ACKNOWLEDGMENTS

The principal investigator of this report is Jonathan Levine, Associate Professor and Chair of Urban and Regional Planning in the Taubman College of Architecture and Urban Planning, University of Michigan. Other team members include Daniel A. Rodríguez, Assistant Professor in City and Regional Planning at the University of North Carolina, Chapel Hill; Jumin Song, doctoral student of Urban and Regional Planning, University of Michigan; and Asha Weinstein, Assistant Professor in Urban and Regional Planning at San José State University.

We greatly appreciate the help of the many people who assisted in the completion of this project. Richard Murphy developed the housing search simulation tool used in the experiment. Katja Irvin conducted the literature review, assisted in the development of the conceptual design and specifications for the housing search simulation, and assisted with the development of the survey instrument. Jessica Zgobis took pictures of properties and worked as a lab assistant during the experiment. Finally, we thank Melissa Goldstein, Alan J. Levy, and Julie L. Stetten of University Housing at the University of Michigan, as well as Jessica Good and Craig Snyder at Sites and Sounds, Inc. (Ann Arbor, Michigan), for providing us with the housing data used in the project.

Thanks are offered also to MTI staff, including Research Director Trixie Johnson, Research and Publications Assistant Sonya Cardenas, Webmaster Barney Murray, and Graphic Artist Shun Nelson. Editing and publication services were provided by Irene Rush, Catherine Frazier. and Beth Blevins.

Table of Contents

Executive Summary 1

Transportation Challenges: Alternatives to the 5
Automobile

Transportation, Information, and Residential Location 9

TRANSPORTATION AND RESIDENTIAL LOCATION 9

THE ROLE OF INFORMATION IN DECISION MAKING 12

TRANSPORTATION CHOICE AND RESIDENTIAL CHOICE 16

Study Methods 19

HYPOTHESES AND OUTCOME VARIABLES 20

THE STUDY SETTING: UNIVERSITY OF MICHIGAN GRADUATE 22

STUDENTS

OVERVIEW OF THE EXPERIMENTAL PROCEDURE 25

DETAILED DESCRIPTION OF THE SIMULATION TOOL 27

PARTICIPANT SELECTION AND RECRUITMENT 38

THE PARTICIPANT SURVEY 39

Summary of Simulation and Survey Results 41

Locational Effects of the Experiment 45

PROPERTIES SELECTED BY PARTICIPANTS 45

LOCATIONAL DIFFERENCES BETWEEN CONTROL AND 47
EXPERIMENTAL GROUPS

ANALYSIS OF SUBPOPULATIONS WITHIN THE EXPERIMENTAL 53

GROUP

ANALYSIS OF SUBPOPULATIONS WITHIN THE FULL SET OF 58
PARTICIPANTS

LIMITATIONS OF THE RESEARCH 63

Conclusion 67

POLICY IMPLICATIONS 67

RECOMMENDATIONS FOR FUTURE RESEARCH 69

Concluding Thoughts 70

Appendix A: Participant Recruitment E-Mail 73

Appendix B: Instructions Given to Participants 75

Appendix C: Participant Survey Questions 77

Endnotes 87

Transportation Challenges: Alternatives to the 87
Automobile

Transportation, Information, and Residential Location 87

Study Methods 90

Locational Effects of the Experiment 91

Abbreviations and Acronyms 93

Bibliography 95

About the Authors 101

Peer Review 103

List of Tables

Correspondence Between Transportation Information Provision and Land Use/ 12 Transportation Behavior Research

Definitions of Accessibility Ratings As Presented in the Information System 19

Outcome Variables 21

Description of Variables and Summary Statistics 28

Summary Statistics of Selected Survey Responses 41

Summary Ratings of Factors Influencing Choice of Housing 43

Summary Statistics for Outcome Measures for All Properties 46
Selected by Participants

Mean Value of Outcome Measures for the Control and Experimental Groups 47

Cross-tabulation of Combined Pedestrian and Transit Accessibility to Central 49
Campus and Home Campus for All Units

Cross-tabulation of Combined Pedestrian and Transit Accessibility to Central 50
Campus and Home Campus for Units More Than 0.5 Mile from Campus

Differences in Location Choices According to Age and UM Transit Use for 53
Participants from the Experimental Group

