ROLE OF WAITING TIME, COMFORT, AND CONVENIENCE IN MODAL CHOICE FOR WORK TRIP Staffan Algers , Stockholm County Council; Stein Hansen, Mtfre og Roms dal Regional College, Norway; and GPran Tegner Joint P lanning Board of Stockholm

This paper is concerned with the development and use of a policy-oriented, disaggregated behavioral choice model for transportation planning prob­lems and emphaslzes the impacts of changes in tr avel comfort, convenienc e, and waiting times. The econometric method chosen was logit analysis, and, in that the logit model can be derived from demand or choice, we can inter­pret logit coefficients as trade-off values. The model is based on survey data for commuters' work trips in the Stockholm metropolitan area in 1968 and 1971. It contains choice variables, socioeconomic variables, and trans­portation policy variables. The most important choice variable is "the use of car for work," which supposedly restricts the possibility for public transit use. The socioeconomic variable is income. The transportation policy variables are t r avel time, travel cost, and the chance of getting a seat. A dummy variable technique is applied to the binary-choice appr oach so that stratification of the choice situation for different types of transit modes within the same model is possible. This allows for differences among the various transit modes in relation to comfort. The survey data are subdi­vided into two income groups to test the hypothesis that the value of time and comfort depends on the level of income. Results from our research are of particular interest when suggested changes in public transportation are compared with each other or with the do-nothing alternative.

•BY MEANS of a questionnaire, the greater Stockholm Labor Force survey of 1968 (AKU-68) presented an excellent opportunity to analyze the greater Stockholm com­muting pattern at the disaggregate l evel. T he Stockholm ar ea trans portation study of 1971 (TU-71) gave us an opportunity to further investigate the unexplored field of evaluation of comfort and convenience at a disaggregate behavioral level. Based on these surveys, a number of special studies were carried out at the Joint Planning Board of Stockholm and the Stockholm County Counci.l in 1970 to 1974 @ ).

This paper is concerned with the application of a disaggregate binary-choice model for the commuters' modal choices reported in AKU-68 and TU-71 and places special emphasis on the impacts of changes in travel comfort, convenience, and transit waiting times.

Our reasons for this choice of emphasis are simple: Research on travel choice has for the past 10 years been concerned with the value of time savings and estimates of time and cost elasticities. The role of comfort and convenience was always referred to as important but rarely was incorporated explicitly as a policy-oriented variable in econometric models.

The first major contributions to empirically clarify travelers' rankings (and, im­plicitly, their relative evaluation) of various transportation system improvements were carried out by sociopsychological research techniques such as paired comparison and semantic scaling. Studies along these lines strongly suggested that comfort and con­venience play a major role when travelers evaluate alternative means of travel.

38

Gustafson, Curd, and Golob (7) found that the following characteristics seem to be more important than lower fares; in order of importance these are

1. Arriving when planned, 2. Having a seat, 3. No transfer trip, 4. Calling without delay, 5. Shelters at pickup, 6. Less waiting time, and 7. Choosing pickup time.

39

All these factors are concerned with time savings or improvements in comfort and con­venience. This indicates what variables to concentrate on in trying to divert automobile users to public transportation, although it should be emphasized that the above ranking depends on the levels of service experienced by those who were interviewed.

In addition to these empirical findings, theoretical developments took place in dis­aggregate choice analyses. DeDonnea (5) modeled travel demand so that a value of time savings would result from an algebraic model of the "economic allocation of time" as contrasted to preceding theoretical models in this field, in which the time values derived contained the composite value of the time savings and the corresponding change in comfort and convenience.

He therefore concluded that an ideal, econometric modal-choice model should ex­press modal choice as a function of three types of explicatory transpor tation policy variables (~): (a) the cost of the trip by each mode, (b) the time of the t rip by each mode, and (c) the objective comfort characteristics of each mode.

Independently, DeSerpa (~) derived similar results. McGillivray (.!E.) used a s imilar theoretical approach to show the relationship between the modern microeconomics of choice behavior and the econometric binary-choice models (logit, probit, and linear probability function). This has been considered the theoretical justification for deriving money values on nonmarket factors such as time, comfort, and convenience from such statistical choice models.

