Results today based primarily on three data sources…...SHRPII C04: TEG Meeting, Washington, DC -...

SHRPII C04: TEG Meeting, Washington, DC - January 14, 2010

Results today based primarily on three data sources…

Seattle 2006 household travel survey (RP)

Seattle 2006, San Francisco 2007, Los Angeles 2009 congestion pricing SP surveys

Seattle 2006 Traffic Choices “experimental RP” data

Comparable experiences with many other data sets


Seattle region (PSRC) RP data

2006 household travel survey

Used 2-day place-based diary

4700 HH, 90,000 trips

HW network times for SOV and HOV 5 periods in the day (AM,MD,PM,EV,NT)

17 periods in the day (mostly 1-hr long)

Separate skims of time on highly congested links


Models estimated on Seattle RP

Time of day choice (1 hour periods)

Mode choice (6 modes)

Joint time of day & mode choice

Two purpose groups: HBW, HBO

Two decision levels: trip-based, tour-based


General RP analysis approach

1. Time and cost only

2. Test specification of time variables

3. Add cost segmentation (income, occ.)

4. Add time segmentation

5. Add other explanatory variables


Findings- basic TOD tests

Based on time coefficient(s) only- No cost difference across TOD alternatives

Using departure time from home works better than using arrival time at work

Using arrival time back home works better than departure time from work

Using a restricted set of alternatives (6-8 hours) works better than all hours of day


Findings- basic TOD tests (2)

Variable of extra time on very congested links is highly correlated with travel time. Works best as a shift variable related to extra time in the worst hour (peak of the peak)

Similar findings from Sacramento data

May be proxy effect for variability


Mode choice nesting structure

Auto Pure Transit Non-motorized

Drive Shared Shared Drive to Walk to Walk/

alone ride 2 ride 3+ Transit Transit bike


Joint mode/TOD nesting structure (trip-based)

Tested seven different structures

HBW Bottom level- nesting of one hour periods into

broader time of day periods

Middle level – nesting of modes into groups

Top level – joint decision across mode groups and TOD groups (logsum close to 1.0)

HBO Bottom level – nesting of modes into groups

Mode groups nested under one hour periods


Income and car occupancy included several ways…

Mode-specific dummy variables

Modifiers to cost effect

Modifiers to travel time effect

Time of day shift variables

Correlations between variables can cause instability


HBW- Imputed VOT by income and occupancy

Income quartile SOV HOV2 HOV3+

Lowest 5.09$ 11.19$ 12.47$

Second 5.94$ 14.51$ 16.75$

Third 6.45$ 16.88$ 19.99$

Highest 9.44$ 42.61$ 70.13$


HBW joint mode and TOD-tour-based

8 departure hours from home x 7 arrival hours at home x 6 modes = 336 alternatives

Same mode nesting structure

No conclusive results yet on TOD nesting


RP analysis- tasks remaining

Add travel time variability measures for Seattle data

Add more “time pressure” variables to tour-based models – demonstrate value of activity-scheduling approach to influence value of travel time

Test transferability of models to Bay Area (BATS 2000) RP data


SP data analysis

Three main data sets…

Seattle : General toll scenarios:

Free vs. tolled routes, Peak vs. off-peak

San Francisco: Downtown cordon pricing:

Before, during or after peak, or transit

Los Angeles: HOT/Express lane scenarios:

Free vs. tolled routes; before during or after

peak, or transit (express bus via HOT lane)


SP analysis approach

Same general approach as for RP, but..

