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Public Transportation

by: Savanna Cruickshank

Public Transportation CE 574

Savanna Cruickshank
GPA 3.54

Michael Kyte

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Michael Kyte
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This 48 page Class Notes was uploaded by Savanna Cruickshank on Friday October 23, 2015. The Class Notes belongs to CE 574 at University of Idaho taught by Michael Kyte in Fall. Since its upload, it has received 10 views. For similar materials see /class/227775/ce-574-university-of-idaho in Civil Engineering at University of Idaho.

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Date Created: 10/23/15
WWW Nm 4 AM TRANSPORTATION RESEARCH RECORD 1623 User and Operator Perspectives in Transit Network Design AVISHAI CEDER AND YECHEZKEL ISRAELI The basic construction of the objective functions of the transit network J x J n n L bot passenger and operator interests is proposed The approach presented A r V r A with decisionmaking techniques in order to allow the user to select from a number of alternatives The nature of the overall formulation is nonlinear and mixed integer programming The problem addressed in this paper is a general one of how to design a new transit network or redesign an existing one given no A tional cost derived from the scheduling components and the passen ger level of service affect the search for the optimal route designs while relying on a compromise between the operator and the user niur r k r L r ows 056 that deal with ideal networks and those based on mathe matical 39 A imilar but different A 39 4 review or prior I r 39 be found in Coder and Wilson 1 Simulation models are presented in Dial and Bunyan 2 Heatliington et al 3 and Vandebona and Richardson 4 These models require a consid erable amount of data and their proximity to optimality is uncertain K K rm quot From aprac tica perspective it is desirable that the route design procedures include interaction and feedback loops betWeen the selection of 1 1 L frequency determination and timetabling along with vehicle scheduling with regard to eet size n t n p u x p r connect every place node in the network to all others From this vast pool or quot J transfers i hen u u us that I maintain network connectivity For each subset thus generated the A 39 d ulculatin H r 39 frequency for each route Next it calculates prespecified optimiza tion parameters for each subset Based on the speci c optimization parameter desired by the user it is then possible to select the most suitable subset The system has been designed as a tool for the plan ning of future transit networks as well as the maintenance of exist ing ones The system ensures exibility by allowing users to either input their own data or to run the system automatically PROBLEM IDENTIFICATION The transit planning process aimed at efficient transport of originw destination transit riders includes four basic elements performed in sequence a network route design 7 setting of timetables c sched uiiugui 39 39r 39 39 39V rtiri 1 Forthisprocess to be cost effective and efficient it should embody a compromise between passenger comfort and cost of service For example a good L J L wueu uau It n at based on a broad range of design parame ters and a choice of objectives re ecting user and operator interest J in Kocur and quot m J L feld 6 and Kuah and Perl 7 These methods are adequate for screening or policy analyses in which approximate design parameters are to be determined radier than a complete design Thus these meth ods cannot represent real situations Mathematical programming models used for Iran it quot 395 39 A the extremely high computational effort required by them These par tial optimization approaches appear in Hasselstrom 8 Dubois et al 9 Silman et al 10 Rea I I Mandi 12 Marwah et al 13 Sharp 14 and Kerich 15 Apart from Hasselstrom s model which is included in the Volvo transit planning package all other models have not actually been applied However Hasselsttom s model is quite complex nonuser oriented and expensive with regard to both the data required and the direct cost and staff time needed for the process W l 39 c enabling 39 1 39 to es can be summarized in six points 39 Cannot handle large size transit networks 39 Does not consider optional objectives and constraints Ba c A I rather than on passenger counts as is done in practice and described by Ceder 1617 0 Cannot simultaneously incorporate three out of the four