3/29/2018
1
Trends in Travel Behavior and
Transit Ridership
CUTR WebinarThursday, March 29, 2018
Steven E. Polzin, PhD.
Outline
What is going on with travel
Trends and underlying causes of transit ridership decline
Implications going forward
3/29/2018
2
U.S. Context and Travel Trends2015/2014 2016/2015 2017/2016 YTD Months Source
U.S. Population 0.8% 0.5% 0.7% - Census
Total Employment 1.7% 1.7% 1.3% 12 BLS
Real GDP 2.9% 1.5% 2.3% 12 BEA (1st est.)
Gas Price -29.3% -14.8% 15.1% 12 EIA
Registered Cars andLight Trucks 2.1% 1.5% 3.0% 12 proj. Hedges Co.
Light Vehicle Sales 5.8% 0.1% -1.8% 12 BEA
Count of Zero-VehicleHouseholds -1.0% -1.9% Census
VMT 2.3% 2.4% 1.2% 12 FHWA
Public Transit Ridership -1.0% to -2.2% -2.3% to -1.6% -3.1, -2.6% 9, 12 APTA and NTD
Amtrak Ridership (FY) -0.3% 1.9% 1.9% 12 Amtrak
Airline Passengers 5.3% 3.9% 3.5% 11 USDOT, BTS
As of March 27, 2018
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
0
500
1,000
1,500
2,000
2,500
3,000
3,500
Jan-
92N
ov-9
2Sep
-93
Jul-
94M
ay-9
5M
ar-9
6Ja
n-97
Nov
-97
Sep
-98
Jul-
99M
ay-0
0M
ar-0
1Ja
n-02
Nov
-02
Sep
-03
Jul-
04M
ay-0
5M
ar-0
6Ja
n-07
Nov
-07
Sep
-08
Jul-
09M
ay-1
0M
ar-1
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n-12
Nov
-12
Sep
-13
Jul-
14M
ay-1
5M
ar-1
6Ja
n-17
Nov
-17
VM
T pe
r Cap
ita,
Ann
ual
Vehi
cle-
Dis
tanc
e Tr
avel
ed (
Bill
ion
Mile
s)
Annual Vehicle-Distance Traveled (Billions)
VMT per Capita
National VMT and VMT per Capita Trend, Moving 12‐Month Total, 1992–2016
8 year reprieve
3/29/2018
3
0
20
40
60
80
100
120
140
160
180
200
0
5
10
15
20
25
1917
1921
1925
1929
1933
1937
1941
1945
1949
1953
1957
1961
1965
1969
1973
1977
1981
1985
1989
1993
1997
2001
2005
2009
2013
2017
Ann
ual T
rips
per
Cap
ita
Ann
ual R
ider
ship
, Bill
ions
Rides, Billion Per Capita Rides
U.S. Transit Ridership and Ridership per Capita
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
6.0
6.5
7.0
7.5
8.0
8.5
9.0
JAN
08M
AY
08S
EP
08JA
N09
MA
Y09
SE
P09
JAN
10M
AY
10S
EP
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N11
MA
Y11
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JAN
12M
AY
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EP
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N13
MA
Y13
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JAN
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Flo
rid
a R
ider
ship
, Mill
ion
s
US
Rid
ersh
ip, H
un
dre
ds
of
Mil
lio
ns
US Florida
U.S. Transit Ridership, Fixed Route, 12‐Month Rolling Average
3/29/2018
4
Top 40 UZAs by 2016 Transit Ridership, 2014‐2016 Change (Millions)
Top 40 urban areas make up 83.9% of U.S. ridership decline from 2014‐2016
Source: NTD Monthly Raw Database
Miami‐Dade Transit
‐14.653, ‐13.4%
Broward County Transit
‐6.302, ‐16.6%
Central FL RTA‐3.011, ‐10.4%
Hillsborough Area Rapid Transit‐1.081, ‐7.2%
