VALUATION OF TRAVEL TIME SAVING WITH REVEALED PREFERENCE DATA
IN JAPAN: FURTHER ANALYSIS
HIRONORI KATO, THE UNIVERSITY OF TOKYO, [email protected]
TAKANORI ODA, CREATIVE RESEARCH AND PLANNING CO., [email protected]
AYANORI SAKASHITA, CREATIVE RESEARCH AND PLANNING CO., [email protected]
This is an abridged version of the paper presented at the conference. The full version is being submitted elsewhere.
Details on the full paper can be obtained from the author.
VALUATION OF TRAVEL TIME SAVING WITH REVEALED PREFERENCE DATA IN
JAPAN: FURTHER ANALYSIS
Hironori Kato, Takanori Oda, and Ayanori Sakashita
VALUATION OF TRAVEL TIME SAVING
WITH REVEALED PREFERENCE DATA
IN JAPAN: FURTHER ANALYSIS
Hironori Kato, The University of Tokyo, [email protected]
Takanori Oda, Creative Research and Planning Co., [email protected]
Ayanori Sakashita, Creative Research and Planning Co., [email protected]
ABSTRACT
This paper presents the latest estimation results of the value of travel time savings of Japanese
road users based on revealed-preference travel data from the nationwide 2005 Road Traffic
Census Survey of Japan. This paper extends our earlier study (Kato et al., 2011) using the
same dataset. It uses standard binary logit and mixed binary logit models in the context of an
expressway versus no-expressway route choice for subgroups categorized by gender,
passengers, type of job, age, departure time, and travel distance, as well as three travel
purposes: home-to-work, business, and private. The mixed logit models assume parametric
distributions—normal and triangular—of travel time and cost coefficients. Then, the
estimation results of the modes are compared among different distribution types.
Keywords: Value of travel time saving, revealed preference, route choice, mixed logit, Japan
INTRODUCTION
The value of travel time saving (VTTS) is widely used for transportation project evaluation.
In many countries, such as the UK (Mackie et al., 2003) and Denmark (Fosgerau et al., 2007),
the national VTTS is estimated mainly with stated-preference (SP) data. Although VTTS
estimation based on SP data may overcome the difficulties associated with revealedpreference (RP) data, the principal drawback of the SP approach is that stated preferences
may not correspond closely to actual preferences (Bonsall, 1983). Thus, VTTS estimation
with RP data could be used to verify SP-based findings. This paper estimates VTTSs using
RP-based travel data from Japan. This paper extends Kato et al. (2011) by carrying out further
analyses. First, VTTSs are estimated with a standard binary logit model for different travel
purposes and different road user subgroups based on age, gender, departure time, and travel
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VALUATION OF TRAVEL TIME SAVING WITH REVEALED PREFERENCE DATA IN
JAPAN: FURTHER ANALYSIS
Hironori Kato, Takanori Oda, and Ayanori Sakashita
distance. Next, VTTS distributions are estimated with a mixed binary logit model based on
travel time and travel cost coefficients in the utility function.
This paper is organized as follows: The next section describes the data used in our empirical
analysis. The VTTS estimation method is then presented based on a standard binary and a
mixed binary logit model in the context of an expressway versus no-expressway route choice.
The estimation results are then explained. The results of the standard logit model are
categorized into individual attribute subgroups and three travel purposes, and those of the
mixed logit model are sorted by distribution function type in travel time and travel cost
coefficients. Finally, the findings are summarized and further issues presented.
DATA
The empirical analysis will use the RP travel data from the 2005 Road Traffic Census Survey
of Japan. This survey was conducted by prefecture governments and major cities under the
supervision of the Ministry of Land, Infrastructure, Transport and Tourism of Japan. The
survey covered the entire nation.
First, the level-of-service data is prepared. If an observed trip made by an individual used a
route including only an ordinary road link, the expressway route of the individual is identified
by searching the minimum-travel-time route in the road network whereas the no-expressway
route of the individual is identified by searching the minimum-travel-time route in the road
network without an expressway link. If an observed trip made by an individual used a route
including both the ordinary road links and the expressway links, the expressway route of the
individual is identified by searching the minimum-travel-time route under the condition that
the observed expressway links are included in the route whereas the no-expressway route of
the individual is identified by searching the minimum-travel-time route in the road network
without the expressway link. The travel time is estimated using the link-based BPR function
while the travel cost is estimated with the link-based toll data. Note that the travel cost does
not include fuel cost and maintenance cost.
Next, the sample-based travel datasets are constructed from the original dataset from this
survey according to the following steps. First, this paper covers home-to-work trips, business
trips, and private trips. It excludes home-to-school and pick-up trips because the sample size
of expressway route choice data for these cases is small compared to other trip types. The
sample sizes of expressway home-to-school and pick-up trips are 31 and 336, respectively,
whereas the number of trips in the home-to-work, business, and private categories are 2,769,
1,125, and 2,080, respectively. This paper also excludes work/school/others-to-home trips
because the original dataset does not distinguish between work-to-home and private- or
business-to-home trips.
Then, intra-zone trips; trips whose origin, destination, vehicle type, or travel purpose is not
available; and trips that include ferry use are eliminated from the original dataset. Intra-zone
trips are excluded because, first, travel data for short-travel-time trips are not sufficiently
accurate and, second, expressway use for intra-zone trips is probably rare. Ferry-use trips are
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VALUATION OF TRAVEL TIME SAVING WITH REVEALED PREFERENCE DATA IN
JAPAN: FURTHER ANALYSIS
Hironori Kato, Takanori Oda, and Ayanori Sakashita
Figure 1–Expressway route choice versus travel time differences between expressway and no-expressway
routes
excluded because our empirical analysis focuses on the choice between expressway and other
routes rather than on modal choice. Trips to and from Shikoku Island are also eliminated from
the dataset because it is connected to Honshu Main Island only by expressway and travelers
cannot choose a no-expressway route.
Finally, trips limited to a no-expressway route choice were eliminated. For example, a person
who resides very far from the expressway or one who travels to a destination very far from
the nearest expressway interchange may not consider the expressway route. Figure 1 plots
expressway route choice rates versus travel time differences between expressway and noexpressway routes. The chart shows that the expressway choice rate increases as travel time
decreases by –20 min to 70 min compared to the no-expressway route, whereas the choice
rate does not vary over time difference ranges of less than –20 min and 70 to 120 min. Choice
rates seem to vary randomly in subgroups with time differences of greater than 120 min,
mainly because the data size of these subgroups is extremely small. The above results suggest
that the expressway is one of the route options when the time difference is between –20 min
and 70 min. Therefore, trips with time differences of less than –20 min or more than 70 min
were eliminated.
