Journalof Public Transportation 73

Characteristicsof Travel Time: Examplesfrom

JianpingWu MikeMcDonald Nick Hounsell Universityof Southampton,UK

Abstract The totaljourney time of Light Rail Vehicles(LRVs) is made up of runningtime, dwelltime (stationstops for passengerboarding and alighting),and signaldelay (delay of LRVsbeing stopped by the regulartraffic signals). Data from operationalsurveys of six modernlight rail systemsin Francehas shown that LRV runningtime was 65-71 percent of the totaljourney time and dwell time was 22-27percent, while signal delay was 7 to 8 percentof the totaljourney time.The average operating speed of theLight Rail Transit(LRT) ranged from 17.7 to 22.8km/h and has an approximatelinear relationship to passengerstop frequency (stops/km). Lightrail dwelltime has beenfound tofollow a log-normaldistribution, although the valuesdiffered significantly between different LRT systems.The meansof the dwell time distributionhave the range of 16 to 31 secondsin off-peakperiods and 21 to 3 7 secondsin peak periods. Factors that influencelight rail dwelltime includethe number pfpassengers at thestops, the numberof standeesin the vehicles,vehicle design (number of doors,door size, lowfloor or highfloor vehicle,etc.), fare collectionsystem, and the locationof LRT stops. Thefindings in thispaper couldbe usedby LRT;ianners and operatorsdirectly in developingand assessingoperating and servicechanges and inproviding input to long-

Winter1997 74 Journal of Public Transportation

rangeplanning procedures. The results can also be usedin microscopicsimulation mod­ elingstudies of LRT in an urban~twork, such as the TRGMSMmodel. ;.

Introduction I Lightrail systemsare increasinglybeing considered as an effectiveand en­ ~ ',-! vironmentallyfriendly alternative to alleviateurban congestion problems in Eu­ ropean and North Americancities. Throughoutthe world,there are more than 300 urban light rail or tramwaysystems in operation(Department of Transport 1995,Bushell 1993). The operatingspeed, dwell time (station stops for passengerboarding and alighting),and signal delay (delay of light rail vehiclesbeing stoppedby the regulartraffic signals) oflight rail vehicles(LRV s) directlycontribute to journey time and affect the numberof vehiclesrequired to operatea given time table.

~I 1 I Beyondthis obviouseffect, these parameters may governthe line capacityin the t systembecause of the on-linestations (stops) and the lack of overtaking(pass­ I ing) opportunities.Further, the parameterdwell time is generallyaccepted to be the majorfactor causing vehicle bunching in segregatedsystems, which, in tum, results in variabilityin headways.Headway variability itself results in higher I I thannecessary passenger journey times and uneven vehicle passenger loads, both of whichinfluence the attractivenessof the service. Despitetheir importance,relatively few studieshave been doneto quantify these parameters.Lin and Wilson(1992) and Fritz (1983)explored the charac­ teristics of light rail dwell time. However,their work was based on a limited surveyand consideredonly dwell time. Levinson (1983) investigated the opera­ tion characteristicsthat includedoperating speed, dwell time and signaldelay on the operationof bus systemsin a cross-sectionof U.S. cities.His workprovided usefulrelationships and parametersto publictransport engineers for planningof servicechanges and impactanalysis of bus systems In the last few years,several LRT systems have been constructedin France (, ,and ),and others are under construction(Urban Net-

Winter1997 Journalof Public Transportation 75 workNews 1994). To understandthe traveltime characteristicsof modemLRT systems,six existingLRT systems in Francewere surveyedin early 1995.In this paper, analysesof LRT total journey time, operatingspee~, signal delay,and dwelltime were carriedout based on the survey.The resultspresented may be used by-LRToperators, planners, and engineersfor light rail systemsplanning, design,and simulationstudies. To help readersunderstand and reviewthe termsused in this paper,defini­ tions are providedin AppendixA.

