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Key Performance Indicators

TRIPP, IIT Delhi

Sneha Lakhotia, PhD candidate K Ramachandra Rao, Professor

Geetam Tiwari, Professor

Transportation Research and Injury Prevention Programme Indian Institute of Technology Delhi

Outline

• Findings from research

• Indicators using AVL data

• Indicators using ETM data

• Insights

• Policy interventions

Tuesday, 09 October 2018 TRIPP, IIT Delhi 2

FINDINGS FROM RESEARCH


Measures of reliability Travel time based • Use of standard

deviation and mean values (Polus, 1978; Paulley et al., 2006; Liu and Sinha, 2007; Li et al., 2010)

• Use of excess values over mean (Strathman et al., 2001; Meyer, 2002, Chang, 2010; Li et al., 2010)

Adherence to schedule • Use of scheduled and

actual arrival/ departure times (Hensher and Prioni, 2002; Sheth et al., 2007; Lin et al., 2008; van Oort and van Nes, 2009; Chen et al., 2009; Eboli and Mazzulla, 2011)

• Use of scheduled and actual wait times (Liu and Sinha, 2007)


Headway based • Use of standard

deviation and mean values (Liu and Sinha, 2007; van Oort and van Nes, 2008; Chen et al., 2009)

• Use of scheduled and actual headways (Lin et al., 2008)

Research gap

• No focus on non-parametric indicators – these are useful when underlying distributions of travel time/ headway are unknown

• Use of instantaneous/spot speeds for assessing speed profiles – ideal to use link speeds for studying traffic flow characteristics correctly


INDICATORS USING AVL DATA


Benefits of using AVL data

Uses of AVL data

Real-time tracking of

fleet

Prediction of arrival times

Extraction of speeds and travel times

Assessment of route

efficiencies

Schedule optimisation


Methodology 1. Segregation of raw GPS data into 2 directions 2. Segregation into 3 time periods –

a) Morning peak – 7 am to 11 am b) Off-peak – 12 noon to 4 pm c) Evening peak – 5 pm to 9 pm

3. Selection of buffer area around bus stops to identify bus arrivals at stops

4. Estimation of stop-based headways based on arrival times at stops 5. Estimation of link-based travel time (TT) based on arrival times at 2

consecutive stops 6. Estimation of space mean speed (SMS) from link-based TT and link

lengths


Method to estimate headways and link speeds


A

B

C

1 2

3

18

19 20

21

22 23

24 25

Note:

• A, B, C represent 3 bus stops

• 2 links are present – AB and BC (links defined based on bus stops, and not route geometry)

• Green dots represent GPS points of a bus moving in the direction A to C

• Red dashed circles represent buffers

Alternate method


A

B

C

1 2

3

18

19 20

21

22

23

24 25

Note:

• A, B, C represent 3 bus stops

• 2 links are present – AB and BC (links defined based on bus stops, and not route geometry)

• Green dots represent GPS points of a bus moving in the direction A to C

• Red dashed circles represent buffers

Comparison of estimates from both methods

Headways


Link speeds

Statistically similar for all the bus stops of all the routes

Statistically similar for more than 90% of the links for all the routes

Indicators important at DEPOT and OC level


Headways for sample route 239DN

13

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Morning peak

020406080

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Off-peak

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Evening peak • Headways worse during off-peak hours

• Identification of problem segments in each time period

• Variation of headways high from the start terminal stop – indicates monitoring of departure time from the depots required

Link speeds for sample route 239DN

14

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Off-peak

0

10

20

30

40

50

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Evening peak • Problem of over-speeding observed in morning peak

• Identification of problem segments (congested and over-speeding) for all time periods

• Helps in creating realistic time tables based on actual speeds

Indicators important at MANAGEMENT level


Average headways

16

• Large headways observed in north and north-west Delhi – important to maintain schedule adherence

• Small headways observed on Ring Road – important to maintain regular headways

Headway variability (HV)