More restricted… which alternatives and variables to include is largely pre-defined by the survey experiment

Tests mainly on segmentation and covariates

Allows “dynamic” analyses, such as departure time switching


Nesting for Seattle SP data

TOD

nests

Off-peak

period

Peak

period

Free

route off-

peak

Toll

route off-

peak

Free

route-

peak

Toll

route-

peak


Nesting for San Francisco SP

Mode

nests

Auto

mode

Transit

mode

Auto

before

peak

Auto

during

peak

Auto

after

peak

Public

transit


Nesting for Los Angeles SP

Level 1: Modes

Auto Transit

Level 2: Periods

Before peak Peak After peak Transit

Level 3: Routes

Toll Free Toll Free Toll Free Transit


Summary of SP Results- Value of SOV In-

vehicle Time ($/hour)

02468

1012141618202224

Lowest

income

quartile

Second

income

quartile

Third

income

quartile

Highest

income

quartile

Seattle-work

Seattle-non-work

San Fran-work

San Fran-non-work

Los Ang- work

Los Ang-non-work


Occupancy results-SP

In-vehicle time coefficient for HOV relative to

SOV Work Non-work

Seattle 1.03 1.39

San Francisco 1.12 1.90

Los Angeles 1.28 1.34

No consistent effects on cost (?!)

SP responses may not reflect values of other

vehicle occupants


Departure time shift disutility- Work - AM peak

-

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

90 80 70 60 50 40 30 20 10 0 10 20 30 40 50 60 70 80 90

Earlier Time shift (min) Later

Equ

iv.

min

ute

s in

-ve

hic

le t

ime

Actual 6-7amActual 7-8amActual 8-9amActual 9-10 am


Departure time shift disutility- Non-work - AM peak

-

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

90 80 70 60 50 40 30 20 10 0 10 20 30 40 50 60 70 80 90


Equ

iv.

min

ute

s in

-ve

hic

le t

ime

Actual 8-9amActual 9-10amActual 10-11amActual 11-12am


Departure time shift disutility- Non-work - PM peak

-

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

90 80 70 60 50 40 30 20 10 0 10 20 30 40 50 60 70 80 90


Equ

iv.

min

ute

s in

-ve

hic

le t

ime

Actual3pm-4 pmActual4pm-5 pmActual5pm-6 pmActual6pm-7 pm


Reliability results- SP

Seattle – each additional percent chance of a

15+ minute delay is equivalent to about 0.4

minutes travel time, for both work and non-

work

San Francisco – each minute of extra delay at

10% probability is equivalent to about 0.15

minutes of travel time for work, and 0.5

minutes for non-work. Not as statistically significant as Seattle results.


SP analysis remaining

Very little – possible refinements to previous models

Compare to results of other SP-based studies


Seattle Traffic Choices data

“Experimental RP” – respondents given an amount of money and then charged by the mile for using main roads

Price varied by time of day/week and facility type

500 vehicles with GPS units, experiment lasted more than a year >>> 750,000 trips

Some data collected during no-toll period


Traffic Choices analysis approach

Determine PSRC TAZ’s for trip ends

Skim best freeway and non-freeway paths for various times of day and attach to trip records

Analyze toll links actually used to determine the type of path chosen

Analyze toll distance and amount actually paid to confirm path choice

Estimate joint time of day / path type models, separately for HBW and HBO trips


Initial results

Based on very many observations

Toll variable very insignificant – high

correlations between toll & travel time

Toll/distance or log(toll) gave better results

Nesting of route type under TOD – stronger

for non-work than for work

Freeway route type constant increases with

the fraction of path distance on freeway


Coeff. T-stat. Coeff. T-stat. Coeff. T-stat.

Nesting routes under TOD 0.735 16.6 0.46 8.1 0.793 19.7

Highway time (min) -0.0272 -30 -0.0251 -24.1 -0.0259 -28.8

Toll (cents) -1.10E-04 -1.2

Toll per mile (cents) -0.0221 -7

Log of toll -0.272 -10.8

Constant-freeway route -0.277 -6.7 -0.233 -5.4 -0.225 -5.5

Fract. Distance on freeway 1.2 13.4 1.62 14.1 1.36 15.6

Initial results (partial) HBW


Initial results (partial) HBO


0.173 17.5 0.25 0.25 Nesting routes under TOD

-0.0289 -43.5 -0.0283 -43 -0.0284 -42.9 Highway time (min)

0.0023 18.5 Toll (cents)

-0.0163 -11.2 Toll per mile (cents)

