appro&imately 4+LP allthese results correspond to bus bunching eects that usually occurs at earlyhours in + stations situated before and after this congested bus station causedby e&cessive journey times due to tra/c conditions in bridges close to these bus

stops as ell as a set of tra/c lights that provide more priority to some junctionshereby motorised users drive. )imilarly, during the 6nter";eak periods thelikelihood of observing average headay times under F0 seconds is roughly E4Lhich is bigger in contrast ith 4+L as the probability of reaching an e&pectedheaday time upper than 120 seconds. This trivial betterment is oing to theeorts of the 8etrovia 5oundation to send more units for pickups and drop"os ofpassengers around this area as ell as passenger demand patterns close to thisone. )imilar results can be inferred for ;8 ;eak periods ith a small variation of2EL as the probability of observing the arrival of articulated buses ith anaverage headay time more than 120 seconds.

5inally, a similar analysis based on the stochastic distribution mentioned beforeis developed for Biblioteca 8unicipal B8 terminal as the beginning of thesouthbound stream of this BRT corridor located nearby the commercial and

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business areas of the @B3. ;articularly based on the probability density functionsplaced at the bottom of the graph above, it can be understood that during '8;eak periods the probability of observing average headay times under F0seconds is roughly E4L hich is bigger in contrast ith 42L as the likelihood ofreaching an estimated headay time upper than 120 seconds. This trivial

betterment is oing to the eorts of the 8etrovia 5oundation to send more unitsfor loading and unloading of passengers around this area as ell as higherpassenger inter"arrival patterns close to the @B3. 8oreover, during 6nter ;eakperiods the likelihood of projected headay times belo F0 seconds is circa 4Lhich is greater than 2+L as the probability of achieving average headay timesbeyond 120 seconds. 5inally, analysing the outcomes during ;8 ;eak periods, itcan be perceived a decrease by 1+L in the likelihood for normal headay timesbeyond 120 seconds and a similar probability of 4JL for achieving averageheaday times loer than F0 seconds. 6n conclusion, it can be observed that theheaday variability is highly observed at the middle of the BRT corridor ithremarkable dierences at the beginning of the northbound and southbound

streams of 8etrobastion trunk.

/"- Mode( Ca(!'rat!on

/"-"1 &a,,en$er Inter5Arr!*a( T!)e,

Taking into account the passenger demand pattern by each bus terminal andstops and folloing the assumptions established for employing a 8-8-1 9ueueingsystem for each bus station, it is necessary to de!ne previously some features in)imio associated ith )ource objects, as can be seen in Table E.4.1., This set ofproperties should be de!ned according to the established values in order to run

accurately the simulation of 8etrobastion BRT corridor.

&ropert+ De0n!t!on !n S!)!o 4a(ue

Ent!t+T+pe

Type of entity created by the )ource object. :ach entityrepresents a class of passenger hose destination is!&ed through se9uence tables

)e9uenceTable

Arr!*a(Mode

8ode used by the )ource object to automaticallygenerate a stream of entity arrivals in )imio.

6nterarrivalTime

Interarr!*a( T!)e

Time interval beteen to successive arrivals,speci!ed using a random sample from a distribution

Random.;oisson

8inEnt!t!e,per

Arr!*a(

#umber of entities that ill be created by a )ourceobject for each arrival event 1

#able ;.0.1. $et of roperties and


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Figure ;.0.1. assenger /nter-7rrival #ime robability 8istributions in $imio

/"-"% &a,,en$er Or!$!n5De,t!nat!on Matr!@

)imilarly, ith the purpose to de!ne the origin and destination patterns of eachpassenger in the proposed mass transit system model, there are a set ofproperties to be de!ned beforehand in )imio as can be shon in Table E.4.2, inorder to spread appropriately the passenger demand across each bus stop of theBRT netork. 6t is necessary to recap that each passenger is represented as an

Ent!t+ o'.et in this microscopic simulator, and its destination !&ed to a speci!cS!n# o'.et.


De,!redSpeed

Type of entity created by the )ource object. :ach entityrepresents a class of passenger hose destination is!&ed through se9uence tables

2 m-sec

In!t!a(Se:uen

e

8ode used by the )ource object to automaticallygenerate a stream of entity arrivals in )imio. 6nputX)ink#



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Figure ;.0.2. 8evelopment of assenger :rigin-8estination atri6 (:8+ in $imio

/"-"- Bu, R!de Capa!t+ Tran,port and Tra*e( Lo$!

