Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

INVESTIGATING THE EFFECTS OF TRANSIT INFORMATIONSYSTEMS ON DIFFERENT USER GROUPS
Yalcin ALVER
Research Fellow
Dept. of Civil & Env. Engineering
Kumamoto University
2-39-1, Kurokami, Kumamoto
860-8555 Japan
Fax: +81-96-342-3507
E-mail: yalcinalver@yahoo.com

Shoshi MIZOKAMI
Professor
Dept. of Civil & Env. Engineering
Kumamoto University
2-39-1, Kurokami, Kumamoto
860-8555 Japan
Fax: +81-96-342-3507
E-mail: smizo@gpo.kumamoto-u.ac.jp

Abstract: This study investigates the effects of Transit Information Systems (TIS). TIS arewide range of systems that provide travelers information about travel options such as; travel
times, delays and/or incidents. In this study, the important transit information types and theirservice levels are investigated in Izmir City, Turkey. The study focused on the transit users;
considered information types for different transit mode segments were identified. In addition,
accepted information types for non-transit users to consider transit as an alternative commute
mode was investigated. For these purposes, a stated preference scenario was prepared which
included the considered important information types of respondents and a function of theiractual travel time. In this study, static pre-transit information systems were considered.
Results indicated that considered TIS between transit and non-transit users are not muchdifferent from each other. However, considered TIS among different transit mode segments issignificantly different.
Key Words: Public transit, Transit information systems, Stated preference scenario.

1. INTRODUCTION
Transit services are one of the essential parts of city life which provides mobility to its users
usually with low prices. The history of public transportation is intimately connected toindustrialization, urbanization, and the separation of residence from workplace. The patronageof public transit grew steadily from 1900s in the world and since then industrialized(developed) countries improved service level of transit and developing countries introduced
transit into their major cities. Nowadays, there is a growing effort to introduce new public
transit systems and improving the service quality of available services.
Population of the world is growing very fast and with industrialization, there is a migrationfrom rural areas to urban areas. Thus, traffic and parking problems rise in big cities. One of
the solutions of this common problem is introducing new infrastructure to provide mobility to
the residents. This consumes much time and money. Another solution is mostly used bydeveloped countries, which have already solved the infrastructure problems, is increasing theattractiveness of public transit by improving its service quality to use them more effectively.
Transit Information Systems (TIS) is one of the most common tools for this purpose, since
information became an indispensable part of our lives. TIS are wide range of systems thatprovide travelers information about travel options such as; travel times, delays and/or
incidents, etc. Increasing value of time in city life impel us to make informed decisions in all
stages of our daily activities. TIS can be used as a powerful instrument to make informed

1745

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

decisions for trips, if it can be provided to users.
This study is conducted in Izmir City, which is the third biggest city in Turkey. Turkey is acountry which can be defined as a developing country. The city is located around a bay along
the Aegean Sea. The population of the metropolitan city is around 2,235,000. The
metropolitan area includes nine municipalities. Being one of the biggest and developed cities
in Turkey, population of the city is growing very fast with immigration from rural areas. The
young population of the city needs mobility. Still, there is lack of transit infrastructure and theongoing projects try to solve this problem.
In the city, five different transit modes are in service. The biggest service capacity belongs tobuses with 1545 buses in 277 different routes. Subway is totally 11.7 km and it has 10
stations. Local train line has 21 stations inside the metropolitan area, but, the service is slow
and frequency is low. Sea transportation is done with 21 commuter boats and ferries between
eight harbors and two ferry harbors. Another transit mode is available which is called“dolmus” in Turkey. This service is provided by minibuses, which can carry 14 passengers.
This service has its own routes and stops. Usually, they do not have custom schedules, but,they are very popular especially in the areas which have a lack of other public transit
activities. In recent years, many significant improvements were done in public transit
services. However, introducing new infrastructure or transit services are costly. Improvements
in transit do not only depend on introducing new services but also improving the service
quality. One of the most effective methods to provide better service is to investigate what
travelers consider and to improve service in the light of these considerations.
This study investigates the important transit information types and its service levels toimprove the satisfaction level of travelers in Izmir City. This will help to keep actual users
with public transit and provide better comfort level. In addition, the effects of TIS to attract
non-transit users to consider transit as an alternative commute mode are investigated. For
these purposes, a stated preference scenario was prepared which included the considered
important information types by the respondent with its different service levels and a functionof actual travel time of respondent. Survey was conducted as face-to-face interviews in four
different areas in metropolitan city.
Discrete choice modeling was used to evaluate the responses of individuals. To investigate theimportant service levels, satisfaction levels of the respondents were asked for the given
scenario. Responses for the satisfaction level has a natural ordering, thus, ordered probit
model was used for the modeling approach. This study focused on four main objectives:• To determine which types of transit information are considered as important by users;
o For transit users, to increase the satisfaction level from the current transit service.
o For non-transit users, to attract more users to increase the likelihood of usingtransit as an alternative commute mode with available TIS.
• To investigate the considered TIS among transit users depending on the different
transit mode segments.
• To identify the effects of significant transit information types and their service levels
for the satisfaction of the transit users by the transit service.
• To determine the socioeconomic characteristics of the transit and non-transit users.