The LightRail Systems The six light rail systemsinvestigated are those locatedin ,, Strasbourg,, Paris, and Rouen.Most of theselight rail systemsoperate at­ gradeand are eithertotally or mostlysegregated from the normalroad vehicles. Undergroundrunning is used in Lille, Strasbourg,and Rouen when light rail passes throughthe railwaystation and busy commercialareas. Flyoverswere found in Grenoble,Rouen, and Paris '"fherelight rail crossesroads with heavy traffic in the suburbanareas. The generalcharacteristics of these light rail sys­ tems are summarizedin Table1, and the main operatingcharacteristics are sum­ marizedin Table2. Four of the six LRT systems(Grenoble, Lille, Paris and Rouen)use four­ door,low-floor light rail vehicles,although they are of differentdesigns and ca­ pacities.The StrasbourgLRT uses six-door,low-floor vehicles, while the Nantes LRTuses eight-doorvehicles with only the middletwo doors being low-floor. All theseLRV s allowedtwo-way movements ( alightingand boarding) of passen­ gers in each door and operatedwith the samerolling stock throughthe day (no differencebetween peak and off-peakperiod services). All the LRTsystems were equippedwith self-serviceticketing machines at the stops. In Grenoble,Lille, Nantes,and Strasbourg,passengers validate their tickets in machinesat the light rail stops, whereas,in Rouen and Paris, in-vehicleticket validatingmachines were used. For the Grenobleand Lille systems,each tick~t-was valid for onejourney in the same directionregardless of the journeylength. However, in Nantes,Paris,

Winter1997 76 Journalof Public Transportation

Table1 GeneralInformation ,. of the LightRail Systems

LRT Systems Line Passen&er LRV Capacity (persons) Length Stops (km) Seats Standees Total Grenoble LRT Line B 5.9 14 46 134 180 (Pl De La Gare-Universitaire) Lille LRT Line 1 10.0 23 74 269 - 343 (Roubaix-Gare Lille Flandres) Nantes LRT Line 2 14.0 30 50 195 245 (frocardiere-Orvault Grand Val) Paris LRT Line 9.0 21 52 126 178 (Saint Denis-Bobiimv-Pablo Picasso) Rouen LRT Line 11.0 22 52 126 178 (Georges Braque-Boulingrin- lf' + Sotteville) I Strasbourg LRT Line 10.0 18 66 201 267 .,\ (Baggersee-Hautepierre Maillon) I!~: It •: Including the start and end tenninus. ii fi I I -~

Table2 MainOperation Characteristics of the LRTSystems

LRT Systems Degree of Segregationof JunctionCrossing Types Operation Citv Centre Suburban At Grade Undere:round Flvover GrenobleLRT Line B Mixed Ooeration Seereeated None Most One - Lille LRTLine 1 Separated Se.e;regated Most Citv Centre None NantesLRT Line 2 Segregated Segregated All None None Some Area ParisLRT Line Sharedwith Segregated Most None One Buses RouenLRT Line Separated Seereeated Most Citv Centre One StrasbourgLRT Line Separated Segregated Most Citv Centre None

Rouen,and Strasbourg,one ticket, validated in a ticketvalidating machine, could be used for one hour for all routes. Light rail stations/stopsmay be classifiedinto three categories:upstream stops (LRTstops locatedupstream of the stop line at traffic-signal-controlled intersections),downstream stops (LRT stops downstream of the stop line at traf­ fic signalcontrolled intersections) and middle-blockstops (LRT stops located in

Winter1997 Journalof Public Transportation 77 the middlearea betweentwo traffic-signal-controlledintersections) according to their locationrelative to intersections.Most frequently,the upstreamand down­ streamstations are used when light rail runs in the centralarea, whereboth nor­ mal road trafficand lightrail passengerflows are high.Middle-block stops were foundmostly in suburbanareas, where normal road trafficflow are not very high and light rail can run at higher speeds.

The OperationalSurveys _ The operationalsurveys were precededby the collectionof informationon LRT routes and timetablesfrom the local transportationauthorities. A general surveyof the LRTcharacteristics was carried out, coveringthe LRT line (name of the line, line length,type of operation/degreeof segregationof rail track, total number of passengerstops and traffic intersections),LRV s (designed capacity, door configuration,fare collectingsystem, rolling stock), LRT stations/stops (lo­ cations,layouts, information and ticketingsystems), intersections (type of inter­ section, type of crossing-at-grade or grade separated,type of signal control, and priority).A photographicrecord also was taken for subsequentreference. Duringthe operationalsurvey, the surveyorin the vehiclerecorded the time of LRVdeparture from the terminus,the wheelstop and start times for each LRV stop, and the reasonsfor stopping( e.g., stop at passengerstops, stop at signalised traffic intersections).Surveys were carried out during weekdaystwice in peak periods (4:00 p.m.-6:00p.m.) and twice in off-peakperiods (10:30 a.m.-12:30 p.m.) for each light rail system.

Analysisand Results In the followinganalysis, peak refers to the averagevalue of the two sur­ veys in peak time, off-peakrefers to the averagevalue of the two surveysin off­ peak time, and averagerefers to the averagevalue of peak and off-peak. JourneyTime The time when a vehicleleft the start terminusto when it arrivedat the end ~ terminus is defined as the total journey time. The'peak, off-peak,and average journeytimes of the six light rail systemsare listed in Table3. The averagejour-

Winter1997 78 Journalof Public Transportation

-:~.