Factor Analysis of Preferences for Property Attributes 56

Differences in Location Choices According to Age and Sex 59
(Experimental and Control Groups Combined)

Differences in Location Choices According to Affordability of Current Housing 60 (Experimental and Control Groups Combined)

Differences in Location Choices According to Preferences for Neighborhood 62
Attributes and Transit Inconvenience (Experimental and Control Groups Combined)

Executive Summary

Where people live, work, shop, and recreate fundamentally determines their local travel options. These locational decisions are, in part, a function of the information that can be collected about the alternative housing opportunities. This report describes tests of an innovative approach to raise awareness about, and possibly increase the use of, nonauto travel options: providing people seeking housing with good information about their bus, walking, and biking options from every housing unit they consider. We hypothesize that individuals who receive information about accessibility to transit, and to important destinations in an area as part of each rental unit listing they see, are more likely to choose to live in high-accessibility neighborhoods than are individuals who do not receive such information. To date, the concept of providing information to modify people's travel behavior has been implemented in a relatively limited number of ways. The most common approach has been to provide up-to-date (or even up-to-the-minute) information on the status of motorized transportation modes (auto and transit) for individuals making travel decisions. Despite the evident value of short- and medium-term approaches to providing travelers with information, both suffer from an inherent weakness: once the traveler chooses a place of residence, the transportation die has largely been cast. In this study, we take a long-term view of the influence of information on travel choices by examining whether information on transportation options, delivered at the moment of residential choice, can alter one's decisions and thereby change one's transportation environment.

We tested our hypothesis in a laboratory setting, asking 236 University of Michigan (UM) graduate students to select their top choices of where to live after reviewing a database of residential properties custom-designed for this study. To assess the influence of the accessibility information, we divided study participants into two groups. The first group (the control group) received unit information with the attributes currently standard in most private and university housing databases, including price, the number of bedrooms, the availability of off-street parking, and whether the property is within one-third of a mile of a UM campus. The second group (the experimental group) received the same information and also information about how far the unit is from a transit stop, transit service frequency at that stop, and the distance to the closest campus. After choosing their preferred rental properties, participants also filled out a survey on their current travel behavior patterns, desired features in housing, and sociodemographic characteristics.

Statistical comparisons between the properties desired by each group suggest two main findings. First, providing bundled accessibility and housing information resulted in the selection of preferred locations that were closer to major destinations, as compared to the selections of individuals without access to that information. Although we cannot know with certainty that travel behavior differences will ensue from the effects identified in our experiment, the fact that the experimental group chose residences that were more accessibile to destinations suggests likely effects that deserve additional exploration. Providing housing seekers with information about their walking and biking options from each unit has the potential to encourage people to choose housing units that are closer to their major destinations than they would otherwise select.

Second, experimental group members selected properties closer to transit lines serving their destinations than did members of the control group. Certain subgroups of the population were especially likely to select housing units closer to transit than they otherwise would have, if offered bundled housing and accessibility information. In particular, older individuals and those who regularly use the University of Michigan's transit system were more likely to pick housing units close to a transit stop when provided with the transit information. Although our research should be tested in other populations and contexts, our results suggest that providing housing seekers with information about their transit options from each unit has the potential to influence certain population subgroups to choose more transit-friendly locations than they would otherwise select.

Our findings have implications for both research and policy. At the level of public policy, the results suggest that information targeted toward individuals who are relocating can be used to enhance the attractiveness of locations that support multiple travel modes. Transportation and urban planners, health promoters, transit agencies, universities, and other institutions interested in promoting walking, bicycling, and transit use will find our results useful. For researchers, the results shed light on an ongoing debate about the connection between transportation and land use. Some observers assert a strong relationship whereby households are guided by the tradeoff between transportation accessibility and housing cost in their locational decisions; others find in the current auto-dominated transportation environment a weakened relationship under which nonaccessibility factors dominate. Our results suggest a third option: the relationship between transportation and land use is neither inherently weak nor inherently strong: it can be either nurtured or thwarted by policy. Appropriate interventions can increase the capacity of transportation accessibility to guide locational decisions, thus strengthening the transportation/land-use relationship. As this research suggests, integrated transportation and housing information offered to people at the time they are choosing a new home may constitute one of those interventions.