1746

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

1.1 Transit Information Systems
TIS provide passengers information on one or more modes of transportation service to assist
decision maker. This assistance can be before the trip (pre-trip) or while already traveling (enroute). En-route transit information can help the users to make their decisions about their trips
on the way or at the stops/stations (wayside) or while they are traveling on the train, bus etc.
(in-vehicle). Basically, information can be provided to the trip maker anywhere starting from
his/her decision process at home to reaching the final destination. Implementation of TIS
necessitates gathering, processing and distributing information on transit routes andschedules. Generally, three types of TIS are considered in the literature; pre-trip, wayside andin-vehicle transit information.
Transit information available before the trip is called pre-trip TIS. Pre-trip TIS help people to
make decisions about the choice of transportation mode, route, and departure time before they
start their trip. Surveys conducted in four different regions of USA showed that pre-trip transit
information is very important for the travelers (Cluet, et al., 2003). Most common pre-trip TIS
are transit route maps, schedules, park and ride information, transit trip itineraries, stop andstation locations, weather etc. Pre-trip TIS is the first chain in the information series and themost important one. Especially, for the non-transit users, travelers’ decision between driving
and transit must be changed before they start their trip. Most important factors that non-transit
users choose driving are the convenience and travel timesavings. Thus, providing detailed
information about transit, such as, waiting times at the stop or seat availability, can ignore
them to choose transit as an alternative. Providing reliable transit information to travelers
before they choose their trip mode enable them to make informed decisions thus, they canconsider alternatives in a more realistic way. Mostly considered TIS in this study are pre-trip
information.

2. PREVIOUS STUDIES
Many studies were conducted about the Advanced Transit Information Systems (ATIS) and its
effects. However, few studies investigated the effect of TIS on transit and non-transit users’
choice decision. Nevertheless, these kinds of studies have started to be popular especially inadvanced countries to improve the service quality and satisfaction of the travelers and to
attract non-transit users to consider transit as an alternative commute mode.
Polak and Jones (1992) investigated travelers’ preferences for different types of travel
information and the effects of such information on travel behavior. The study was conducted
as a stated preference design for whom uses personal computers at home. Respondents were
informed about the car and transit (bus) travel times from home to the city. Result of thisstudy indicated that there is a significant demand for such information, even among car users.
Hall et al. (1994) investigated the effectiveness of Southern California Rapid Transit District
telephone information service. Schweiger (1995), Hickman and Day (1996) reviewed some of
the transit information systems.
Kitamura et al. (1995) investigated the effect of pre-trip information systems by conducting
in-laboratory interviews. Their finding showed that age is an important variable that defines
market segments for pre-trip information systems. Shank and Roberts (1996) reviewed thebenefits of TIS and found that traveler information technologies could result in shifts from the
car alternative to transit alternative. They informed that in the areas; Seattle, Washington andBoston, MA, which they conducted the surveys, 5-10 % of the car users could shift from car
1747

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

to transit if they were informed. Khattak et al. (1996) and Adler & McNally (1994) conducted
experiments to study the effects of information on traveler behavior.
Mehndiratta et al. (2000) investigated the user characteristics of Travel Advisory Telephone
System (TATS) in San Francisco Bay Area and also investigated from what elements of ATIS
information, provided by this system, do the users derive value. Abdel-Aty (2001) conducted
another comprehensive study to investigate the transit information service mostly desired by
non-transit users. They conducted a telephone interview survey in Sacramento and San Jose
areas of California. The likelihood of commuter choice was investigated under given
information. They got promising results, which showed 38% of the non-transit users would
like to consider transit as an alternative commute mode if transit information was available.
He found that frequency of service, number of transfers, seat availability, fare, and walking
times to the stops were considered information types by non-transit users.
Cluet et al. (2003) indicates the results of customer preference for transit ATIS in Seattle, Salt
Lake City, Columbus and Providence/Kingston in USA. These results show that timetables
are the most preferred pre-trip information. Traditional (static) information is preferred to realtime information for pre-trip planning. These surveys showed that transit users want to obtainpre-trip information in printed form and via computer. In this survey, the overall level ofpreference for information while at the wayside is substantially less than for pre-trip planning.

3. SURVEY DESIGN AND MODELING METHODOLOGY
A questionnaire survey was conducted in Izmir City, Turkey. Survey was conducted in four
different locations in the metropolitan area of the city to collect samples from different
socioeconomic groups and different respondents whose transit choices are different. Data wascollected by in-person interviews from May 22 to Jun 17, 2005. Totally 645 interviews wereconducted during this time period.
Prepared survey has two parts, first part covers the questions to identify the socioeconomic
characteristics of the respondent and some other significant factors that could effect the trip
decision. Main part of the questionnaires was prepared in two different forms. First one is fortransit users and second one is for non-transit users who want to consider transit as analternative commute mode at least once in a week. The main structure of the questions was
same for transit and non-transit users with slight differences and investigates the consideredsignificant information types and their service levels. Questions for transit users include extra
questions about the actual transit mode and satisfaction level from the current service
conditions.
3.1 Questionnaire
3.1.1 First part: socioeconomic characteristics and some other important factors
This part of the questionnaire mainly investigates the general socioeconomic characteristicsof the respondents. Considered personal and socioeconomic characteristics are; gender, age,income, education level and occupation. In addition, some questions were asked to clarify theother significant factors that could effect the trip decision. These additional questions are; carownership, flexibility of work starting time, car-pooling and if the respondent travels tomultiple locations from work/school. Finally, all respondents were asked actual work/school
trip time. Sample characteristics of the survey are indicated in Table 1. The sample group1748