plan ning components network design timetable setting and vehicle scheduling In particular the models cannot evaluate the network without de ning the vehicle requirements for each route and thus Input Network nodes and arcs Travel time on each arc 39 n demand Set of terminals Maximal deviation from Create routes 1 shortest pa r Route length constraints Identfo mfg 2 Create minimal set of r s and transfers 3 Set Covering Problem Required policy bus capacity Demand assignment procedure minimum frequency existing for frequency calculation given by user Calculate values for optimization criteria 21 h r 4 passenger ours waiting ime empty passenger hours operational comaaims elgu Calculate values for L d ad A c 39 39 2 2 heading travel times eet size ts on the Generate more suboptunal effecuveness of each set 6 of solutions salmon Z139 7 2 Multiobjective user decision s f of quotbestquot solution 7 FIGURE 1 Methodology for designing routes and networks a later section Following is the problem formulation Consider a connected network composed of a directed graph G MA with a nite number of nodes 1N l connected by IAl arcs The following notations are used Route progressive path initiated at a given terminal and terminated at a certain node while traversing given arcs in sequence Transfer path progressive path that uses more than one route r set 0 routes TR tr set of all transfer paths N set of nodes located on route r N set of nodes located on transfer path tr 1 passenger demand between i and j i j EN riding on route r d1 passenger demand between i and j along the trans fer ath tr it vehicle frequency associated with the route r m minimum frequency reciprocal of policy head way required i t5 travel time between i and j on transfer path tr TRANSPORTATION RESEARCH RECORD 1623 t overall travel time on route rbetween its start and L maximum passenger load on route r w passenger waiting time on route r dquot desired occupancy on each vehicle load standard an a 1 transfer tr contains r II quot 39 0 otherwrse A D r 1 4 Z and L min 21 a 212110 j 42 2 W110quot j 132 EH I LieV ijeN r min 2 FS 2 where PH ij passenger hours between nodes i and j ijeN de ned as passengers riding time in a transit vehicle on an hourly basis it measures how much time is spent by passengers in vehicles between the two nodes WH 21 waiting time between nodes i and jijeN de ned as L r 1 x a r i between the two nodes EH emptyspace hours on route r defined as the unused h FS eet size number of transit vehicles needed to provide all trips along the chosen set of routes Objective Function Z 1 The objective function Z is based on the socalled load pro le It is a histogram describing the number of passengers on board the transit vehicle versus the transit route length in distance or time units Figure 2d provides an example of such a load pro le The rst element of Z is total passenger hours in the routing system 2 PHij 2 2d3t3 2 Edl tlf lt3 JEN reR LieN Ire Nquot TR fie where for the example in Figure 2 2 PH 2039 330 10290 9 500 passenger minutes JEN The second element in 21 is the total wait time hours both at the transit system stops and during transfers This element represents the use perspective and its calculation is based on the maximum load point method for deriving the transit vehicles frequency 16 during the time covered by the passenger demand matrix a x mold0 4 If the minimum frequency requirement is the result of h then the load pro le will have no in uence on the frequency determination as shown in Figure 3 The expected wait time for passengers on route r is half of the transit vehicle headway in which passengers arrived randomly at Ceder and Israeli a b 20 10 2 3 4 o e a 1 L150 mo 80 is 2 tillquot 0 3 a 39 39 39 A t39 v a matrix in pass per hour given mu unvcl drug in minutes given C d 390 E 330 quot5 5 80 240 1 l 2 3 100 90 i 2 4 150 50 100 Feasible routesin both 20 30 dimcmms 3 Travel time minutes The load pro le of route l 2 3 derived FIGURE 2 Example of the determination of load pro le the transit stop and the headway is distributed in a deterministic manner 17 WV g 5 hence 1 r r WHz2KL zdanziganj 6 1eN reR r 115M Hem In the example shown in Figure 2 one obtains 60 W 2 no1 263050050 100 so 50 90 2136 hours where d0 equals 50 The third element of Z describes the total empty space hours or emptyscat hours when 1 equals the number of seats on the transit Passengers FIGURE 3 Load pro le regarding the load level when the minimum frequency constraint holds where PH is