Willingness to pay for travel information - Semantic Scholar

Transportation Research Part C 11 (2003) 137–159 www.elsevier.com/locate/trc

Willingness to pay for travel information Asad J. Khattak

a,*

, Youngbin Yim b, Linda Stalker Prokopy

c

a

c

Carolina Transportation Program, Department of City and Regional Planning, University of North Carolina at Chapel Hill, 3140 New East Building, Chapel Hill, NC 27599, USA b California PATH Program, Department of Civil Engineering, Institute of Transportation Studies, University of California at Berkeley, 1357 South 46th Street, Building 452, Richmond, CA 94804, USA Department of City and Regional Planning, University of North Carolina at Chapel Hill, 3140 New East Building, Chapel Hill, NC 27599, USA Received 12 May 2000; accepted 6 November 2001

Abstract Improved travel information received via electronic sources can inform people about travel conditions and help them make travel decisions. The personal benefits of high quality travel information may motivate individuals to pay for information. This study analyzes travelersÕ willingness to pay for better quality information received from a traveler information system offered through a public–private partnership in the San Francisco Bay Area. The data were collected in 1997 through a computer-aided telephone interview of individuals who called traveler advisory telephone system (TATS) and were willing to be interviewed (N ¼ 511). The survey results indicate that the average number of times per month the respondents called TATS was 4.80 (TATS was a free service at the time). The average use of the system would decline if the service was not improved but a service charge was initiated. People indicated that they were more willing to pay for a customized service. The impacts of travel information, travel context and socioeconomic variables on willingness to pay for information were analyzed by estimating a random-effects negative binomial regression model of revealed and stated TATS calling frequency. The results indicate that customized travel information, longer trips, worktrips, and listening to radio traffic reports are associated with higher TATS calling frequency and with greater willingness to pay for information. Overall, the consumer response to purchasing travel information services seems cost-sensitive and future efforts can focus on commercialization of travel information, beginning with where demand for information is relatively inelastic and improvement or customization of travel information is achievable. 2003 Elsevier Science Ltd. All rights reserved.

*

Corresponding author. Tel.: +1-919-962-4760; fax: +1-919-962-5206. E-mail addresses: [email protected] (A.J. Khattak), [email protected] (Y. Yim), lstalker@ email.unc.edu (L.S. Prokopy). URL: http://www.planning.unc.edu/facstaff/faculty/khattak.htm. 0968-090X/03/$ - see front matter 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0968-090X(03)00005-6

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A.J. Khattak et al. / Transportation Research Part C 11 (2003) 137–159

Keywords: Traveler behavior; Advanced traveler information systems; Survey research; Modeling; California

1. Introduction Electronic media are increasingly facilitating the dissemination of dynamic information in urban areas of the United States. Such information may allow travelers to adjust their travel and activity participation decisions in response to network conditions. New telephone services are emerging as a source of higher quality travel information for which consumers may be willing to pay. However, most traveler advisory telephone systems (TATS) are presently offered free of charge as they are undergoing field tests and evaluation. Though the ultimate aim of field experiments is to develop systems that can be financed through user fees (or third party sponsors, i.e., advertisements) and not public expenditures. This paper examines whether people who are using a TATS are also willing to pay for both present levels of service and for a customized service. To make more informed investment decisions about supporting free of charge travel information services, the public sector needs to know how much, if at all, individuals are willing to pay for existing and customized services. Analyzing willingness to pay for information helps inform the debate on subsidization of travel information services (Kanninen, 1996). Traveler demand for information in the geographically diverse nine-county San Francisco Bay Area is studied. Since September 1996, a federally funded field operational test (FOT) of a traveler information system called the TravInfo TATS has been in operation in the Bay Area. Callers receive current travel information on selected routes and transit options. Studying the caller behavior in this program provides an ideal basis for determining whether individuals are willing to pay for equivalent or improved (customized) services. This paper analyzes the survey responses for individuals who have used the Bay Area TATS service at least once. Because survey participants are already familiar with the service, their responses provide a good resource to gauge their willingness to pay for customized travel information. To understand the effects of information attributes, individual characteristics and travel context on individualsÕ use of and willingness to pay for travel information, a random-effects negative binomial regression model is estimated. The model combines revealed-preference data on telephone calling frequency with stated-preference data on willingness to pay for information. In this paper, revealed preference refers to reported past calling behavior and stated preference refers to how respondents say they will access information if they had to pay for information.

2. Conceptual structure Three key elements of the transportation system are the network (i.e., supply), the users (i.e., demand), and the travel information system. The transportation network capacity in urban areas is often exceeded during the peak periods and capacity reduction can occur due to incidents (Al-Deek et al., 1998). In response to network conditions, individuals may change their travel decisions such as mode, departure time and route. Their decisions are also influenced by other contextual factors, travel information and their own attributes. While traveler decision-making is


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relatively extensively investigated, the willingness to access and pay for information services is not. Individuals may be more likely to pay for higher quality travel information when: • Travel time uncertainty is high, e.g., if incident induced congestion occurs frequently. • Information is available to a selected few individuals, e.g. if only a few individuals know about an incident, then they may be able to divert to relatively uncongested alternate routes, while uninformed drivers take the congested route. • Perceived benefits of information use (e.g., travel time savings and anxiety reduction) exceed the perceived costs of information acquisition. The travel information system often consists of private sector information vendors (also known as Information Service Providers or ISPs) and government agencies that collect and process travel information. The governmentÕs goal is not only to disseminate it to the general public, but also to use it for dynamic traffic control and to promptly respond to incidents. Travel information in many large US cities is currently being supplied to the general public mostly through private information vendors, though some public resources are used to collect, process and disseminate travel information. In the following paragraphs, the paper focuses on developments in travel information systems and usersÕ willingness to pay for travel information. Currently, private information vendors supply travel information to radio and television stations (Malchow et al., 1996). By listening to the radio, watching television or browsing the Internet, individuals indirectly pay for these services with their time. Traffic information providers earn their revenues from advertising fees. Public-sector traffic information suppliers often do not market their services directly to the end users, in this case, the travelers; rather, the information is provided directly to the disseminators such as radio or television stations or ISPs. The ISPs augment the publicly available information with their own travel monitoring, often through airborne and ground observers/reporters. In large US cities, about one-half of all commercial radio and television airtime is sold to sponsors for advertisement of their products. The demand for traffic reports has increased recently, and commercial networks perceive travel information services as good revenue generators. Most of the cost to suppliers is equipment (typically surveillance aircraft) and personnel needed for operations and data collection. Supplying travel information typically produces economies of scale, with decreasing marginal cost and increasing marginal revenues. For this reason, the trend in the traffic information market is toward one supplier in small cities, with competition existing only in larger metropolitan areas. The viability of private information vendors gives limited evidence that individuals may be willing to pay for private advanced traveler information systems. However, it is unclear exactly what attributes of travel information individuals are willing to pay for. Travel time information is probably most valuable when uncertainty of travel conditions is high, e.g., in incident induced congestion situations (Khattak et al., 1996a,b), and those benefiting most will be more willing to pay for the information. There is limited evidence regarding willingness to pay for dynamic travel information (see Table 1). Polydoropoulou et al. (1997) estimated willingness to pay indirectly through a latent variable model. The remaining studies are based upon surveys that gathered willingness to pay data, although in some cases it is unclear how data was collected. All of the findings suggest that under certain conditions, some urban travelers are willing to pay for information, provided that the

