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importantTRANSCRIPT
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*A Presentation on CAPACITY ANALYSIS OF BUS RAPID TRANSIT SYSTEM USING MICROSIMULATION
Presented by Anshuman Sharma(13524005)Under the guidance ofTransportation Engineering Group Deptt. of Civil Engineering Indian Institute of Technology Roorkee
Dr. M. Parida Dr. Ch.Ravi Sekhar Professor Senior Scientist Transportation Planning Deptt. CSIR-CRRI, New Delhi
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*OUTLINE OF PRESENTATIONNEED OF THE STUDYOBJECTIVESMETHEDOLOGYSTUDY AREA DATA COLLECTIONDEVELOPMENT OF SIMULATION MODELCAPACITY ESTIMATIONPOLICY MAKING PROCEDURECONCLUSIONS
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NEED OF THE STUDY*
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*Capacity has an influence over speed, travel time and reliability of a public transit
Therefore , it affects quality of service
It is very essential not only to know the present condition but also to analyze whether the current system will be able to accomplish the demand in future
Not much work has been done regarding ways to calculate capacity of Transit lanes (for example BRT lanes)
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OBJECTIVES*
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*To estimate bus lane capacity using empirical and simulation model.To suggest modifications that can be incorporated in TCQSMComparison of both the models.Implementation of simulation model to schedule the departure headway of BRT buses.
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METHODOLOGY *
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*Study ObjectivesData Collection and ExtractionBus Lane Capacity AnalysisSimulation ModelTCQSM ModelComparison of Both the Models Implementation of Simulation model for Policy measureInput ParametersBus Stop demand dataBus Stop location dataInput ParametersRoadway GeometricsBus headwaysDwell time
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STUDY AREA *
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*Study Area: 6.1 km length of BRT transitway of Bhopal city
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*Sectional view of Bhopal BRTS
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Important termsAverage Dwell TimeFailure RateCritical Bus StopBus Lane Capacity*
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DATA COLLECTION *
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*Bus volume dataDwell time dataVideo graphic surveySignal phasing data
Primary Data
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*
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*
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*Video graphic BRT data
Time interval (minutes)0-1515-3030-4545-6060-7575-9090-105105-120120-135135-150150-165165-180180-195195-210210-225225-240Bus flow per fifteen minutes5453764743454453Projected hourly flow of buses20162012282416281612162016162012Speed (kmph)38.9851.7148.6647.2644.6552.1243.8250.9542.5449.6443.2138.4750.0940.8147.4137.69
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*Dwell time data
Bairagarh to Collectorate(upstream)Collectorate to Bairagarh(downstream)Bus Stop numberBus Stop NameDwell Time (s)CvDwell Time (s)CvMin.AverageMax.Min.AverageMax.1Collectorate10.114.524.731.4 %71319.328.3 %2VIP Guest House8.214.322.124.2 %8.413.923.430.6 %3Lalghati617.133.341.7 %4.720.636.944.5 %4Halalpura10.119.136.138.1 %5.117.740.251.2 %5Sundarvan Garden4.58.917.450 %7.110.914.920.8 %6Pump House4.611.423.151.2 %6.111.422.851.9 %7Sant Hirdaram Chouraha4.812.922.245.5 %4.97.413.242 %8Bairagarh6.615.536.351.8 %59.422.463.4 %9Chanchal Chouraha67519070.6 %6.422.647.252 %10Kali Mata Mandir610.126.654.1 %4.810.237.683.6 %
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*Signal Phasing Data
Name of the IntersectionNo. of phasesCycle length (s)Lalghati4205DirectionRed (s)Green(s)Amber(s)Lalghati to Collectorate110905
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Simulation ModelAdvantage
*The transportation system can be modified as per our conditions
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Methodology adopted for developing Simulation*
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Development of base network*
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Public Transit line and Public Transit stop*
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*
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*
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Vehicle parameters *Signal controls
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Model Calibration Speed Distribution of BRT buses
*
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Driver Behaviour Parameters ( Mehar et al. (2014); Yu et al. (2006))
*
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Model ValidationChi-square goodness-of-fit value between observed and simulated frequencies for the BRT bus speeds Error in average speeds of BRT busesGeoffrey E. Havers (GEH) statistic *
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The Chi-square statistic value evaluated was 8.06 and at 5 % level of significance the Chi-square critical value was 9.49. Thus, Chi-square test results indicate the acceptance of null hypothesis, which infers, there is no difference between simulated and observed data at 5 % level of significance.