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

represents the city population; the amount of young population (<39 years old) and malepopulation are not different from the 2000 population statistics of Izmir City, 68.1% and50.4%, respectively. Different from transit users the main characteristics of non-transit users
are; old age (40 years old<=), high income (1,000 YTL (833 $)<=) and high car ownershiprates. In addition, car-pooling is very popular among non-transit users.
Table 1 Sample characteristics
Socioeconomic characteristics
GenderAge
IncomeEducation
Car ownership
Commute to multiple locations
Carpool
Flexibility of work starting time

Male
FemaleYoung ( <39 years old)
Old ( 40 years old<= )Low ( <1,000 YTL)High (1,000 YTL <= )Low (high school and lower)
High(higher than high school)
Yes
No
Yes
No
Yes
No
No flexibility
There is flexibility

Total Transit users Non-transit Users
54%
49%
69%
46%
51%
31%
71%
74%
22%
29%
26%
88%
75%
73%
19%
25%
27%
81%
29%
30%
27%
71%
70%
73%
54%
46%
82%
46%
54%
18%
65%
68%
57%
35%
32%
43%
32%
25%
55%
68%
75%
45%
63%
65%
58%
37%
35%
42%
YTL is Turkish money unit (1.2 YTL ≃ 1 $)3.1.2 Main part
The main part of the questionnaires was prepared in two different forms for transit and nontransit users. Before starting the main part, respondents were asked, “Have you used transit atleast once in last seven days as a commute/school trip?” to identify if he/she is a transit user
or not. Then the respondents were asked two types of questionnaire sheets depending on theiranswers. The structure and questions in both sheets are almost same. The only difference was
the way how the questions were asked, because one of them was prepared for transit users to
identify their satisfaction level from the available transit service with TIS and the other one
was for non-transit users to identify the likelihood of using transit service under available TIS.
According to the answers, 493 (76%) of the respondents were classified as transit users and152 (24%) of them as non-transit users. Transit users were asked the questions, which wereprepared for them. Non-transit users were asked one more question before the main part ofquestionnaire, to separate the non-transit users who would not want to consider transit as analternative commute mode under available information. Thus, non-transit users were asked,“If you have more information, might you consider transit as an alternative commute mode atleast once in a week?”. Respondents who had answered “yes” were asked the main part of thequestionnaires and the ones who answered “no” were not asked any more questions. 107(70.4%) of the non-transit users answered “no” and their interviews were finished and 45(29.6%) of the non-transit users told they would consider transit as an alternative commute
mode if they had more information available.
3.2 Hypothetical Stated Preference Scenario
The main structure of the questionnaires includes a stated preference scenario, which depends
on the chosen transit information types and actual commute time of the respondents. As
1749

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

discussed, main objective of this survey was to investigate the important information types,
which are considered as significant by the transit users. In addition, important information
types for the non-transit users to increase the likelihood of using transit as an alternative
commute mode were investigated. Design of the hypothetical scenario both for transit andnon-transit users were the same.
Before the hypothetical scenario, each respondent was asked to pick three information types
from the list, which are significant for him to increase his satisfaction from the current transit
service or to consider transit as an alternative commute mode. Respondents were also asked torank these information types as first, second and third important information types. Ten transit
information types were shown to the respondents (Abdel-Aty, 2001):• Park & Ride information.
• Information about frequency of service.
• Fare information.
• Information about walking time to/from the station.
• Information about transfers, and transfer locations.
• Transit route map.
• Information about operating hours.
• Information about waiting times at the stop/station.
• Information about seat availability.
• Information about station locations.
A hypothetical scenario was prepared by using these chosen information types and actual
commute/school trip time of the respondent. The chosen information types were presented in
the scenario with their randomly chosen service levels. These service levels were prepared
before the experiment to use in the hypothetical scenarios and only information types wereshown to the respondents not the service levels. Generally four service levels were decided
for each information type. In the preparation of the service levels, three points wereconsidered; the current transit service conditions during peak hours, the current transit service
conditions during normal hours (not peak hours), and the improved transit service conditions.
The actual commute time was presented in the scenario by multiplying it randomly by one ofthese parameters; 0.75, 1.00, 1.25, and 1.50. These multiplications were prepared to present
the worse or better travel timesaving. This can be understood longer or shorter travel time fornon-transit user when he/she changes the mode or a change in the service conditions for thetransit user.
In the scenario, a function of actual travel time and randomly selected service levels of eachchosen important information types were presented. Then, respondents were asked for theirsatisfaction level under the given conditions. For transit users, satisfaction level of the given
service level was asked and for non-transit users, they were asked if they would be satisfied
with the given scenario to consider transit as an alternative commute mode. Satisfaction level
was asked in a five-ranked scale where one means very dissatisfied and five very satisfied.
As an example, consider that a transit user had chosen seat availability as an important
information type in the first place, transit route map and information about frequency ofservice second and third, respectively. If he told his normal travel time is 30 minutes, then the
scenario was created as shown in Table 2 and worded as follows:

