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Transit Benchmarking

October 1, 2012

APTA Annual Meeting



Presentation Structure

1. Benchmarking and the RTSC at Imperial College London

2. Introduction to the Benchmarking Groups

3. Overview of the Benchmarking Process and Methodology

4. Managing the Data – A Balanced Approach

Questions and Discussion

2


1. Benchmarking and the RTSC at Imperial

College London

3


Benchmarking is the Search for Best Practices That Lead to

Superior Performance

4

A systematic process of continuously measuring,

comparing and understanding organizations’ performance

and changes in performance…

Of a diversity of key business processes

Against comparable peers

To gain information which will help the participating

organisations to improve their performance

(Adapted from the definition by Lema and Price)


Improving Transit Performance Through Benchmarking

Identify best practices and understand:

Strengths and weaknesses

Where improvements are most likely achievable,

helping to set challenging but achievable targets

What has/hasn’t worked elsewhere

How to reduce cost, improve service quality & safety

How good ideas can be implemented, at both the

strategic and day-to-day levels

Rarely is there a challenge that another operator hasn’t also faced

Focus has to be positive, not punitive!

5


The Railway and Transport Strategy Centre (RTSC) at Imperial

College – Focus on Independent, Comparable Benchmarking

World leader in public transit

benchmarking

Urban transit operations

Transportation economics & policy

Fall 2012 rankings show Imperial

College London as 6th in the world

Often considered to be the “MIT of Europe”

The RTSC has an international team of 15 - a wide

variety of experience and expertise

Part of the Centre for Transport Studies, within the

Department of Civil and Environmental Engineering

6


2. Introduction to the Benchmarking Groups

7


RTSC History and Experience – 18 Years of Successful

Worldwide Benchmarking Projects

Significant benefits have driven continued participation: e.g. New York, London

have both been CoMET members for 18 years and IBBG members for 8 years

8

1994 Group of Five metros (subway systems) formed

1996 Community of Metros (CoMET) founded for large metros

1998 Success of CoMET leads to formation of

Nova group for medium-sized metros

2004 International Bus Benchmarking Group (IBBG)

established for urban bus operators

2010 International Suburban Rail Benchmarking Group

(ISBeRG) established for suburban/regional rail operators

2011 American Bus Benchmarking Group (ABBG) established

for mid-sized bus operators in North America


65 Public Transit Operators Worldwide Are Part of the

Benchmarking Effort

9

London

Madrid

Lisbon

Barcelona

Oslo

Paris

Brussels Copenhagen

Munich

Milan

Naples

Moscow

Delhi

Beijing

Tokyo

Shanghai

Taipei

Guangzhou

Hong Kong

Bangkok

Singapore

Melbourne

Sydney

Buenos Aires

Sao Paulo Rio de Janeiro

Santiago

Vancouver

Mexico City

Toronto

Montreal

New York

Austin

Salt Lake City

San Francisco

Cleveland

Brisbane

Seattle


0.0

0.5

1.0

1.5

2.0

2.5

3.0

0

100

200

300

400

500

600

Bil

lio

n P

as

se

ng

er

Jo

urn

eys

Ne

two

rk L

en

gth

(K

m)

Network Length (Km) Passenger Journeys

Because Public Transit Generally Has Returns to Density and

Not Scale, We Can Compare Organizations of Different Sizes

10

Network Size and Annual Passenger Journeys (2011)

CoMET Metros Nova Metros


3. Overview of the Benchmarking Process

11


Four Key Principles Guiding the Benchmarking Groups

Collaboration – giving and taking the good and the bad; members help

each other improve, and the greatest benefits come from active

cooperation and participation

Confidentiality – completely open information

exchange within the groups and complete

confidentiality to the outside

Members can be open and honest

Anonymization protocols/tools for external

dissemination where appropriate

Speed – moderate group size and study

scope, with fast online interactions

Independence – flexibility to focus on areas of

most immediate interest to members

12


Elements of the Imperial College Benchmarking Model

KPI System – to compare

performance, identify lines of inquiry

Case Studies – In-depth research on

topics of common interest, to identify

best practices

Clearinghouse Studies – Shorter,

faster studies to quickly draw on

group knowledge and experience

Website with Online Forum – Peers

consult with each other, providing

quick answers

Meetings – attended by senior

management, plus expert workshops

and Imperial College visits

13

0

50

100

150

200

250

300

SP* Os* Tk* Sy Mu LO SF* LI MN* Ch*

To

tal C

ap

ac

ity p

er

Ca

r

Railway-Defined Capacity per Car (Seated + Standing, 2010)

Metros Group Average


Why We Look at Key Performance Indicators

Benchmarking is NOT only a comparison of data or

a creation of rankings

The structured KPI comparisons can be used for:

Stimulating productive “why” questions

Identify lines of inquiry, where drill-down is needed via studies

Identifying high priority problems, strengths and weaknesses

Identifying and monitoring trends and the best practices behind

them for potential transfer and implementation

Internal motivation – setting challenging but achievable targets

Supporting dialogue with stakeholders (confidentiality permitting)

But…the benefits of measurement should outweigh

the cost of data collection

CONFIDENTIAL 14


American Bus Benchmarking Group 2012/2013

Key Performance Indicator System

Growth & Learning G1 Passenger Boardings (5-year % change)

G2 Vehicle Miles and Hours (5-year % change)

G3 Passengers per Revenue Mile & Hour

G4 Staff Training (by staff category)