Another approach or iginally introduced by Luce (12) was fu rther developed by Marschack (ll), McFadden (ill, and Charles River Associates (4). This showed that the logit model of choice analysis can be derived from probabilistic utility theory.

One may therefore conclude that applying the logit model to analyze individual travel choices is compatible with the theory of demand, and trade-off values .may consequently be derived.

Both the algebraic and the probabilistic point of departure rest on some strong as­sumptions regarding separability or independence of irrelevant alternatives (g_, i, fil, but the lack of better alternative means for analyzing our sample data made our choice of the econometric approach quite easy.

SELECTION OF EXPLANATORY VARIABLES

An econometric study of travel behavior based on a random cross-sectional sample of individuals can be carried out in two ways :

1. The socioeconomic characteristics that vary between individuals are included ex­plicitly as variables in the model.

2. The sample is subdivided into categories so that socioeconomic variables are not needed as explanatory variables of the modal choices.

Both approaches were used in this study, although it is difficult to succeed with category analyses because the subsamples usually turn out to be too small. Which approach to use depended on whether the variable in question influenced the traveler's evaluation of the other variables or whether it influenced the a priori probability to use a car.

40

The basic issue, however, had to do with the transportation policy variables inas­much as one aim of the study was to provide public planning officers, politicians, and transit operators with a firmer knowledge of what public transportation system char­acteristics to focus on when they suggest urban transportation options in the future.

Travel time, travel costs, and travel comfort and convenience are the major groups of transportation policy variables. As in many other similar studies, each segment of travel time and travel cost is identified. But unlike most previous studies in this field (exceptions include~ lb .!!!., ~. our models also include travel comfort and con­venience variables, the most important of which seem to be "number of transfers" and "chance of getting a seat" (7).

The use of each trip segment as a basi.s for separate explanatory variables is well­founded in modern microeconomic consumer theory (~ .?., ~ and is compatible with the intuitive policy-oriented segmentation that is relevant to the supply of transportation services. The travel segments used as explanatory variables not only reflect the trip segmentation perceived by the travelers; they also reflect the action parameters of transportation planning authorities.

This raised the question of whether travelers perceive the same variable values as those measured by traffic engineers. We assumed that no bias of this kind was present; this assumption was supported by the findings of Lansing and Hendricks (.!Q_) in the United States, Quarmby (17) in the United Kingdom and by recent findings in Sweden (1) (Figs. 1 and 2). - This is an important assumption because both perceived and engineering data had to

be used in the same models as a consequence of the limitations of perceived data for the travel mode not chosen for the journey to work.

It was also assumed that the travelers in our samples have a good knowledge of the values of the attribute dimensions of the alternatives available. This was a reasonable assumption because we were dealing with work trips. This assumption seemed to be supported by a recent study (1) and TU-71, in which 68 percent of the automobile com­muters reported that they were conscious of public transit costs and 84 percent were conscious of the time it took to travel by public transit. Among transit users, similar percentages were 40 and 76 respectively. Similar results were also derived by Lansing and Hendricks (10).

Socioeconomic variables are important for making valid predictions for future situations with significantly different socioeconomic structures within and between the zones of the study area. In addition to sex, age, and income level, a variable express­ing the use of the automobile while the respondent was at work was included because we believe that frequent car use during working hours makes commuting by public transit less attractive. From AKU-68, we found that about 50 percent of the automobile com­muters also used their cars during working hours. The frequency of car use during work time turned out as follows according to TU-71:

Time of Use

Daily At least once a week At least once a month Only occasionally Never

Total

Percentage of Use

14 20 11 20 35

100

A brief definition of the variables and the nature of the data (in parentheses) used in AKU-68 follow. Both reported and estimated values refer to round trips. Cost and income are measured in Swedish crowns (Kr).

Figure 1. Perceived versus estimated travel time for automobile and public transit users in Stockholm .

.Transit travel time 40

+ Auto users Auto travel time

35 0 Tran1it users

30 30

~ 25 25 ·;::

Qi 20 20 > g

al 15 0 I- + 15 > 'iii e 10 + 10 ., Q.

5 5

0 0 0 5 10 15 20 25 30 35 0 5 10 15 20 25 30

estimated travel time

Figure 2. Perceived versus estimated travel time for automobile and public transit users in Uppsala.