1750

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

Table 2 Example hypothetical scenario
Actual travel time (30 min.)30 *1.5 = 45 min
1. choice(seat availability)
50% chance
2. choice(transit route map)
Available
3. choice(frequency of service)
10 min.
“Please think that if your travel time is 45 minutes (30*1.5), the chance of seat availability is50%, a transit route map is available with you and frequency of the transit service is 10
minutes. Under listed service conditions above, please rank your satisfaction level to keep on
using transit from the listed satisfaction levels below.”
Very dissatisfied

Dissatisfied

NeutralSatisfied

Very satisfied

This scenario was asked to the respondents for two times. Respondents chose important
information types once. In the second scenario, random number, which is multiplied by thetravel time and service levels of chosen information types were changed randomly. Second
scenario helps to collect two data sets from a respondent.
3.3 Modeling Methodology
An ordered probit approach was used to model the satisfaction levels from the given
hypothetical scenarios, which represent different information types provided by TIS. In somecases, multinomial choice variables are naturally ordered. In these cases, if the dependent
variable takes more than two values, ordered probit model is appropriate to solve the discrete
choice problem. The ordered probit model is a fairly straightforward extension of the binary
probit model which can be used in cases where there are multiple and ranked discrete
dependent variables. The multinomial logit or probit model would fail to account for theordinal nature of the dependent variable. In addition, the regression model cannot be used tosolve the problem, because if the responses are coded from zero to five, the regression model
will assume the differences between the responses two and three same as the differences
between four and five, however, they are only ordinal ranking.
In the survey design, as discussed, the satisfaction levels of the respondents were investigated
for the given hypothetical scenarios, which depend on the chosen information types with its
service levels. The unobserved dependent variable, y* , is described as follows;
yi* = xi β + si γ + ε(1)Explanatory variables are formed of two parts; vector of explanatory variables describing theservice levels of information types, xi, and the socioeconomic characteristics of respondentand actual travel time, si. β and γ are the vector of coefficients for service levels of
information types and socioeconomic characters with travel time, respectively. The error term,
ε , is normally distributed with mean zero and variance one, N[0,1]. The observed ordinalvalue yi, takes on values zero through m according to the following scheme:
yi = j ⇔ µ j −1 < yi* ≤ µ j(2)Where j = 0,……,m. In the equation (2) the threshold parameters µs are unknown parameters
to be estimated with coefficients vector β. In ordered probit modeling, it is not possible toestimate the coefficients of a constant term and four cut points (thresholds) in five-category
1751

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

case. The distribution curve can be shifted on the X-axis, the value of cut point’s changes, but,probabilities are kept constant. For that reason, in this study three thresholds are estimated in
five-category case (Greene, 2000). Thus, in the estimation some threshold values are set asshown; µ −1 = −∞ , µ 0 = 0 and µ m = +∞ . In the study, dependent variable was collected as thesatisfaction level for the scenario. Satisfaction levels from very dissatisfied to very satisfied,were converted to a five scale ranking to use in the estimation. Then the probabilities of eachsatisfaction level can be written as follows:P[ yi = 0] = Φ(− xi β )P[ yi = 1] = Φ(µ1 − xi β ) − Φ(− xi β )P[ yi = 2] = Φ(µ 2 − xi β ) − Φ (µ1 − xi β )P[ yi = 3] = Φ(µ 3 − xi β ) − Φ(µ 2 − xi β )P[ yi = 4] = 1 − Φ(µ 3 − xi β )(very dissatisfied)(dissatisfied)(neutral)(satisfied)(very satisfied)(3)

Where, Φ(......) represents the areas under normal distribution curve.

4. MODELING RESULTS
In this section estimated models are discussed under two subsections. In the first approachsignificant information types are identified for transit and non-transit users, separately.
Depending on the low car ownership rate related with low economic welfare and expensivegas prices in Turkey, most of the users are using public transit for commute trips. As a second
approach the differences between the transit modes by means of significant information types
and their service levels are investigated.
As discussed, ordered probit model was used for the modeling and the responses for the
hypothetical scenarios were used as the dependent variable. In the ordered probit model, twosets of parameters were estimated. First one is, the threshold parameters which indicate thecut points on the normal distribution curve, associated with specific values of the defined
explanatory variables. In five categories four thresholds are needed; first threshold was set tozero ( µ 0 = 0 ) and, other three of them were estimated. Second one is, explanatory variables,
which were considered in two groups; service levels of each information types andsocioeconomic variables with actual travel time of the respondent. In the model, explanatory
variables were defined as dummy variables. Information types were presented with theirservice levels as dummy variables. Socioeconomic variables were also defined as dummy
variables after combining some characteristics into significant and appropriate groups. Travel
times of the respondents were used as they were presented in the scenario (not dummy
variable). Many modeling attempts were done and most significant models are presented inthis paper.