total passenger hours on route r vehicle This element represents an unproductive measure for the opcrator Its formulation is ZEH 2maxL7tmu39dat 213110 f 7 r rel iJeN In the example of Figure 2 EH 330 240 10 900 passenger minutes The rst part of Equation 7 describes do 2 VH where VH is total vehicle hours on route r as a measure for the capacity of the transit network total passenger hours times the desired occugzancy In Equation 1 or in Z ah a2 and a3 are weighting factors while 39 39 39 P110117 Fquot iqdonned lUL implicit A A and thus Z will eventually include a E3 2 PH i j JEN Objective Function Z The second objective function 22 is an estimate for the eet size required to carry on the entire matrix of the passengers demand The method used for evaluating the eet size is based on the de cit function theory 18 This theory provides techniques to assign the minimum number of vehicles to carry out a given timetable A de cit function is simply a step function that increases by one at a time at each trip departure and decreases by one at a time at each trip arrival Such a function may be constructed for each terminal in a multiterminal transit system The sum of the maximal de cit func tion values over the schedule horizon and across all the terminals is the minimum number of vehicles required The maximal value of the de cit function can be reduced by introducing deadheading empty trips into the schedule as well as shifting the departure times within bounded tolerances 19 This study does not include all the detailed procedures of the de cit function theory and con centrates rather on estimating the minimum fleet size required for a L A Lquot V D u r 39 39 m it allowed For this purpose the lowerbound calculation 20 is used based on the determined frequencies in the Z calculation The nal outcome of this is the value 22 which represents an estimate for the operational component s minimum required eet size Overall View The complete formulation including the constraints of the network design problem can be found in Ceder and Israeli 21 and Israeli and Ceder 22 The nature of the overall formulation is nonlinear nonlinear and mixedvinteger programing Its analog problem is L J 39 quot L quot hv Magnanti and Wong 23 with a nonpolynomial hard computational complexity Thus conventional approaches are incapable of providing a solution even with a relatively high degree of simpli cation The solution approach is composed of two stages In the rst stage the problem dimension is reduced through the construction of a skeleton feasible route network that meets a maximum travel time constraint The skeleton network is the basis for an optimization routine to determine the shortest direct and indirect via transfers paths between each pair of nodes The second stage relies on a proce dure that incorporates optimization and enumeration processes in order to derive the minimal Z objective function This procedure while searching for min 2 creates various Z solutions each asso ciated with a different z solution Finally the most desirable set of Z Z is derived through known techniques in multiobjective pro gramming 2425 Further details of the theoretical dimension of this methodology can be found elsewhere 2627 whereas a more speci c stepwise description of the approach is shown in the next section Components The overall system comprises seven components as shown in Fig are l In the rst component the system generates every possible route from all terminals which meets the route length factor con straints in other words its algorithm screens out routes according to given boundaries on the route length In addition there is a limit on the travel time between each origin destination OD pair That is a given demand cannot be assigned to a transit route if its travel time exceeds the minimum travel time on the network by more than a given percentace The second component is based on an algorithm that produces 1 My 39 39 T e rst step of the algorithm is to establish additional direct routes between OD pairs characterized by high OvD demands a predetermined OvD demand value These direct routes are actually initiated or terminated at nonterminal nodes and consequently deadheading trips are re sponsible for their connection to the terminals Also a low 0D de mand without a direct route is not considered for obtaining service The transfers are created using a mapping algorithm This applies to the disconnected OD pairs as well as to all the 0D pairs In L y a J L Mai r routes