Jacksonville Transportation Authority
+0.491, +4.0%
Pinellas SuncoastTransportation
Authority‐1.826, ‐12.9%
PalmTran‐2.479, ‐20.8%
Gainesville RTS‐1.272, ‐11.8%
South Florida RTA‐0.150 ‐1.5%
City of Tallahassee‐0.650, ‐15.0%
Top 10 Agencies in Florida by 2016 Transit Ridership, 2013‐2016 Change (Millions)
Top 10 agencies make up 87.7%
of Florida ridership from 2013‐2016
Source: NTD Monthly Raw Database
3/29/2018
5
9.0%
5.1%
2.7%
0.6%
1.2%
5.0%
0%
2%
4%
6%
8%
10%
12%
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Mode Share, U
sual Commute Car, truck, or van ‐‐
carpooled Public transportation
Walked
Bicycle
Other means
Worked at home
Declining Carpooling and Growing Work‐at‐Home Dominate Trends
10 year trend –
2017‐2007
3‐year trend –
2017‐2014
1 year trend –
2017‐2016
Trending – Q4 2017 ‐
Q4 2016
All bus ‐15.2% ‐11.3% ‐5.1% ‐3.4%
Light rail (LR & SR) 20.7% 1.4% ‐0.8% ‐3.6%
Commuter rail 5.6% ‐1.3% ‐1.7% ‐1.3%
Heavy rail 9.8% ‐3.8% ‐0.4% 1.2%
Demand Response (DR‐DT) 10.4% ‐1.3% ‐0.5% 0.2%
Total ‐3.4% ‐7.1% ‐2.8% ‐1.4%
All bus ‐3.5% 3.7% 0.5% 0.3%
Light rail 54.9% 17.4% 6.8% 5.2%
Commuter rail 16.5% 1.1% 0.9% 1.3%
Heavy rail 7.8% 3.0% 1.8% 0.7%
Demand Response 15.5% 1.2% 0.1% 0.8%
Total 4.7% 3.2% 0.9% 0.7%
National
Service miles
Transit Ridership and Service Summary ‐ National
3/29/2018
6
Transit Ridership and Service Summary ‐ Florida
All bus ‐18.4% ‐20.5% ‐7.6% ‐5.6%
Light rail ‐50.0% 2.5% ‐4.1% 2.7%
Commuter rail 22.4% ‐2.4% 2.0% 6.5%
Heavy rail 15.3% ‐9.4% ‐6.7% ‐3.8%
Demand Response ‐0.2% 6.6% 4.3% 6.4%
Total ‐15.1% ‐18.6% ‐7.0% ‐4.9%
All bus ‐4.6% 1.2% ‐0.8% ‐1.9%
Light rail ‐20.2% 2.4% ‐1.7% ‐16.6%
Commuter rail 270.2% 1.9% 1.1% 7.7%
Heavy rail ‐9.0% ‐7.5% ‐8.9% ‐18.8%
Demand Response 9.1% 17.1% 5.8% 5.7%
Total 0.8% 5.7% 1.0% 5.7%
Florida
Ridership
Service miles
10 year trend –
2017‐2007
3‐year trend –
2017‐2014
1 year trend –
2017‐2016
Trending – Q4 2017 ‐
Q4 2016
Where are We Headed?
2012‐2014
2018
?
Transit ridership near 60 year high
Millennials are different
We passed peak VMT
We are urbanizing and CBD’s are thriving
Developers embrace transit
Strong referendum success
TNC’s address first‐mile/last‐mile issue
2015‐2017
Millennials buy cars and move to suburbs
Transit ridership loss accelerates in 3rd year of decline
VMT and VMT/Capita continue growth
Growth and migration resume historic patterns
System conditions, reliability, health care costs, etc. plague transit operators
How much will that subway cost? When will Hawaii's rail system open? How is that new streetcar doing?
TNC’s can cannibalize transit ridership
Why do we need transit with CAV?
3/29/2018
7
Framework for Understanding Changes in Transit Ridership
1. Demographics and Land‐Use
3. Competition
2. Transit Service Quality
How much of ridership’s change is explained by these factors?