The observed average speed of BRT buses is 45.5 km/h against simulated average speed of 45.16 km/h. The error between observed and simulated speed is 0.7 % (
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Comparison between modeled and observed traffic volume was made using Geoffrey E. Havers (GEH) statistic. This used in traffic simulation models to compare two sets of traffic volume. The formula for estimation GEH statistics is given in equation.
GEH = sqrt ((2 (M-C) ^2)/(M+C))Where M is the traffic volume obtained from simulation model and C is the observed traffic volume
*Average GEH statistic calculated was 1.4 (
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*Views of the Simulation
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CAPACITY ESTIMATION- SIMULATION MODEL *CAPACITY ESTIMATIONSpeed Reduction (SR) approachFailure Rate (FR) approach
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Failure Rate Approach
*Cumulative Queue Delay was observed at Critical bus stop Cumulative queue delay is defined as the total delay of all the BRT buses waiting in queue behind the buses occupying the loading areas.Cumulative delay was divided by 3600 to obtain Failure Rate. Failure Rate given as input to TCQSM model to calculate capacity
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*
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Speed Reduction Approach (Jacques and levinson (1997) )
* Throughput of the BRT buses was increased in the simulation model until the reduction in speed is up to 20 % of the average operating speed at existing headway. To increase the throughput, headways of the BRT buses were decreased as 75, 50, 25, 10, 5, 2, and 1 percent of the existing headway and given as input in the simulation model.The discharge (bus/h) observed at this condition is the capacity (bus/h) of the bus lane Existing Headway of TR-1, SR-1, SR-5 and BRT TR-4 are 10, 15, 12, and 18 minutes
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*5% of existingheadwayLimiting Discharge
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Comparison of Simulation Models with TCQSM model*
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Policy Making procedure*Simulation gives an advantage of incorporating changes in the existing transportation system and thereby analyzing the system for different scenarios Understanding the relationship between capacity and failure rateScheduling the headways of the BRT buses
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Critical bus stop capacity (lane capacity) values for different failure rate percentages *Interrupted
Interrupted
Uninterrupted
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*
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According to TCQSM, failure rate is implemented as a design value to estimate a lane capacity that reflects a desired level of operational reliability.*Operating marginDwell time = 50 s10 sNo Failure Failure
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Problem with setting Failure rate as design value*Implementation of a design failure rate is less achievable because of its probabilistic nature.Operating margin is measured in seconds, decision-makers can easily understand and implement it
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Variation of critical bus stop capacity and operating margin with failure rate *
Chart1
30.2239450404123.444
34.0327146222100.116
36.24230570188.83
37.118650059584.726
37.804234074281.648
38.592063511578.246
39.506854125674.466
41.035394656368.526
42.671362774362.64
43.450073796259.994
45.130879550754.594
46.313313239751.03
50.372597307140.068
53.149307371733.534
54.337513584430.942
55.580061755628.35
acdvvv
Operating Margin (seconds)
Stop Capacity Vs Failure rate
Operating margin Vs Failure rate
Failure rate (%)
Bus Stop Capacity (buses/h)
Sheet1
Total delay in Queue in SecondsFailure Rate (%)
Seed 302597.19
Seed 352617.25
Seed 402537.03
Seed 452587.17
Seed 502567.11
Seed 552527.00
Seed 602587.17
Seed 652577.14
Seed 702547.06
Seed 752527.00
7.110.22536916
Sheet2
60090010807206009001080720
1.59001350162010806009001080720
1.48401260151210086009001080720
1.3780117014049366009001080720