and their related transfers such that connectivity between nodes is maintained and their total deviation from the shortest path is mini mized This problem is de ned as a set covering problem SCP which is hard to solve 28 The SC can determine the minimal set of routes from the matrix of the feasible routes In this matrix each row represents either a feasible route or a transfer In the fourth component the entire 0D demand is assigned to the chosen set of routes The assignment algorithm that has been developed 22 includes steps that are related to a routechoice decision investigation that is the algorithm includes a probabilis tic function for passengers who are able to select the bus that arrives first or alternatively wait for a faster bus The passengers strategy isto 39 39 L 39 139 t Tlie 39 g d is similar to that developed by Marguier and Ceder 29 but with a different probabilistic functiOn e fth component completely represents the operator s pep spective It is directed at finding the minimum fleet size required to meet the demand as well as to satisfy the determined frequency on each route This objective is designated Z in the formulation and it complements the operator s portion in 2 which is to minimize emptyspace hours The 39 I L w c Lou uuctiiig of routes in order to search for additional 2 22 values in the vicinity or uroir 39 m 39 39 39 J incre mental changes in the set of routes much like the reduced gradient methods Given the set of routes associated with the minimum Z value the single route that is the worst contributor to Z is deleted and then the SCP is solved in the third module followed by the execution of the fourth and fth components This process could continue but there is no guarantee that a previous alternative will not be repeated To Overcome this problem a new matrix is constructed with me idea TRANSPORTATION RESEARCH RECORD 1623 of finding the minimal and worst set of candidate routes for possible deletion in each iteration that is a new SC matrix is constructed in which the candidate routes are the columns and each row represents a previous set of routes that was already identi ed in the vicinity of the optimal 2 Z setting The solution to this new SCP matrix is a set of J A A r r 39 1 During this process a number of unique collections of routes are termed pro hibited columns as they are the only ones that can transport a certain demand Theso prohibited columns are assigned an arti cially high cost value so as not to be included in the solution This process also involves some bounds in order to converge on a desired number of iterations or number of Z 2 solutions The complex procedure of this component can be found in Israeli and Ceder 27 The seventh and nal component of the system involves multi objective programming of the two objective functions Z and 22 Given the alternative sets of routes derived in the sixth module the purpose is to investigate the various alternatives regarding the most efficient 2 Z2 solution The method selected in this component is called the compromise set method 3031 it ts linear objective functions 32 for discrete variables The outcome of this method is the theoretical point in which Z 2 attains its relatively minimal value The results can be presented in a table or a two dimensional graph that shows the tradeoff between Z and A The decision maker can then determine whether to accept the proposed solution In the latter case for example the decision maker can see how much Z is increased by decreasing Z to a certain value and vice Versa SUMMARY This paper outlines the basics of a transit network design system TL L 39 9m 7quot A A 39 39 mun networks The system comprises seven main components each of which uses the data created by its predecessor or data given by the user to carry on the next step in the analysis see Figure l The system is believed to be a useful tool set for the following applications 9 Optimal design for a new transit network 0 Optimal design for expansion or curtailment of an existing transit networ 0 Assessment of the performance of an existing transit network from the aspects of or operator ef ciency and b passenger level of service and 0 Sensitivity analysis of transit network performance for a variv ety of system parameters such as different eet sizes different lev els of service changes in passenger demand changes in frequencies changes in travel time and more Finally it is worth mentioning that the challenge which was ultimately met was to incorporate three