Transit travel time

100

90

80 Auto travel time

70 70

~ GO

·;:::

Qi 50 50 > g

40 40 'O ., > 'iii 30 e 30 Q)

Q. 20 20

10 10

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

estimated travel time

41

42

M T.

Tt

Twait a .

Twalk a

T~a1t

Tw•lk t TIV

t nv c ~ CP ca CR a

c. NOT

K A y

uoc

mode: 0 = t1·ansit, 1 = a utomobile (reported); door-to-door automobile travel time, min/ day (r epor ted for automobile users as perceived data, estimated fo r t ransit us ers as engineering data); door-to-door transit travel time, min/ day (reported for transit users, esti­mated for automobile users) ;

= waiting time for automobile, 0 min/ day (estimated); ~ walking time to automobile, 10 min/ day (estimated); = mor ning plus evening waiting time for transit mode, only waiting time for

firs t t r ansfer measured (estimated); = walking t ime to transit (estimated);

line-haul time on transit, Tt -Tr 1k -T~·1 t (estimated); time in vehicle for automobile, T. -T:•1k (estimated);

= transit cost/ day (estimated); automobile parking costs/ day (reported);

= automobile running costs/ day (estimated); automobile cost, c: + CR (estimated); number of transfers (reported); sex: 0 = male, 1 = female (reported); age years (reported); individual income/ day (reported); and use of car during working hours : 0 = if not used, 1 = if used (reported).

Some of the engineering values used need some further comments. Great impor­tance has been attached to T~ait in previous modal-choice studies because estimated coefficients indicate a waiting-time value 2 to 3 times that of line-haul or driving time saved. The number of statistically significant estimates on these coefficients are, however, rather limited, and they mostly imply a particular arrival behavior, namely that the average waiting time is half the time interval between two transit modes re­gardless of the length of this interval.

It seems reasonable to assume that a worker's knowledge of the journey is such that the assumption mentioned onl y will hold true for t ransit lines with a high frequency, say 5 min or so between trains (or buses). If intervals are larger , people will sys­tematically arrive at the station so that they may avoid long waiting times.

So that the effect of the latter hypothesis on waiting-time values (the role of transit frequency) could be tested, Tr 1t was measured by a formula (16). The waiting time on each one-way t rip was computed as 1. 79 + 0.14 t for 5 ,;; t, where t = minutes between 'trans it arrivals. For t < 5, the one-way waiting time was equal to 1/2 t. It is important to note that T~•it does not include transfer times; only waiting for the first transit mode is measured by T~· 1 t.

One purpose of this paper is to investigate the role of the assumed waiting behavior of commuters so that our knowledge of the value of time saved during waiting may be improved. By a comparison of different assumptions about the arrivals at transit stations, the differences in empirical results will indicate upper and lower limits on the value of time saved during travel. The range between these limits will in practice be a function of the distribution of the transit frequencies on the public transportation network in the area under investigation. In other words, a highly developed transit system with high frequencies will probably reveal little variation in the waiting-time values, whereas a system with low frequencies will imply larger ranges between upper and lower limits.

T. for transit users was estimated by using origin-destination data and exogenously computed average link speeds and by assuming the use of the shortest route.

T~•lk was measured for each observation as the walking distance between a home address and the nearest transit station or bus stop. A similar procedure was applied at the workplace . A walking s peed of 5 km/ hour was assumed.

C~ was constructed to r eflect the dependence on traffic conditions (peak or off peak) and individual tax deduction possibilities . By use of the speed-fuel consumption model of the British Transport and Road Research Laboratory and traffic speed data for

43

Stockholm, the automobile running costs were estimated at 2.46 Swedish Kr/ 10 km in peak periods and 2.25 Swedish Kr in off-peak periods. When the automobile was re­ported to be used for work purposes, the cost for the individual was defined as zero because of tax deduction allowances.

c: data were basically provided by the survey. For those respondents who answered that no parking was available, 20 Swedish Kr were coded because this would be the cost of using a parking lot. Ct was estimated by using the lowest monthly cost alternative.

NOT was derived through the reported work trip composition for transit users and from the alternative transit arrangement reported by automobile users. Inasmuch as transfer times were not explicitly recorded, the coefficients of the NOT variable mea­sured the value of transfer inconvenience.