As a result of data screening, 146,409 observations were obtained, including 82,068 home-towork, 12,305 business, and 51,555 private trips. Table 1 shows the descriptive statistics of our
dataset. First, 77.2% of home-to-work, 85.6% of business, and 61.5% of private trips are
made by males. These numbers indicate that most car drivers are male. Second, people in
agricultural occupations account for 1.8%, 15.8%, and 5.1% of home-to-work, business, and
private trips, respectively. This reflects the small agricultural population in Japan. Persons
who hold retailing/service jobs undertake 22.5% of home-to-work and 32.3% of business trips.
This means that retailers and/or service workers travel more for business purposes than other
workers do. Third, people in their fifties make 39.0% of business trips. This means that senior
workers travel on business more than young workers do. Those in their sixties make 30.5% of
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VALUATION OF TRAVEL TIME SAVING WITH REVEALED PREFERENCE DATA IN
JAPAN: FURTHER ANALYSIS
Hironori Kato, Takanori Oda, and Ayanori Sakashita
Table 1–Descriptive statistics of the sample dataset
Home to work Obs. Business % Obs. % Private Obs. % Gender Male Female 63516
18746
77.2 22.8 10583
1780
85.6 14.4 31921 19863 61.6 38.4 Job Agriculture Production/Transport Retailing/Service Office work/Tech. Others 1413
15141
17909
30015
15251
1.8 19.0 22.5 37.6 19.1 306
704
4465
3573
2916
2.6 5.9 37.3 29.9 24.4 2181 3075 7677 7809 7553 7.7 10.9 27.1 27.6 26.7 Age 20–29 30–39 40– 49 50–59 60– 11634
17124
20269
24020
7406
14.5 21.3 25.2 29.9 9.2 521
1552
2827
4605
2307
4.4 13.1 23.9 39.0 19.5 3668 6934 8620 11999 13741 8.2 15.4 19.2 26.7 30.6 Departure Time 0000–0659 0700–0759 0800–0859 0900–0959 1000–1059 1100–1159 1200–1759 1800–1959 2000–2359 16262
40331
16445
3848
1263
882
4845
1697
1298
18.7 46.4 18.9 4.4 1.5 1.0 5.6 2.0 1.5 663
1262
2271
3707
3943
3148
13607
755
341
2.2 4.2 7.6 12.5 13.3 10.6 45.8 2.5 1.1 1172 1769 3442 6092 7681 5484 23091 4219 1398 2.2 3.3 6.3 11.2 14.1 10.1 42.5 7.8 2.6 Travel distance (km) 0–10 27120
31.1 10309
34.9 23963 44.2 11–20 33987
38.9 8815
29.9 16627 30.7 21–30 31–40 41–50 51–70 71–100 15070
6138
2607
1743
607
17.3 7.0 3.0 2.0 0.7 4009
2294
1417
1611
1059
13.6 7.8 4.8 5.5 3.6 5950 2964 1716 1836 1148 11.0 5.5 3.2 3.4 2.1 private trips. The fact that retired people have more free time for private trips than the
younger generation does could account for this. Fourth, 47.1% of home-to-work trips
commence during the 0700–0759 time frame. This means that, on average, the time between
0700 and 0800 is the peak hour in Japan. Fifth, trip distances in 70.0% of home-to-work,
64.1% of business, and 75.0% of private trips are 20 km or less. Finally, the correlation
between travel time and cost is 0.765 in expressway trips. This is probably because the
expressway toll is determined on the basis of route distance whereas travel time is highly
dependent on trip distance. This may be one of the unavoidable characteristics of the RPbased dataset.
13th WCTR, July 15-18, 2013 – Rio de Janeiro, Brazil
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VALUATION OF TRAVEL TIME SAVING WITH REVEALED PREFERENCE DATA IN
JAPAN: FURTHER ANALYSIS
Hironori Kato, Takanori Oda, and Ayanori Sakashita
METHODS
Two methods are used for empirical analysis of VTTSs based on the above dataset: a standard
binary logit model and a mixed binary logit model. Both of these are applied to expressway
and no-expressway route choices. Although various specifications could be applied to a utility
function including a non-linear utility function, this paper assumes a linear indirect utility
function with travel time and travel cost as variables. Application of more complex utility
functions and introduction of other variables such as individual attributes, other service
factors, route-specific variables are our further research issues.
Binary Logit
The utility functions of using the expressway and no-expressway routes are assumed to be
Vex, n  TT TTex, n  TC TCex, n   ex, n
(1)
Vno,n  TT TTno,n   no,n
(2)
where Vi , n is the indirect utility function of an individual n who has chosen the route option
i ; TTi , n is the travel time of route option i for individual n ; TCi , n is the travel cost of route
option i for individual n ;  i,n is the error component of route option i for individual n ; TT
is the coefficient of travel time; and TC is the coefficient of travel cost. Route option i  ex
represents the expressway route, and i  no the no-expressway route. As a linear utility
function is assumed, the VTTS is derived as TT TC .
The probability of choosing the route option i in the binary logit is shown as
Pi ,n 

1
1  exp V j ,n  Vi ,n

(3).
Then, the unknown coefficients are estimated by maximizing a log-likelihood with respect to
 as
max LL   max   i ,n  ln Pi ,n  
(4)


where  i,n is equal to 1 if individual n chooses route option i and 0 otherwise.
n
i
Mixed Binary Logit
A mixed logit model can incorporate taste variation with a flexible substitution pattern in a
standard logit model. The probabilities in a mixed logit model are formulated by the integral
of the probability in a standard logit over a density of parameters (McFadden and Train, 2000;
Train, 2003),
(5)
Pi ,n   Li ,n   f  d
where Li ,n   is the standard logit probability of equation (3) and f   is a density function.
We will use a mixed logit model assuming random coefficients. The function f   is specified
to be continuous; in particular, the density of  is normal and triangular. The choice
probability under this assumption is expressed as
Pi , n   Li ,n     d
(6)
13th WCTR, July 15-18, 2013 – Rio de Janeiro, Brazil
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VALUATION OF TRAVEL TIME SAVING WITH REVEALED PREFERENCE DATA IN
JAPAN: FURTHER ANALYSIS
where     is a density function with parameters  . The unknown parameters are estimated
by maximizing the log-likelihood function with respect to  :
max LL   max   i ,n  ln Pi ,n  
(7).