Table3 Light,RailTotal Journey Time ,... Differences LRT Systems Line Length Total Journey Time (minutes) BetweenPeak and (km) Off-Peak Peak Off-Peak Average (%) GrenobleLRT 5.9 20.0 19.0 19.S s LilleLRT 10.0 36.0 32.0 34.0 -11 NantesLRT 14.0 48.0 43.0 45.S 12 ParisLRT 9.0 35.S 32.S 34.0 9 RouenLRT 11.0 34.0 32.0 33.0 6 StrasbourgLRT 10.0 31.0 29,0 30.0 6'

ney time was foundto varybetween a minimumof 19.5 minutes(Grenoble) and a maximumof 45.5 minutes(Nantes). The differencein totaljourney times be­ tween peak and off-peakvaried between5 percent (Grenoble)and 12 percent (Nantes).While total journeytime is not a very useful indicatorof operational characteristicsbecause it is mainlycontrolled by LRTline lengths,the percent­ age of each componentof the LRTtotal journey time may be consideredto re­ flect its operationefficiency and servicequality. Lightrail journey time consistsof threeparts, running time, dwell time, and delaycaused by trafficsignals. The resultsshown in Figures1 and 2 indicatethat signaldelay took 7 to 8 percentof lightrail totaljourney time, dwelltime 22 to 27 percent, and runningtime 65 to 71 percent for the six surveyedlight rail systems.Detailed discussions on LRToperating speed, running time/speed, sig­ nal delay,and dwelltime are containedin the followingsections. OperatingSpeed Operatingspeed, which is calculatedby dividingthe lightrail line lengthby total journeytime, is an importantindex of LRT operationand servicequality, sinceit removesthe influenceof LRTline length.Unlike total journey time, light rail operatingspeeds showedmuch less variationbetween lines, rangingfrom 17.71km/h in Lille to 20.02 km/h in Rouen and Strasbourg(see Table4). The differencein operatingspeed between peak and off-peakperiods varied between

Winter1997 Journal of Public Transportation 79

Signal Signal Delay Delay 8% 7% Dwell Dwell time time 22% 27%

Figure1. Compositionof totaljourney Figure2. Compositionof totaljourney time(peak time). time(off-peak time).

Toble4 LightRail Operating Speed

DifferenceBetween LRT Systems AverageOperating Speed (km/h) Peak and Off-Peak

Peak Off-Peak Average (%) GrenobleLRT 17.70 18.63 18.17 5 LilleLRT 16.67 18.75 17.71 13 NantesLRT 17.50 19.67 18.59 12 ParisLRT 15.21 16.62 15.92 9 RouenLRT 19.41 20.63 20.02 6 StrasbourgLRT 19.35 20.69 20.02 7 differentlight rail systems.The Grenoble,Rouen, and StrasbourgLRTs had a 5 to 7 percentdifference in operatingspeed betweenpeak and off-peakperiods, whilethe Nantesand Lille LRTshad differencesofup to 12 to 13 percent. Manyfactors may have affectedlight rail operatingspeeds. A main factor may be the frequencyof passengerstops; as shownin Figures3 and 4, light rail operatingspeeds decreased as the numberof passengerstops increased for both the peak and off-peakperiods. Appropriate linear relationships have been found betweenthe operatingspeeds ~d the averagefr~quency of passengerstops for both peak and off-peakperiods by regressionanafysi_s (Manugistics 1992) with the generalforms as below:

Winter1997 80 Journal of Public Transportation

Y = 30.08-5.76*X (PeakTime with R2 = 0.64) (1)

I '\ ... I. Y = 31.27- 5.61*X ,: (Off-PeakTime with R2 = 0.68) (2) I where X is the averagefrequency of passengerstops (stops/km)and Y is light rail operatingspeed (km/h). LRVRunning Time and Running Speed The percentageof runningtime out of the total journey time may be taken as an index of LRT operationefficiency. It was shown in Figures 1 and 2 that light rail's runningtime was approximatelytwo-thirds of the totaljourney time,

22.00 ..------, 23.00 ------,

f 20.00 21.00 it ::!. i i ~' 'i 1.1 ' GI al ~ 19.00 ·~. ~18.00 tn tn C ,. C 1L :; ~ ~ 17.00 !l . i 16.00 0. 0 • 0 • 15.00 ------14.00 ------1.70 1.90 2.10 2.30 2.50 1.70 1.90 2.10 2.30 2.50 Stop Density (stops/km) Stop Densities (stops/km) .JI',, • Surveyed -Predicted • Surveyed -Predicted