HV

% of stops

(morning

peak)

% of stops

(off peak)

% of stops

(evening

peak)

Insuff.

data 1.3 0.6 1.2

0 - 1 5.1 5.2 0.4

1 - 2 6.4 4.3 3.3

2 - 3 19.4 8.2 4.8

> 3 67.9 81.9 90.3

Headway variability (HV)


HV

% of stops

(morning

peak)

% of stops

(off peak)

% of stops

(evening

peak)

Insuff.

data 1.3 0.6 1.2

0 - 1 5.1 5.2 0.4

1 - 2 6.4 4.3 3.3

2 - 3 19.4 8.2 4.8

> 3 67.9 81.9 90.3

• Low HV in north-west Delhi – good performance despite large headways

• East Delhi and southern Delhi near border experience very high HV

• Requires revised scheduling and increase in fleet supply

HV – Density maps


Morning peak Off-peak

Evening peak

Ring Road

Karkardooma

Pragati Maidan Patel Chowk

Shivaji Park

Kalkaji

Low High

Stops on Ring Road and Karkardooma experience high HV all day

Travel time variability (TTV)


TTV

% of links

(morning

peak)

% of links

(off peak)

% of links

(evening

peak)

Insuff.

data 9.4 12.0 19.1

0 - 1 44.1 42.2 35.1

1 - 2 37.2 37.8 38.0

2 - 3 5.0 5.1 5.2

> 3 4.3 2.8 2.5

Travel time variability (TTV)

Tuesday, 09 October 2018 TRIPP, IIT Delhi

TTV

% of links

(morning

peak)

% of links

(off peak)

% of links

(evening

peak)

Insuff.

data 9.4 12.0 19.1

0 - 1 44.1 42.2 35.1

1 - 2 37.2 37.8 38.0

2 - 3 5.0 5.1 5.2

> 3 4.3 2.8 2.5

• TTV is mostly low • Links with high TTV

identified – can be target for improvement

• TTV increases in off-peak and evening peak

• However, links with worst TTV highest in morning peak

21

TTV – Density maps



Evening peak

Sarita Vihar

Ashram

Patparganj Connaught Place

RK Puram

Low High

Links with high TTV • Sarita Vihar in

morning • Ashram in off-peak • Patparganj in

evening

Link speeds


SMS

% of links

(morning

peak)

% of links

(off peak)

% of links

(evening

peak)

Insuff.

data 12.2 19.2 18.7

<10

km/h 25.1 27.8 30.3

10 – 20

km/h 43.9 36.1 36.2

20 – 30

km/h 16.7 15.3 13.3

30 – 40

km/h 2.1 1.5 1.5

> 40

km/h 12.2 19.2 18.7

Link speeds


SMS

% of links

(morning

peak)

% of links

(off peak)

% of links

(evening

peak)

Insuff.

data 12.2 19.2 18.7

<10 km/h 25.1 27.8 30.3

10 – 20

km/h 43.9 36.1 36.2

20 – 30

km/h 16.7 15.3 13.3

30 – 40

km/h 2.1 1.5 1.5

> 40 km/h 12.2 19.2 18.7

• More than quarter of links face congestion

• Less than 20% links face over-speeding

• NH-19 and eastern part of Ring Road experience congestion

INDICATORS USING ETM DATA


Benefits of using ETM data

Uses of ETM data

Maximum load

sections

In-vehicle passenger

volume

High boarding

passengers Route

optimisation

Fleet utilisation


Route-level



Passenger demand assessment

• Passenger travel on a transit line is shown by a series of diagrams Boarding and Alighting Diagram

Passenger Volume

• All these diagrams are plotted for a set of discrete passenger stops


Boarding/Alighting at fare stage stops for sample route 239DN

Tuesday, 09 October 2018 29

-5

5

15

25

35 Evening peak

Boarding Alighting

-5

5

15

25

35 Off-peak

Boarding Alighting

010203040 Morning peak

Boarding Alighting

• Identification of stops with high boarding and alighting numbers – longer dwell times