Hironori Kato, Takanori Oda, and Ayanori Sakashita


The value of Pi ,n   is simulated by sampling over  and is given by
SPi ,n   
n
i
1 R
 Lni  r ,  
R r 1
(8)
where R is the number of random draws  r , which is taken from the distribution function of
 . Finally,  is computed as the solution of the simulated log-likelihood maximization
problem as
max SLL   max   i ,n  ln SPi , n  
(9).


n
i
Although the Monte-Carlo method could be utilized to simulate choice probability, its slow
convergence rate is a drawback. Quasi Monte-Carlo approaches have been attempted to speed
up convergence by a more selective approach in the choice of sampling points to evaluate
choice probability. Then, instead of Monte-Carlo random draws, we apply Halton draws
following Bhat (2001), using 125 draws as in Cirillo and Axhausen (2006).
Weighted Likelihood Maximization
The weighted likelihood maximization procedure is used for parameter estimation because the
sampling rate varies among zones in the survey. Let nm be a respondent whose vehicle is
registered at zone m . The weight of the individual, nm , is defined as follows:
 nm 
Hm
Nm
(10)
where N m is the number of respondents at zone m and H m is the number of registered
vehicles at zone m . Then, the maximization of the log-likelihood function in the binary logit
and the mixed binary logit is respectively redefined as
max LLW     nm   i , nm  ln Pi , nm  
(11)


    nm   i, nm  ln SPi, nm  
nm
W
max SLL
nm
i
(12).
i
RESULTS
VTTS Estimation by Traveler Attributes versus Travel Purpose
Table 2 shows estimation results based on the standard binary logit, originally shown in Kato
et al. (2011). The summaries of results are as follows: First, personal VTTSs of home-to-work,
business, and private trips are estimated to be 24.5, 33.9, and 21.0 JPY per min per person,
respectively. The estimated personal VTTS of business trips is nearly equal to or a little lower
than the average wage rate in Japan. Second, the personal VTTS of males is a little higher
than that of females. Third, the estimated personal VTTS when driving alone is equal to 26.6
JPY per min per person while the estimated average personal VTTS when driving with
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VALUATION OF TRAVEL TIME SAVING WITH REVEALED PREFERENCE DATA IN
JAPAN: FURTHER ANALYSIS
Hironori Kato, Takanori Oda, and Ayanori Sakashita
Table 2–Estimation results by travel purpose, gender, passenger, job, age, departure time, and travel distance
based on the standard logit model
Num of Ave. pax. VTTS per Travel time Travel cost Initial LL Final LL
obs. per car person Coeff. 82068 12328 51621 ‐7440.6 105703 ‐1763.3 40314 ‐19161.4 ‐7153.5 ‐21713.9 ‐4603.0 ‐7091.3 122427 ‐2027.6 23590 ‐225.2 ‐968.8 ‐2115.9 ‐589.7 ‐3300.0 ‐5393.6 3880 18811 29925 1.39 1.10 1.17 22.6 21.4 26.4 ‐53.6 ‐42.8 ‐7243.1 ‐4648.5 ‐2626.7 ‐1717.1 41254 25613 1.12 1.20 26.5 24.9 ‐0.007 ‐0.005 ‐0.005 ‐0.005 ‐0.005 ‐30.9 ‐41.9 ‐46.7 ‐53.7 ‐42.3 ‐2595.6 ‐4572.1 ‐5608.1 ‐7245.9 ‐4551.9 ‐709.3 ‐1541.3 ‐1971.1 ‐2743.7 ‐1722.5 15763 25532 31605 40447 23323 ‐28.3 ‐40.1 ‐30.7 ‐24.2 ‐23.0 ‐19.5 ‐39.6 ‐16.1 ‐12.3 ‐0.004 ‐0.006 ‐0.005 ‐0.005 ‐0.005 ‐0.005 ‐0.006 ‐0.006 ‐0.006 ‐36.5 ‐50.6 ‐35.9 ‐28.0 ‐26.8 ‐23.1 ‐46.6 ‐19.8 ‐14.1 ‐3116.2 ‐6682.3 ‐3485.2 ‐1991.8 ‐1858.0 ‐1363.5 ‐5963.9 ‐1111.6 ‐525.9 ‐1499.4 ‐2148.0 ‐1140.5 ‐792.3 ‐699.5 ‐477.6 ‐1854.0 ‐323.6 ‐180.8 16716 40751 19559 10815 9887 7117 31474 5951 2694 ‐18.5 ‐25.7 ‐20.2 ‐15.6 ‐10.5 ‐8.1 ‐6.0 ‐6.7 ‐0.016 ‐0.009 ‐0.005 ‐0.003 ‐0.002 ‐0.001 ‐0.001 ‐0.001 ‐29.3 ‐54.0 ‐40.1 ‐24.4 ‐14.4 ‐9.3 ‐5.8 ‐6.4 ‐10258.1 ‐8987.2 ‐3606.4 ‐1562.8 ‐767.6 ‐412.0 ‐241.5 ‐318.7 ‐577.5 ‐1827.5 ‐1540.2 ‐980.1 ‐609.1 ‐353.6 ‐218.2 ‐291.1 51712 51582 21670 9506 4597 2472 1470 2000 T‐stat. Coeff.
T‐stat.