4.1 Considered Information Types for Transit and Non-transit Users
Totally, 645 data was collected and 493 of them were classified as transit users. Among thenon-transit users, 45 of them agreed to consider transit as an alternative commute mode if
transit information is available. Thus, 45 data was collected from non-transit users. Survey
was designed with two scenarios to obtain more data. The number of non-transit users who
considers using transit under available information is quite small. However, since two

1752

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

observations were collected from one respondent to use, using two responses from same
respondent can widen data set. On the other hand, using two data sets from one respondent in
the model without any correction causes some biases, because the data sets are correlated(unobserved heterogeneity). Some studies have accounted for the correlation between
repeated observations, a brief review can be found in Abdel-Aty and Abdalla (2004).
Alternatively, Bunch et al. (1993) ignored the effect of heterogeneity by indicating that insmall numbers of repeated observations from each individual the properties of parameter
estimates themselves do not rely on the strict independence assumption and the benefits ofusing a much larger pooled data is more important than this concern. The effect of unobserved
heterogeneity on the estimated results could be corrected by dividing the t-statistics to thesquare root of the number of repeated observations (Louviere & Woodworth, 1983;
Mannering, 1987). However, it is indicated by the same references that, this method isconservative and over corrects the t-statistics. Thus, in the estimation of significant
information types for transit and non-transit users, since two hypothetical scenarios were usedfrom one respondent, the t-statistics are a little inflated and are not corrected to account for
the unobserved heterogeneity effects. Dividing the t-statistic by 2 can do it in a conservativeway.
Estimated models for transit users and non-transit users who would consider transit as analternative commute mode are presented in Table 3. For transit users, income and having a car
are significant (95%) socioeconomic factors, which affect the satisfaction level from thecurrent transit service under information. If the respondent is in low-income group and does
not have a car, his satisfaction level is higher. Probably, he does not have any other alternativeand cannot compare the transit service with car. For non-transit users, flexibility of work
starting time is the most significant factor with 95% significance level and also gender issignificant with 90% significance level. If the respondent has no flexibility on work starting
time, he is most likely to use his car. Male drivers consider transit as alternative more thanfemale drivers do. Travel time is significant only for non-transit users. Probably for long trips,
non-transit users are more likely to use transit because of high travel costs and uncomfortable
driving for a long time under congested conditions.
Frequency of service is significant with one service level for both transit and non-transit
users; 30 minutes waiting time affects significance level in a negative way for transit users
and 15 minutes in a positive way for non-transit users. 0.5 YTL (0.42 $) is considered ascheap transit fare and increase the satisfaction level for both transit and non-transit users andthe effect is higher for transit users. Operating hours is considered as a significant information
type with three service levels for transit users and with one service level for non-transit users.
Waiting times at the stop is considered as significant with two service levels for transit users
and with one service level for non-transit users, all decrease the satisfaction levels fromtransit service. Also, far distances between the station and house of the respondent effect thesatisfaction in a negative way. Number of transfers was considered as significant information
with two service levels and both decrease the satisfaction level for transit users. In addition,
walking time to/from the station was considered as an important information type with twolevels for non-transit users and decreases the likelihood of using transit service. Usuallyconvenience is considered more by non-transit users thus, walking distance is significant for
them.

1753

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

Table 3 Estimated ordered probit models for transit and non-transit users
Transit users
Non-transit users
Estimate (tEstimate (tstatistics)
statistics)
0.4654 (27.91) ** 0.6507 (8.46) **0.8598 (25.76) ** 0.9869 (5.98) **2.2386 (22.27) ** 2.3210 (3.67) **Thresholds
T1
T2
T3

(µ1)
(µ2)
(µ3)
Service level of transit information
0.7513 (2.39) **X2 Frequency of service (1 if frequency is 15 min, 0 otherwise)-0.4052 (-3.43) **X4 Frequency of service (1 if frequency is 30 min, 0 otherwise)
0.4295 (3.71) ** 1.1093 (1.88) **X5 Fare information (1 if fare is 0.5 YTL.(0.42 $), 0 otherwise)-1.4901 (-2.67) **X8 Walking time to/from the station (1 if walking time is 5 min., 0 otherwise)-0.8684 (-1.91) **X9 Walking time to/from the station (1 if walking time is 10 min. , 0 otherwise)-0.5895 (-4.20) **X12 Transfer information (1 if 2 transfer is needed, 0 otherwise)-0.7441 (-3.32) **X13 Transfer information (1 if 3 transfer is needed, 0 otherwise)
X15 Operating hours (1 if operating hours is between 6.00am-midnight, 0 otherwise) 0.4170 (3.38) ** 1.0132 (2.03) **X16 Operating hours (1 if operating hours is between 6.00am- 2.00 am, 0 otherwise) 0.3761 (3.03) **0.8895 (6.47) **X17 Operating hours (1 if operating hours is 24 hours, 0 otherwise)-0.3099 (-2.92) **X19 Waiting times at the stops (1 if waiting time is 20 min., 0 otherwise)-1.1625 (-6.81) ** -1.9782 (-2.43) **X20 Waiting times at the stops (1 if waiting time is 30 min., 0 otherwise)-1.2083 (-3.02) **X25 Distance of the stop to the resident of the respondent (1 if 750m, 0 otherwise)
X26 Distance between the station and respondents home (1 if 1000m, 0 otherwise) -1.3697 (-5.95) **Socioeconomic characteristics and some other significant factors-0.0016 (-1.15) 0.0116 (1.80) **S1 Actual travel time of the respondent
0.5774 (1.68) *S2 Gender (1 if male, 0 otherwise)
0.5641 (8.54) **S4 Income (1 if lower than 1,000 YTL.(833 $), 0 otherwise)
0.2819 (4.19) **S7 Having a car (1 if respondent has no car, 0 otherwise)-0.7527 (-2.04) **S8 Flexibility of work starting time ( 1 if there is no flexibility, 0 otherwise)
Number of samples
975
90
Log likelihood at zero-2402.73-189.08
Log likelihood at convergence-1348.81-101.02* 0.10 significant ** 0.05 significant