-0.145 -5.4 Log of toll

-0.125 -4.7 -0.138 -5.3 -0.125 -4.8 Constant-freeway route

1.15 18.9 1.75 28.4 1.61 26.4 Fract. Distance on freeway


Traffic Choice travel time reliability measures

Analyze all GPS travel times between pairs of

network nodes

Determine mean, st.dev., 80th and 90th % time for each node pair/hour period

Aggregate node pairs into TAZ pairs and

calculated weighted average of mean, st.dev.,

80th and 90th % time

For each path type, apply st.dev./mean ratio to the network-based times


Results using reliability Standard deviation of travel time negative and

significant for HBW – similar to coefficient for

travel time

But… wrong sign for HBO trips.

St.deviation per mile gave similar results

Toll coefficient now negative (included

observations from post-tolling period)

Imputed VOT quite low ($5/hr)

Based on relatively few observations


Additional results HBW

VOT ($/hr) 5.29$ 4.50$ 4.56$


Highway time (min) -0.0529 -7.2 -0.0435 -5.7 -0.0479 -6.5

St. dev. Highway time (min) -0.0402 -5.4

St. dev. HW time/distance -0.156 -5.3

Toll (cents) -0.006 -9.1 -0.0058 -8.8 -0.0063 -9.5

Constant-freeway route -3.31 -12.5 -3.28 -12.3 -3.41 -12.8

Fract. Distance on freeway 4.72 11.1 4.57 10.7 4.87 11.5


Additional results HBO

3.55$ 5.26$ 4.09$ VOT ($/hr)


-0.0231 -6.9 -0.0342 -8 -0.0266 -7.6 Highway time (min)

0.0355 4.6 St. dev. Highway time (min)

0.0984 3.9 St. dev. HW time/distance

-0.0039 -5.6 -0.0039 -5.7 -0.0039 -5.6 Toll (cents)

-1.01 -7.2 -1.01 -7.2 -0.957 -6.8 Constant-freeway route

2.45 8.4 2.41 8.2 2.36 8.1 Fract. Distance on freeway


Traffic Choices analysis remaining

Try to extend reliability measures to more node pairs and zone pairs… Use a lower threshold of number of

observations needed

Use only for periods with the most demand (or aggregate across other periods)

Use observed pairs to estimate synthesis equation

Match in household variables (income)


Summary comparison –absolute values of beta(time)

-0.14 -0.12 -0.1 -0.08 -0.06 -0.04 -0.02 0

HBW mode & tod, tour-based

HBW mode & tod, trip-based

HBO mode & tod, trip-based

HBW mode choice, trip-based

HBW tod choice, trip-based

Traffic Choices- HBW 1

Traffic Choices- HBW 2

Traffic Choices- HBO 1

Traffic Choices- HB0 2

Seattle SP- HBW

Seattle SP- HBO

San Francisco SP- HBW

San Francisco SP- HBO

Los Angeles SP- HBW

Los Angeles SP- HBO


Conclusions (1)

Consistent willingness to pay for travel time savings across RP and SP data sets and types of models, but…

With important systematic differences by travel purpose, income and car occupancy


Conclusions (2)

For meaningful analysis of congestion pricing, time of day choice models are essential

Ideally, such models take advantage of data on current departure time or preferred departure or arrival time > time of day shift models


Conclusions (3)

Consistent evidence about hierarchy of types of travel decisions across RP & SP data sets… Path type choice (toll/non-toll) is at the

lowest level,conditional on other choices

For commuting, similar time periods nested underneath mode choice, but broader time of day periods generally above mode choice (similar to Dutch results)


Conclusions (4)

The effect of travel time variability is important, above and beyond the mean travel time

For OD-level analysis, std. deviation works better than 80th or 90th pctile, particularly when divided by distance

For application, simulation approaches best in the longer term (L04), but other approaches (link-based measures or synthesized OD measures) may be more feasible in the shorter term

Results today based primarily on three data sources…...SHRPII C04: TEG Meeting, Washington, DC -...

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Transcript of Results today based primarily on three data sources…...SHRPII C04: TEG Meeting, Washington, DC -...