Travel purpose Home to work Business Private ‐0.153 ‐0.130 ‐0.164 ‐56.9 ‐0.006 ‐27.5 ‐0.003 ‐53.0 ‐0.005 ‐74.1 ‐30.0 ‐60.7 ‐14212.2 ‐2233.3 ‐9868.0 ‐4503.3 ‐1152.3 ‐3357.0 Gender Male Female ‐0.146 ‐0.184 ‐72.8 ‐0.005 ‐39.2 ‐0.007 ‐88.0 ‐48.4 Passengers Drive alone Drive with passenger(s) ‐0.158 ‐0.142 ‐73.5 ‐0.006 ‐38.7 ‐0.004 ‐91.3 ‐42.7 Job Agriculture Production/Transport Retailing/Service ‐0.149 ‐0.153 ‐0.147 ‐12.8 ‐0.005 ‐26.2 ‐0.006 ‐39.2 ‐0.005 ‐15.5 ‐35.3 ‐46.5 Office work/Tech. Others ‐0.155 ‐0.149 ‐44.4 ‐0.005 ‐35.3 ‐0.005 Age 20–29 30–39 40–49 50–59 60–69 ‐0.155 ‐0.160 ‐0.159 ‐0.149 ‐0.149 ‐22.9 ‐34.5 ‐39.1 ‐44.5 ‐36.2 Departure time 0000–0659 0700–0759 0800–0859 0900–0959 1000–1059 1100–1159 1200–1759 1800–1959 2000–2359 ‐0.115 ‐0.165 ‐0.170 ‐0.148 ‐0.152 ‐0.150 ‐0.169 ‐0.173 ‐0.151 Travel distance (km) 0–10 11–20 21–30 31–40 41–50 51–60 61–70 71–100 ‐0.223 ‐0.128 ‐0.087 ‐0.074 ‐0.053 ‐0.051 ‐0.044 ‐0.042 JPY/min 1.04 1.13 1.53 24.5 33.9 21.0 1.21 1.24 24.7 21.9 27.6 15.4 1.00 2.35 1.15 1.22 1.15 1.16 1.35 20.1 23.8 26.2 25.8 22.8 1.07 1.05 1.12 1.33 1.43 1.44 1.40 1.33 1.26 24.5 26.8 28.3 24.6 22.6 20.7 21.8 22.1 21.5 1.23 1.17 1.17 1.23 1.32 1.40 1.50 1.60 11.4 12.1 14.4 18.4 20.0 25.1 27.3 28.1 Source: Kato et al. (2011)
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VALUATION OF TRAVEL TIME SAVING WITH REVEALED PREFERENCE DATA IN
JAPAN: FURTHER ANALYSIS
Hironori Kato, Takanori Oda, and Ayanori Sakashita
Table 3–Estimated VTTSs of individual attribute subgroups categorized by travel purpose (JPY/min per person)
Home to work Business Private Gender Male 24.4 (25.3)
34.1 (38.6)
21.4 (33.6)
Female 23.8 (24.6)
29.4 (33.5)
19.5 (28.3)
Drive alone 25.0 (25.0)
37.3 (37.3)
28.4 (28.4)
Drive with passenger(s) 14.1 (32.5)
19.8 (45.5)
15.2 (35.9)
Agriculture Production/Transport Retailing/Service Office work/Tech. 24.9 (26.3)
20.3 (20.6)
24.7 (25.6)
25.6 (26.3)
20.5 (33.5)
32.2 (36.7)
34.9 (38.7)
36.4 (41.4)
24.9 (26.3)
20.6 (30.4)
23.5 (35.4)
22.4 (32.7)
Others 25.8 (27.1)
29.4 (34.0)
21.5 (32.3)
Age 20–29 30–39 40–49 50–59 19.3 (19.7)
25.4 (26.2)
26.6 (27.4)
24.3 (25.3)
19.8 (34.4)
32.9 (38.0)
34.1 (38.3)
34.8 (39.1)
17.2 (26.5)
18.5 (31.8)
22.0 (31.8)
24.4 (34.6)
60– 22.8 (24.1)
33.2 (37.5)
21.4 (33.1)
0000–0659 0700–0759 0800–0859 0900–0959 1000‐1059 1100–1159 1200‐1759 1800–1959 22.3 (23.1)
25.0 (25.7)
26.0 (27.0)
25.2 (26.4)
29.8 (31.8)
26.0 (27.2)
22.5 (23.7)
27.9 (29.0)
35.3 (44.2)
37.8 (44.5)
35.8 (42.2)
36.2 (39.9)
31.6 (34.9)
39.0 (42.9)
30.3 (34.2)
27.3 (31.6)
28.7 (43.6)
26.7 (40.0)
24.0 (35.6)
20.9 (32.4)
20.4 (31.6)
16.2 (25.5)
19.7 (30.1)
19.9 (29.0)
2000–2359 24.0 (25.3)
38.8 (45.9)
18.7 (27.4)
0–10 13.8 (14.4)
25.7 (28.7)
7.3 (10.6)
11–20 21–30 31–40 41–50 51–60 61–70 13.3 (13.7)
16.0 (16.5)
20.2 (21.2)
22.9 (23.9)
31.6 (33.6)
33.1 (35.2)
15.6 (17.4)
14.4 (16.1)
27.2 (30.8)
31.5 (36.9)
36.0 (44.3)
47.9 (60.0)
9.4 (13.8)
11.5 (17.7)
13.7 (23.0)
15.3 (27.4)
18.3 (33.5)
21.3 (41.1)
71–100 36.7 (42.2)
46.9 (56.2)
21.5 (43.1)
Passengers Job Departure time Travel distance (km) Note: Values in parentheses are estimated VTTSs per vehicle. passengers is equal to 15.4 JPY per min per person. The difference between the driver’s and
the passengers’ VTTS is probably due to the fact that the driver experiences less fatigue from
driving time saved while passengers will not since they do not drive the car. Fourth, the
personal VTTS of production/transport workers is apparently lower than the estimated
personal VTTSs of other jobs. Fifth, the estimated personal VTTS of individuals in their
twenties is the lowest among all age groups and is followed by that of individuals in their
sixties. Sixth, the estimated personal VTTS of trips starting between 0800 and 0859 is the
highest in a day, followed by that of trips between 0700 and 0759 and between 0900 and 0959.
VTTSs between 0700 and 0959 are higher because traffic congestion is serious during the
morning peak hours. Finally, the longer the travel distance, the higher is the estimated
personal VTTS. This study examines the characteristics of VTTSs in more detail with the
13th WCTR, July 15-18, 2013 – Rio de Janeiro, Brazil
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VALUATION OF TRAVEL TIME SAVING WITH REVEALED PREFERENCE DATA IN
JAPAN: FURTHER ANALYSIS
Hironori Kato, Takanori Oda, and Ayanori Sakashita
standard logit model using the same dataset. Table 3 shows the estimated VTTSs of
individual attribute subgroups categorized by travel purpose.
First, the estimated personal VTTSs for home-to-work, business, and private trips are 24.4,
34.1, and 21.4 JPY per min per person, respectively, for males and 23.8, 29.4, and 19.5 JPY
per min per person, respectively, for females. Personal VTTSs for business and home-to-work
trips are, respectively, about 16% and 2.5% higher for males. The difference for business
VTTSs reflects the wage rate difference between males and females in Japan. The lower
difference for home-to-work VTTS is probably because both male and female commuters
experience the same disutility with respect to travel time, such as traffic congestion during
morning peak hours, and the same arrival time constraint in home-to-work trips; thus, these
factors influence the VTTS more than the wage rate does.