1.2720108012968646009001080720
1.166099011887926009001080720
160090010807206009001080720
0.95408109726486009001080720
0.84807208645766009001080720
0.74206307565046009001080720
0.63605406484326009001080720
0.53004505403606009001080720
0.42403604322886009001080720
0.31802703242166009001080720
0.21201802161446009001080720
0.16090108726009001080720
6009001080720
6009001080720
6009001080720
6009001080720
6009001080720
6009001080720
6009001080
900
900
900
900
900
900
900
900
Sheet3
Cumulative queue delay at critical bus stop from simulation (s)failure rate in %ZOperating marginBus Capacity TCQSM model
150401.112.28612330
1401153.191.85410034
1301805.001.6458936
1202105.831.5698537
1102356.531.5128238
1002657.361.4497839
903028.391.3797440
8036810.221.2696941
7044312.311.166343
6048013.331.1116043
5056215.611.0115545
4062017.220.9455146
3082422.890.7424050
2096226.720.6213453
10102028.330.5733154
5112131.140.5252856
390.0487804878
380.0731707317
7.360.264
31.142.114
5117
7107
997
1187
1376
1566
1756
1946
2135
2325
2515
25.214
25.413
25.313
25.214
25.114
2610
26.29
26.38
2610
1156.695526.85.638
2147.44126.76.151
3138.486526.656.407
4129.8329684026.95.126
5121.4775274.614
6113.423
7105.6685
898.214
991.0595
1084.205
1177.6505
1271.396
1365.4415
1459.787
1554.4325
1649.378
1744.6235
1840.169
1936.0145
2032.16
2128.6055
2225.351
2322.3965
2419.742
2517.3875
2615.333
2713.5785
2812.124
2910.9695
3010.115
319.5605
329.306
339.3515
349.697
Sheet3
Failure rate (%)
Bus Capacity in buses/h
Variation of Critical Stop Capacity with Failure Rate
y = 0.7918x + 32.198
R = 0.98
acdvvv
Operating Margin (seconds)
Stop Capacity Vs Failure rate
Operating margin Vs Failure rate
Failure rate (%)
Bus Stop Capacity (buses/h)
Relationship between Failure Rate, Operating Margin and Bus stop Capacity
Percentage of Existing Arrival rate of Buses
Failure rate (%)
Variation of Failure Rate with Percentage of Existing Arrival Rate
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Variation of failure rate with respect to different percentages of existing headway *
Chart1
1.11111111111
3.19444444442
53
5.83333333334
6.52777777785
7.36111111116
8.38888888897
10.22222222228
12.30555555569
13.333333333310
15.611111111111
17.222222222212
22.888888888913
26.722222222214
28.333333333315
31.138888888916
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
Percentage of existing headway of buses
Failure rate (%)
Sheet1
Total delay in Queue in SecondsFailure Rate (%)
Seed 302597.19
Seed 352617.25
Seed 402537.03
Seed 452587.17
Seed 502567.11
Seed 552527.00
Seed 602587.17
Seed 652577.14
Seed 702547.06
Seed 752527.00
7.110.22536916
Sheet2
60090010807206009001080720
1.59001350162010806009001080720
1.48401260151210086009001080720
1.3780117014049366009001080720
1.2720108012968646009001080720
1.166099011887926009001080720
160090010807206009001080720
0.95408109726486009001080720
0.84807208645766009001080720
0.74206307565046009001080720
0.63605406484326009001080720
0.53004505403606009001080720
0.42403604322886009001080720
0.31802703242166009001080720
0.21201802161446009001080720
0.16090108726009001080720
6009001080720
6009001080720
6009001080720
6009001080720
6009001080720
6009001080720
6009001080
900
900
900
900
900
900
900
900
Sheet3
Cumulative queue delay at critical bus stop from simulation (s)failure rate in %ZOperating marginBus Capacity TCQSM model
150401.112.28612330
1401153.191.85410034
1301805.001.6458936
1202105.831.5698537
1102356.531.5128238
1002657.361.4497839
903028.391.3797440
8036810.221.2696941
7044312.311.166343
6048013.331.1116043
5056215.611.0115545
4062017.220.9455146
3082422.890.7424050
2096226.720.6213453
10102028.330.5733154
5112131.140.5252856
390.0487804878
380.0731707317
7.360.264
31.142.114
5117
7107
997
1187
1376
1566
1756
1946
2135
2325
2515
25.214
25.413
25.313
25.214
25.114
2610
26.29
26.38
2610
1156.695526.85.638
2147.44126.76.151
3138.486526.656.407
4129.8329684026.95.126
5121.4775274.614
6113.423
7105.6685
898.214
991.0595
1084.205
1177.6505
1271.396
1365.4415
1459.787
1554.4325
1649.378
1744.6235
1840.169
1936.0145
2032.16
2128.6055
2225.351
2322.3965
2419.742
2517.3875
2615.333
2713.5785
2812.124
2910.9695
3010.115
319.5605
329.306
339.3515