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Table 1 Literature synthesis Source

Data

Findings

In 1998, the monthly call volume of the TATS traffic and transit information services was constant––about 8000 calls per month for traffic and 30,000 per month for transit information. Survey participants were fairly satisfied with the service. Polydoropoulou et al. Surveys of users and For non-users, the higher the expected benefit from (1997) non-users (SmarTraveler, ATIS, the higher the willingness to pay (measured as a Boston) latent variable estimated by the importance placed on reliability, relevance and coverage of a system). For users, satisfaction with the service is important in determining utility. Travelers were very sensitive to ATIS usage charges and demand is highly elastic. During test time, 1993–1996, the SmarTraveler service Englisher et al. (1996, 1997) Surveys of users and grew to 60,000 calls per week per 20,000 callers. When non-users (SmarTraveler, users were asked if they would pay a fee, projected use of Boston) SmarTraveler decreased by 36% at 10 cents a call. At a flat fee of $2.50 per month, about half the respondents said they would be very unlikely to subscribe. Cellular phone users were ‘‘quite price sensitive’’. Remer et al. (1996) Behavioral response (Trav- Some travelers are willing to pay for certain features of link project––Minnesota) an information service. Hobeika et al. (1996) Interviews and surveys of Travelers are willing to pay for pre-trip and en route I-95 corridor travelers: travel information. Maine to Virginia Harriss and Kanheim TravelersÕ attitudes survey Willingness to pay for access to improved information is (1995) (New York) high among travelers of all income groups. Small urban area commuters who perceived a high need Khattak et al. (1995) Surveys of travelers and for ATIS were willing to pay more. multivariate statistical models (Centre County) Cellular phone subscribers were not inclined to use the Yim et al. (1991) Behavioral survey of cellular providerÕs traffic information service at a per-call San Francisco Bay Area charge of $1. residents Yim et al. (1998)

Behavioral survey of San Francisco Bay Area residents

information is satisfactory and the perceived benefit from the service is high. Some of the studies reveal that current information system users are willing to pay for information that is currently provided free of charge (Remer et al., 1996; Polydoropoulou et al., 1997; Wolinetz et al., 2001). However, charging for travel information services is not currently widespread in the US. Also, variations in willingness to pay across urban areas have not been investigated through comparative studies. The rationale for investing public resources in collecting travel information is that studies show that the content, quality and medium of travel information influence traveler behavior and may reduce traffic congestion and delays (Wenger et al., 1991; Schofer et al., 1993; Khattak et al., 1995). In incident-induced congestion, the perception of delay and quality of real-time (delay) information are valuable in travelersÕ adjustment decisions. Survey research shows that a majority of individuals residing in urban areas access, use and respond to travel information. Travel in-


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formation is acquired through various sources, including direct observation and electronic sources. Greater uncertainty in travel conditions, possibly due to incident and other bottlenecks, motivates people to acquire real-time travel information. On repetitive trips, the acquired information is then interpreted and compared with historical perception of travel times between the origin and destination. The acquired information is processed to either revise the normal or planned trip pattern or to make no change (Ben-Akiva et al., 1996). Higher perceived accuracy of traffic reports and longer driving time are associated with higher frequency of pre-trip route changes (Abdel-Aty et al., 1995). Khattak et al. (1996a,b) have found that longer unexpected delays and information received from electronic media (as opposed to selfobservation of traffic congestion) increases the probability of changing several pre-trip decisions. As the market penetration of travel information increases, the benefits of information may begin to diffuse. The potential for peak period congestion relief through travel information may be limited, given that alternate routes are also congested and other alternatives (such as switching to transit) are often not feasible. However, there is greater potential for information services when travel conditions are uncertain. The following theoretical and policy issues have been identified from the literature as requiring further study: • What is the link between willingness to pay for information and information attributes, sociodemographic and contextual factors? • Will the users of a free and subsidized information system pay for existing and improved (customized) services? • Where should future travel information policies and programs in large and medium sized urban areas be targeted?

3. Survey context and implementation TravInfo is a FOT sponsored by the Federal Highway Administration (FHWA) of the US Department of Transportation (DOT) and the California Department of Transportation. Combining public and private sector resources, TravInfo has developed a multi-modal traveler information system for the San Francisco Bay Area (Fig. 1). Specifically, the test location encompassed nine counties with a population of approximately six and half million people and a diverse transportation network traveled by single-occupancy vehicles, high-occupancy vehicles such as vanpools and buses, other motorized vehicles and bicycles, as well as light rail, rapid rail, commuter rail, cable cars and ferries. Two important objectives of TravInfo are to generate a market for travel information, and to encourage private firms to offer traveler information (Yim et al., 1998). TATS is a new service offered by the TravInfo partnership. During a two-week period in April 1997, callers to TATS had their calls randomly intercepted to generate a sample of system users. Callers were asked if they could be contacted within 48 hours to answer questions about their call to TATS. To prevent multiple surveys of the same individual, repeat interceptees were rejected from the follow-up call pool. Though calls were randomly intercepted, the sample obtained was not purely random as attempts were made to

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Fig. 1. TravInfo coverage region.