operational planning ele 39 at quot 39 39rtetwnrk psian frequency timetable setting and vehicle scheduling REFERENCES 1 Coder A and N Wilson Bus Network Design Transportation Research Vol 20B No4 1986 139331 334 2 Dial R l1 and R E Bunyan Public Transit Planning System Sociov Economic Planning Science Vol 1 l968 pp 345 362 53 H 5 Ta 9 Coder and Israeli 3 Heathington K W J Miller R R Knox G C Hoff and J Bruggman Computer Simulation of a Demand Scheduled Bus System Offering DoortoDoor Service In Highway Research Record 91 HRB National Research Council Washington DC 1968 pp 26 40 Vandebona U and A J Richardson Simulation of Transit Route Operations In Transportation Research Record 1036 TRB National Research Council Washington DC 1985 pp 36 40 Kocur G a d C Held ckson Design of Local Bus Service with Demand Equilibration Transportation Science Vol 16 No 2 1982 pp 149 170 Tsao S and P Schonfeld BranchedTransitServices An AnalysisJoun nol ofTransportation Engineering Vol 110 NO 1 1984 pp 112 128 Ku G and 1 Perl Optimization of Feeder Bus Routes and Bus Stop Spacing Journal of Transportation Engineering Vol 1 14 No 3 1988 pp 3414354 Hasselstrom D Public Transportation Planning PhD dissertation Department of Business Administration University of Gothenburg Sweden 1981 Dubois D G Bel and M Libre A Set of Methods in Transportation Network Synthesis and Analysis Operations Research Vol 30 N0 9 1979 pp 797 808 Silman L A Z Barzily and U Passy Planning the Route System for Urban Buses Computers and Operations Research Vol 1 1974 pp 201 211 1 Rea T C Designing Urban Transit Systems An Approach to the Route Technology Selection Problem PE 204 881 University of Washington Seattle 1971 Mandl C E Evaluation and Optimization of Urban Public Transporta tion Networks European Journal of Operation Research Vol 5 1979 pp 396 404 Marwah B R F S Umrigar and S B Patnaik Optimal Design of Bus Routes and Frequencies for Ahmedab d In Transportation Research Record 994 TRB National Research Council Washington DC 1984 1 47 is u a gt39 on 0 pp 4 Sharp G P Public Transit System Network Models Consideration of Guideway Construction Passenger Travel Delay Time and Vehice Scheduling PhD dissertation Georgia Institute of Technology Atlanta 1974 5 Keudel W ComputerAided Line Network Design DIANA and Mini mization of Transfer Times in Networks FABIAN Proc International Workshop on ComputerAided Transit Scheduling J R Daduna and A WrPrI Pd International 1 quot39 39 39 39 Sciences No 308 SpringerVerlag Berlin 1988 Ceder A Bus Frequency Determination Using Passenger Count Data Transportation Research Vol 18A No 5 6 1984 pp 439 453 3 18 E N N N N v N s N U N Ox 2 N on N 0 to o m N Ceder A and Israeli and Ma nanti T L an Gtocoec ea A D R Ha Israeli and Minieka E t39m39 quot 39 quot J Cedar A and H Stern De cit Function Bus Scheduling with Dead heading39 39 39 39 C quot Vol 14 No l 1981pp 19 34 H Stern A Graphical Person Machine Interactive 39 ln 39 k nth urns Approach IUI TRB National Research Council Washington DC 1982 pp 69 74 Gm H anaA Pd r A rmnmv At n u i r v r Size Problem Transportation Science Vol 17 No 4 1983 pp 471483 Coder A and Y Israeli Scheduling Considerations in Designing Tran sit Routes at the Network Level Proo 5th International Workshop on A39AA39F 4 r to IM can Nn 286 H A Man 113 136 A eder Public Transportation Assignment with Pas rt 0 Springer Verlag Berlin 1992 pp IST39l39l J B Lesort 6d Elsevier Science and Pergamon Publishing 1996 pp 561588 g 39 R L Wong Network Design and Transportation Planning Models and Algorithms Transportation Science Vol 18 No 1 1984 5 11 r Co on L Multiobjective Programming and Planning Academic rk 1978 Press New o 39 nsen and L Duckstein Multiobjective Deci sion Analysis with Engineering and Business Applications John Wiley ons New Yor Cedar Transit Route Design Using Scheduling and MultiObjective Programming Techniques In ComputerAided Transit Scheduling SpringerVeriag Berlin 1995 6 39 Y and A Cedar MultiObjcctive Approach for Designing Tran sit Routes with Frequencies In Advan ed Met ads in Transportation Analysis 1 Bianco and P Toth eds Springer Verlag Berlin 1996 pp 157482 I Marcel Dckker New York 1978 pp 181 234 Marguier P H 1 and A Cedar Passenger Waiting Strategies for Over lapping Bus Routes Transportation Science Vol 18 No 3 1984 pp 207230 Zeleny M Compromise Programming In Multiple Criteria Decision Making 1 L Cochrane and M Zeleny eds University of South Carolina Press Columbia 1973 1 Concept of Compromise