Figure3. Operatingspeed and stop Figure4. Operatingspeed and stop frequency(peak periods). frequency(off-peak periods).

with a 6 percentdifference between peak and off-peakfor the surveyedlight rail systems. The differencesin runningtimes as a percentageof totaljourney times be­ tween differentlight rail systemsare shown in Table 5. The minimumrunning time share was 58 pe~centin Paris, and the maximumwas 74 percent in R~uen. ~ Significantdifferences between peak and off-peakperiods were also found in t , some light rail systems,e.g. 20 percentin Nantes and 14 percentin Lille. l ·, ' I•

Winter1997 Journalof PublicTransportation 81

Tobie5 LRVRunning Time

RunningTime in Percentageof DifferenceBetween Peak LRT Systems Total JourneyTime (%) and Off-Peak

Peak Off-Peak Average (%) GrenobleLRT 69.45 75.11 72.28 8.14 LilleLRT 61.39 69.91 65.65 13.87 NantesLRT 61.98 74.60 68.29 20.36 ParisLRT 59.72 56.62 58.17 -5.19 RouenLRT 71.56 77.38 74.47 8.13 StrasbourgLRT 67.32 70.62 68.97 4.90

Runningspeed is definedas the ratio of the light rail line lengthto its run­ ning time. As shownin Table6, this differsbetween different light rail systems with a minimumof 25 km/h in GrenobleLRT and a maximumof 29 km/h in StrasbourgLRT. The differencesbetween peak and off-peakperiods were insig­ nificantfor most of the LRTsystems except for the Paris LRT,where the differ­ ence was as high as 13 percent. The factorsthat mayinfluence running speed include the degreeof segrega­ tion (e.g., mixed,segregated, or separatedoperation), vehicle accelerationand decelerationcapabilities, maximum cruise speed, and drivercharacteristics. For example,in Grenoble,the LRT shares the roadspace with other road traffic (mixed operating)in the city area. The movementof LRV is sometimesimpeded by

Toble6 LRVRunning Speed

DifferenceBetween Peak LRT Systems AverageLight Rail RunningSpeed (km/h) and Off-Peak

Peak Off-Peak Average (%) GrenobleLRT 25.49 24.81 25.14 3 LilleLRT 27.15 26.82 26.99 1 NantesLRT 28.24 26.18 27.21 8 ParisLRT 25.47 29.35 27.41 -13 RouenLRT 27.13 26.66 ,'~6.89 2 StrasbourgLRT 28.75 29.30 29.02' -2

Winter1997 82 Journal of Public Transportation

pedestriansand other road vehicles,reducing the LRTrunning speed. However, in Strasbourg,the LRT uses-~gregated right-of-wayfor the whole line, with undergroundrunning in the centralarea. A higherLRV running speed (29 km/h) was thereforerecorded. SignalDelay . Althoughthe averagedelay at trafficsignals constituted only 7_ to 8 percent of the totaljourney time, as shownin Figures 1 and 2, the differenceswere sig­ nificantbetween different light rail systems.It may be seen fromTable 7 that the ., signal delay of LRVs in Lille was about 18 percent of the total journey time, while no signaldelay was observedin the StrasbourgLRT in peak period.In the I off-peak,Paris LRT had the highestsignal delay of 14percent of the totaljourney ~ :I time, while the StrasbourgLRT had only 1 percent.Differences between peak ;l and off-peakperiods were found to be very significantfor some light rail sys­ l tems. For example,the peak-periodsignal delay was abouttwice as high as that r.: I. in off-peaktime for the Lille and NantesLRTs. I l The amountof signaldelay depends predominantly on the densityof signal­ controlledintersections, the form of signalcontrol and the prioritymeasures for the LRTvehicles. Providing high priorityto light rail vehiclescan significantly· increaseoperating speeds. For example,for the six lightrail systemsstudied, if a full signalpriority had been assigned(no signal delays for all the light rail sys­ tems), an average increase in operatingspeed would be from 1 to 16 percent,

Table7 LRVSignal Delay

LRT Systems Signal Delay in Percentage of Total Journey Time (%)

Peak Off-Peak ,, Average GrenobleLRT 8.00 8.47 8.24 LilleLRT 18.03 8.63 13.33 NantesLRT 6.77 3.47 5.12 ParisLRT 9.77 13.97 11.87 RouenLRT 3.88 5.88 4.88 StrasbourgLRT 0.00 1.10 0.55

Winter1997 Journalof Public Transportation 83 varyingbetween systems as shownin Table8. Furthermore,according to a study by Wu and McDonald(1996), high priorityfor LRVscan significantlyreduce delayat signalledintersections without necessarily causing significant extra de­ lay to non-priorityvehicles.