Second, the estimated personal VTTSs of home-to-work, business, and private trips are 25.0,
37.3, and 28.4 JPY per min per person, respectively, for driving alone and 14.1, 19.8, and
15.2 JPY per min per person, respectively, for driving with passengers. Personal VTTSs of
driving with passengers are lower than those of driving alone by 43.6%, 46.9%, and 46.5%
for home-to-work, business, and private trips, respectively. These figures may mean that the
personal VTTS ratio of driving with passengers to driving alone is not much affected by
travel purpose. These results may also show that the ratio of VTTSs of car passenger to that
of drivers is much lower than that of other studies such as Fosgerau et al. (2007) showing that
the value of travel time is 20% to 35% lower for a car passenger than the car driver.
Third, the personal VTTSs for business trips in production/transport, retailing/service, office
work/technology, and others are higher than those for other travel purposes in the
corresponding type of job. In agriculture, the personal VTTS for business trips is lower than
those for other travel purposes. These differences may reflect the number of passengers in a
car. In business trips, the average number of passengers, including the driver, is 1.63 for
agriculture and 1.14 for production/transport, 1.11 for retailing/service, 1.14 for office
work/technology, and 1.16 for others. Our results suggest generally that the more passengers,
the lower the average personal VTTS.
Fourth, among different age subgroups, business and private personal VTTSs are highest for
individuals in their fifties, followed by those in their forties. This reflects the fact that people
in their forties and fifties have a higher wage rate than others. Home-to-work personal VTTS
is highest for those in their forties, followed by those in their thirties. One of the possible
reasons is that workers in their thirties and forties are middle-level managers, such as a subchief or vice manager working under a manager, who are strictly required to arrive at the
workplace before the given start time of work; this leads to higher willingness-to-pay for
saving commuting time. In the Japanese labor market, people in their twenties are less
responsible in their commuting behavior, while those in their fifties or older are managers
with more flexible commuting options.
Fifth, the relationships between departure time and estimated personal VTTS may not be well
characterized. The estimated home-to-work personal VTTS is highest for trips starting
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Hironori Kato, Takanori Oda, and Ayanori Sakashita
between 1000 and 1059, probably because individuals with higher income, such as managers
and employers, tend to commute after the morning peak hours. Trips commencing between
1800 and 1959 yield the second highest VTTS in this category, perhaps because those who
work in the evening and night hours tend to have a higher wage rate than daytime workers.
The estimated personal VTTS is highest for business trips starting between 1100 and 1159. A
possible reason is that the most valuable business activities are typically implemented just
after lunchtime and many businesspersons commence their business trips before lunch for
after-lunch business activities. The 1100 to 2359 time slot follows, with the second highest
VTTS for business trips, probably because those who work in the evening tend to have a
higher wage rate than daytime workers. Finally, the estimated personal VTTS for private trips
starting between 0000 and 0659 is the highest in a day, followed by those between 0700 and
0759. This may reflect the fact that leisure trips commencing early in the morning have a
higher value than those starting at other times because individuals can enjoy longer leisure
time in a day.
Sixth, the estimated personal VTTSs of home-to-work and private trips increase with travel
distance. For business trips, however, they decline from 0 to 30 km but increase from 30 to
100 km as the travel distance increases. The different characteristics of urban and inter-urban
trips may account for business trips’ U-shaped VTTS over travel distance. The marginal
utility with respect to travel time may decrease as travel time increases in urban business trips
in which the time constraint is not very serious. However, the marginal utility starts increasing
when the travel time is longer because less time is allocated to leisure activities, which results
in higher marginal utility of leisure time in a condition of declining marginal utility with
respect to the leisure time. However, as similar results have not been reported so far in other
countries, we are afraid the estimated VTTS for business trips of less than 10 km might be an
anomaly.
Finally, we calculate the distance elasticities of VTTS with the estimated results using the
equation
VTTS  DIST 
(13)
where VTTS is the personal VTTS (JPY/min) and DIST the travel distance (km). The
representative distances of the travel distance subgroups are the means of subgroup
bandwidths: 5, 15, 25, 35, 45, 55, 65, and 80 km. Figure 2 shows regression curves of the
estimated VTTS distributions versus distance by travel purpose. The figure shows that
distance elasticities of VTTS are estimated to be 0.36 for all travel purposes, 0.39 for hometo-work trips, 0.77 for business trips, and 0.41 for private trips. Note that VTTS estimates for
trips of 10 km or less are eliminated from business VTTS estimation for the reason mentioned
earlier. Thus, the results imply that the distance elasticity of inter-urban business trips is much
higher than that of other trips. From meta-regression analysis in the UK, VTTS distance
elasticity is estimated to be 0.08 for leisure trips and 0.45 for business trips (Abrantes and
Wardman, 2011). By comparison, our distance elasticity based on RP data is slightly higher.
One of the possible reasons is that our estimation does not include fuel cost in travel cost data.
Longer-distance travelers tend to prefer the expressway route partly because the average fuel
cost is lower—fuel is consumed more efficiently in the expressway than in the no-expressway
route. This means the difference between the actual expressway and no-expressway route
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VALUATION OF TRAVEL TIME SAVING WITH REVEALED PREFERENCE DATA IN
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Hironori Kato, Takanori Oda, and Ayanori Sakashita
Figure 2–Travel distance vs. estimated personal VTTSs by travel purpose
costs, where both expressway charge and fuel cost are considered, is smaller than the
calculated cost difference, in which only the expressway charge is reflected. Thus, the
estimated willingness-to-pay may be a little biased, and larger than the actual willingness-topay. As incorporating fuel cost in the route-choice model could require additional data such as
fuel consumption by travel speed, by acceleration/deceleration pattern, and by vehicle type, it
may be quite difficult to do so. Thus we have identified this issue as a topic for further
research.
Estimated VTTSs for Peak and Off-peak Hours
People who travel in congested road networks expectedly experience unreliable travel times.
A number of recent studies have analyzed the reliability of transportation services. Two
different approaches have been proposed to model travel-time variability effects so as to allow
for economic analysis (Hollander, 2006). The first approach is the mean-variance approach, in
which it is assumed that travelers see travel-time variance as a direct source of inconvenience
(e.g., Jackson and Jucker, 1982; Senna, 1994). The second approach is the scheduling
approach, which considers that the entire utility attributed to travel-time variation can be
captured by modeling travelers’ earliness and lateness considerations when choosing the time
at which they depart for their journey (e.g., Bates et al., 1995; Small et al., 1999). Although
these studies highlight travel-time variability in their modeling approach, the value of
reliability may be captured in a more simple way, by a comparison of VTTSs under reliable
versus unreliable conditions. Accordingly, this study simply compares estimated VTTSs of
trips made during congested versus less congested hours. The original dataset of the 2005
Road Traffic Census Survey contains peak-hour and off-peak-hour travel data. “Peak hours”
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Hironori Kato, Takanori Oda, and Ayanori Sakashita
implies that the departure time is in peak hours of the day, while “off-peak hours” mean the
departure is during off-peak hours. Peak hour is defined as hours in which the traffic volume
in an origin zone of a road network is the highest in a day.