Customer C1 Customer Information (scheduled and real-time)

C2 On-Time Departure Performance (0 <> + 5)

C3 Passenger Miles per Revenue Capacity Mile

C4 Passenger Miles per Revenue Seat Mile

C5 Lost Vehicle Miles

Internal Processes P1 Peak Fleet Utilization (not used split by cause)

P2 Network Efficiency (revenue miles & hours per

total miles & hours, non-revenue split by category)

P3 Staff Productivity

(total vehicle hours & miles per labour hour)

P4 Staff Absenteeism Rate (by staff category)

P5 Mean Distance/Time Between Road Calls

Financial F1 Total Cost per Total Vehicle Mile & Hour

F2 Total Operating Cost per Total Vehicle Mile & Hour

(F3 service operation, F4 maintenance, F5 administration)

F6 Service Operation Cost per Revenue Mile & Hour

F7 Total Operating Cost per Boarding & Pax Mile

F8 Operating Cost Recovery

(fare revenue & commercial revenue per operating cost)

F9 Fare Revenue per Boarding & Pax Mile

Safety S1 Number of Vehicle Collisions per Vehicle Mile & Hour

(preventable & non-preventable)

S2 Number of Staff Injuries per Staff Work Hours

S3 Staff Lost Time from Accidents per Staff Work Hours

S4 Number of Passenger Injuries per Boarding & Pax Mile

S5 Number of 3rd Party Injuries per Vehicle Mile & Hour

Environmental E1 Diesel Fuel Consumption

E2 CNG Fuel Consumption

(per total vehicle mile, per pax mile, and per capacity mile)

E3 CO2 Emissions per Total Vehicle Mile & Pax Mile

15


KPI Challenges: Reaching Comparability Takes Time and is a

Continuous Effort, Building on 18 Years of Experience

One-time benchmarking studies are typically not

successful, as it takes iterative cycles and ongoing

work to achieve comparability

Confidentiality permits an open and honest information

sharing environment

Comprehensive KPI definitions and handbook

Understanding of context is key to interpret

performance (use of profile reports and regional data)

Data availability/quality: sufficient level of detail and

subcategories (e.g. staff categories) necessary

Drill-down of detailed cost and performance data, with

studies going deeper into areas of interest

16


4. Managing the Data – A Balanced Approach

17


Normalization of Data

Performance data needs to be normalized for scale as far as reasonably possible and desired Passenger boardings range in the IBBG:

80 million (Brussels) to 2.3 billion (London)

For each KPI, the most suitable denominator was chosen:

Passenger boardings, passenger miles

Vehicle miles, vehicle hours (revenue / total)

Capacity miles (seat / all)

Staff hours (total / categories)

Financial data needs to be expressed in comparable units before being normalized.

Inflation corrected

The International Groups use the World Bank’s Purchasing Power Parity Index

18


IBBG Example: Quantification of the Variety in Service

Characteristics Between ‘Similar’ Agencies

Type of service characteristic N µ Min Max CV

Average passenger trip length - km 13 4.6 2.8 8.0 1.6 0.35

Network efficiency - % of deadheading km 13 10.4 7.3 17.3 3.5 0.34

Weighted average vehicle planning capacity 12 71.2 52.1 94.8 15.7 0.22

Average commercial speed – km/h 11 17.3 12.0 23.3 3.3 0.19

Weighted average vehicle weight - tonne 12 12.5 11.2 14.9 1.0 0.08

N = Number of bus organizations in sample

µ = Sample average

Min = Minimum value

Max = Maximum value

= Standard deviation

CV = Coefficient of variation

19


IBBG Example: Variability of Speed Between Members

0

5

10

15

20

25

Bus12 Bus4* Bus2 Bus6 Bus11 Bus8 Bus9 Bus7 Bus5 Bus10 Bus1 Bus3

Commercial Speed - 2010Km/h

Operator A produces 11

revenue kms more per

revenue hour than C

A C B

20


IBBG Example: Effect of relative ‘speed’ position on

performance normalised by vehicle kms and hours (1)

C B

(PPP)

21


IBBG Example: Effect of relative ‘speed’ position on

performance normalised by vehicle kms and hours (2)

C B

(PPP)

22


Imperial College Framework for Balanced Normalization

23

Total Tonne

Miles

Revenue Vehicle

Miles

Revenue Vehicle

Hours

Passenger

Miles

Passenger

Boardings

Vehicle Utilisation

Performance

Revenue Capacity

Miles

Bus Planning

Capacity

Vehicle Weight

Commercial

Speed

System Utilisation

Performance

Trip Length

Network Efficiency

Total Vehicle

Capacity / Miles

Total Vehicle

Hours


Conclusions – “Rarely is There a Challenge That Another

Operator Has Not Already Faced”

65 transit operators across the world comparing performance

and sharing ideas – the benchmarking has continued for 18

years due to clear purpose and benefits

Commitment to continuous improvement, with senior-level

support and adequate staff resources required for success

Benchmarking is becoming an essential and highly cost

effective tool for transit managers to meet their increasing and

complex challenges

24


Thank You!

Questions?

Alex Barron

Senior Research Associate

ABBG Project Manager CoMET and Nova Deputy Manager

Railway and Transport Strategy Centre

Imperial College London

Email: [email protected]

25

Mark Trompet

Senior Research Associate

Bus Benchmarking Program Manager IBBG Project Manager

Railway and Transport Strategy Centre

Imperial College London

Email: [email protected]

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