Toble8 OperatingSpeed and S_ignal Delay

OperatingSpeed OperatingSpeed OperatingSpeed Increase When LRTSystems WithoutSignal Delay As They Were WithoutSignal Delay {km/h) (km/h) (%) Peale Off-Peale Peale Off-Peale Peale Off-Peale Average GrenobleLRT 19.24 20.76 17.70 18.63 8.7 9.26 8.98 LilleLRT · 20.33 20.52 16.67 18.75 21.99 9.44 15.72 NantesLRT 18.77 20.24 17.50 19.53 7.26 3.59 5.43 ParisLRT 16.86 19.31 15.21 16.62 10.83 16.24 13.54 RouenLRT 22.20 21.91 19.41 20.63 4.04 6.24 5.14 StrasbourgLRT 19.35 20.92 19.35 20.69 0.00 1.12 0.56

DwellTime Generally,the percentageof dwelltime ( or stationstops for passenger board­ ing and alighting)had an averageof22 to 27 percentof the totaljourney time, as shownin Figures 1 and 2. However,this varied significantlybetween different systems.As shown in Table 9, the averagedwell time per stop was only 14.4 secondsin the GrenobleLRT, but was 28.9 secondsin Strasbourgduring the off-

Table9 LRTDwell Time

- Mean Dwell time Per Stop Difference Between Peak and LRT Systems (seconds) Off-Peale Peak Off-Peak Average (%) Grenoble LRT 20.82 14.40 17.61 44.55 LilleLRT 20.21 18.74 19.47 7.86 NantesLRT 31.03 19.51 25.27 59.07 ParisLRT 32.49 28.68 30.59 13.28 RouenLRT 23.86 15.31 1~ 55.78 Strasbourg LRT 35.75 I 28.94 32.35 23.54

Winter1997 84 Journalof Public Transportation

peak. In the peak hour, the StrasbourgLRT had an averagedwell time of 35.8 secondsper stop,but this was.only20.2 secondsin Lille.Significant differences \.... in dwelltime were also foundfor some lightrail systemsbetween peak and off- peak periods.For example,in the Grenoble,Nantes, and RouenLRTs, the differ­ enceswere 45 to 59 percent. FactorsAffecting light RailDwell Time _ Duringthe light rail operationsurvey, several factors were observedto in­ fluencedwell time. • Little differencewas seen betweenthe dwell times for one passenger and three or four passengersbecause of the multiple,wide, two-way (alighting and boarding)door systemsand the low-floorvehicles used. · • As the numberof standeesnear the doorwayincreased, the timeof alight­ ing or boardingper passengerincreased significantly. This occurred par­

1f ~I ticularlywhen there was an-in-vehicleticket validatingmachine near ,11 the door. • One unpredicta~leelement on LRVdwell time is the driver's character­ istics.The time from LRVwheel stop to door open and the time of last passengerboarding to door closure/wheelstart was observedto vary from driverto driver. LRTDwell Time Models Review Previouswork on vehicle dwell times (or the related measure,passenger servicetimes) has been focusedon bus systems,with relativelylittle attention paid to light rail systems.Typically, least squaresregression has been used to relate vehicle dwell time to the numbersof passengersboarding and alighting, with separatemodels estimatedfor differentoperating characteristics likely to affectdwell time, such as the restrictionon door usagefor boardingand alight­ ing, fare collectionmethod, door configuration,and high-flooror low-floorve­ hicle (Leivine et al. 1994; Marshall et al. 1990; Guenthnerand Sinha 1983; Zografosand Levinson 1986; Levinson 1983; Guenthner and Hamat 1988; Ceder and Marguier1985; Kraft and Deutschman1977; Cundill and Watts1973).