The 1968 waiting times are estimated by the British formula (16). Because we are particularly interested in the sensitivity of our conclusions with respect to waiting­time behavior, the underlying distribution of time intervals between transit arrivals should be known. A brief definition of the variables in TU-71 follows. The data for all the variables except UOC were reported by automobile and transit users; UOC was reported only by car users. Costs are measured in Swedish Kr.

Ta = door-to-door travel time for automobile, min/ day; T t = door-to-door transit time, min/ day; Ca = total travel costs for automobile/ day; ct total travel costs for transit/ day;

NOT number of transfers/ day; S chances of getting a seat for longer part of trip, yes or no; and

UOC use of car during working hours: 1 =used daily or more than once a week during working hours, 0 = otherwise.

ANALYSES OF EMPIRICAL RESULTS

In a recent paper (20), we presented a number of highly significant parameter estimates based on the data sets described. One striking feature of these estimates was the ex­traordinary high values of time saved while waiting. It was concluded (20) that our particular formula choice for waiting-time behavior caused high values in comparison to the waiting-time values revealed in modal-choice studies of other metropolitan areas .

The formula used (20) would have yielded waiting-time values identical to what is considered normal if the transit system supply were such that all frequencies were 5 min or less. This, however, was not at all the case in Stockholm because 75 percent of the commuters faced transit frequencies of 7.5 m in or more (Table 1). The larger the share of commuters is on transit lines with low frequency, the larger the dis­crepancy will be between the expected waiting-time value according to the formula and that derived by assuming that all waiting times are equal to one-half of the headway, which could be considered a random waiting time. The relationships between transit headway and random waiting time and between transit headway and expected waiting time are shown in Figure 3.

According to observed waiting times and transit headways in Stockholm in 1971 (~), there are strong reasons to believe that commuters really become conscious of waiting time as transit headways are reduced. This applies particularly to those who depend on transit lines with frequencies of more than 10 min but also to those who depend on transit lines with frequencies between 5 and 10 min.

Table 2 gives the empirical results of the logit analyses of the 1050 observations in AKU-68, which show complete automobile availability. The variables are similar to those used previously. In addition, it should be noted that the waiting-time variable (from the British formula) is T~•tt, but the traditional waiting-time variable is T~· 1 t (0). The symbol T~ illustrates so-called excess time (not including transfer time) and con­tains the sum of T~ait(o) and T~• 1 k.

The most striking feature of Table 2 is the drastic change in the waiting-time coef­ficient when T~ait is replaced by T~• 1t(O). All the other coefficients remain unchanged

44

Table 1. Distributions of transit frequencies (AKU-68).

Minutes Between Tran sit Arrivals

0 to 2.5 2.5 to 7.5 7.5 to 12.5 12.5 to 17.5 17 .5 to27 .5 > 17.5

Number of Observations•

23 247 459 109

51 161

Percentage of Distribution

2.2 23.5 43. 7 10.4

4.9 15.3

Note: Table 1 is based on mean morning and evening frequencies. 8 1050 total.

Figure 3. Expected and random waiting times as functions of transit headway.

WAITING TIME (minutes)

1~

12

11 RANDOM WAITING TIME

10

9

-- EXPECTED WAITING 5 ..... - TIME _ .... --_ .... _ .... j .... -_ .... ----'7~--

~ ~

~

0 TRIP HEADWAY

4 b 8 10 11 1~ 1& 18 20 22 24 2&

Table 2. Empirical results of AKU observations.

Model

4

Variable LC I-Value LC I-Value LC I-Value LC

T~a1l 0.0871 2.74

T:"'' (0) 0.0244 2.55 0.0244 2.55 T~a 1t -0.0076 0.69 -0.0072 0.64

T: 0.0118 AT'y 0.0070 1.96 0.0070 1.94 0.0073 2.04 0.0078

c: -0.1166 10.5 -0.1167 10.4

Ct -c: 0.1012 3.34 0.0998 3.25

tl.C 0.1148 10.9 0.1144

NOT 0.1870 2.82 0.1871 2.76 0.1979 2.99 0.2147

uoc 0.6612 3.07 0.6710 3.07 0.6235 3.29 0.6499

Constant 0.7278 2.80 1.0942 4.15 0.9889 5.38 0.9063

Note: LC= togit coefficient, t•values indicate level of statistical significance.