Solutions and the Method of the Displaced Ideal Computers and Operation Research Vol 1 No 4 1974 pp 479 496 Duckstein L and S Opricovic Multiobjective Optimization in River Basin Development Water Resources Research Vol 16 No 1 1980 Coder A Methods for Creating Bus Timetable Vol 21A No 1 1986 pp 59 83 Publication of this paper sponsored by Committee on Bus Transit Systems CE 574 Public Transportation Class 18 2006 October 2 Website of the Day Railway Technical httpwwwrailwaytechnicalcomsitht1 html Railway Technical Web Pages at Signa ing Railway Technical Articles Traction Pages Fare Collection Finance tor Railways Terminology Pagea the n Statistics Stories Track Railway Suppliers Search this site here Home Page About us Site Map Links Page Search Contact us Site Sponsors The Development and Principles of UK Signalling PRC Rail Consulting Ltd An outline of the history UKmit my t quot Including Wl39heaspeci signals F r u Iquot Y A u l A l Iquot W amp International railway system consultancy 3mm 39 Emeil PRO Rail Consulting University of Birmingham Contents M80 in Railway Systems Engineering Pioneer Signalling The Time Interval System Line Capacity Fixed Signalling Distant Signals Interlocking A Blocks The Track Circuit V ry k ri n 39 Tu n m m I Mum F 393 FourAspect Signalling A Safe Braking Distance The Overlap programme for Railway Systems Engineering lrtroduction it man quot 39 quot usnl 39 quot Over 3 ammun 7 auigm 39 39 39 I quot Herele an attempt to explain in simple term lU standards w a it call wumo i Reading assignment Vuchic pp 577582 TCRP 100 Part 5 Chapter 1 Rail capacity fundamentals p 51 gt p 5 10 Chapter 2 Train control and signaling p 511 gt p 5 18 Chapter 5 Operating issues p 531 gt p 548 Chapter 6 Planning applications p 553 gt p 554 Chapter 8 Light rail capacity LRT Study questions What are the elements used to define a mode Right of way B longitudinally separated A fully grade separated Vehicle guidance Guided rail Guided vehicle Steel wheels on steel rails suppon Propulsion Electric linear induction motors Transit unit Driver driven driving and control Type of network Rail networks supplemented by bus lines and operation LRT Study questions What is light rail transit Peak Peak Peak Trains Cars Head Hour HourLoad HourLoad h h Ci Lo ation ay 5 pdir pft pm Boston Green Line Subwayquot 45 90 80 96 16 53 Calgary South Line 11 33 320 4950 2 1 6 8 Denver entral 12 24 300 3000 14 47 Edmonton Northeast LRT 12 36 300 3220 12 40 Los Angeles Blue Line 9 18 400 2420 16 54 ewark City Subway 30 30 120 1760 14 46 Philadelphia SubwaySurface 6O 60 60 4130 15 50 Portland Eastside MAX 9 16 400 1980 16 51 Sacramento Sacramento LRT 4 12 900 1310 15 49 Toronto Queen at Broadway 51 51 7O 4 30 0 19 61 Dir Route Length Avg Weekday Veh Operated mi km 39 Region Type ard gs in Max Service Baltimore LR 576 927 27400 40 oston LRSC 510 821 255600 154 Buffalo LR 124 200 23200 23 Calgary LR 404 650 132100 81 Cleveland LR 308 496 14100 25 Dallas LR 408 656 37700 48 ver LR 280 451 22500 29 LR 139 224 38000 31 Galveston TX VT 52 84 300 4 Guadalajara LR 298 480 149000 NA Houston LR 140 225 scheduled 2004 opening Jersey City HudsonBergen LR 138 222 3100 12 Kenos a I VT 19 31 150 1 Little Rock VT 42 68 scheduled 2004 opening Los Angeles LR 824 1326 0 51 Memphis VT 58 93 3500 9 Mexico City LR 323 520 55000 NA Minneapolis LR 232 373 scheduled 2004 opening Monterrey LR 286 460 0 NA w reans SCVT 160 257 14900 23 Newark City Subway LR 83 134 16900 16 ttawa DLR 100 161 5800 2 Philadelphia LRSC 693 1115 83100 108 Pitts ur LRSC 348 560 24600 47 Portland MAX LR 649 1044 73600 56 Portland Streetcar SC 48 77 4200 4 anlun Green Lme Subway Calgary 7m Avenue Ma Pmladelphm Subway Edmonton Northeast Llne DenverCenlral L05 Angelss Blue Line Pumand Easlslde MAX Newark thy Subway Sacramento 1Cemra I El i I 0 2000 400 GOOD 5000 0 000 12600 Ava sak rage Weekday Riders 15 M mes eak Hour l 33 Wm W in n Light Rail San Diego a b nght Rail Portland Oregon d n b e V E NC m R t h m L C cl Streetcar Philadelphia e Streetcar San Francisco WW 7 7 f Vintage Trolley Memphis LRT Study questions What are the factors that control the number of trains that can travel through a section of line Exhib39t 5 29 Headway Components of y d Hea Lines Operating Qpacityms vy Rail Transit at or Close to VanmuverrMetrotown EB San Francisco BAiTeEmbarcadero WB Newarkdoumal Square was Calgarerity Han EB New YorkrGrand Centlal NB Caigaryrsrd Street E5 Calgawdst Street WB TulontorKing SB New YarkrGrand Cenh