RIDERSHIP TRENDS Office of Performance Management &

Innovation

February 27, 2017

Summary

• FMCB is considering a ridership goal for its strategic plan

and this presentation is designed to inform that discussion

• Ridership is a key measure of our service

• Ridership trends are difficult to analyze due to changing

methodologies and the large number of internal and

external factors impacting it

• Multiple ways to analyze ridership provide useful insights

• Overall ridership is on pace with job and population growth

• The T’s ability to serve and grow ridership depends on

capacity in time and space

2

Why have a ridership goal?

A ridership goal could inform:

• Capital decision-making about capacity

• Service planning and operating decisions

• Fare policy decisions

• In order to achieve environmental, social, and economic

goals for transit, the MBTA may want to maintain or increase

our market share for trips in the Boston region

• As population and employment increase, this means increasing

ridership and our capacity

3

How do we define and measure ridership?

Ad from 1982 issue of Passenger Transport magazine

• Measure unlinked passenger

trips defined by National

Transit Database (NTD)

guidelines

• Different methods of collecting

data by mode

• Heavy/Light Rail: Automated Fare

Collection (AFC)

• Bus: AFC and Automated

Passenger Counters (APCs)

• Commuter Rail: Conductor Counts

• Methodologies have changed

over time

4

What we report • To NTD*:

• Monthly ridership, by mode, from AFC system with adjustments for

non-interaction and transfers. Non-AFC from manual counts

(Commuter Rail and Boat) or RIDE software

• Yearly ridership, by mode, by day type and overall.

• Bus collected using on-board APC scaled to total service provided.

• Other modes as above with additional checks.

• On MBTA Back on Track Dashboard:

• Average weekday ridership for the last available month, from AFC

system with above adjustments.

*An error was discovered in the FY15 bus ridership reported to NTD due to a

methodology change. This presentation includes a corrected number.

5

What affects ridership?

FY03 FY05 FY07 FY09

FY11 FY13 FY15

Charlie system

implemented

Winter

2015

Govt. Center

closed

Automated

Passenger

Counters on

buses

Late

Night

Service

National

Recession

Base fare

increase 25%

Base fare

increase 5% Base fare

increase 18%

Base fare

increase

7%

TNC adoption

rate rises

Service change

Methodology change

Fare change

External factors

6

No single analysis tells the complete story

Unlinked Passenger Trips (UPT) is an imperfect measure, but

allows comparisons to other systems

We analyze the change in Unlinked Passenger Trips

• Over different timeframes to see trends

• Compared to external factors for context

• Compared to our service levels to measure efficiency

• By mode for comparison

• By day type to see changes in peak and off peak ridership

7

TRENDS

8

Month over month 2016 weekday ridership steady

Source: MBTA AFC system with non-interaction factors applied

0

250

500

750

1,000

1,250

1,500

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Th

ou

sa

nd

s

Average Weekday Ridership all AFC modes 2014 2015 2016

9

2016 Saturday ridership decreasing, aligns

with the end of Late Night

Source: MBTA AFC system with non-interaction factors applied

0

250

500

750

1,000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Th

ou

sa

nd

s

Average Saturday Ridership all AFC modes 2014 2015 2016

10

Sunday ridership has small fluctuations

Source: MBTA AFC system with non-interaction factors applied

0

250

500

750

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Th

ou

sa

nd

s

Average Sunday ridership all AFC modes 2014 2015 2016

11

Ridership growth on pace with job and population

growth

Source: NTD, BLS, US Census

Ridership is total UPT as reported to NTD

Jobs = Average total employment for the 17 inner core cities and towns

80

85

90

95

100

105

110

115

2008 2009 2010 2011 2012 2013 2014 2015

Jobs, Population and Ridership Indexed to 2008 Ridership Index - All Modes Population Index (inner core) Jobs Index (inner core)

12

Commute trip mode share is outpacing

population and job growth in the Boston region

Sources: US Census, Bureau of Labor Statistics Local Area Unemployment Statistics

80

85

90

95

100

105

110

115

2008 2009 2010 2011 2012 2013 2014 2015

Fiscal Year

Population Index (inner core) Jobs Index (inner core) Commute via Transit index

13

Trends differ by mode

Source: NTD, MBTA AFC system w/ adjustment for 2015 Bus (AFC = Automated Fare Collection)