I-Value

1.81

2.18

10.9

3.25

3.45

3.71

45

with regard to both absolute value and level of statistical significance. The trade­off values implied by the estimates in Table 2 are given in Table 3.

Tables 2 and 3 give the important role of the assumed arrival behavior of com­muters at transit stations and the actual intraregional variations in the level of service of public transit in terms of headways with regard to waiting times.

The explanatory power of the models discussed is not significantly different, but it should be noted that our initial model (based on the British formula for waiting time) yields the smallest constant term of the four logit models discussed.

Table 3 gives travel-time values equal to about 20 percent of the gross wage rate for all four models. This finding is quite compatible with those of similar previous studies in other urban areas. Assuming traditional waiting-time behavior (models 2 and 3) regardless of transit headways, waiting-time values equal to 3 times the travel­time values are revealed. This finding is quite compatible with similar previous studies in other cities.

When the alternative formula for waiting time is introduced (model 1) the Stockholm waiting-time value becomes 12 times the value of travel time. This is interesting be­cause this formula seems more compatible with actual travel behavior than the most commonly used formula.

A similar disaggregate modal-choice model based on commuter behavior was re­cently carried out in Sweden (AIB-74) (1). In AIB-74, a commuter data sample was collected from several workplaces. The sample sizes were 220 in Uppsala (32 560 inhabitants), 452 in Vasteds (117 946 inhabitants), and 188 in Stockholm (699 238 in­habitants) respectively or a total of 860 observations.

The AIB-74 study used the same set of explanatory variables as in our study, with two exceptions: (a) It also contained the time it took the commuter to walk from where the automobile was parked to the workplace, and (b) all variables were reported. This means that the waiting-time variable for transit like all other variables is to be re­garded as perceived by the commuters. Four geographically different models were estimated, one for each city, and there was one pooled model. As in our study, the AIB-74 study produced highly significant parameter values for most of the transporta­tion policy variables.

If the perceived waiting-time variable were used in the AIB-74 study, the waiting­time value would become between 7 to 12 times the value of the in-vehicle travel time.

This finding further supports our belief that using half the headway as a definition of actual waiting time leads to inaccurate waiting-time values. It also supports the hypothesis that commuters tend to be conscious of waiting time. As a matter of fact, the value of waiting time saved relative to in-vehicle times seems to be similar in our study (based on the British formula) and in the AIB-74 study (based on perceived times).

Now one must decide whether the waiting-time value estimated in our study should be used in other cities instead of waiting-time values equal to three times the travel­time values. Our findings indicate that this depends on the level of service of the transit system in terms of the mean intraregional transit frequency and the distribution of these frequencies.

Our findings further indicate that the relative waiting-time value will never be less than previous findings (three times the travel-time value). It seems as if previous studies in other cities have established a lower boundary of waiting-time values rela­tive to travel-time values for metropolitan areas with high per capita incomes.

Our findings indicate further that this relative waiting-time value will increase rapidly as the quality of the transit system is lowered in terms of frequency between departures.

The conclusion is, therefore, that our findings about the commuters' evaluation of waiting time saved relative to travel time saved are only transferable to another city if the transit system frequency characteristics are equal and the economic activity level is the same.

A dummy variable technique was then applied on the binary choices in a way that made it possible to identify the different types of transit modes within the same model and consequently to reveal possible differences between the various transit modes with respect to comfort of the transfer. A variety of binary-choice situations, such as

46

automobile to underground, automobile to bus, automobile to commuter train, automobile to bus/underground, automobile to bus or train, and automobile to other transit mode com­binations, were, therefore, simultaneously treated in the choice equation. The coefficients of the following model were estimated on the AKU-68 modal-choice sample:

p =

where

and where

0L(x, • )

l+eL(x,a)

+ (aa + beZ + Cs Y + dsX + esQ)NOT

at> be, cs, ds, and es = parameters (i = O, ... , 6); Z = 1 if transfer is between buses; Z = O if otherwise; Y = 1 if transfer is between underground lines; Y = 0 if otherwise; X = 1 if transfer is between commuter train and underground; X = 0 if otherwise; Q = 1 if transfer is between rail (commuter train or undergound)

and bus; and Q = 0 if otherwise.

