*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

*OUTLINE OF PRESENTATIONNEED OF THE STUDYOBJECTIVESMETHEDOLOGYSTUDY AREA DATA COLLECTIONDEVELOPMENT OF SIMULATION MODELCAPACITY ESTIMATIONPOLICY MAKING PROCEDURECONCLUSIONS

NEED OF THE STUDY*

*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)

OBJECTIVES*

*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.

METHODOLOGY *

*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

STUDY AREA *

*Study Area: 6.1 km length of BRT transitway of Bhopal city

*Sectional view of Bhopal BRTS

Important termsAverage Dwell TimeFailure RateCritical Bus StopBus Lane Capacity*

DATA COLLECTION *

*Bus volume dataDwell time dataVideo graphic surveySignal phasing data

Primary Data

*

*

*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

*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 %

*Signal Phasing Data

Name of the IntersectionNo. of phasesCycle length (s)Lalghati4205DirectionRed (s)Green(s)Amber(s)Lalghati to Collectorate110905

Simulation ModelAdvantage

*The transportation system can be modified as per our conditions

Methodology adopted for developing Simulation*

Development of base network*

Public Transit line and Public Transit stop*

*

*

Vehicle parameters *Signal controls

Model Calibration Speed Distribution of BRT buses

*

Driver Behaviour Parameters ( Mehar et al. (2014); Yu et al. (2006))

*

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 *

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 % (

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 (

*Views of the Simulation

CAPACITY ESTIMATION- SIMULATION MODEL *CAPACITY ESTIMATIONSpeed Reduction (SR) approachFailure Rate (FR) approach

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

*

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

*5% of existingheadwayLimiting Discharge

Comparison of Simulation Models with TCQSM model*

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

Critical bus stop capacity (lane capacity) values for different failure rate percentages *Interrupted

Interrupted

Uninterrupted

*

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

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

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

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

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

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.

*

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

*

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. *

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

*

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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Thank You

*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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