Demand
Supply
Framework used in Metro analysis
Considers agency control
3/29/2018
8
Framework for Understanding Changes in Transit Ridership
1. Demographics and Land‐Use Age
Geographic Distribution across Metros – Migration and Growth Trends, International Migration Trends
Geographic Distribution within Metros (within proximity of service?/gentrification)
Income
Licensure Levels
Auto Ownership
Poverty Levels (SNAP enrollment)
Unemployment
Reduced College Student Ridership (APTA report)
Core Values
3.23.5
4.04.3 4.2
4.03.6
2.9
2.0
0
1
2
3
4
5
5‐14 15‐24 25‐34 35‐44 45‐54 55‐64 65‐74 75‐84 85+
Tirps per person per day
Age group
1.0%
2.9%
2.6%
1.8%2.0%
1.6% 1.5%
1.2% 1.1%
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
5‐14 15‐24 25‐34 35‐44 45‐54 55‐64 65‐74 75‐84 85+Share of trips taken via transit
Age group
0
10
20
30
40
50
5‐14 15‐24 25‐34 35‐44 45‐54 55‐64 65‐74 75‐84 85+
Persons (M
illions)
2015
2010
2000
1990
1980
Aging Population has a Negative Impact on Ridership
3/29/2018
9
Top 10 Largest-Gaining Counties (Numeric Change): July 1, 2015 to July 1, 2016
Largest-Declining Counties or County Equivalents (Numeric Change): July 1, 2015 to July 1, 2016
County PopulationNumeric Change
Percent Change
Transit Commute Share 2015
County PopulationNumeric Change
Percent Change
Transit Commute
Share 2015
Maricopa County,Arizona
4,242,997 81,360 1.95 2.3%Cook County,Illinois
5,203,499 -21,324 -0.41 18.8%
Harris County,Texas
4,589,928 56,587 1.25 2.8%Wayne County,Michigan
1,749,366 -7,696 -0.44 2.5%
Clark County,Nevada
2,155,664 46,375 2.2 4.2%Baltimore city,Maryland
614,664 -6,738 -1.08 19.6%
King County,Washington
2,149,970 35,714 1.69 12.6%Cuyahoga County,Ohio
1,249,352 -5,673 -0.45 5.1%
Tarrant County,Texas
2,016,872 35,462 1.79 0.6%Suffolk County,New York
1,492,583 -5,320 -0.36 6.8%
Riverside County,California
2,387,741 34,849 1.48 1.4%Milwaukee County,Wisconsin
951,448 -4,866 -0.51 6.2%
Bexar County,Texas
1,928,680 33,198 1.75 2.6%Allegheny County,Pennsylvania
1,225,365 -3,933 -0.32 9.1%
Orange County,Florida
1,314,367 29,503 2.3 3.2%San Juan County,New Mexico
115,079 -3,622 -3.05 0.3%
Dallas County,Texas
2,574,984 29,209 1.15 2.9%St. Louis City,Missouri
311,404 -3,471 -1.1 9.7%
Hillsborough County,Florida
1,376,238 29,161 2.16 1.7%Jefferson County,New York
114,006 -3,254 -2.78 0.0%
Average 3.4% Average 7.8%
Migration and Growth are Higher in Low Transit Use Areas
Improving Vehicle Availability Coincides with Declining Transit Ridership
‐10%
‐5%
0%
5%
10%
15%
20%
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Percent Change in Transit Ridership and Zero‐Vehicle Households from 2005
Ridership Percent Change from 2005 Percent Change Zero‐Vehicle Households from 2005
1.3 million fewer persons lived in zero vehicle households in 2016 than in 2014.
3/29/2018
10
229
3810
0
50
100
150
200
250
300
An
nu
al T
ran
sit
Trip
s P
er
Per
son
Vehicles in Household 0 1 2+ Total
Change in Population(5 and up), 2014-2016
-1.094 million -1.440 million +5.360 million +4.265 million
Estimated Transit Trip Change
-251 million -55 million +67 million -239 million
Total Population(5 and up), 2016
19.036 million 73.889 million 221.115 million 295.004 million
Each Fewer Resident in a Zero-Vehicle Household is Estimated to Reduce Annual Transit Trips by 191
Impact of Greater Auto Availability
Note: Fixed-route transit ridership was 10,331 million in 2014 and 9,881 million in 2016, declining 449 million trips.
Transit trip rates based on 2009 National Household Travel Survey and Census data suggest 240 million, or 53%, of the decline is explained by changes in vehicle availability.
Sources:2009 NHTS,U.S. Census,NTD
191
Zero‐Vehicle Household Trend
VEHICLES AVAILABLE – U.S.
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
No vehicles available
8.70% 8.80% 8.90% 9.10% 9.30% 9.20% 9.10% 9.10% 8.90% 8.70%
• Nearly half of all transit trips are made by residents of zero‐vehicle households – 44.6% in 2001 NHTS, 48.1% in 2009 NHTS, 43.0%
in 2017 NHTS
• We do not know what share of zero‐vehicle households are zero‐vehicle by choice, law, physical/medical condition, or income
• The share of zero‐vehicle households ranges from 4% in Utah to 12.6% in Massachusetts then 29% in New York and 37.3% in DC
choice
legal
medical
income
8.7%
?