The other variables are as defined previously for AKU-68. If Z=Y=X=Q=O, this refers to transfers between other combinations of transit modes, such as bus plus rail modes with transfers or underground plus bus transfers and others. The mean number of transfers is thus highest for the other combinations, e.g., bus to rail and rail to rail (Table 4).

By use of the illustrated dummy variable technique, the parameters for the different types of transfer can be derived as follows. The estimated, traditional policy param­eters are significant. The transfer dummies are not at all significant, but the approach indicates one possible way to use data to understand how different transfers are re­garded by the travelers.

The derived values of travel time and waiting time are equal to those found in model 1 in Table 3. The (highly speculative) transfer-comfort values derived from the esti­mates in Table 5 are given in Table 6.

Not much can be relied on in Table 6, but it is interesting to note that the two most significant transfer types, bus to bus and other transit mode combinations, are valued somewhat higher than the average transfer value (1.85 Swedish Kr) derived above from the other models. This may indicate that transfer discomfort is regarded as more onerous for bus modes and mixed bus-rail mode combinations than for pure rail modes.

There are several intuitive explanations that support this. First, transfers between different bus lines take place at bus stops that have fewer conveniences compared with commuter train and underground stations that are well-equipped with climate shelter and other convenience facilities. Second, our values of transfer discomfort reflect time spent walking to and waiting for transit. As buses in the greater Stockholm area generally depart less frequently than the underground, the time spent waiting for a transfer-on the average-is higher for the bus than for the underground. Because of the mix of buses and cars in the street, buses also tend to give rise to a lower regu­larity than rail modes on exclusive rights-of-way. This also tends to prolong time spent waiting for transfers. Third, neither the different bus route schedules nor

Table 3. Trade-off values.

Trade-Of[ Value• in Model

Variable 4,

Waiting time saved per hour 51.64 14.65 12 .74

Excess time saved per hour 6.22

Travel time saved per hour 4.15 4.20 3.80 4.09

Each transfer 1.85 1.87 1.72 1.88

' In Swedish Kr.

Table 5. Empirical results from model based on type of transit transfer.

Dummy Parameter

Table 4. Structure of transfers (AKU-68).

Type of Transit Transfer

Bus to bus Underground to underground Rail to rail Bus to rail Other combinations

Total

Number ol Observations

276 185 117 200 272

1050

Table 6. Transfer comfort values.

Value

Number of Transfers (mean value)

0.77 0.32 1.45 2.0 4.6

2

Item Variable Estimate I-Value Cost per Line-Haul Time

Type of transit transfer Transfer per Transfer Underg round to underground ~+CG 0.0300 0.16 Type of Transit Transfer (Swedish Kr) (min) Rail to rail as+ d6 0.1009 0.39 Bus to rail a.ti+ es 0.1566 0.92 Underground to underground 0.31 4.4 other combinations ... 0.2091 2.76 Rail to rail 1.03 14.8

Other variables Bus to rail 1.60 23.0 Bus to bus 3.44 49.5

T~•1t 0.0786 2 .45 Other transit combinations 2.14 30.8

T:"-T!" 0.0068 1.90

Cl-C: 0.0978 3. 24

c: -0.1152 - 10.38

uoc 0.6776 3.12

Constant o. 74133 2.83

Table 7. Modal-choice characteristics and income level.

Variable

AT AC NOT uoc Constant Value of Door-to-Door Value ol

Model LC I-Value LC I-Value LC I-Value LC t-Value LC I-Value Time• Transfer"

Total sample 0.0084 2.46 0.1136 11.02 0.212 3 3.37

Low income 0 .0068 1.30 0.1319 8.38 0.2421 2.32

High income 0 .0089 1.93 0.0983 6.83 0 .2359 2. 85

Note: LC • logit coefficient. t·values indicate level of statistical significance,

•swedish Kr/hour, hSwedish Kr/transfer.

Table B. Modal choice and seating comfort (TU-71 ).