Table 4 summarizes the estimated VTTSs in peak and off-peak hours by travel purpose based
on the peak/off-peak VTTS ratio. This shows that the estimated personal VTTS of trips
starting during the peak hours is higher than that of trips during off-peak hours by 21.9% for
all travel purposes, 9.5% for home-to-work trips, 13.3% for business trips, and 13.8% for
private trips. These differences are caused mainly by the unreliability of travel time in
congested traffic. Calfee et al. (2001) found that VTTSs in congested traffic are valued 200%
higher than in free-flow conditions whereas Abay et al. (2003) found the time values in
congested traffic to be only 25% higher. Wardman and Ibanez (2012) show evidences of the
congestion multiplier in the UK and the US. The preferred time relatives are 1.14 for busy,
1.23 for light congestion, 1.31 for heavy congestion, 1.45 for stop-start, and 1.78 for gridlock
in the UK, whereas they are 1.02 for busy, 1.05 for light congestion, 1.21 for heavy
congestion, 1.43 for stop-start, and 1.60 for gridlock in the US. As exact peak hour traffic
conditions are not available in our database, our results cannot be directly compared with UK
and US results.
Table 4–Estimated peak/off-peak VTTSs per person by travel purpose (JPY/min)
All Home to work Business purposes Off‐peak 21.9 (28.9) 23.1 (24.2) 32.3 (36.5)
Private 20.3 (31.0) Peak 26.7 (29.7) 25.3 (26.1) 36.6 (41.8)
23.1 (35.2) Peak/Off‐peak ratio 1.22 (1.03) 1.10 (1.13) 1.13 (1.15)
1.14 (1.14) Note: Values in parentheses are estimated VTTSs per vehicle. Distributions of VTTSs Estimated with the Mixed Logit Model
Table 5 shows the estimation results of route choice by travel purpose based on the mixed
logit, incorporating random coefficients of utility functions. Two types of distributions
(normal and triangular) plus fixed are applied to the coefficients of travel time and travel cost;
thus a total of nine patterns of random coefficients models, including a standard logit model,
are estimated for home-to-work, business, and private trips.
First, the final log-likelihood (LL) is lowest in the standard logit (fixed-fixed) for all travel
purposes; the final LLs in the mixed logit models are higher. This is reasonable because the
mixed logit model with randomized coefficients could reflect taste variation among
individuals more appropriately. Second, the coefficients of mean travel time and travel cost
are estimated to be significantly negative in all models. The coefficients of variation are also
significant in all models. Third, the estimated coefficients do not seem to differ very much
among the models, except for the standard logit (fixed-fixed) model, in which the absolute
values of coefficients are significantly lower than in other models. Fourth, among the hometo-work and business trip models, the triangular-triangular model has the highest final LL,
followed by the normal-triangular model, and then the triangular-normal model. Among the
models for private trips, the triangular-triangular model has the highest final LL, followed by
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VALUATION OF TRAVEL TIME SAVING WITH REVEALED PREFERENCE DATA IN
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Hironori Kato, Takanori Oda, and Ayanori Sakashita
Table 5–Estimation results based on the mixed logit model by travel purpose
Travel time Travel cost Travel time (mean) (mean) (var.) Dist. of TT Dist. of TC Coeff. t‐stat. Coeff. t‐stat. Coeff. t‐stat. Home‐to‐work (N=82,068, Initial LL=‐14212) Normal Normal ‐0.65 ‐11.2 ‐0.047 ‐10.7 0.36 8.8 Normal Triangular ‐0.50 ‐14.8 ‐0.039 ‐14.5 0.26 9.8 Normal Fixed ‐0.23 ‐36.6 ‐0.009 ‐48.1 0.17 25.5 Triangular Normal ‐0.65 ‐14.1 ‐0.033 ‐14.6 0.33 13.3 Triangular Triangular ‐0.65 ‐10.3 ‐0.052 ‐9.9 0.91 7.9 Triangular Fixed ‐0.23 ‐37.0 ‐0.009 ‐49.9 0.41 26.4 Fixed Normal ‐0.36 ‐20.7 ‐0.030 ‐17.5 n.b.d n.b.d Fixed Triangular ‐0.35 ‐20.8 ‐0.033 ‐16.8 n.b.d n.b.d Fixed Fixed ‐0.15 ‐56.9 ‐0.006 ‐74.1 n.b.d n.b.d Business (N=12,328, Initial LL=‐2233) Normal Normal ‐0.71 ‐5.5 ‐0.029 ‐5.4 0.42 4.6 Normal Triangular ‐0.60 ‐6.3 ‐0.026 ‐6.2 0.34 4.9 Normal Fixed ‐0.23 ‐15.3 ‐0.006 ‐19.2 0.19 10.4 Triangular Normal ‐0.50 ‐5.4 ‐0.043 ‐5.5 0.31 5.2 Triangular Triangular ‐0.70 ‐5.4 ‐0.030 ‐5.3 1.03 4.6 Triangular Fixed ‐0.23 ‐16.0 ‐0.006 ‐20.2 0.44 11.5 Fixed Normal ‐0.39 ‐8.5 ‐0.020 ‐7.0 n.b.d n.b.d Fixed Triangular ‐0.38 ‐9.1 ‐0.020 ‐7.3 n.b.d n.b.d Fixed Fixed ‐0.13 ‐27.5 ‐0.003 ‐30.0 n.b.d n.b.d Private (N=51,621, Initial LL=‐9868) Normal Normal ‐0.64 ‐13.1 ‐0.035 ‐12.9 0.36 10.3 Normal Triangular ‐0.62 ‐12.5 ‐0.036 ‐12.9 0.34 9.0 Normal Fixed ‐0.30 ‐29.6 ‐0.009 ‐36.2 0.22 22.1 Triangular Normal ‐0.52 ‐14.5 ‐0.034 ‐14.7 0.27 13.1 Triangular Triangular ‐0.65 ‐12.6 ‐0.035 ‐13.1 0.99 9.9 Triangular Fixed ‐0.31 ‐29.6 ‐0.009 ‐37.3 0.54 23.0 Fixed Normal ‐0.39 ‐19.8 ‐0.026 ‐15.9 n.b.d n.b.d Fixed Triangular ‐0.37 ‐20.6 ‐0.027 ‐16.2 n.b.d n.b.d Fixed Fixed ‐0.16 ‐53.0 ‐0.005 ‐60.7 n.b.d n.b.d Travel cost Final LL
(var.) Coeff. t‐stat. 0.024 10.2 ‐2863 0.051 13.8 ‐2835 n.b.d n.b.d ‐3952 0.045 10.8 ‐2858 0.069 9.5 ‐2823 n.b.d n.b.d ‐3963 0.016 16.0 ‐2964 0.045 15.7 ‐2908 n.b.d n.b.d ‐4503 0.018 5.2 ‐812 0.039 5.8 ‐809 n.b.d n.b.d ‐1024 0.062 4.8 ‐811 0.045 5.1 ‐806 n.b.d n.b.d ‐1025 0.014 6.4 ‐843 0.033 6.7 ‐836 n.b.d n.b.d ‐1152 0.019 12.2 ‐2041 0.048 12.3 ‐2018 n.b.d n.b.d ‐2785 0.044 10.5 ‐2044 0.046 12.5 ‐2017 n.b.d n.b.d ‐2796 0.015 14.5 ‐2151 0.038 15.1 ‐2111 n.b.d n.b.d ‐3357 Note: n.b.d means not available by definition. the normal-triangular model and then the normal-normal model. These results indicate that
models with a triangular distribution of coefficients tend to have a higher log-likelihood than
other types.