?
?
?
3/29/2018
11
Transit Use Correlates with Need‐Based Program Participation
0%
30%
60%
90%
120%
150%
0%
5%
10%
15%
20%
25%
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
SNAP Users Percent Change from 2002
Ridership Percent Change from 2002
Percent Change U.S. Transit Ridership and SNAP Enrollment
Ridership Percent Change from 2002
SNAP Users Percent Change from 2002
Other Hypothesized Demographic Factors
Resumption of suburban growth trend
International immigrants may have a different demographic profile than in the past
International immigrants may be arriving at, or moving to, less transit intensive areas
Undocumented immigrant drivers license authorization may be spurring auto ownership
3/29/2018
12
Are Core Values that Impact Travel Changing?
Do we value autonomy, privacy, flexibility, convenience, etc. more than in the past?
Money
Cost
Reliability
Travel
BehaviorComfort
Safety
Time Cost
Convenience
Flexibility
Image Environmental,
Social Impact
Framework for Understanding Changes in Transit Ridership
2. Transit Service Quality Fares (levels, convenience, ease of use)
Level of Service (coverage, frequency, hours of operation)
Speed (access, wait, in vehicle, transfer, egress)(tolerance for waiting in our immediate gratification culture)
Reliability
Safety/Security
• Accident Safety, In‐Vehicle/Facility Crime
Image
• Cleanliness
• Interpersonal Compatibility ‐ Increased homeless/mentally ill ridership (APTA report)
• Status/Persona
Environmental Impacts
Awareness/Marketing (trip planning, real time information, digital fare payment, etc.)
Amenities (Wi‐Fi, shelter, convenience retail, etc.)
3/29/2018
13
Average Fare Revenue per Passenger Trip and Passenger Mile (2017 Dollars)
$0.00
$0.20
$0.40
$0.60
$0.80
$1.00
$1.20
$1.40
$1.60
$1.80
Average Fare Reven
ue
per Passenger Trip per Passenger Mile
Pre 2014 data from APTA Fact Book, Post 2014 data from NTD
Service Supply
0
1
2
3
4
5
6
7
8
9
10
DE
C02
AP
R03
AU
G03
DE
C03
AP
R04
AU
G04
DE
C04
AP
R05
AU
G05
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C05
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R06
AU
G06
DE
C06
AP
R07
AU
G07
DE
C07
AP
R08
AU
G08
DE
C08
AP
R09
AU
G09
DE
C09
AP
R10
AU
G10
DE
C10
AP
R11
AU
G11
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C11
AP
R12
AU
G12
DE
C12
AP
R13
AU
G13
DE
C13
AP
R14
AU
G14
DE
C14
AP
R15
AU
G15
DE
C15
AP
R16
AU
G16
DE
C16
AP
R17
AU
G17
Hun
dred
s of
Mill
ions
(T
rips
and
VR
M)
12-Month Rolling Average of U.S. Transit Ridership and Service, Fixed Route
Ridership Service
3/29/2018
14
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
DE
C02
MA
Y03
OC
T03
MA
R04
AU
G04
JAN
05JU
N05
NO
V05
AP
R06
SE
P06
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B07
JUL0
7D
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07M
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PR
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G14
JAN
15JU
N15
NO
V15
AP
R16
SE
P16
FE
B17
JUL1
7Hun
dred
s of
Mill
ions
(T
rips
and
VR
M)
12-Month Rolling Average of U.S. Transit Ridership and Service, Metro Bus
Ridership Service
Service Supply
Service Supply
0
5
10
15
20
25
30
35
40
45
DE
C02
MA
Y03
OC
T03
MA
R04
AU
G04
JAN
05JU
N05
NO
V05
AP
R06
SE
P06
FE
B07
JUL0
7D
EC
07M
AY
08O
CT
08M
AR
09A
UG
09JA
N10
JUN
10N
OV
10A
PR
11S
EP
11F
EB
12JU
L12
DE
C12
MA
Y13
OC
T13
MA
R14
AU
G14
JAN
15JU
N15
NO
V15
AP
R16
SE
P16
FE
B17
JUL1
7
Mill
ions
(T
rips
and
VR
M)
12-Month Rolling Average of U.S. Transit Ridership and Service, Light Rail
Ridership Service
3/29/2018
15
Service Supply
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5D