Transportation Challenges: Alternatives to the Automobile

In the last three decades, a strong core of policy research and innovation has focused on developing methods to provide travelers with convenient, affordable alternatives to driving. Among the more prominent approaches have been efforts to expand transit service, urban design interventions that make walking and biking safer and more pleasant, and land-use planning techniques designed to make walking, biking, and transit more convenient. Another set of approaches has focused on the question of how to make the public aware that good alternatives to driving exist. The best transit system in the world is useless if people do not realize that it could efficiently serve their travel needs. This report describes research testing an innovative new approach to raising awareness about nonauto travel options: providing people seeking housing with information about the bus, walking, and biking options as part of each housing unit listing they review.

The concept of using information provision to improve people's travel options has been implemented in a relatively limited number of ways, and the most common approach has been to provide up-to-date (or even up-to-the-minute) information on transportation modes for individual trip-making decisions. For example, some transit systems have installed electronic message boards at bus stops to alert riders when the next bus will arrive. Several private companies and public agencies are developing systems for alerting drivers to congestion levels on freeways, sometimes predicting trip times based on current congestion levels. These efforts attempt to give travelers transportation information that will enable them to select the modes, or mix of modes, that best serve their needs for a particular trip.

A second, medium-term approach to using information provision to induce people to shift away from solo driving is to provide travelers with one-time, personalized counseling on their travel alternatives. For some time, programs have helped commuters to find a way to carpool, including matching up carpool partners, and some large employers hire commute managers to help their employees identify alternatives to driving. Moving beyond just commute trips, the German firm Socialdata has developed a program it calls "individualized marketing," which in the United States has been tested under the program name TravelSmart®. In the TravelSmart program, participants are asked what travel options they would like to learn more about, provided with the information, and given the option to speak with someone to have further questions answered.See Socialdata America 2004, "TravelSmart Individualized Marketing Pilot Project Final Report" (Portland, OR.: Socialdata America, Ltd., 2004). Available at www.trans.ci.portland.or.us/Options/TravelSmart/Report.htm (Accessed on 6/29/2005).

Despite the evident value of these short- and medium-term approaches to providing travelers with information, both suffer from an inherent weakness: once the traveler chooses a place of residence, the transportation die has largely been cast. That is, the relative quality of one's transportation options is determined principally by the transportation options available from one's home. If you live in an area with no or little transit service, even the most accurate and up-to-date information on public transit service in your city is unlikely to induce you to ride the bus. If the closest grocery store to your home is five miles away, no amount of information is likely to induce you to walk there on a regular basis. Transportation decision making involves both short- and long-run decisions, and the choice of where to live is the most important long-run choice. An information policy that focuses only on the daily modal choice may be overlooking valuable opportunities to affect travel behavior.

This study attempts to address the travel information problem at the root by investigating the impact of providing transportation information when people are moving to a new home. It asks whether information on transportation options, delivered at the moment of residential choice, can alter one's residential location and thereby change one's transportation environment. The study was designed as a simulation experiment, whereby a randomly selected group of graduate students (the experimental group) were asked to choose their top five preferred homes from a database of available rental properties. The database provided integrated information on the bus, walking, and biking options from each unit. A control group went through the same process but used a housing database without integrated transportation information. We were thus able to test what impact the integrated transportation information might have on the rental units the experimental group chose.

Members of the experimental group selected residences significantly closer to their campus destinations than those who were exposed only to the conventional information; such a choice, if played out in actual residential location, would make both walking and cycling more realistic transportation options. We found that providing integrated transit information did not induce the experimental group as a whole to select locations closer to bus lines, but certain identifiable subpopulations did locate closer to transit lines.

These results suggest that providing integrated transportation and housing information at the time of a residential decision can influence a household's locational choices. This finding may be relevant to organizations interested in reducing use of the single-occupant auto by their affiliates, or in ensuring mobility to a carless population. These organizations may include universities, agencies promoting commuting alternatives, firms with travel demand management programs, or organizations that help people transition from public assistance to employment.