Winter1997 Journal of Public Transportation 85

Lin and Wilson(1992) and Fritz (1983)suggested that modelsfor light rail transitfor the GreenLine of MassachusettsBay Transportation Authority (MBTA) were similarto the bus dwell time modelsdescribed above. These LRT dwell timemodels, either linear or non-linear,simply related the lightrail dwelltime to the numberof passengersalighting, boarding, and presenton the vehicle.Also, as thesedwell time models were developed from only one lightrail line survey,it is unlikelythat they can be used more generallyfor other modem light rail sys­ temsbecause of the differenceson LRVcapacity, door configuration, fare collec­ tion system,etc. It was beyondthe scopeof this studyto collecta rigorousand comprehen­ sive data set capableof findingan adequatedatabase for multivariableanalysis. It was thereforedecided to investigatethe distributionof LRV dwell times at stops and identifyany significantdifferences between lines/stops using appro­ priate statisticalanalysis. This workis describedin the followingsection. 1heDistribution of LRTDwell Time The differencesin LRTdwell time were foundto be significantfrom LRT systemto system,stop to stop and time to time.However, a statisticalanalysis of these results indicatesthat LRT dwell time followeda log-n~rmaldistribution whichhas a generalform of:

2 - I ( (In(x)-µ) ) f( x ) - ----=- exp - xa--fii 2a-2 whereµ is the mean and a is the standarddeviation. Figure 5 shows a typical distributionof LRTdwell time; Table 10 lists the samplesizes of LRTdwell time data,which were used in the statisticalanalyses for the six surveyedLRT systems. TheK-S {Kolmogorov-Smimov) test (Manugistics1992) results of the over­ all goodness-of-fitbetween the LRTdwell time and the theoretical(log-normal) distributionfor all the six LRT systemsare shownin Table 11. It shows in the table that the significancelevels of K-S test for all the six LRTsystems are sig­ nificantlygreater than 0.05,which indicates a gobt1~tof the LRTdwell time to the theoreticallog-normal distribution.

Winter1997 86 Journalof Public Transportation

Although a Dwell Ti~e Distribution '\.\,.. CRouen) more complex ,... causalrelationship 28

for dwell time 24 could not be de­ 20 veloped,given the :71 ~ 16 Cl) data available in ::I ti'" this study, the ...Cl) 12 main (~ctors that '"" result in the vari­ ance of the LRT

dwelltime are dis­ 0 20 40 60 80 r cussed in follow­ Dwell Ti•e (seconds) ing sections. Figure5. Exampleof LRTdwell time distribution.

Table10 Sampleof Sizesof LRTDwell Time Data LRTSystems SampleSize Peak Off-Peak Total GrenobleLRT 28 28 56 LilleLRT 46 46 92 NantesLRT 60 60 120 ParisLRT 42 42 84 RouenLRT 44 44 88 StrasbourgLRT 36 36 72

DifferenceBetween Different LRT Systems AlthoughLRT dwell time followsa log-normaldistribution, a significant differencewas foundbetween different LRT systems. Figure 6 showsthe differ­ encein the parametersµ(mean) and a (standarddeviation) between the different LRTsystems. Two sampleK-S tests were carriedout to assessthe overalldifference of LRTdwell time betweendifferent LRT systems. The results shownin Table 12

Winter1997 Journalof Public Transportation 87

Tobie11 K-STest Results for Log-NormalDistribution

Dwell Time Data Significance Level ofK-S Test Significant Fit Grenoble 0.541 V Lille 0.506 ✓ Nantes 0.248 ✓ Paris 0.513 ✓ Rouen 0.243 ✓ Strasbourg 0.273 ✓

Parameters of Log-Normal Distribution ofLRT Dwell Time

40 ------. 35 EJMean i 30 11!11Standard Deviation C: 0 ~ 25 ~ ~ 20 ~ e 15 ~ 10 ll.

Grenoble Lille Nantes Paris Rouen Strasbourg LRT Systems

Figure6. Differenceon parametersµ(mean) and a (standarddeviation) betweendifferent LRT systems. indicatethat significantdifferences· exist for mostof the comparedpairs, as shown by a K-S significancelevel ofless than 0.05. DifferenceB~een Peakand Off-Peak Time The differencein LRT dwell times betweenpeak and off-peakperiods is also significant,except for the Lille and StrasbourgLRTs (with K-S significance level test results greaterthan 0.05).Results are shownin Table 13. Parametersfor LRTDwell Time Models , Table 14 summarizesthe parameters,µ (meifr)and a (standarddeviation), of the log-normaldistribution for each of the LRT systemsstudied under both