EC
02M
AY
03O
CT
03M
AR
04A
UG
04JA
N05
JUN
05N
OV
05A
PR
06S
EP
06F
EB
07JU
L07
DE
C07
MA
Y08
OC
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MA
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JAN
10JU
N10
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V10
AP
R11
SE
P11
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B12
JUL1
2D
EC
12M
AY
13O
CT
13M
AR
14A
UG
14JA
N15
JUN
15N
OV
15A
PR
16S
EP
16F
EB
17JU
L17Hun
dred
s of
Mill
ions
(T
rips
and
VR
M)
12-Month Rolling Average of U.S. Transit Ridership and Service, Heavy Rail
Ridership Service
0
5
10
15
20
25
30
35
40
45
DE
C02
MA
Y03
OC
T03
MA
R04
AU
G04
JAN
05JU
N05
NO
V05
AP
R06
SE
P06
FE
B07
JUL0
7D
EC
07M
AY
08O
CT
08M
AR
09A
UG
09JA
N10
JUN
10N
OV
10A
PR
11S
EP
11F
EB
12JU
L12
DE
C12
MA
Y13
OC
T13
MA
R14
AU
G14
JAN
15JU
N15
NO
V15
AP
R16
SE
P16
FE
B17
JUL1
7
Mill
ions
(T
rips
and
VR
M)
12-Month Rolling Average of U.S. Transit Ridership and Service, Commuter Rail
Ridership Service
Service Supply
3/29/2018
16
Framework for Understanding Changes in Transit Ridership
3. Competition
Communication Substitution for Travel
Trip making levels (telecommuting, e‐commerce, distant learning, online banking etc.)
TNC availability/LOS/price
Bike/Bikeshare
Auto Cost
• Fuel Cost
• Purchase/Lease/Finance Cost
• Parking Cost/Other Auto Costs
Roadway Congestion/Speed
$0.00
$0.50
$1.00
$1.50
$2.00
$2.50
$3.00
$3.50
$4.00
0
2,000
4,000
6,000
8,000
10,000
12,000
Ave
rag
e U
.S. G
as P
rice
Un
linke
d P
asse
ng
er T
rip
s (M
illio
ns) U.S. Average Gas Price U.S. Ridership
*Inflation adjustment performed using Bureau of Labor Statistics inflation calculator using CPI, UPT for 2015 and 2016 from Bureau of Transportation Statistics, Gas prices from EIA
Gas Prices and Transit Ridership, 1994‐2016
3/29/2018
17
Ridership trends are context specific and vary significantly across geography.
The reasons for soft ridership differ across contexts with telecommuting, TNC’s, service reliability, auto ownership trends, fares, and other factors having different impacts in different markets.
Transit has historically had the lowest mode loyalty (mode of last resort).
Declining fare revenues and/or dampened public willingness to increase subsidies resulting from soft ridership could contribute to continuing declines in ridership.
Key Issues – Travel Behavior
Influences on Transit Choice (Hypothesized)
Economic Status
TNC Availability
Car Affordability
E‐commerce, Telework
Safety, Reliability, Quality
Gas Price
Fares
Speed
Geograp
hic and Economic Distribution of
Population
Service availability
Gentrification/Housing Affordability
3/29/2018
18
Wild Guess as to Attribution of Causes of Ridership Decline
Increased auto availability, 53
Aging, 0.5
Migration trends/gentrification, 2.23 Transportation network
companies (Uber, Lyft), 6
Telecommuting/e‐commerce, etc., 9
Bikeshare, carshare, 1
System safety/reliability, 8
Personal safety/cleanliness, 8
Gas prices, 7
Service supply, 0
Fares, 1
Leap year, 0.27Weather, 2
Parking cost/availability, 0
Commuter benefits program changes, 0
Enhanced traveler expectations, 1
Strong employment growth and growing real income could continue to undermine transit dependency and jeopardize ridership.
Urban civility may influence future ridership trends.
Demographic trends in proximity to transit services (TOD) will influence future ridership.
Increasing roadway congestion could favor premium transit services but undermine mixed traffic transit operations.
System condition and quality of industry execution may influence ridership.