impose quotas on traffic and transit calls, and on males and females, to better emulate the population (Yim et al., 1998 contains further details). A computer-aided telephone interview (CATI) method was utilized in the follow-up calls. During the follow-up calls, a comprehensive survey was conducted which took an average of 15 min to complete. 2175 calls to TATS were intercepted during the two weeks, representing 11.4% of the average number of calls in a twoweek period. Only 27% of interceptees agreed to participate in the survey. However, of the 587 people who agreed to a follow-up call, 511 (87%) completed the CATI survey. To assess the generalizability of the survey results, Yim et al. (1998) conducted exploratory analyses examining how respondents compared to another survey of the Bay Area residents. Specifically, a Bay Area-wide survey was conducted in 1995 using random digit dialing (for details about this relatively representative survey, see Yim et al., 1997 and Khattak et al., 1999). A comparison of variables such as age, income, and ethnicity revealed some differences with the Bay Area-wide survey. The TATS callers were slightly older than respondents in the Bay Areawide survey. In general, TATS callers requesting transit information had lower incomes and were more frequently Black/African–American than were the Bay Area-wide survey participants. TATS callers requesting traffic information had higher incomes and were more frequently white or Asian American than were the Bay Area-wide survey participants (Yim et al., 1998). Given the differences between the TATS users and the relatively representative Bay Area-wide re-


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spondents, it is not possible to generalize the results. TATS users represent active information seekers. Polydoropoulou et al. (1997) outlined additional biases inherent in willingness to pay studies. These include: • Prominence bias: the respondent considers only the most important attribute of the service in making a choice; • Inertia or justification bias: the respondentÕs answers to stated preference questions are influenced by their actual choice; • Policy response/strategic bias: the respondent does not want to lose a free service and so strategically answers the questions, or after hearing about a lower service cost is unwilling to respond to higher cost alternatives; • Non-commitment bias: the respondent overstates his or her willingness to pay as there is no actual payment involved; and • Cognitive dissonance bias: the respondent currently associates the product with zero price and is unable to assess its true worth. Also paying for the service can create dissonance between the belief that travel information should be free and the actual/intended behavior. In a case where current users of a system are asked how much they would be willing to pay for the current service, policy response bias and cognitive dissonance bias may dominate (Polydoropoulou et al., 1997).

4. Survey design and questions The survey structure is given in Fig. 2. One of the key objectives is to determine whether users are willing to pay for what is currently a free service (other than local phone charges). In the TATS survey, the respondents were asked a series of questions about their current and future use of the system. To ascertain current usage of the system (revealed preference), the following question was asked: ‘‘Last month, about how often did you call TATS––or was this your first call to TATS?’’ The response categories were a mix of weekly and monthly frequencies. There are six stated preference questions, which are asked to examine how much respondents would be willing to pay for the service. The first three questions are posed in the following manner: ‘‘Suppose TATS was only available for a per-call service charge. About how many times a week do you think you would call TATS if the charge was 25 cents (50 cents, 1 dollar) per call?’’ Note that we asked for the frequency of calling TATS at various payment points that increased consecutively (from 25 cents to 1 dollar). This is the preferred procedure for asking

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Fig. 2. Survey structure (number of respondents).


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frequency questions, though the preferred procedure for asking questions about how much a respondent may be willing to pay for a product or service is to first ask about a higher payment point and lower it subsequently, if the respondent is unwilling to pay. In the above question, if the respondent answers that he or she would not call at 25 cents, then he or she is not asked the two subsequent questions. But for those who are asked subsequent questions, a potential validity threat known as strategic bias may exist; after respondents hear that they could possibly only pay 25 cents per call, they may be less likely to respond positively to the higher call charges (i.e., reduce their stated calling frequency more than they would otherwise), making their willingness to pay responses more conservative. Often contingent valuation studies circumvent this problem by asking random sets of respondents only one of the three questions, and then compiling the answers. However, for the purposes of this study, the fixed sequence of questions is useful because it obtained more information about each respondent. Another way to ask the sequence would be to start at the highest level of payment and ask the questions in reverse order. This could avoid the type of strategic bias mentioned above, but may result in Ôfalse positiveÕ responses to the high levels of payment which are later regretted when it is known that a lower payment could be made. In either sequence of payment levels there is potential for bias. The second question that is asked to ascertain willingness to pay relates to customized information access. Prior to the question about payment for this telephone service, respondents are told what the information system would offer: ‘‘Suppose TATS offered personalized information access which would reduce the time it took to get information and to make the system easier to use. You would input a personal identification number and the system would give you traffic reports on all routes you have pre-chosen. For instance, say you routinely want information on Highway 101, the Bay Bridge, and Interstate 580. The system could be programmed to give you exactly that information without having to request each routeÕs information separately.’’ The question about payment reads: ‘‘Suppose personalized information access was only available for a per-call service charge. About how many times a week do you think you would use personalized information access if the charge was 25 cents (50 cents, 1 dollar) per call?’’ In all, there are seven questions about frequency of telephone information service use––the first deals with current usage at no cost, while the other six pertain to usage with a per-call service charge. It is important to note that the first question (revealed preference) asked for frequency in terms of times per month, while the latter questions asked it in terms of times per week. Different time periods were used because cognitively it is relatively easy to recall calling frequency over a month, but it will be difficult to anticipate future travel conditions over a month––hence the time scale for the stated preference questions was condensed to a week. We also note that the stated preference questions assume that there is room for improvement in terms of customizing information. Although this is a relatively realistic assumption, given the state of TATS information at the time of the survey, we do not mean to imply that people were not satisfied with the current service.

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5. Analysis 5.1. Descriptive statistics To compare the responses, the data were changed into times per month as presented in Table 2. In some cases, it was necessary to generate times per month using a random variable. The data are right censored at 20 times per month because the categorical responses made us collapse responses higher than 20, i.e., values above 20 were coded as 20. In cases where a respondent stated that they would not pay to receive information for 25 cents a call, the responses for 50 and 100 cents were coded as zero. Similarly, if a respondent stated that they would not pay 50 cents, it is assumed they would not pay for a 1-dollar call, and that response also became zero. In a few cases, respondents did not answer the question about 25 cents a call, and in these cases, the responses are left coded as a missing value. Based upon this coding, the average number of times per month the respondents currently use the system is 4.80 (standard deviation, r ¼ 6:21). Polydoropoulou et al. (1997, p. 6) report 5.4 calls per week for a typical respondent in the Boston Area. Although we did not perform statistical tests on the difference in average monthly calls to the two systems, it is interesting to note that, qualitatively speaking, those people who call the Bay Area TATS do so less often than

Table 2 Coding of revealed and stated preference answers for survey respondents Answers