349.697
Sheet3
Failure rate (%)
Bus Capacity in buses/h
Variation of Critical Stop Capacity with Failure Rate
y = 0.7918x + 32.198
R = 0.98
acdvvv
Operating Margin in seconds
Failure rate in %
Bus Capacity in buses/h
Percentage of Existing Arrival rate of Buses
Failure rate (%)
Variation of Failure Rate with Percentage of Existing Arrival Rate
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Failure rate, critical stop capacity (lane capacity), and headway for different desired operating margin *Existing Headway of TR-1, SR-1, SR-5 and BRT TR-4 are 10, 15, 12, and 18 minutes
Operating Margin (s) set by decision makersAchieved Failure rate (%)Critical Bus Stop Capacity (bus/h)Headway (minutes)TR-1SR-5SR-1BRT TR-4806 %38111613197010 %40101512186013 %45697105515 %485769
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Conclusions Related to simulation modelValidation of simulation model was carried out based on three commonly adopted statistical tests namely Chi-square, error in average speeds and GEH statistic. The error between observed and simulated average speed was 0.7 %. The GEH statistic observed for Bhopal BRT transitway was 2.4. The errors in estimated capacity between simulation model and TCQSM model were 4.8 % and 7.3 % for FR (failure rate) and SR (speed reduction) approaches, respectively. The result implies that the FR approach is the most reliable simulation approach to estimate bus lane capacity. The study reports that there is a linear relationship between critical stop capacity and failure rate. The linear relationship between critical stop capacity (lane capacity) and failure rate can be represented by Y = ax + b. Here, Y is critical stop capacity (bus/h), x is failure rate (%), and a,b are constants with unit of bus stop capacity (bus/h). The constants and depend upon average dwell time, coefficient of variation of dwell times, g/C ratio, number of loading areas at critical bus stop.
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This study suggests the use of design operating margin against failure rate to achieve a desired level of operational reliability. This is because the operating margin is more implementable parameter on ground as compared to failure rate. In addition, setting up a particular designed operating margin, may increase or constraint the existing capacity provided by the bus lane of BRT transitway
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RecommendationsThe TCQSM guidelines could be applied to other BRT systems of India, to quantify the capacity of exclusive bus lane (homogeneous conditions).This study recommends the use of video graphic survey to collect dwell time data at those bus stops, where passenger demand is high.The actual variation of dwell time could be studied and hence capacity estimation equations can be further modified. Similar changes regarding variation of dwell time can be incorporated while developing simulation model. This study can be further extended to examine the effect of intersections on the bus flow and thereby on lane capacity.Although the simulation model developed in this study was precise; to further improve the accuracy of the model for Indian BRT corridors in VISSIM, more calibration parameters (CC2 to CC9) should be investigated. *
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PublicationsINTERNATIONAL JOURNALAuthorship: Anshuman Sharma, M. Parida, Ch. Ravi Sekhar, Ankit KathuriaType of publication: Peer reviewed JournalReferred publication: YesTitle: Bus Lane Capacity Analysis: A Case Study of Bhopal Bus Rapid Transit System (BRTS)Journal and page numbers: Public transport: Planning and Operations (Submitted on: 4th March 2015, Manuscript under review) NATIONALAuthorship: M. Parida, Anshuman Sharma, Ch. Ravi SekharType of publication: SouvenirTitle: BRTS: A Sustainable Public Transport OptionJournal and page numbers: 75th Annual Session of Indian Road Congress, January 18-22, 2015, Bhubaneswar, India