Key Issues – Travel Behavior
3/29/2018
19
Is there an inflection point where service becomes more attractive to choice travelers?
What transit quality of service is required to make it attractive?
What transit service concepts are sustainable in low to moderate density dispersed activity environments?
Ridership
Productivity
Accessibility
Speed
Frequency Convenience, etc.
?
Key Issues – Strategic
What do These Curves Really Look Like?
Better Service attracts travelers but capacity
overwhelms market size and resources unless
densely developed and well funded
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
60 57.5 55 52.5 50 47.5 45 42.5 40 37.5 35 32.5 30 27.5 25 22.5 20 17.5 15 12.5 10 7.5 5 2.5Probab
ility of Taking Tran
sit
Minutes between Vehicles
Mode Share on Transit
CaptiveChoice
0%
5%
10%
15%
20%
25%
30%
60 57.5 55 52.5 50 47.5 45 42.5 40 37.5 35 32.5 30 27.5 25 22.5 20 17.5 15 12.5 10 7.5 5 2.5
Pro
bab
ilit
y o
f T
akin
g T
ran
sit
Minutes between Vehicles
Probability of a Given Trip Being on Transit for Choice Traveler
Transit expansion fails to attract many new travelers?
3/29/2018
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Research on Ridership Trends
APTA. “Understanding Recent Ridership Changes: Trends and Applications.” Policy Development and Research. Nov. 2017.
Agency Initiatives: “Falling Transit Ridership: California and Southern California.” UCLA Institute of Transportation Studies. Dec. 2017.
FDOT, Understanding Ridership Trends in Transit – in progress
TCRP J‐11/Task 28, Synthesis, “Analysis of Recent Public Transit Ridership Trends”, Georg Tech.
Pending:
TCRP A‐43, “Recent Decline in Public Transportation Ridership: Analysis, Causes, Responses,” $400,000.
TCRP H‐56, “Reinventing Transit Networks for a New Mobility Future,” $300,000.
How Should Stakeholders Respond?
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Key goals
1. Mobility 2. Resource efficiency 3. Economic
competitiveness
New Technologies are Leading to New Mode Concepts and Business Models
May be best addressed with multiple
1. Technologies and services
2. Mixes of public and private providers
3. Different pricing and funding strategies
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Today’s modal silos will disappear
We won’t worry about the future of transit but instead worry about the future of mobility
Upcoming seminar
CUTR Webcast Series Presents:
Understanding the Effects of Demographic and Socio-Economic Factors on Public
Transit Ridership Trends
Kurt LehmannGraduate Research AssistantCUTR · USF
April 12th · 12pm-1pm
More Information: https://www.cutr.usf.edu/2018/
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You may find the new book of interest. I was the principal author of Chapter 5, Upgrading Transit for the Twenty‐First Century, of the recently released text, Three Revolutions Steering Automated, Shared, and Electric Vehicles to a Better Future. https://islandpress.org/book/three‐revolutions
If you’d like to purchase a copy of the book from Island Press, use the code 4SPERLING, which is good for a 20% discount. You can also order it from Amazon, Barnes and Noble, and your local independent bookseller.
http://scholarcommons.usf.edu/jpt/vol21/iss1/Joel Volinski Prologue Reflections on the Future of Public Transportation
Dan Boyle The Future of TransitRalph Buehler Can Public Transportation Compete with Automated and Connected Cars?Graham Currie Lies, Damned Lies, AVs, Shared Mobility, and Urban Transit FuturesJill Hough and Ali Rahim Taleqani Future of Rural TransitSteven E. Polzin Just Around the Corner: The Future of U.S. Public TransportationKari Watkins Does the Future of Mobility Depend on Public Transportation?Carol Schweiger Improved Mobility through Blurred LinesSusan Shaheen and Adam Cohen Is It Time for a Public Transit Renaissance?: Navigating Travel Behavior,
Technology, and Business Model Shifts in a Brave New WorldEric Schreffler Better Integrating Travel Choices into Future Urban Mobility Systems: The Day
the Highways Stood StillJerome Lutin Not If, but When: Autonomous Driving and the Future of TransitMichael Manville, Brian D. Taylor, and Evelyn Blumenberg
Transit in the 2000s: Where Does It Stand and Where Is It Headed?Jarrett Walker To Predict with Confidence, Plan for Freedom
The Future of Public Transportation
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Thank You!