Number of calls per month

Revealed preference: ‘‘Last month, about how often did you call TATS––or was this your first call to TATS?’’ Five or more times a week 20 Three or four times a week Random variable between 12 and 16 One to two times a week Random variable between 4 and 8 One to three times a month Random variable between 1 and 3 Less than once a month 1 First call 1 Stated preference (same answer options for both same service and customized service): (1) ‘‘Suppose TATS was only available for a per-call service charge. About how many times a week do you think you would call TATS if the charge was 25 cents [50 cents, 1 dollar] per call?’’ (2) ‘‘Suppose personalized information access was only available for a per-call service charge. About how many times a week do you think you would use personalized information access if the charge was 25 cents [50 cents, 1 dollar] per call?’’ More than seven times a week 20 Seven times a week 20 Six times a week 20 Five times a week 20 Four times a week 16 Three times a week 12 Two times a week 8 Once a week 4 Less than once a week Random variable between 1 and 3 Would not use the service 0


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Boston SmarTraveler callers. The difference may be partly due to the short period of time TATS has been in service and the difference in service and costs, in addition to differences in congestion levels, socioeconomics and spatial structure. SmarTraveler callers do not have to pay for cellular call fees. Cellular calls made up over 60% of the total calls. TravInfo TATS callers had to pay a regular cell charge for the call and it was found that 30% of the TATS calls came from cell phones. Though this is not an indication that they are willing to pay for the service or they think that they are paying for the service. Given that most cellular services include a number of base minutes that are free, users may not consider cellular phone calls to TATS as paying for the traveler information. The difference may also be due to differences in survey questions and coding––note that the TATS data are censored at 20 calls per month. Englisher et al. (1997) reports increases in call frequency over time for the Boston SmarTraveler system. TATS may experience a similar increase the longer it is in service. Table 3 gives average stated usage levels if a service charge per call is instituted for the same level of service and for a customized service. The average use of the system will decline if the service is not improved (customized) but a service charge is instituted. Higher service charges result in fewer calls. However, if a service charge were instituted for customized service, average usage would increase at 25 cents a call, and then decrease. This result underscores the strong demand for customized information. At all levels of payment, respondents state that they would be more willing to pay for the customized service than to pay for the status quo. TATS users were asked if they would prefer to Ôpay per callÕ or pay a monthly fee for either the existing service or for the customized service. If there were to be a charge for the existing service, a majority of TATS users (70% of those who answered the question) would prefer to pay per call. For the customized service, 57% of TATS users (who responded to the question) would prefer to pay per call versus monthly. In the willingness to pay questions, only a fee per call option for payment was used. This should be a more intuitive mode of payment for respondents to relate to, with questions about monthly fees for a currently free service being a cognitively more difficult situation for the respondents. Asking about willingness to pay a monthly fee for a currently cost free service may result in greater cognitive dissonance and may make infrequent users more likely to state that they would not pay for the service in comparison to their responses to a fee per call situation. 5.2. Composition of the panel data set To understand what individual-level characteristics are significantly correlated with the use of a telephone traffic information service, whether free-of-charge, or with a charge per call, a panel data set was created. While it is possible to estimate seven different regression models for each Table 3 Average usage with fees, current service and customized service Fee per call

Average usage, current service

Average usage, customized service

$0.25 $0.50 $1.00

4.12 (r ¼ 5:37) 2.55 (r ¼ 4:23) 1.03 (r ¼ 2:76)

7.09 (r ¼ 6:62) 4.36 (r ¼ 4:23) 1.75 (r ¼ 3:79)

Note: Current average use of TATS ¼ 4.80 (r ¼ 6:21).

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level of information service or payment, there is a random effect inherent within each individual that cannot be predicted. These unobservable individual-level characteristics will affect respondentsÕ willingness to use the service across all revealed and stated preference categories, therefore the responses were merged into a panel data set as described below. Following the recoding of the responses into consistent units (number of times called/would call TATS per month), the data was made into a panel data set. Each revealed or stated preference response category for an individual became its own row in the data set, with an indicator variable denoting the response category. For each individual there should be seven observations (assuming a response was given for each question––or a default of zero times per month entered for some of the stated preference questions). Typically in a panel data set, for each individual there will be an observation for each time period, and an indicator variable for the time period. In this case, instead of time periods, there is an indicator variable corresponding to level of payment and service. The coding is demonstrated in Table 4 for two sample respondents. Notice that only one of the seven indicator variables can have a value of 1 for any row in the data set. The variable Ôtimes per monthÕ corresponds to the number of times per month a respondent said that they would call when asked the question that corresponds to that indicator variable. The goal of the regression analysis is to understand what factors motivate respondents to call (or say they would call) a certain number of times per month. By including indicator variables for each stated preference category, it is possible to examine if respondents are significantly more or less likely to access travel information than their current access frequency (which serves as the reference category). For each individual, all other explanatory variables (income, travel time, etc.) remain the same across service levels. Table 4 Example of coding of indicator variables and dependent variable for two hypothetical respondents Respondent number

Times per month calls/ would call

Current use

25 cents same service

50 cents same service

1 dollar same service

25 cents pers. service

50 cents pers. service

1 dollar pers. service

Additional attributes

1 1 1 1 1 1 1 2 2 2 2 2 2 2 ...

13 12 4 0 12 4 0 20 20 12 4 16 12 4 ...

1 0 0 0 0 0 0 1 0 0 0 0 0 0 ...

0 1 0 0 0 0 0 0 1 0 0 0 0 0 ...

0 0 1 0 0 0 0 0 0 1 0 0 0 0 ...

0 0 0 1 0 0 0 0 0 0 1 0 0 0 ...

0 0 0 0 1 0 0 0 0 0 0 1 0 0 ...

0 0 0 0 0 1 0 0 0 0 0 0 1 0 ...

0 0 0 0 0 0 1 0 0 0 0 0 0 1 ...

Xi vector Xi vector Xi vector Xi vector Xi vector Xi vector Xi vector Xi vector Xi vector Xi vector Xi vector Xi vector Xi vector Xi vector ...