Then, using the estimation results shown in Table 5, distributions of VTTSs per vehicle are
calculated by simulations (Hensher and Greene, 2003). From these distributions, we generate
50,000 draws of travel time and travel cost coefficients and calculate the ratios over the two
values. Then, VTTS distributions over those draws are computed. The mean is also calculated
based on the 50,000 draws after two percentiles, the highest and lowest, are removed from the
VTTS distribution so most of the sample distribution is reflected, according to Cirillo and
Axhausen (2006). As presented by Daly et al. (2012), the above models do not yield finite
moments in the VTTS distribution. This means that the VTTS could be infinitely high or low
when the cost coefficient is nearly equal to zero in simulations. Thus we assume that the
distributions of the cost coefficient are bounded away from zero in computing VTTSs.
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VALUATION OF TRAVEL TIME SAVING WITH REVEALED PREFERENCE DATA IN
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Hironori Kato, Takanori Oda, and Ayanori Sakashita
Table 6 summarizes the distributions of coefficients and VTTSs per vehicle computed from
the 50,000 random draws. Figures 3, 4, and 5 illustrate the distributions of VTTSs per vehicle
for eight models of home-to-work, business, and private trips, respectively. First, over 75% of
the computed coefficients of travel time and travel cost are non-positive for all models. The
triangular-normal models have a significantly lower share of negative travel cost coefficients
in the population; they produce a significantly higher share of negative VTTSs in the
population, which leads to lower VTTS means for all travel purposes.
Second, 0% to about 25% of the VTTSs computed have negative values. VTTSs below or
equal to zero account for, on average, a share of 9.1 for home-to-work, 12.0 for business, and
9.6 for private trip models. Although we had expected the share of negative VTTS for private
trips to be higher than those for business and home-to-work trips, the share of private trips is
the lowest among them.
Third, the normal-normal models yield, on average, VTTSs of 17.2 JPY per min per vehicle
for home-to-work, 28.5 JPY per min per vehicle for business, and 22.9 JPY per min per
vehicle for private trips; travel time coefficients are negative for over 95% and travel cost
coefficients for over 94% of the population for all travel purposes. VTTSs are zero or
negative for 6.1% of the population for home-to-work, 9.5% for business, and 6.1% for
private trips. These results are similar to those shown by earlier studies, including Cirillo and
Axhausen (2006) showing 10% and Algers et al. (1998) indicating 11% of negative VTTSs in
the population.
Fourth, triangular-triangular models, which have the highest final LL among different
distributions, produce an average VTTS of 16.2 JPY per min per vehicle for home-to-work,
27.6 JPY per min per vehicle for business, and 24.1 JPY per min per vehicle for private trips;
travel time and travel cost coefficients are negative for over 94% of the population for all
travel purposes. VTTSs are zero or negative for 6.9% of the population for home-to-work,
10.0% for business, and 8.2% for private trips.
Finally, both the normal-normal and triangular-triangular models yield lower VTTS means
than the fixed-fixed models for all travel purposes. The ratios of the average normal-normal
model VTTS to the standard logit model VTTS are 0.68 for home-to-work, 0.75 for business,
and 0.71 for private trips. The ratios of the average triangular-triangular model VTTS to the
standard logit model VTTS are 0.64 for home-to-work, 0.73 for business, and 0.75 for private
trips. One of the possible reasons for this is because we assumed that the distributions of the
cost coefficient are bounded away from zero in computing VTTSs to avoid the infinite
estimates of VTTSs. This assumption could also eliminate the extremely high VTTSs; and
this may lead to lower means of VTTSs than the real ones. To overcome the potentially
biased results, we may need to apply another approach including the use of a finite mixture
model, the non-parametric estimation procedures, and re-parameterization in VTTS space as
suggested by Daly et al. (2012) and Train and Weeks (2005).