• Helps identify directional flow in different time periods

• Stops with high boarding volumes should be target for improvements in bus performance

Demand indicators • Coefficient of flow variation =

Degree to which passenger volume peaks along a route Ratio of maximum passenger load to average number of passengers High estimate indicates that supply may need to be increased or that

route needs to be split into smaller routes

• Coefficient of passenger exchange = Portion of passengers that are exchanged over a line, i.e. turnover rate Ratio of number of passengers who boarded to the number of passengers

who do not replace the alighting passengers High estimate indicates that passengers make shorter trips – route could

be split into more routes with less length


0

10

20

30

40

Off-peak

Passenger variables for sample route 239DN


0

10

20

30

40

Evening peak

05

10152025303540

Morning peak

Morning peak

Off-peak

Evening peak

Average passenger volume

16 7 5

Coefficient of flow variation

2.1 1.9 1.8

Coefficient of passenger exchange

1.1 1.0 1.0

City-level



TRIPP, IIT Delhi 33

Morning peak Off peak Evening peak Badarpur Border 119,906 38,888 33,156 Anand Vihar ISBT 96,198 88,003 57,405 Badarpur Village 75,340 24,758 24,931 Shakarpur 34,929 32,439 32,094 AIIMS Ring Road 23,904 32,611 29,762 AIIMS 23,499 27,273 24,473 ISBT Maharana Pratap Bus (T) 12,658 13,195 25,403 Madanpur Khadar Crossing 4,353 15,490 19,990

Anand Vihar ISBT ISBT Maharana Pratap Bus (T)

Shakarpur

Madanpur Khadar Crossing

Badarpur Village Badarpur Border

AIIMS Ring Road AIIMS

Highest passenger boarding stops

TRIPP, IIT Delhi 34

Morning peak Off peak Evening peak Anand Vihar ISBT Terminal 175,294 133,617 133,112 Badarpur Border 121,639 104,038 130,956 New Delhi Railway Station Gate 2 45,510 38,025 34,404 Punjabi Bagh Terminal 44,036 23,873 15,281 Mehrauli Terminal 37,559 25,564 23,738 Mangla Puri Terminal 32,740 36,559 33,648 Kapashera Border 29,764 29,258 12,063 Shyam Giri Mandir 17,418 17,068 25,542

Anand Vihar ISBT Terminal

Shyam Giri Mandir Punjabi Bagh Terminal

New Delhi Railway Station Gate 2

Badarpur Border Mehrauli Terminal Kapashera Border

Mangla Puri Terminal

Highest passenger alighting stops

On-board passengers – Density maps

TRIPP, IIT Delhi 35


Evening peak

Akshardham Temple Ashram

Kalkaji

Low High

• High density locations experience crush-load conditions

• Need to be addressed by increasing supply of buses

Route Length

Frequency

Time period 7 – 11 am

12 – 4 pm

5 – 9 pm

Low

(> 10 min)

Short

(< 25 km)

Long

(> 25 km)

High

(< 10 min)

Short

(< 25 km)

Long

(> 25 km)

36

• Median route length considering all bus routes – 25 km (selected as criterion for classification)

• Ideally high frequency routes should be classified as less than 6 mins • However, almost no routes in Delhi have frequency less than 10 mins

(thus selected as criterion for classification)

Classification of routes and time period

Frequency distribution of route lengths of 25 sample routes


0

2

4

6

8

10

0 5 10 15 20 25 30 35 40 45 50No

. of

rou

tes

fro

m s

amp

le

Route length (km)

Average route length from sample = 26 km Median route length from sample = 27 km

Passenger demand for Delhi


• Average passenger volume: Monthly assessment of routes where per bus average volume > 70 Monthly assessment of routes where per bus average volume < 20 Focus on low frequency and long routes for demand > 70 Focus on low frequency and short routes for demand < 20