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5.3. Hypothesized relationships Table 5 lists the variables hypothesized to influence an individualÕs decision to use and pay for a given level of service. The normal length of time it takes the respondent to make the trip about which they called TATS, measured in minutes, is expected to have a positive effect on the number of times the respondent calls TATS. Longer urban trips have more chance for congestion and delays. Kitamura et al. (1994) studied travel behavior for users of a privately operated telephone traffic information system in the Bay Area. He found that as commute distance increased, respondents were prone to making more frequent calls. Englisher et al. (1996) found a similar trend

Table 5 Description of independent variables Variable name Description of variable

Mean (standard deviation)

Percentage responses missing

Expected direction

Typmins Tripfreq Transit

Normal trip time, in minutes Frequency of similar trips in a typical week Indicator variable, coded 1 for respondents who used public transit for trip called about Indicator variable, coded 1 if respondent was calling for information about a trip to work Indicator variable, coded 1 if respondent encountered unexpected traffic congestion on trip Frequency with which respondent tunes into radio traffic report before leaving home. 0 ¼ never, 1 ¼ less than once a week, 2 ¼ one to two times a week, 3 ¼ three to four times a week, 4 ¼ five or more times a week Frequency with which respondent tunes into television traffic report before leaving home. Coding same as radio traffic reports Indicator variable, coded 1 if total family income was between $20,000 and $39,999

53.76 (34.60) 4.33 (3.79) 0.430

57.5 61.0 48.1

Positive Positive Negative

0.497

26.0

Positive

0.392

71.0

Positive

1.88

0.00

Positive

0.990

0.00

Positive

0.237

8.0% for all income

Indicator variable, coded 1 if income was between $40,000 and $59,999 Indicator variable, coded 1 if income was between $60,000 and $79,999 Indicator variable, coded 1 if income was $80,000 or higher Indicator variable, coded 1 if respondent has a cellular phone in any of household vehicles Indicator variable, coded 1 if last grade or year of school that respondent completed was college completion or higher

0.178

–

Positive; reference category income < $20,000 Positive

0.119

–

Positive

0.239

–

Positive

0.369

16.8

Positive

0.600

0.78

Unclear

Worktrip Trafcong Freqrad

Freqtv

Inc2030

Inc4050 Inc6070 Incmax Cellphn College

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among users of a publicly run service in the Boston area. The logarithm of length of time is used in the modeling. Over 57% of respondents did not answer the question about their normal trip time; as can be seen in Fig. 2 some of these were not asked the question (46.5% of the total respondents). Respondents who called TATS for general information or who did not make the trip on the day they called were not asked about their normal trip time. The number of times a respondent makes a similar trip in a typical week is hypothesized to increase the number of times they call TATS, partly because they will know the route to inquire about. The average number of times TATS users make similar trips in a typical week is 4.3. The logarithm of this variable is used in the modeling. Forty-three percent of respondents called TATS to inquire about public transit. Assuming that dynamic travel information is more valuable in uncertain travel conditions, transit callers are, on average, less likely to call TATS frequently because they have less control over the route taken. Traffic congestion will not play into their decisions as much as it does for respondents who travel in personal vehicles. Due to the CATI skip pattern, a large number of respondents did not respond to the question about transit (48.1%). The respondents could call to receive information regarding trips undertaken for a variety of purposes. If the most recent call was about a trip to work (almost half the sample), then it is expected that the respondent will be more likely to call frequently in the future as their time may be relatively more valuable than that of a respondent calling for personal business. Twenty-six percent of respondents did not respond to the question about trip purpose due to skip patterns. If the respondent encountered unexpected traffic congestion on the trip they called about, it is hypothesized that they will be more likely to call frequently in the future, as this is indicative of congestion problems and travel time uncertainty. Alternatively, people may not access traffic information more frequently than before simply because they experienced a traffic problem. Over 70% of respondents did not respond to this question, and there are no a-priori hypotheses about the direction of this indicator variable. One may hypothesize that the more a respondent uses other forms of information, TV or radio, the more he or she will call TATS. That is, information seekers (surveyed in this study) may want to receive information from multiple sources. Alternatively, one information source may substitute for others, so it is possible that callers who rely on TATS do not use other information sources (or those who rely on radio and TV, do not call TATS frequently). There are four indicator variables for income; the reference category is the lowest income group. It is hypothesized that as income increases, respondents will be more likely to call TATS frequently due to their higher value of time. An indicator variable was created to account for respondents who did not provide their income (described below); these respondents probably belong to higher income groups and the missing income variable should have a positive influence on the call frequency. Only 8.0% of the respondents did not answer the question concerning income. The remainder of the respondents are distributed fairly evenly among income categories, with no category containing fewer than 10% of the respondents. Thirty-seven percent of respondents have cellular phone access in any of their household vehicles. It is expected that respondents with a cellular phone will be more likely to call frequently given the convenience provided by having phone access in the vehicle. Sixty percent of respondents have at least a college degree. It is unclear what effect this will have on frequency of calling.


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Due to high numbers of missing values in the independent variables, it was preferable to create indicator variables to account for the missing independent variables. This was done in the following way: • The missing data are coded as zero in the original variable. The original variable is now available for every respondent. • For respondents who did not answer the question, a second variable is created which is coded as 1 if the datum was missing, and 0 if the datum was not missing. This variable represents the behavioral pattern of respondents who did not answer the question. Once the original variables have been recoded following the above steps, all the indicator variables must be included together in a regression model and interpreted simultaneously. If the created indicator variable for missing data is statistically insignificant then the non-respondents are not significantly different from the respondents who provided information. 5.4. Random effects negative binomial model In the panelset format, the dependent variable is a count variable. Using linear regression models for a count variable can lead to inefficient, inconsistent and biased estimators (Long, 1997). The most common model used for count variables is Poisson regression, where the Poisson distribution determines the probability of occurrence. One of the assumptions of the Poisson model is that the conditional mean and the conditional variance of the dependent variable are equal. As a result, a common problem in estimating a Poisson regression is over-dispersion where the conditional variance exceeds the conditional mean. This can be mediated by allowing the variance to differ from the mean. The negative binomial model is an extension of the Poisson model, which allows for over or under-dispersion. In estimating a negative binomial model, an additional parameter, h, is generated. A statistically significant h indicates that this model is preferable to the Poisson model. In the Poisson regression model, the conditional mean of y given x is represented in the following way: li ¼ expðxi bÞ, where li is the conditional mean of y for individual i, and xi b is the linear predictor, the vector of characteristics of person i, and the effects of xi . In the negative binomial model, the mean is replaced with a random variable: l~i ¼ eðxi bþei Þ

ð1Þ

where ei is a random error term that is uncorrelated with xi . In the negative binomial model, variation in the conditional mean is due both to variation in x among individuals and to unobserved heterogeneity. The relationship between the conditional means in the Poisson and the negative binomial models can be illustrated: l~i ¼ eðxi bÞ eðei Þ ¼ li eðei Þ ¼ li di

ð2Þ

where di is defined to equal expðei Þ. In order to identify this model an assumption about the distribution of this error term is needed. The most frequent assumption, and the one used in this analysis, is that di has a gamma distribution.