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VALUATION OF TRAVEL TIME SAVING WITH REVEALED PREFERENCE DATA IN JAPAN: FURTHER ANALYSIS
Hironori Kato, Takanori Oda, and Ayanori Sakashita
Table 6–Distributions of coefficients and VTTSs per vehicle computed from 50,000 random draws with the estimation results
Distribution of VTTS per vehicle (JPY/min) VTTS per vehicle: Distribution of Distribution of Beta TT: below and Beta TC: below and below and including Beta TT Beta TC including zero (%) including zero (%) 5% 25% Median 75% 95% zero (%) Home‐to‐work Normal Normal Normal Triangular Triangular Triangular Fixed Fixed Fixed Business Normal Normal Normal Triangular Triangular Triangular Fixed Fixed Fixed Private Normal Normal Normal Triangular Triangular Triangular Fixed Fixed Fixed Normal Triangular Fixed Normal Triangular Fixed Normal Triangular Fixed 96.5 97.3 90.4 100.0 95.8 90.3 100.0 100.0 100.0 Normal Triangular Fixed Normal Triangular Fixed Normal Triangular Fixed 95.4 96.1 88.7 100.0 95.0 88.6 100.0 100.0 100.0 Normal Triangular Fixed Normal Triangular Fixed Normal Triangular Fixed 94.7 94.3 100.0 75.4 94.5 100.0 92.4 92.3 100.0 96.5 96.3 91.4 100.0 94.1 90.7 100.0 100.0 100.0 6.1 ‐1.4 7.4 13.3 22.3 60.3 5.5 ‐0.5 6.9 12.1 20.7 65.4 9.6 ‐6.4 12.3 25.1 38.1 56.7 23.6 ‐84.7 4.2 10.3 21.0 94.4 6.9 ‐1.4 6.1 11.8 20.6 64.5 9.7 ‐5.8 12.4 26.1 39.5 57.7 3.2 5.7 8.5 11.6 17.7 49.5 3.4 5.0 7.3 10.1 15.8 53.2 0.0 n.b.d. n.b.d. n.b.d. n.b.d. n.b.d. 9.5 ‐24.8 10.8 21.7 38.9 123.2 9.1 ‐43.8 10.7 20.7 37.3 129.6 11.4 ‐14.2 17.3 39.4 61.9 93.3 24.6 ‐46.9 1.7 5.7 11.6 51.9 10.0 ‐35.9 10.0 21.1 38.2 131.9 11.4 ‐12.2 17.8 40.8 63.5 93.9 7.6 ‐58.9 12.0 17.5 28.9 98.0 7.7 ‐74.5 11.3 16.7 27.6 101.6 0.0 n.b.d. n.b.d. n.b.d. n.b.d. n.b.d. 6.1 ‐1.7 9.6 17.5 29.6 82.6 6.6 ‐2.3 8.8 16.1 28.2 88.3 8.6 ‐6.7 16.7 33.4 49.8 73.1 22.8 ‐66.6 3.7 8.4 16.7 72.6 8.2 ‐3.5 8.6 17.4 30.8 96.3 9.3 ‐6.7 16.7 34.6 52.2 75.8 3.9 6.5 10.1 14.0 21.7 63.1 4.0 6.2 9.1 12.8 20.4 69.6 0.0 n.b.d. n.b.d. n.b.d. n.b.d. n.b.d. 97.2 97.1 100.0 76.4 97.1 100.0 96.8 96.6 100.0 97.2 96.8 100.0 77.2 97.4 100.0 96.1 96.0 100.0 Note: n.b.d means not available by definition. 13th WCTR, July 15-18, 2013 – Rio de Janeiro, Brazil
15
Mean 17.2 16.6 25.2 8.7 16.2 26.0 15.1 13.7 25.3 28.5 26.9 39.6 4.7 27.6 40.7 21.3 19.8 38.2 22.9 22.0 33.3 7.0 24.1 34.5 18.2 17.2 32.2 VALUATION OF TRAVEL TIME SAVING WITH REVEALED PREFERENCE DATA IN JAPAN: FURTHER ANALYSIS
Hironori Kato, Takanori Oda, and Ayanori Sakashita
Figure 3–Distributions of VTTSs per vehicle by combination of distributions of time and cost coefficients computed from 50,000 random draws (home-to-work trips)
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VALUATION OF TRAVEL TIME SAVING WITH REVEALED PREFERENCE DATA IN JAPAN: FURTHER ANALYSIS
Hironori Kato, Takanori Oda, and Ayanori Sakashita
Figure 4–Distributions of VTTSs per vehicle by combination of distributions of time and cost coefficients computed from 50,000 random draws (business trips)
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VALUATION OF TRAVEL TIME SAVING WITH REVEALED PREFERENCE DATA IN JAPAN: FURTHER ANALYSIS
Hironori Kato, Takanori Oda, and Ayanori Sakashita
Figure 5–Distributions of VTTSs per vehicle by combination of distributions of time and cost coefficients computed from 50,000 random draws (private trips)
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VALUATION OF TRAVEL TIME SAVING WITH REVEALED PREFERENCE DATA IN
JAPAN: FURTHER ANALYSIS
Hironori Kato, Takanori Oda, and Ayanori Sakashita
CONCLUSIONS
This paper presented the latest estimation results of the value of travel time savings for
Japanese road users. It used RP travel data from the nationwide 2005 Road Traffic Census
Survey of Japan. A standard binary logit and a mixed binary logit model were used in the
context of an expressway versus no-expressway route choice for subgroups based on gender,
number of passengers, type of job, age, departure time, and travel distance categorized into
three travel purposes: home-to-work, business, and private. Distance elasticities of VTTS are
also estimated. The mixed logit models assumed parametric distributions—normal and
triangular—of travel time and cost coefficients. The results showed that the mean VTTSs
produced from the estimated mixed logit models are lower than VTTSs estimated with the
standard logit models for all travel purposes.
Further research issues are as follows: First, this study applied the simplest form of linear
utility function consisting of only travel time and cost, but factors other than travel time and
travel cost may significantly influence VTTSs. Other types of utility functions incorporating
individual attributes, as well as non-linear utility functions, should be explored. Second, this
study used only normal and triangular distributions for travel time and cost coefficients in the
mixed logit models. Model estimation with other types of distributions, such as censored logit
and log-normal distributions, should also be tried. Third, a comparison between RP and SP
data-based VTTS estimation may be valuable. Additionally, VTTS estimation could be
updated using, for example, data from the 2010 Road Traffic Census Survey of Japan, which
were not available at the time of this study. Thus, the temporal change in VTTS could be
analyzed by a comparison of the 2005 and 2010 estimates.
ACKNOWLEDGEMENTS
This study was financially supported by a research project of the Japan Road Technology
Convention under the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) of
Japan. The authors thank Professor Takayuki Ueda (University of Tokyo), Professor Kazusei
Kato (Nihon University), Professor Masayoshi Tanishita (Chuo University), Dr. Yuichi Mohri
(Institute of Behavioral Science), and Mr. Katsumi Uesaka (MLIT, Japan) for their support.
We are also grateful to Mr. Tetsuya Tsujima (University of Tokyo), who performed the
analysis of the mixed logit models.
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Abrantes, P.A.L. and Wardman, M.R. (2011) Meta-analysis of UK values of travel time: An update,
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13th WCTR, July 15-18, 2013 – Rio de Janeiro, Brazil
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VALUATION OF TRAVEL TIME SAVING WITH REVEALED PREFERENCE DATA IN
JAPAN: FURTHER ANALYSIS
Hironori Kato, Takanori Oda, and Ayanori Sakashita
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13th WCTR, July 15-18, 2013 – Rio de Janeiro, Brazil
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