Table E.4.4 summarise a set of properties associated ith a 4eh!(e o'.etthatmust be de!ned previously in )imio, ith the aim of representing the operationof buses running across both streams of 8etrobastion BRT corridor. :ach o=ehicle object used is then declared through a list of transporters that ill be

associated to a set of Tran,2er Node o'.et,speci!ed in the rider se9uencetable aforementioned designed for passenger departure patterns.


R!deCapa!t+

6nitial carrying capacity of a =ehicle object related to itsdynamic and single ride station.

*0 pa&.

LoadT!)e

Time re9uired for this =ehicle object to load an entityi.e. load time per entity

Random.:&ponential 2

sec

Un(oadT!)e

Time re9uired for this =ehicle object to unload an entitye.g. the unload time per entity

Random.:&ponential 2

secDe((T!)e

8inimum dell time re9uirement for this =ehicle objecthen loading and unloading entities at a transfer-basicnode

1+ sec.

De,!redSpeed

6nitial desired speed value for this =ehicle object duringthe movement of entities toards a transfer-basic node

21 km-h

In!t!a(Node

6nitial node location of this =ehicle object at thebeginning of the simulation run, de!ned as the $homelocation%.

Transfer node

Rout!n$T+pe

Route behaviour of this =ehicle object ill follothrough a !&ed route se9uence to transport riders totheir speci!c locations

5i&ed Route

RouteSe:uen )e9uence table that de!nes the route se9uence thatthis =ehicle object ill loop Bus )e9uence

EE


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e

Capa!t+T+pe

'vailability of this =ehicle object to perform tasks,indicating that each object of this type follos a orkschedule.

Aork )chedule

Wor#Shedu(e

#ame of the schedule that de!nes the $7n"shift, 7"shift% availability of this =ehicle object over time.

)cheduleY=ehicle

Ran#!n$Ru(e

)tatic ranking rule used to order re9uests aiting toseie this =ehicle object for a process task.

5irst 6n 5irst 7ut5657

Lo$Re,our

e

6ndicates hether usage related events for this =ehicleobject are to be automatically logged.

True



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sections among bus stops as ell as for connecting ;ath objects performing theoperation of docking bays along ith Ser*er o'.et,acting as a real bus stationor terminal ith a !&ed capacity established according to their physicaldimensions.


&athT+pe

Type of tra/c movement to be adopted for this ;athobject and integrated ith other of its category

Bidirectional

TraD!ret!on

Rule employed to manage tra/c entry onto thisbidirectional ;ath object

5irst in :ntryIueue

De,!redD!ret!on

6nitial desired direction of tra/c movement for thisbidirectional ;ath object

5orard

Tra*e(erCapa!t+

6nitial ma&imum number of traveling entities that maysimultaneously occupy this link

6n!nitylane

1 bus berthEntr+

Ran#!n$

Rule used to rank entry onto this ;ath object among

competing entities

5irst 6n 5irst

7ut 5657Dran to

Sa(e

)peci!es hether this ;ath dran length in the 5acilityAindo of )imio is the length to be used for thesimulation logic

5alse

Lo$!a(Len$th

6nitial length to be employed for the simulation logic ofcompeting entities

3istancemetres

A((o&a,,!n$

Rule that allos or denies overtaking manoeuvresthroughout this ;ath object.

5alse

SpeedL!)!t

8a&imum desired speed at hich an entity can travelalong this ;ath object

F0 km-h

ReahedEnd

;rocess that occurs hen an entity%s leading edge hasreached the end of this path

Tra/c (ight'dd"7n

;rocess

#able ;.0.;. $et of roperties and


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Figure ;.0.;. odelling #ra4c 9ights and its association to !us 9anes in $imio

Chapter #3; in 201+ oriented to develop more greenercities across the orld.

Append!@ A" Or!$!n5De,t!nat!on Matr!@ 2or AM &ea# &er!od 5 Year %81)'.

. 5ilipe, (. and 8acSrio, R. 2014 ' 5irst Glimpse on ;olicy ;ackaging for6mplementation of Bus Rapid Transit ;rojects. Research in Transportation:conomics, 4* 1, 1+0"1+.

J. Gunaan, 5.:. 201E 3esign and 6mplementation

[armijos, 2015] design of a discrete-event model simulation for a brts in ecuador

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