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It is possible to extend the above model to panel data sets. In this paper, a random-effects model rather than a fixed-effects model is estimated (Hausman et al., 1984; Shankar et al., 1998). The choice between fixed-effects and random-effects models can be controversial, but in this case it is clear that only a random-effects model will give meaningful results. In a fixed-effects model, time-invariant characteristics are differenced out. By estimating a random-effects model, it is acknowledged that there are unobservable characteristics of the individual that will influence their responses in each time period (or, in this case, for each response category). A limiting assumption of the random-effects model is that the random effect, which controls for unobserved variables, is uncorrelated with the included independent variables. For a Poisson regression, the random-effects model is estimated by making the predicted mean of a random variable the same as in the simple negative binomial model: l~it ¼ lit ai ¼ eðxit bÞ þ l0 þ ui

ð3Þ

where ai is a random individual specific effect which is captured both by the constant, l0 and by the error term, ui . Each observation has a subscript for the individual, i, and the response category, t. Notice that there are now observations both for individuals and across response categories (there are a maximum of seven responses per individual). It is assumed that the mean is randomly distributed across individuals. In the random-effects negative binomial model, lit varies randomly across response category even if the xit Õs are constant. Due to the variance components of this model, there are two parameters in the distribution for di ; distributing di as a beta random variable, the parameters ‘‘a’’ and ‘‘b’’ are estimated. From these parameters the mean and variance of di can be computed. It is assumed that the over-dispersion parameter is randomly distributed across groups. Group effects are conditioned out of this model, and it is not possible to generate either marginal effects or predicted values (Greene, 1995; Hausman et al., 1984). It is possible to estimate this model for an unbalanced dataset in which not every individual has the same number of responses. This is the case for these data, as some of the questions about stated and revealed preference had missing answers. Finally, the model is specified as right censored at 20 calls per month due to collapsing the higher responses into a single value (20). The mean and variance of the dependent variable are 3.56 and 29.27, respectively (standard deviation is 5.41). Thus the data are over-dispersed. Overall, the random-effects model accounts for over-dispersion, serial correlation and heterogeneity at the observation level (when the mean-variance ratio grows with higher means). 5.5. Model results Table 6 shows the estimation results. Frequently with random-effects models it is possible to get statistics that provide the amount of correlation that is explained by each individual. However, this is not possible for the random-effects negative binomial model, though some informal statistics can be considered. The log-likelihood at convergence shows a substantial increase in the random-effects negative binomial model ()5162.3) compared with the negative binomial model ()5413.7). So allowing variance effects to differ within and between individuals is useful. The rhosquared value (q2 ) is an informal goodness of fit statistic that favors the random-effects model. The standard errors for the random-effects model, reflected in the t-statistics, are comparatively


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Table 6 Negative binomial and random-effects negative binomial regression models Variable

Meana

Negative binomial model

Random-effects Negative binomial model

Coefficient (z-score)

Coefficient (z-score)

Stated preference variables Exist25c Exist50c Exist100c Pers25c Pers50c Pers100c

0.165 0.162 0.162 0.113 0.115 0.116

)0.132 ()0.982) )0.667 ()5.22) )1.63 ()13.32) 0.417 (2.36) )0.141 ()0.977) )1.14 ()8.91)

)0.297 ()3.49) )0.886 ()7.55) )1.94 ()13.3) 0.222 (2.97) )0.487 ()4.79) )1.61 ()10.5)

Contextual variables Log of typmins Typdum Log of freqtrip Freqdum Transit Transdum Worktrip Workdum Trafcong Congdum Freqrad Freqtv

1.79 0.529 0.610 0.543 0.181 0.448 0.438 0.246 0.145 0.634 2.00 1.03

0.372 (3.63) 1.49 (3.54) )0.142 ()1.49) )0.337 ()2.13) )1.20 ()1.90) )1.24 ()1.92) 0.0793 (0.643) 0.402 (3.44) 0.0115 (0.091) 0.571 (0.913) 0.0797 (3.71) 0.0362 (1.42)

0.192 (2.75) 0.782 (2.56) )0.284 ()4.32) )0.103 ()0.908) )0.409 ()1.78) )0.460 ()1.75) 0.165 (1.98) 0.102 (1.14) 0.0787 (1.04) 0.0570 (0.260) 0.0457 (2.98) 0.0282 (1.52)

0.227 0.198 0.128 0.292 0.0665 0.382 0.635

)0.137 ()1.01) )0.534 ()3.68) )0.00346 ()0.023) 0.0314 (0.398) )0.618 ()3.77) 0.178 (2.00) )0.234 ()2.77) 0.651 (1.575) 2.15 (22.3)

)0.131 ()1.09) )0.470 ()3.82) )0.204 ()1.59) 0.0602 (0.489) )0.679 ()3.81) 0.0907 (1.56) 0.0150 (0.260) )0.174 ()0.572)

Socio-economic variables Inc2030 Inc4050 Inc6070 Incmax Incdum Cellphn College Constant h a b N Log-likelihood function LðBÞ Log-likelihood with const. LðCÞ q2 ¼ 1 ½LðBÞ=LðCÞ

2.12 (7.03) 5.81 (6.75) 2511 )5413.726 )5662.079 0.044

424 )5162.279 )5578.952 0.075

For the stated preference questions, revealed preference is the base. a Means are for the variables included in the model and are not representative of means and standard deviations in the actual population (see text regarding coding of missing values for further discussion). p-value less than 0.10. p-value less than 0.05.