Both transit and non-transit users considered six information types as significant. Fiveinformation types are same among transit and non-transit users. However, there aredifferences on the considered service levels for same information types. In addition, walking
distance to/from the station is considered as significant among non-transit users different fromtransit users and only transit users consider number of transfers as significant information.
4.2 Considered Information Types Depending on Transit Mode Segments
As a result of the survey 76% of the commute/school trips are done by transit service in Izmir
City. One of the main reasons of this high transit usage percentage is low car ownership rate
and the other one is the high gasoline prices (1.95 $/lt.) in Turkey. TIS can be used for two
important purposes for transit users. First one is to provide a more comfortable service to thecurrent transit users, second one is also related with the further, to keep actual users as atransit user in the future. As indicated in the survey design, most of the non-transit users are in
old age (40 years old<=) and high-income (1,000 YTL(833 $)<=) group. It is the reality thatmost of the young population cannot afford a car today in Turkey, however they would
convert to use their car when they will afford it. For the listed reasons above, the applicabilityof TIS must be investigated more deeply especially for transit users. Increasing economic
welfare will cause increasing traffic problems in near future, if the investments forinfrastructure fall behind. Successful TIS applications can help to solve such problems.

1754

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

The transit service in Izmir City has five different modes as discussed. The quality of thesemodes is not similar. Local train service is not a common transit mode for intra-citycommuting. Only two respondents answered that they use this mode in daily commute or
school trips. Thus, this mode was eliminated. Subway and commuter boats have more
comfortable service than bus and dolmus modes. In addition, subway and boat have reliable
and fast service. These similarities between transit modes gave us an idea to consider twosegments of transit modes and to investigate the effects of TIS separately for these mode
segments. Comparing ridership attraction for subway/boat and bus/dolmus modes, what kindof transit information considered as urgent is important. Subway/boat services tend to havethe advantage of having more information over bus/dolmus services. Subway/boat services
operate on an easily identifiable right-of-way and have higher frequency rates thanbus/dolmus service. In some advanced countries there is reliable bus frequency information
but still the off-hand information in Turkey for subway/boats are considered to be better thanbus/dolmus modes due to mentioned reasons. The available TIS in Izmir City for these
considered four modes are indicated in Table 4, but all these information are not easily
accessible. Usually, most of the transit information for subway and boat modes available via
internet and most of the users are not aware of this information. Available TIS is similar
among the mode segments and it can be seen that it is totally different between the mode
segments. At waiting time, level of comfort at subway stations and commuter boat harbors areusually higher than bus/dolmus stops. Also comfort level in subway and boat are higher thanbus/dolmus modes, former can provide better ventilation and more seats. On the contrary of
the poor service and lack of information bus/dolmus stops are generally be within walking
distance of a greater number of people acting as their own feeders and serves in larger areas.
Table 4 Available information for transit modes
SubwayFare information
Information about operating hours

Commuter boat
Bus
Fare information
Fare information
Information about operating hours Information about operatinghours
Frequency information
Frequency information
Transit route map
Transit route map
Information about station locations Information about station
locations

DolmusFare information

Depending on these differences in the quality of service, responses to hypothetical scenarios
vary among transit users. From this point, we thought that significant information types forboth segments could be different from each other. Thus, two additional models were estimatedseparately for subway/boat and bus/dolmus modes.
4.2.1 Subway and boat modes
Due to the discussed reasons above subway/boat modes are gathered in a segment. Especially,
differences in the service quality and high travel timesavings of this segment attract different
socioeconomic groups. Considered TIS and satisfaction levels from available information
types for subway/bus users have some differences from those of other transit segment users.
Thus, it is important to consider the significant information types separately for two transit
segments. Understanding the characteristics and needs of different types of users is one of themain cornerstones in providing and deciding successful applications of TIS. For this purpose
significant information types are investigated for these two mode segments separately. The
final and significant model for subway/boat segment is indicated in Table 5.

1755

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

Table 5 Results of the estimated model for subway/boat and bus/dolmus segments.
Subway/boat
Bus/dolmus
Estimate (tEstimate (tstatistics)
statistics)**
0.6106 (10.06)
0.5330 (16.61)****1.2841 (10.73)
0.8726 (13.50)****
2.7213 (5.57)
2.0027 (9.00)**