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INTERNATIONAL CONFERENCEAuthorship: Anshuman Sharma, M. Parida, Ch. Ravi Sekhar, Ankit Kathuria Type of publication: Conference Refereed conference: Yes Title: Capacity Analysis of BRTS: A Case Study of Bhopal BRTS Journal and page numbers: 13th Transportation Practitioners meeting, July 1-2, 2015, London, UK (Manuscript accepted)NATIONAL CONFERENCEAuthorship: Anshuman Sharma, M. Parida, Ch. Ravi Sekhar, Ankit KathuriaType of publication: Peer reviewed conferenceRefereed publication: YesTitle: Bus Lane Capacity Estimation for Bhopal BRTS: An Empirical Approach Conference: 3rd Conference of Transportation Research Group of India (CTRG), December 17-20, 2015, Kolkata, India (Abstract accepted, Manuscript under review)
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TCRP Report 165 (1999),Transit Capacity and Quality of Service Manual, First Edition, Transportation Research Board, National Research CouncilTCRP Report 165 (2003),Transit Capacity and Quality of Service Manual, Second Edition, Transportation Research Board, National Research CouncilTCRP Report 165 (2013),Transit Capacity and Quality of Service Manual, Third Edition, Transportation Research Board, National Research CouncilTCRP Report 90, (2003), Bus Rapid Transit, Volume 1: Case Studies in Bus Rapid Transit, Transportation Research Board, National Research CouncilUrban Transport, http://www.adb.org/sectors/transport/key-priorities/urban-transport. Accessed JUNE 20th, 2014Verma A., and Dhingra S.(2006),Developing Integrated Schedules for Urban Rail and Feeder Bus Operation.Jounal of Urban Planning and Development,ASCE, 132(3), 138146Vuchic V. (2005), Urban Transit: Operations, Planning and Economics. John Wiley & SonsWidanapathiranage R., Bunker J., and Bhaskar A. (2014), Modelling the BRT station capacity and queuing for all stopping busway operation. Public Transport, 7(1), pp.2138Widanapathiranage R., Bunker JM., and Bhaskar A. (2013b), A microscopic simulation model to estimate bus rapid transit station bus capacity. Paper presented at the Australasian Transport Research Forum Proceedings, Queensland University of Technology, BrisbaneWisDOT (2014), Model Calibration-Wisconsin Department of Transportation (WisDOT).http://www.wisdot.info/microsimulation/index.php?title=Model_Calibration#The_GEH_Formula. Accessed on 10 November 2014Wright L., and Hook W. (2007), Bus Rapid Transit (BRT) Planning Guide: Institute for Transportation and Development Policy. New York, USAYu L., Yu L., Chen X., Wan T., and Guo J (2006), Calibration of Vissim for Bus Rapid Transit Systems in Beijing Using GPS Data. Journal of Public Transportation, pp.239257ak J., Fierek S., and Kruszyski M. (2014), Evaluation of Different Transportation Solutions with the Application of Macro Simulation tools and Multiple Criteria Group Decision Making/Aiding Methodology. Procedia - Social and Behavioral Sciences, 111, pp.340349
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Thank You
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*REFERENCES
TRB (2013) Transit Capacity and Quality of Service Manual. Third edition, Transportation Research Board, National Research Council, Washington, D.C.
Mushule, N. (2012) Bus bay performance and its influence on the capacity of road network in Dar es Salaam. Am. Journal of Enineering and Applied Science, 5, pp.107113
Jaiswal, S., Jonathan, M., Ferreira, L. (2010) Modelling Bus Lost Time: An Additional Parameter Influencing Bus Dwell Time and Station Platform Capacity at a BRT Station Platform. Paper presented at the 89th Annual Meeting of Transportation Research Board, National Academics, Washington, D.C.
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Chi square test for Normal Distribution:H0 = Boarding lost time follow normal distributionA = Boarding lost time do not follow normal distributionMatlab Code:BLT an array containing all the Boarding Lost Time Data is defined[h,p,st] = chi2gof (BLT)h = 1p = 2.2156e-007st = chi2stat: 30.6451 df: 2 O: [58 55 17 9 13] E: [45.2458 35.8163 34.0771 22.4818 14.3790]Since h=1 null hypothesis can be rejected at the 5% level of significance therefore Boarding lost time data do not follow normal distribution.
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Loading AreaCumulative of Effective Loading Areas1121.7532.4542.6552.75
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