0.6559

0.1780

0.6572

Variable

.:IT S·t.T T, S·T, T. .:IC c. NOT uoc Constant

3.54 1.1270

3.34 1.4335

2.71 0.7153

Value of time, having a seat Value of time, not having a se at Value of transfer Value of travel time by car

7.00 4.44

6.20 3.10

3.03 5.45

Model 5

0.0547 (4.25) -0.0235 (1.88)

0.1374 (5.50)

0.0535 (0.61) 0.9470 (2.43) -0.4234 (1.69) 13.62 23.88 0.40

1.98

1.83

2.40

Model 6

0.0385 (6.97) -0.0111 (3.35) -0.0498 (7.11 )

-0.1020 (3 .55) 0.1729 (1.74) 1.0216 (2 . 74) 0.4234 (1.20) 16.08 22.63 1.70 29.29

Note: Numbers in parentheses are t·values that indicate level of statistical significance.

48

the locations of bus stops are usually synchronized. Distance and time spent waiting for transfers tend to increase more for buses.

The transfer value with respect to other mode combinations is our most reliable transfer value. As this type of transfer mainly refers to the combinations of bus plus several rail modes or underground plus several bus routes, this comfort value should be interpreted as reflecting either feeder bus or rail transfers in the outskirts of the metropolitan region or city bus or rail transfers in the inner city of Stockholm. The convenience level is generally rather low, especially at the suburban commuter train and underground stations. The distance and the difference in the physical convenience level between train station platforms and feeder bus stops are usually considerable. In 1968, none of the stations was equipped with escalators or elevators.

However, the convenience level of transfers between different underground lines and between the commuter train and the underground (located in the inner city of Stockholm) is exceedingly high compared with the other transfers discussed. Transfers between rail modes always take place across a platform or by elevators or escalators. These enable commuters to be unaffected by weather, and there are other conveniences such as kiosks and ~inishops.

Our point estimates given in Tables 5-and 6 are certainly compatible with these observations, but, because of the extremely low significance levels associated with these dummy coefficients, further research is needed to provide conclusive evidence to transit planners.

The AKU-68 data are insufficient for deriving more information through this kind of transfer differentiation. Consequently, there is an urgent need for research to develop similar modal-choice models that emphasize differentiating comfort values into dif­ferent types of transit use.

Effects of income level on trade-off values were also studied. Because of sample limitations, only two income categories were used: (a) low income (< Swedish Kr 30 000/ year) and (b) high income (>Swedish Kr 30 000/ year). The logit model used for this purpose was somewhat less detailed than the ones used previously in that total door­to-door cost and time differences were used. We first estimated the coefficients for the whole sample (1050 observations) and then estimated for each of the two income groups. The results are given in Table 7.

Table 7 shows that the statistical level of significance is highly sensitive to sub­divisions of the sample. The time difference variable of the low-income category is significant at 80 percent only, which cannot be considered satisfactory. The other coefficients are significant at 95 percent. Attempts to subdivide further by income led to completely unsatisfactory results in regard to level of significance.

The conclusion to be drawn from the findings presented in Table 7 is that both value of time and transfer seem to increase with income. This is quite in line with similar studies in other cities, although little evidence so far exists in regard to the evaluation of transfers as a function of income.

The final resuits in this paper are based on the TU-71 data, 352 observations in all. These data made it possible to test the hypothesis that people who do not have a seat during the trip value their travel time higher than people who have seats.

Two logit models were used for this purpose. The first (20) contained cost and time differences (door-to-door) in addition to NOT and UOC. Theother one is somewhat more general in that door-to-door times and costs enter separately for each mode, except for the slight modification that public transit costs (Ct) is not included. The reason for this is that the uniform 50 Swedish Kr/month season ticket applies to the whole commuting population in the study area and can be considered a constant rather than a variable.

The seating question is dealt with similarly in both models. A dummy variable, S, takes the value one if the individual has a seat and zero if otherwise. It is incorporated in the model by attaching it to the time variable in the following way:

(a1 + a2 • S)T t

49

or

If a sufficiently large sample were obtained it would have been useful, as an alterna­tive, to stratify by S to see if there were any variations in values of time when the stratification procedure was used in contrast to our dummy method. However, limited sample size made such a stratification unsuccessful in terms of significant coefficients. The estimates obtained via our two models are given in Table 8.