Thresholds
T1
T2
T3

(µ1)
(µ2)
(µ3)
Service level of transit information-0.8103 (-1.99)** -0.6069 (-3.13)**
X4 Frequency of service (1 if frequency is 30 min, 0 otherwise.)0.8279 (2.50)**
X5 Fare information (1 if fare is 0.5 YTL (0.42 $), 0 otherwise)
0.6739 (1.90)**
X6 Fare information (1 if fare is 0.75 YTL (0.63 $), 0 otherwise)
0.4713 (1.65)*
X8 Walking time to/from the station (1 if walking time is 5 min., 0 otherwise)-0.7839 (-2.38)**
X11 Transfer information (1 if 1 transfer is needed, 0 otherwise)-1.7420 (-1.01) -1.0025 (-2.20)**
X13 Transfer information (1 if 3 transfer is needed, 0 otherwise)
0.8254 (2.17)**
X15 Operating hours (1 if operating hours is between 6.00 am - midnight, 0
otherwise)
0.3159 (1.51)*
X16 Operating hours (1 if operating hours is between 6.00 am- 2.00 am, 0
otherwise)
2.0874 (1.90)** 0.5446 (2.27)**
X17 Operating hours (1 if operating hours is 24 hours, 0 otherwise)-0.4924 (-1.56)*
X19 Waiting times at the stops (1 if waiting time is 20 min., 0 otherwise)-1.0708 (-3.51)**
X20 Waiting times at the stops (1 if waiting time is 30 min., 0 otherwise)-0.4751 (-1.30)
X21 Seat availability (1 if 50% seat is available in the coming transit, 0 otherwise)-0.9251 (-1.51)*
X22 Seat availability (1 if 75% seat is available in the coming transit, 0 otherwise)-0.4347 (-1.30)
X24 Distance between the station and respondents house (1 if 500m, 0 otherwise)-1.1930 (-2.46)**
X26 Distance between the station and respondents house (1 if 1000m, 0 otherwise)
Socioeconomic characteristics and some other significant factors
0.0164 (2.50)** -0.0043 (-1.55)*
S1 Actual travel time of the respondent
0.3727 (2.78)**
S4 Income (1 if lower than 1,000 YTL.(833 $), 0 otherwise)
0.4408 (2.10)**0.1963 (1.45)*
S5 Education level (1 if less than high school, 0 otherwise)
0.4684 (3.70)**
S6 Having a car (1 if respondent has no car, 0 otherwise)
Number of samples
113
308
Log likelihood at zero-278.399- 796.024
Log likelihood at convergence-149.997-442.058* 0.10 significant ** 0.05 significant

Travel time and education are the only two significant variables among the socioeconomic
characteristics and other trip attributes. Generally, subway/boat modes are fast transit modes
and users of both modes consider travel time more than other mode users. Subway and boat
users consider three transit information types significant (95%) with five service levels.
4.2.2 Bus and dolmus modes
Bus/dolmus users were considered in another group among transit users. The biggest share of
the transit users belong to this group. Depending on our survey, 73% of transit passengers use
even bus/dolmus as their commute mode. Nevertheless, they do not have enough transit
information available. Finally, most significant estimated model for bus/dolmus users isindicated in Table 5.
In the estimation, travel time and education level are 90% significant. Income and having car
dummy variables are 95% significant. Having a car dummy variable is very significant in thisestimation, however, it was not in the previous model (subway/boat segment). The carownership rate is high among the subway and boat users. Users who have car available for
their commute/school trips prefer subway and boat modes because they are as fast as car
mode (sometimes faster) and also comfortable. However, this is not the case for bus anddolmus users. Usually they have no choice except using these modes because they do not
1756

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

have car. In the model five information types were considered as significant (95%) with six
service levels. Significant information types for two mode segments are indicated in Table 6
with number of significant service levels in parenthesis.
Table 6 Significant information types among mode segments
Subway/boat
Frequency information (1)Information about operating hours (2)Fare information (2)Bus/dolmus
Frequency information (1)Information about operating hours (1)Information about transfer and transfer locations (2)Information about waiting times (1)Information about station locations (1)Considered information types vary among mode segments. Frequency of service andoperating hours are common transit information types. Bus and dolmus users consider
additional three information types as significant. Subway and boat users consider only one
information type different from other mode segment. Available information about frequency
and operating hours increases the satisfaction level of both mode segment users. However,providing other information types must be considered separately for different mode segments.
These would increase the effectiveness of TIS. In addition, cost for TIS applications will be
reduced by providing only considered information types for the transit modes. Consideredinformation types must be investigated carefully and these kinds of investigations must be
done periodically, because interests of groups can change with changing conditions.

5. CONCLUSIONSThis study depends on the data collected by in-person interviews from 645 respondents in
Izmir City, Turkey. Most of the residents use transit as their commute mode in big cities
because of the income level and high gas prices. Mainly, the effects of TIS on the satisfactionlevel of the transit users were investigated. TIS have an important role to increase the comfort
level of the current users. In addition, the effects of TIS to attract non-transit users to use
transit as an alternative commute mode were investigated. 29.6% of the non-transit users
declared that they would consider transit if more information was available.
In the survey, ten transit information types were presented. A stated preference scenario wasprepared considering the actual travel time of the respondent and chosen most important three
information types. Satisfaction levels of respondents were investigated with these scenarios.
By using ordered probit modeling. Finally, four models were considered. First two models
were estimated for transit and non-transit users, separately. Then, two additional models were
estimated by separating transit users into two segments. Survey results showed that 76% of
the commute and school trips were done by public transit in Izmir City. Thus, in the second
part, study focused on transit users. Transit modes have some significant differences from theviewpoint of service quality and available transit information. Thus, transit modes are divided
into two segments. Subway/boat modes have more reliable, fast and comfortable service withsome available transit information. On the other hand, bus/dolmus modes do not have reliable
and comfortable service and available TIS is just a few. Depending on the differences indifferent transit modes, expectations from the available TIS and satisfaction levels of transit
users from this information are different. Thus, subway/boat modes are combined in asegment and bus/dolmus modes are combined in another segment.