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lower for several of the estimates. The random-effects model is statistically better than the ‘‘crosssectional’’ negative binomial model, so this model is presented and discussed. The indicator variables for the stated preference questions being answered are all statistically significant (revealed preference is the base category). The three variables asking respondents whether they would be willing to pay for the current level of service are all negative. This indicates that respondents will use the service less often than they currently do if they have to pay for it. As the per-call charge increases, this variable increases in significance and magnitude. It is interesting to note that respondents are likely to call TATS more often than they currently call if a customized service is provided even if it costs 25 cents per call (reflecting a high demand for customized information). However, as the cost increases to 50 cents, respondents will use the service less frequently. This evidence supports the original findings in examining the average times per month a respondent stated they would call. Having controlled for the effect of individual and context characteristics and the unobservable random-effects, it is possible to conclude that respondents are willing to accept a small per-call fee for customized travel information services. Among the socio-economic variables, the results for the income variables are mixed. It appears that as income increases, individuals are likely to call slightly less frequently, or say they would call, TATS. However, only the $40,000–$50,000 income category is statistically significant (5% level), compared with the ÔbaseÕ category of household income below $20,000. Furthermore, the indicator variable for missing values of income, which is assumed to include mostly high-income individuals, is negative and statistically significant. Overall, income does not seem to have a strong systematic influence on frequency of calling for information among the TATS users. As frequency of calling is associated with willingness to pay in the survey, income level does not appear to be systematically related to willingness to pay for information. This is not consistent with Harriss and Kanheim (1995), who found a strong willingness to pay for customized information by all income groups. Having a cellular phone does not significantly increase call frequency (10% level), despite 31% of the calls coming from cellular phone users during these initial stages of the field test. Having a college degree is also statistically insignificant (10% level) in the random-effects model. Among context variables, travel time and trip frequency are statistically significant ‘‘exposure’’ variables. As the log of time-length of the trip called about increases, respondents are significantly more likely to call frequently. Longer travel times increase calling frequency, mainly because there are greater chances of encountering congestion and there are more route options on longer urban trips. As the log of trip frequency increases, respondents are likely to call less frequently, which is the opposite of the earlier expectations. It is possible that respondents who travel more frequently for a particular trip purpose presumably already know, based on earlier experiences and other information sources, whether congestion is likely to be a problem, reducing their calling frequency. The trip purpose effect is captured by work trips, which are undertaken frequently. Those who called TATS about a work trip are significantly more likely to call frequently compared with those who called TATS for other trip purposes. These are non-discretionary trips (must be made to earn a living) and are usually made during congested periods. Transit users are less likely to be frequent callers when compared with personal vehicle users, although this effect is only significant at the 10% level. The effect of encountering unexpected congestion is statistically insignificant (10% level). The frequency of listening to the radio is


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Table 7 Relationship between calling TATS and behavioral changesa Frequency of calling (times Did not make trip after per month) calling TATS

Changed route after calling TATS

Changed departure time after calling TATS

1 2–7 8–16 20 Chi-square

(20/100) 20.0% (14/74) 18.9% (11/50) 22.0% (9/35) 25.7% 0.753 (p-value ¼ 0.861)

(24/99) 24.2% (16/74) 21.6% (11/49) 22.4% (3/35) 8.6% 3.964 (p-value ¼ 0.265)

(85/187) 45.5% (15/90) 16.7% (6/56) 10.7% (5/40) 12.5% 44.836 (p-value ¼ 0.000)b

Note: Individuals could have changed both route and departure time. a No respondents changed their mode after calling TATS. b Significant at 5% confidence level.

positively associated with a higher frequency of calling TATS, but the effect of watching television is not statistically significant (10% level). Though TATS captured some of those who do not listen to radio or watch television travel reports (Yim and Miller, 2000), it seems that during the initial stages of this field-test those who tuned in to the radio called TATS more frequently. This may change if TATS continues to provide a high-quality travel service and more people start substituting their travel information needs with the new media. One implication of the finding is that at least initially, travelers can be informed about the new TATS service through the radio, although the TATS service was not advertised on the radio at the time of this survey. More generally, to attract potential subscribers, new travel information services can advertise on the more conventional media and highlight their unique attributes such as customization. 5.6. Links of telephone calling to travel behavior Studies have found that electronic travel information induces behavioral response in terms of route and departure time changes (Abdel-Aty et al., 1995; Khattak et al., 1995). Nearly half (46.7%) of those who learned about traffic problems from TATS changed their travel behavior (Yim et al., 1998). Table 7 provides limited evidence relating frequency of calling TATS to actual behavioral changes for this sample. In the survey, respondents were asked if they changed their behavior in terms of the trip they called TATS about. (However, there is no information regarding whether or not they received positive or negative traffic information. Therefore evidence regarding the relationship between frequency of calling and propensity to change behavior must be interpreted with caution.) The only behavioral change that is significantly associated with frequency of calling is canceling a trip after calling. Those calling only once per month are significantly more likely to cancel their trip than users at any other frequency level (5% level). The frequency of calling TATS had no statistically significant effect on decisions to change routes or departure times for this data set.

6. Validity and limitations of the analysis The validity of stated preferences in willingness to pay studies is always a concern. The test of validity in the absence of real-life data is whether the methods were appropriate and the results are

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reasonable. For this study, a survey research firm administered the survey instrument using the CATI technique. The estimated models are largely consistent with a-priori expectations and with the findings from other studies. Furthermore, by combining stated and revealed behavior, the study is able to assess the reasonability of stated changes relative to past calling behavior. However, we recognize that revealed preference data is only as good as the respondentsÕ recall of past behavior. Variability in the dependent variable for each stated preference level and for revealed preference is an indicator of validity because it illustrates that there is sufficient variation in demand for the service at each price to justify an examination of its determinants. We determined that our data were valid in terms of variation by successfully estimating seven separate models––one for the revealed preferences and six for stated preferences––reflecting that there was sufficient variability in each data subset to support panel estimation. Two potential data limitations need to be reiterated. One is strategic bias resulting from the sequence of ordering of the willingness to pay categories, from lowest to highest costs. While we used the preferred procedure for eliciting responses to frequency of use at various price points, knowing that a 25 cent cost is possible may result in Ôfalse negativeÕ responses for the higher cost categories. Thus, frequency of use at the 50 cent and dollar costs may be somewhat under-estimated. This implies that the willingness to pay results for 50 cents and 1 dollar may be conservative. Finally, the method of using a random variable to assign frequency of calls per month introduces a level of variation not directly accounted for in the random-effects model. While the random-effects model accounts for randomness in individualsÕ responses, some of the randomness accounted for can also result from the assignment of categorical responses to specific call frequency values.