• Maximum load: Immediate attention at cockpit when per bus load increases beyond 70 (theoretical capacity) Focus on low frequency and long routes

• Coefficient of passenger exchange: Monthly assessment of routes where coefficient > 1.5 Currently no focus required on any type of route

ROUTE AVERAGE PASSENGER DEMAND

Morning Off-Peak Evening

Low freq – short route 33 (±23) 31 (±22) 31 (±18)

Low freq – long route 53 (±27) 39 (±25) 38 (±20)

High freq – short route 34 (±09) 32 (±06) 32 (±05)

High freq – long route 44 (±13) 34 (±10) 36 (±11)

Origin-Destination matrices • 97% of the OD pairs are not connected in the

current sample of 25 routes • Maximum passenger demand –

Morning peak – Badarpur to Punjabi Bagh (32,100) Off-peak – Badarpur to Anand Vihar (14,505) Evening peak – Madanpur Khadar to Badarpur (23,372)

• Distances for the OD pairs have been extracted from Google Distance Matrix API – used for calculating trip length distributions


Trip length distributions

Tuesday, 09 October 2018 TRIPP, IIT Delhi 40 Average: 10.94 km

0%

10%

20%

30%

40%

0 5 10 15 20 25 30 35 40

Distance (km)

Morning peak

0%

10%

20%

30%

40%

0 5 10 15 20 25 30 35 40

Distance (km)

Off-peak

0%

10%

20%

30%

40%

0 5 10 15 20 25 30 35 40

Distance (km)

Evening peak Average: 11.82 km

Average: 12.05 km

INSIGHTS


Observations • Reliability performance is poorest for routes less than 25

km in length Highest HV Highest TTV Lowest speeds

• Passenger load performance is poorest for low frequency routes, particularly routes greater than 25 km in length

• Speeds and HV is worst during the evening peak and off-peak period

• TTV is worst during the morning peak period


Correlations


Alighting seen to show highest correlation with HV (~0.5),

followed by boarding (~0.4)

TTV_M SMS_M HV_M TTV_O SMS_O HV_O TTV_E SMS_E HV_E

Boarding 0.20** -0.01 0.40** 0.13** -0.05 0.43** 0.11* -0.06 0.35**

Boarding per bus 0.06 -0.06 0.08 0.04 -0.12* 0.15** 0.05 -0.11* 0.30**

Alighting 0.25** 0.01 0.49** 0.18** -0.04 0.50** 0.19** -0.08 0.29**

Alighting per bus 0.11* -0.09 0.17** 0.08 -0.16** 0.24** 0.13** -0.17** 0.20**

Spatial observations • High HV and high speed issues are observed on the

southern part of Ring Road • High TTV is highly localised and differs by time of day • High load sections observed at these locations:

Akshardham Temple Ashram Andrews Ganj Kalkaji

• Stops with high overall boarding and alighting are adversely related to reliability


POLICY INTERVENTIONS


Indicators assessed at different levels

Depot and OC level

• Stop-based headways

• Headways at start terminals

• Link-based speeds

Management level

• City-wide headways

• HV

• TTV

• City-wide speeds

• Stop-based passenger demand

• City-wide passenger demand

• Origin-destination matrices


Policy interventions • As reliability is negatively associated with overall

stop boarding and alighting, it is suggested to make infrastructure improvements to locations with high HV and TTV

• This is likely to cause a cyclic effect and even out boarding and alighting volumes (which could be getting accumulated due to bus bunching)

• Focus required on southern part of Ring Road for addressing higher speeds and HV


Target interventions • Reliability

Links with high HV and TTV Links with high HV and TTV which are recurrent Links with high delays Links with high delays which are recurrent

• Passenger demand Average passenger load per bus exceeds 70 Average passenger load per bus is less than 20 Coefficient of passenger exchange exceeds 1.5


THANK YOU FOR YOUR ATTENTION


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