1757

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

Results of estimated first two models showed that mostly considered significant information
types are same among transit and non-transit users (5 information types). However, responses
for various service levels are different among transit and non-transit users. In addition, transit
and non-transit users considered one information type as significant different from each other.
For transit service providers, it is important to consider the needs of different individual
groups. Non-transit users’ life standard is usually high because of high income. Thus, their
expectation from transit service is different from others. Additionally, the socioeconomic
characteristics of the transit and non-transit users were investigated. It is found that somesocioeconomic factors are significant in this decision. Gender and flexibility of work startingtime are significant among non-transit users, and income and having a car are significant fortransit users.
Subway/boat users considered three information types with five service levels as significant.
On the other hand, bus/dolmus users considered five information types with seven service
levels. Two of the considered information types are the same among the segments. Different
from bus/dolmus, subway/boat users consider information on fare as significant. However, forbus/dolmus users waiting times and station locations are significant information types,
because there is no available such information for bus/dolmus modes. In addition, number oftransfers is another significant information type for bus/dolmus users, because transfer
information is not available and transfers are uncomfortable for this group. The mostsignificant socioeconomic factors for both subway/boat and bus/dolmus users are actual travel
time and education level. In addition, income and having a car are considered as significant
among bus/dolmus users.
This study shows that people would like to be informed about the service levels of transit andthis information affect their satisfaction by transit service. Considered TIS does not varymuch among transit and non-transit users. However, considered TIS among different mode
segments is significantly different from each other. To provide more effective andcomfortable service, TIS can be considered as an effective tool. However, before providing
information considered information types for different groups must be investigated deeply.
This will be achieved by providing more effective information with minimum cost.
TIS have a promising effect to improve the service quality of transit service. TIS provide
improvements for two different groups of individuals. First one is for transit users by
improving their satisfaction levels from the available public transit services. Mostly young
population uses transit in Turkey. When they have economic independence and can afford acar, they can quickly change their mode. Thus, it is important to improve service quality oftransit to compete with car mode. Second group is non-transit users. To solve many problems
such as traffic congestion, air pollution caused by exhaust gases etc., it is important to attract
non-transit users to use transit instead of car. With a little effort and with small budgets atleast some of the static transit information can be provided for the users and the positiveeffects of this information would be more than expected. Especially, in developing countries
TIS must be introduced parallel with new transit services and infrastructure investments,
because the need for information is growing fast both in developing and developed countries
in all stages of life.

1758

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

REFERENCES
Abdel-Aty, M., (2001) Using ordered probit modeling to study the effect of ATISon transit
ridership, Transportation Research C9, 265-277.
Abdel-Aty, M., and Abdalla, M.F., (2004) Modeling drivers’ diversion from normal routes
under ATIS using generalized estimating equations and binomial probit link function,
Transportation, No. 31, 327-348.
Adler, J.L and McNally, M.G., (1994) In-laboratory experiments to investigate driver behavior
under advanced traveler information systems, Transportation Research 2C, 149-164.
Bunch, D., Bradley, M., Golob, T., Kitamura, R., and Occhiuzzo, G., (1993) Demand forclean-fuel vehicles in California: a discrete-choice stated preference pilot project,
Transportation Research A, No. 27(3), 237-253.
Cluett. C., Bregman, S., and Richman, J., (2003) Customer preferences for Transit ATIS:
Research report. Report No. FTA-OH-26-7015-2003.1, US Department of Transportation,
Washington, D.C.
Greene W., (2000) Econometric Analysis, Fourth Edition, Prentice-Hall, New Jersey.
Hall, R., Colletter, E., and Yim, Y., (1994) Evaluation of transit telephone information at
SCRTD, Transportation Research Record, No. 1451, 49-57.
Hickman M. and Day T., (1996) Assessment of information systems and technologies at
California Transit Agencies, Transportation Research Record, No. 1521, 49-57.
Khattak, A.J., Polydoropoulou, A., and Ben-Akiva, M.E., (1996) Modeling revealed andstated pretrip travel response to advanced traveler information systems, Transportation
Research Record, No. 1537, 46-54.
Kitamura R., Reddy P., Vaughn K. and Jovanis P., (1995) Transit pre-trip information systems:
On experimental analysis of information acquisition and its impacts on mode use, In
Proceedings of the second World Congress and Intelligent Transport Systems, Yokohama,Japan, Vol. II, 720-729.
Louviere, J., and Woodworth, G., (1983) Design and analysis of simulated consumer choice or
allocation experiments: an approach based on aggregate data, International Journal ofMarketing Research, No. 20, 10-14.
Mannering, F., (1987) Analysis of the impact of interest rates on automobile demand,
Transportation Research Record, No. 1116, 10-14.
Mehndiratta, S., Kemp, M., Peirce, S., and Lappin J., (2000) Users of a regional telephonebased traveler information system – a study of Travinfo users in the San Francisco Bay
Area, Transportation, Vol. 27, 391-417.
Polak J. and Jones P., (1992) The acquisition of pre-trip information: A stated preference
approach, In proceedings of 71st Annual Meeting of Transportation Research Board,
Washington, DC.
Schweiger C., (1995) En-route transit information systems: The state of art in North America,
In Proceedings of the second World Congress and Intelligent Transport Systems,
Yokohama, Japan, Vol. II, 985-990.
Shank D. and Roberts D., (1996) Assessment of ITS benefits-results from the field, Intelligent
Transportation Systems (ITS), reprint for the 1996 ITS America Annual Meeting, Houston,TX, 15-19 April.

1759