Winter1997 88 Journalof PublicTransportation

Table12 TwoSample K·S Tet5 of DwellTime Between Different LRls ApproximateSignificance Level of SignificantlyDifferent LRTSystems Two Sample K-S Test Grenoble - Lille 2.05E-2 "J Grenoble --- Nantes 5.35E-2 X Grenoble - Paris 4.86E-7 v Grenoble -- Rouen 6.0lE-2 X Grenoble - Strasbourg 0.00 v - Lille - Nantes 4.llE-2 "J Lille ---- Paris 4.24E-8 "J Lille - Rouen 2.53E-2 v Lille --- Strasbourg 0.00 v Nantes - Paris 3.l9E-6 v Nantes - Rouen 9.60E-2 X Nantes -- Strasbourg 0.00 v l Paris --- Rouen 8.32E-3 v Paris --- Strasbourg 9.62E-9 v Rouen ----- Strasbourg 1.16E-9 v itl ,,;1 Tobie13 TwoSample K·S Tests of LRTDwell Time Between Peak and Off-Peak Period

ApproximateSignificance Level of SignificantlyDifferent LRT Svstems Two SampleK-S Test Grenoble 7.87E-4 -..J ] Lille 3.03E-l X Nantes 6.80E-3 v ·i! "J ,I Paris 2.82E-3 I Rouen l.68E-2 "J ) Strasbourg l.59E-l X

~, I peak and off-peakperiods. The mean,µ, of LRTdwell time has a range of 16 to 31 seconds in off-peakperiod and 22 to 37 seconds in peak period. The LRT dwelltime modelstogether with the parametersin Table14 may be used as refer­ ence for LRTsystem operation analysis and LRTnetwork simulation modeling study. I Dwelltime is an importantfactor influencing light rail operatingspeeds. It ~1·,, may be seen from Table 15 that if a dwell time of 5 secondscould be saved at " 11 each stop, the operatingspeed for the light rail systemswould increase by about 5 to 6 percenton average.

Winter1997 Journalof PublicTransportation 89

Table14 Log-NormalDistribution Parameters of LRTDwell Time in Peakand Off-Peak Periods

LRT Systems Parameters in Peak Period Parameters in Off-Peak Period µ (mean) cr (Standard Deviation) µ (mean) cr (Standard Deviation) Grenoble 22.994 7.728 15.738 4.655 Lille 21.896 7.162 19.977 5.793 Nantes 26.642 12.783 20.102 8.129 Paris 35.259 14.628 30.787 15.912 Rouen 24.602 12.017 19.463 6.662 Strasbourg 36.805 13.253 30.646 8.404

Tobie15 Increaseon OperatingSpeed When with 5 SecondsDwell Time Savings

LRT Systems OperatingSpeed With OperatingSpeed OperatingSpeed Increase With Dwell Time Saving WithoutDwell Time Dwell Time Saving (km/h) Saving (km/h) (%) Peak Off-Peak Peak Off-Peak Peak Off-Peak Average GrenobleLRT 18.80 19.85 17.70 18.63 5.83 6.14 5.99 LilleLRT 17.60 19.94 16.67 18.75 5.32 5.99 5.65 NantesLRT 18.46 20.74 17.50 19.53 5.21 5.81 5.51 ParisLRT 16.00 17.56 15.21 16.62 4.93 5.38 5.16 RouenLRT 20.52 21.88 19.41 20.63 5.39 5.73 5.56 StrasbourgLRT 20.34 21.82 19:35 20.69 4.84 5.17 5.01

Conclusions Lightrail's totaljourneytimeconsists ofLRV running time, dwell time, and signal delay.Generally, the signal delay takes 7 to 8 percent of light rail total journeytime, dwell time 22 to 27 percent,and LRV running time 65 to 71 percent. LRTsystems should keep the numberof passengerstops as low as possible, I subjectto passengerconvenience, since light rail's operatingspeed decreases approximatelylinearly as the frequencyof passengerstops increase. LRT priorityin at-gradecrossing intersections will significantlyimprove LRT's operatingspeed. It is also desirableto eliminatepedestrian and normal road trafficinduced delay by using a segregated1~mway.

Winter1997 90 Journal of Public Transportation

Traffic Signal Controlled Intersections _J ~ ~ ~ ~ L I ~ nl~u~~Stop Stop ~~dStop Figure7. Relative locations of theLRT stops. Significantdifferences in dwell time have been found between different light rail systems,and betweenpeak and off-peakperiods. Generally, the dwell time followsa log-normaldistribution with the mean,µ, in the rangeof 16to 31 secondsin off-peakperiods and 22 to 37 secondsin peak periods,according to