14

Government

Center closure

0

20,000,000

40,000,000

60,000,000

80,000,000

100,000,000

120,000,000

140,000,000

160,000,000

180,000,000

200,000,000

FY 08 FY 09 FY 10 FY 11 FY 12 FY 13 FY 14 FY 15

Total UPT by Mode

Commuter Rail Demand Response (The RIDE) Ferry Heavy Rail Light Rail Bus

Source: NTD

0

20

40

60

80

100

120

140

2007 2008 2009 2010 2011 2012 2013 2014 2015

Nu

mb

er

of

un

lin

ke

d p

asse

nge

r tr

ips p

er

reve

nu

e

veh

icle

ho

ur

Fiscal Year

Commuter Rail Ferryboat Heavy Rail Light Rail All Bus Total Fixed Route

Ridership by service hours differs by mode

15

Government

Center

closure

CAPACITY

16

Capacity affects ability to meet demand

• Capacity constraints are spatial and temporal

• Bottlenecks (single links or stations) can reduce

capacity on entire lines

• Questions to consider:

• In the short-term, can we increase ridership where

we have capacity off-peak and lower volume routes?

• In the medium and long-term, where and when do

we need to increase capacity?

17

Time of day capacity constraints

Source: MBTA AFC system, Keolis conductor counts and train schedule

[Ridership by 15 min – weekdays fy16.xlsx]

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

3:00 AM 5:00 AM 7:00 AM 9:00 AM 11:00 AM 1:00 PM 3:00 PM 5:00 PM 7:00 PM 9:00 PM 11:00 PM 1:00 AM Bo

ard

ings

or

Stat

ion

En

trie

s b

y 1

5-m

inu

te p

eri

od

Average weekday FY16

Red Line Orange Line Blue Line Light Rail All bus Commuter Rail Notes:

• Commuter Rail boardings based on departure time of train from its origin, not actual passenger boarding time

• Commuter Rail counts average of October 3-7, 2016

• Other boardings are average weekday in FY16

• Counts are unadjusted for behind-gate transfers or non-interaction boardings, undercounts morning peak on Light Rail

18

Bottleneck capacity constraints (Focus40 analysis)

Source: MassDOT / MBTA Focus40

Map shows percent of theoretical capacity

utilized from 8:00-8:30 AM on an average

weekday

• Bottlenecks can be caused by high ridership segments, low speeds caused by dwell time or operating constraints

• Solutions depend on the cause

19

Capacity constraints also exist on the bus network

20

Discussion

• Should the MBTA have a ridership goal?

• Over what timeframe?

• How should the ridership goal inform operating, capital, and

fare policy decision-making?

21

APPENDIX

22

Heavy Rail Average UPT by Day Type

Source: NTD, MBTA AFC system w/ adjustments

0

100

200

300

400

500

600

FY 08 FY 09 FY 10 FY 11 FY 12 FY 13 FY 14 FY 15 FY 16

(prelim)

UP

T

Th

ou

sa

nd

s

Weekdays Saturdays Sundays

23

Light Rail Average UPT by Day Type

Source: NTD, MBTA AFC System w/ adjustments

0

50

100

150

200

250

300

FY 08 FY 09 FY 10 FY 11 FY 12 FY 13 FY 14 FY 15 FY 16

(prelim)

UP

T (

Th

ou

sa

nd

s)

Weekday Saturday Sunday

24

Government

Center closure

Bus Average UPT by Day Type

Source: NTD

0

50

100

150

200

250

300

350

400

450

FY 08 FY 09 FY 10 FY 11 FY 12 FY 13 FY 14 FY 15 FY 16

(prelim)

Ave

rage

UP

T (

Th

ou

sa

nd

s)

Weekday Saturday Sunday

25

Commuter Rail Average UPT by Day Type

Source: NTD

0

20

40

60

80

100

120

140

160

FY 08 FY 09 FY 10 FY 11 FY 12 FY 13 FY 14 FY 15

Ave

rage

UP

T (

Th

ou

sa

nd

s)

Weekday Saturday Sunday

26

Census Commute to Work Share

Source: US Census and American Community Survey, 17 inner core communities

52.1% 47.3%

7.3%

24.5%

27.1%

9.4%

10.7%

3.0% 4.2%

1.7% 3.4% 0%

10%

20%

30%

40%

50%

60%

2000

Census

2005-

09

2006-

10

2007-11 2008-

12

2009-

13

2010-14 2011-15

Drove Alone Carpooled Public Transit

Walked Worked from Home Other (Taxi / TNC, Bike, Motorcycle)

27