7. Conclusions and future research This study analyzes the responses of active information seekers in the Bay Area. Their willingness to pay for travel information is analyzed by estimating models with revealed and stated preference data. The advantage of modeling preferences is that stated (future) behavior is ‘‘informed’’ by past behavior. The random-effects negative binomial model adds rigor to the analysis because it accounts for the over-dispersion in the data, as well as randomness and heterogeneity. While we have attempted to reduce potential biases, e.g., by professionally surveying respondents using CATI and by using the appropriate statistical methods, we recognize that certain biases inherent in survey research, such as self-selection and policy response bias, may remain. The results show that (at least) some active information seekers are willing to pay for traveler information. After controlling for socioeconomic and context variables, the responses indicate that the average use of TravInfo TATS will decline if the service is not customized but a service charge is initiated. Higher service charges will result in fewer calls, as expected. Interestingly, if a service charge is initiated for customized service, average usage might increase at 25 cents a call, and then decline at higher payment levels. At all levels of payment, the respondents stated that they would be more willing to pay for the customized service than to pay for the status quo. The modeling results indicate that individuals are more willing to pay for travel information via the


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telephone when the information is customized, their trips are longer, the trip is work related and the mode is automobile. From a policy perspective, the public sector should continue to support the TATS service while encouraging the commercialization of travel information. This study has found that there is significant demand for the TATS service, though the frequency of calling during the initial stages of TATS seems relatively low. There is some evidence from earlier studies (e.g., Englisher et al., 1997; Bower, 1997) that system usage may grow with time. Results from our study indicate that commercial radio can facilitate the growth of customer base by informing travelers about TATS––though TATS did not advertise their service on radio at the time of the survey. In particular, advertising to listeners who are willing to call frequently and pay for the information can be valuable. Any marketing efforts must be able to distinguish the TATS service from other services in terms of additional benefits it provides to customers, particularly the customization of travel information and the higher information quality aspects of the service. The consumer response to purchasing travel information services seems rational, i.e., individuals faced with certain trip types seem more willing to pay for travel information services if the information is properly customized. Future public sector efforts in other urban areas may focus on first developing a ‘‘free of charge’’ information service through a public–private partnership and then gradually introducing charges where demand is relatively inelastic. For example, charges can be introduced in areas and on roads where a substantial portion of the travelers takes relatively longer automobile commute trips and where customization and improvement of travel information is possible. Thus future deployment efforts for traveler information systems may focus on urban areas that are experiencing growing traffic congestion but where only limited travel information services exist. Many research issues remain to be addressed. First, the willingness to pay results should be validated with research performed in other areas such as Boston. In fact, a meta-analytic approach to understanding and interpreting experiences with FOTs, conducted throughout the US, is critical to synthesizing studies about ATIS. Second, the most desirable and valuable service improvements need to be further investigated. Such improvements may include (1) alternate route suggestions, (2) broader route coverage (major arterial roadways as well as freeways/interstates), (3) coverage of the specific routes the caller takes, (4) comparative travel times between alternate routes, (5) 24-h public transit information, (6) access to a TravInfo staff member to ask questions, (7) forecasts of traffic conditions, (8) roadway conditions experienced at alternate departure times, (9) traffic conditions at specific locations and bottlenecks, (10) services information (for example, ‘‘yellow pages’’ information), (11) recommended method of travel including public transit options, (12) the specific time the traffic crash or incident occurred (so caller can self-determine whether the incident will be cleared by the time they get to that spot), (13) an estimate of when a bottleneck will be cleared, (14) information on parking availability at park-and-ride and other parking lots, (15) a notification service to the highway patrol for an emergency towing service, (16) summary reports on major incidents on all freeways and highways (like what is reported on radio or television), (17) emergency disaster information––for instance, in the event of an earthquake, flood, brush fire, or other disaster, (18) weather information, and (19) traffic conditions at specific locations relevant to the callerÕs trip. Third, in addition to considering willingness to pay for TATS as a stand-alone service, there is a need to investigate (where appropriate) ‘‘bundling’’ the traveler information with other services such as cellular services, entertainment and fast Internet

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access services. Including new services will add value to travel information services and the bundled products can be marketed effectively by the private sector. There may even be potential for new companies to offer such bundled products. Fourth, linking the content of travel information received by individuals to specific behavioral changes at the individual and network levels should be investigated. It will be interesting to study real-life behavioral changes that occur as the quality and customization of travel information improves. Finally, research is needed to understand response to new information dissemination media such as the Internet, personal digital assistant units, and in-vehicle or hand-held information devices. Specifically, it will be interesting to understand willingness to pay for the emerging means of providing traveler information, especially if they are bundled with other products and services. Nevertheless, the research and the results contained in this paper provide helpful information at the early stages of travel information market development. This and the suggested research can ultimately help information service providers formulate strategies that improve their businesses.

Acknowledgements This work was performed as part of the collaboration between the California PATH Program at the University of California and Carolina Transportation Program at University of North Carolina. The contents of this paper reflect the views of the authors who are responsible for the facts and the accuracy of the data presented herein. The authors wish to acknowledge (1) the TravInfo Evaluation Oversight Team for their comments, (2) GLS Research for their assistance in survey administration, and (3) Professor Paula Kantor for review and valuable comments. We thank the California PATH Program and the Carolina Transportation Program for continued financial support. References Abdel-Aty, M., Vaughn, K., Kitamura, R., Jovanis, P., Mannering, F., 1995. Models of commutersÕ information use and route choice: initial results based on Southern California commuter route choice survey, Transportation Research Record, 1453, Transportation Research Board, Washington, DC, pp. 46–55. Al-Deek, H., Khattak, A., Thananjeyan, P., 1998. A combined traveler behavior and transportation system performance model with ATIS. Transportation Research––Part A 32A (7), 479–493. Bower, B., 1997. The impact of real-time information on travel behavior, Conference Proceedings of 67th ITE Annual Meeting, Boston, MA. Ben-Akiva, M., Bowman, J., Goupta, D., 1996. Travel demand model system for the information era. Transportation 23, 241–266. Englisher, L., Juster, R., Bregman, S., Kosses, D., Wilson, A., 1996. Consumer response to the SmarTraveler ATIS: findings from the second year evaluation, In: Proceedings of the 1996 Annual Meeting of ITS America, Houston, Texas, vol. 1. Englisher, L., Bergman, S., Pepin, S., Wilson, A., 1997. Promoting advanced traveler information systems among cellular and land-line phone users. Transportation Research Record, 1588, Transportation Research Board, Washington, DC, pp. 63–69. Greene, W.H., 1995. LIMDEP Version 7.0 UserÕs Manual, Econometric Software, Inc., Plainview, NY. Harriss, P., Kanheim, C.S., 1995. Public Interest in, and Willingness to Pay For, Enhanced Traveler Information as Provided by IVHS in the New York Metropolitan Area, ITS Annual Meeting, Washington, DC.


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