Our immediate reaction is that model 6 gives more reasonable results than model 5. The level of significance in model 6 is much better and "the value of having a seat", which is the difference between the two time values for public transit, seems more reasonable in model 6. We found the seating value of model 5 somewhat high (20). In model 6, this value is 65 percent of the same estimate in model 5. -

The absolute level of the time values is extremely high compared to those derived in AKU-68 and other studies. A 30 percent increase compared with AKU-68 data for inflationary and real income increases should be expected, but those values given in Table 8 are just too high to be readily accepted.

The opposite conclusion is to be drawn for the transfer value. Instead of an ex­pected 30 percent increase, the transfer value is slightly less than those of AKU-68. The difference is, however, so small that it is tempting to conclude that the result has added further support to our beliefs about the commuters' willingness to pay to have one transfer less. This is not incompatible with the findings of Watson (22).

CONCLUSIONS AND SUGGESTIONS FOR FURTHER RESEARCH

The analytic approach used in this study has yielded important empirical results. Most models used are satisfactorily significant. In those models with no category subdivision, almost every transportation policy parameter is highly significant and supports the following conclusions.

The travel-time value differs substantially with the type of trip segment. The models estimated reveal a significantly higher value of the time spent waiting for transit than of the corresponding line-haul time on transit.

The commuters tend to be conscious of waiting time. When a more realistic as­sumption about actual waiting time is used instead of half the headway, the waiting­time value becomes between 7 to 12 times the value of the in-vehicle travel time. Our findings indicate that the waiting-time value depends on the level of service of the transit system in terms of transit headway.

The level of the travel-time value also depends to a large extent on the in-vehicle comfort in terms of seating opportunities. The overall travel-time value decreases substantially when commuters can enjoy a seat as compared to when they cannot.

A transfer-comfort value is deduced and amounts to about 1.90 Swedish Kr/transfer. In terms of travel time, the commuter is equally well-off with 1 less transfer and 24 min longer for door-to-door time. This transfer-comfort value tends to be valued as more onerous for such transfer combinations as bus to bus and bus plus several rail modes than for an average transfer or transfers between rail or underground modes.

·When the sample is stratified into different categories such as income groups, travel-time groups, and types of transfers, the level of statistical significance drops markedly in the subgroups compared to the overall models formulated.

For income groups, the results support the hypothesis that the travel-time or transfer-comfort value increases with income.

These results may be useful in various transportation planning situations:

1. Derived values of time, comfort, and convenience should be used in benefit-cost

50

analyses of changes in the structural design of the transportation system in urban regions.

2. Impacts on the modal split, due to changes in transport policy such as fares, comfort of parking and transit, and policy issues on convenience, can be predicted by applying this kind of modal-choice model.

The results of this analysis indicate an urgent need for further research and de­velopment. Existing and future traffic surveys should be better adapted to handle planning problems that are relevant for policy-making authorities, e.g., by permitting relevant modal- and traffic-choice analyses in which subdivisions into trip segments, multiple-choice situations, and different socioeconomic groups are practicably possible.

Inasmuch as one of the comfort and convenience variables estimated in these models (number of transfers) seems to reflect a rather composite factor of discomfort, it is necessary to focus more investigations on model components. Among these components, number of transfers may reflect the time spent walking to a transfer point and waiting­time discomfort as well as the uncertainty of getting a seat in the vehicle entered at the transfer point.

ACKNOWLEDGMENTS

We wish to acknowledge the following persons and agencies. Kristina Lennartsson and Lars Wikstrom of the Department of Planning and Building Control in Stockholm; Ole Havard Hansen of M~re og Romsdal Regional College; Nils Bruzelius of the Stockholm County Council; AB Stor-Stockholms Lokaltrafik; the National Central Bureau of Statis­tics; Ulf Nyberg of the Joint Planning Board of Stockholm; J. G. Cragg of the University of British Colombia; and David A. Hensher, Commonwealth Bureau of Roads, Melbourne, Australia. Finally, we wish to thank the Economic Research Fund of the Bank of Nor­way and the National Swedish Council for Building Research for financial support, which made our international cooperation much more efficient. The entire responsibility for the contents including all conclusions derived from the analytic results presented rests solely on the authors.

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