a SB TorontchIDDr NB VancouverrBul mrd WB San Francisco MUNLMontgomew WB Vancouverruroadway H3 New Yorerueens Piaza WB ITrain Carmel Sepamtian NewarkrExchange Place EB 0 50 100 150 200 250 300 Seconds Exhibit 543 2 48 7 Gmde Seperated Line Capacity Cab Signaling 4o 7 7 B 38 77 7 7 7 7 7 35 77 7 7 7 7 7 7 if 7 it i 2 light rail 3 light rail 4 light rail 6 heavy rail 8 heavy rail Train Length cars NOTE Combination of dwell time and operating margin ranges from 55 5 upper bound to 7D 5 lower bound NOTE Line Capacity trainshdir m l l l l l r l l l l l l l y 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 0000 Single Track Length ft Assumes 35mph speed llmlt 180ft train length 205 dwell time and 205 operatan margin Exhibit 547 Single Track Line Capacity TwoCar Light Rail Trains An alternative gure using metric units appears in AQQEn IX A Exhibit 549 Light Rail Line Capacity Exclusive Lane Operation H N N 1 Line Capacity trains h H 90 120 150 Longest Traffic Signal Cycle Length 5 Travel Time s 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 5000 6500 7000 7500 3000 Single Track Length ft No stations 1 station stations NOTE Assumes speed limit of 35 mph lSOfoot train length 205 dweli time 205 operating margin and other data as per Exhibit 565 The remnmended closest headway is twice this time LRT Study questions What is the purpose of train control and signaling LRT Study questions What is the operating margin LRT Study questions What are the components of dwell time SkyTraln Vancouver TTC Toronto SvaralnSE Vancouver crs Calgary BART San Frantisco SkyTrain Vancouver Muni San Frantism EI39S Edrrmnmn MTS San Diego TriMet Pordand PATH New York SkyTrain Vancouver W Toronto ErS Edmonton CTS Calgary Mun San Francisco BART San Franclsco MTS San Dlego TrIMet Doltland Mun San Franclsco NYCl39AIRT New York PATH New York NVCTAIND New York SkyTraln Vancouver Munl San Franclsco 0 1 2 3 4 5 6 Time per passenger per single stream 5 NOTE Lever boardrnu except Hg or dmvn Steps where m loaned Exhibit 514 Dwell Time Components of Four Rail Transit Stationslmsl El Doors Open Passenger Flow Doors Open No Passenger Flow l Doors Closed Waiting to Depart Individual Train Observations Note that the scale of the Grand Central Station chart is twice that of the other chars In this series Dwell Time 5 Average headway 153 seconds Number of passengers observed 586 Flow time averages 38 of total dwell a San Francisco BART Montgomery Station LRT Study questions What is fixed block control sanequot Tram m IO R 6 am G G gt 1 Signals turn red behind a train as it enters each block IO lOR IOG lO 2 Trains may pass green amp yellow signals but stop 81 wait at red IOv lOR lOR lOG 3 Empty bloch separate trains yellow signals warn next one is red CHIb IOG IQv IOR IOR 4 Minimum headway combines dwell and block traversal limes Trai s ation Trai s ation Trai s ation LRT Study questions What are typical quotweakest linksquot in a light rail transit system D CH 1 First train arrives at station M 2 Second train arrives crosses to opposite platform 3 First train departs crosses to opposite track ESE 4 Third train arrives after first train has cleared crossover IOR ys IOR yR 1 First train arrives at junction 2 Opposing train arrives waits mm m K IOG yR IOR R 3 First train clears interlocking 4 Second train proceeds Station 2 Rear of leading train Distance Station 1 5 f Dwell Op 5 Time MarginTiE IV Front of following train Time NOTE Acceleration and braking curves omitted for clarity Beaverton Central oEeaverton TC Beaverton Sunset TC OWashinglon Park Expo Center I Delta ParkFIR Kenton 0 Lombard Street 0 Portland Boulevard 3 Killingsworth Street uGoing Street oOverlook Russell Street Hollywood TC I Downtown Portland Portland Airport 0 NE 60th Ave NE 82nd Ava Mt Hood Ave Cascades WarkroseSumner TC 0 Gateway TC Q 9 a lt lt gt 390 395 lt a a 32 3 1 u u Lu 23rd Ave Mar5ha St Rose Quarter TC E g 39 8 lt4 Johnson St 92 g g 4 E 8 lt I a mo 4 Gilsan g E K 2 A c 0 LU t o z W Everett 8 T 0 E as f 0 Old TownChinatown a Burnside SIICouch S g bra E E V PGE Par on 68 O Skldmore Fountain g 3 Kings HillSW Salmon St 3 I 114077Lg 5 003 3 ya Ill3rd 5 quot7700 41 s R 3 ED 5 5 Portland State Mve g Unwersn M0 y quot1907 0 f 3rdHarrison g 151Harrison 9 Rwerplace Downtown Exam 2 Preparation Closed book closed notes To include Homework problems Reading assignments Class discussions Study questions Topics Quality of service Bus transit capacity


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