I a,, the study based on the surveyof the six light rail systemsin France.LRT fare collectionmethods, LRV floor height (high or low),and door configurationsare importantfactors ofLRT dwelltime on high passengerflow routes. The resultsand findingsin this papercan be used directlyby LRTplanners and operatorsin developingand assessingLRT operatingand servicechanges and providinginput to long-rangeplanning procedures. Further, the LRT dwell timemodel is the essentialcomponent of LRTsimulation models, which ~e increas­ mglyconsidered by LRTplanners and operators for system design and evaluation. ❖ AppendixA: TermDefinitions The termsused in this paperhave the followingdefinitions: LRT: LightRail Transit(sometimes termed Light ) LRV(s): LightRail Vehicle(s) DwellTime: LRVstation stops for passengerboarding and alighting SignalDelay: DelayofLRVs being stopped by the regulartraffic signals UpstreamStop: LRTstopslocated upstream of the stop line at trafficsignal con­ trolledintersection (see Figure7)

Winter1997 Journalof PublicTransportation 91

DownstreamStop: LRTstops locateddownstream of the stop line at traffic signal controlledintersections (see Figure7) MiddleBlock Stop: LRTstops located in the middlearea betweentwo trafficsignal controlledintersections (see Figure7)

Acknowledgments The authorswish to thank the local transportand LRT operationauthorities in Grenoble,Lille, Nantes, Paris, Rouen, and Strasbourgfor their kind supportduring our LRToperation characteristics survey.

References Bushell,C. (Ed.).1993. Jane :S, Urban Transport Systems 1992-93. Ceder,A., and P.H. J. Marguier.1985. Passenger Waiting Time at TransitStops. Traffic Engineering+Control 26 (June):327-329. Cundill,M.A., and P. F. Watts.1973. Bus Boardingand AlightingTimes. Transport and RoadResearch Laboratory, Crowthorne, Laboratory Report 521. Departmentof Transport.1995. Light Rapid Transit ( and Related)Systems. Buses and TaxisDivision of the Departmentof Transport,London. Fritz,M. S. 1983.Effect of Crowdingon LightRail PassengerBoarding Times. Trans­ portationResearch Record 908: 43-50. Guenthner,R. P., and K. Hamat.1988. Transit Dwell Time under ComplexFare Struc­ tures.Journal ofTra,nsportation Engineering (ASCE) 114(3) (May): 367-379. Guenther,R. P.,and K. C. Sinha.1983. Modeling Bus DelaysDue to PassengerBoarding and Alighting.Transportation ~esearch Record 915: 7-12. Kraft,W. H., and H. Deutschman.1977. Bus Passenger Service Time Distributions. Trans­ portationResearch Record 625: 37-42. Leivine,J.C., and G. W. Torng.1994. Dwell-Time Effects of Low-FloorBus Design. Journalo/Transportation Engineering (ASCE) 120: 914-929. Levinson,H. S. 1983.Analysing Transit Travel Time Performance. Transportation Re­ searchRecord 915. Lin, T. M., and N. H. M. Wilson.1992. Dwell Time Relationships for Light Rail Sys­ tems.Transportation Research Board. Transportatign Research Record 1361: 287-295. Manugistics,Inc. 1992.Statgraphics, Vol. 6. '··

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Marshall,L. F., H. S. Levinson,and L. C. Lennon.1990. Bus ServiceTime and Capaci­ ties in Manhattan.Transpor4f!.ion Research Record 1266: 189-196. UrbanNetwork News. December 1994/January 1995: 10. Wu,J., and M. McDonald.1996. TRGMSM: A SimulationModel for LightRail Transit (LRT)At-Grade Crossing Design. Traffic Engineering and Control,Vol. 37, No. 3: 73-177. Zografos,K. G., and H. S. Levinson.1986. Passenger Service Times for a-No-FareBus System.Transportation Research Record I 051: 42-47.

Aboutthe Authors DR. JIANPINGWu has six years of researchexperience on LRToperation, particu­ larlyat-grade operation at signalizedtraffic intersections, and developedthe microscopic simulationmodel TRGMSM for LRTat-grad~ operation study. He has alsobeen involved in other research activities including bus priority in London, fuzzy logic enhanced motorwaytraffic simulation, and route optimizationfor electricdelivery vehicles. PRO­ FESSOR MIKE McDONALD is Directorof TransportationResearch Group, University of Southampton,and has beenresponsible for over 100resea~ch contracts for the Transport ResearchLaboratory, Department of Transport,Engineering Research Councils, the Eu­ ropeanUnion, and other localand centralgovernment agencies. DR. NICK HouNSELL has 17 years of researchexperience in trafficmanagement, public transport, and intelligent i transportationsystems, specializing in publictransport priority and advancedtraffic con­ trol systems,including substantial applications in London.He also has initiatedmajor Europeanprojects on this topic,including leading a collaborativestudy into publictrans­ port priorityin EuropeanUrban Traffic Control Systems.

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