The current study is based on an experimental design, a rarity in studies of land use and travel behavior. Given the nature of the subject, it is almost never possible--or ethical--to assign randomly selected individuals to control and experimental groups to be exposed to different transportation or land-use environments. This study is experimental in that it is based on random assignment of subjects to different information environments. Differences observed between the control and experimental groups can be attributed--within a margin of statistical error--to the effect of being exposed to integrated transportation and housing information. Although the design is experimental, the study is based on a simulation and stated preferences, rather than actual choice of a residence. How the behavior revealed in the experimental simulation would map to actual residential choice remains uncertain, but the results are promising enough to support the need for a follow-up to this study that would use a similar design but track actual residential choices by members of control and experimental groups who used the database for their actual housing search, not a simulated one.

The remainder of this report is organized as follows. The next chapter discusses the existing research relevant to this study and shows how the current research fits into that larger picture. The following chapter explains the specific research design used in the study. A summary of simulation and survey results follows, and the main results section summarizes the effects of providing integrated transportation accessibility and housing information to residential decision makers. We conclude by suggesting the implications of the study for both policy makers and future research.

Appendix A shows the e-mail sent to recruit participants. Appendix B presents the instructions given to the participants who were chosen. Appendix C presents the survey questions that participants were given. Endnotes, a list of abbreviations and acronyms, and a bibliography follow.

Transportation, Information, and Residential Location

This study investigates the intersection of research on choice of travel mode, choice of residential location, and how people use information to make decisions. In transportation, studies of mode choice have measured the components of individuals' decisions to travel by foot, transit, automobile, or other modes. Yet the primary shaper of the relative quality of these modes is location. Where one lives, works, shops, and recreates will fundamentally shape the travel options available. Theories about locational choice--especially choice of one's place of residence--are thus central to research on the land-use/transportation interaction. One's choice of location is, in part, a function of the information that can be obtained about it and competing choices. Economists employ the "perfect information" assumption to define competitive markets, yet empirical research suggests that people use information in ways that are more subtle and complex.

These three areas--transportation mode choice, location choice, and use of information--have increased in policy prominence as well. The decrease in highway construction over the last two decades has heightened emphasis on finding innovative strategies that improve operations and management of current assets. Use of innovative information technology (IT) applications is one option that can sustain and revitalize our transportation systems. In contrast to a general focus on adequate information for the modal choice decision, this study considers the provision of bundled transit and housing information as an advanced application enabled--but not currently deployed or under development--by current IT technology. Several transit agencies have adopted IT-based information dissemination strategies, but none have measured their impact on travel behavior, including ridership changes.See Charles River Associates, "Building Transit Ridership--An Exploration of Transit's Market Share and the Public Policies That Influence It" (Washington, D.C.: Transportation Research Board, 1997). The paucity of research on the behavioral impacts of providing integrated transit information limits opportunities to improve the performance of transportation alternatives.

TRANSPORTATION AND RESIDENTIAL LOCATION

The Current Debate

Although the current study is motivated by the potential for policy to affect travel behavior, its immediate focus is on the residential location decision. Residential location involves two basic choices: housing type and residential environment. These correspond to the geographer's two aspects of location: site and situation. "Site" refers to the characteristics of a place, including attributes of physical layout, social composition, and climate. "Situation" is the position of the location in relation to other places--its relative location. Transportation access--or the ease of reaching one's destinations--is an attribute of situation that circumscribes residential location choice. Thus, while people may focus most overtly on their site requirements in choosing where to live, theories of residential search suggest that people limit their housing search to neighborhoods that fill their situational needs.See Frans Dielman and Clara Mulder, "The Geography of Residential Design," in Residential Environments: Choice, Satisfaction, and Behavior, edited by J.I. Aragonés, G. Francescato, and T. Gärling (Westport, CT: Bergin & Garvey, 2002). The outcome is often a compromise between situation and site.See Clara Mulder and Pieter Hoimeijer, eds., Residential Relocations in the Life Course, Population Issues. An Interdisciplinary Focus (New York: Kluwer Academic/Plenum Publishers, 1999).

Given residential locators' interest in both situation and site, it is not surprising that research on locational choice has divided between two schools. The first school, which follows Alonso, focuses on the relative costs of transportation and housing in determining residential locational choice.See William Alonso, Location and Land Use (Cambridge, MA: Harvard University Press, 1964). Within this framework, a household has two options: it could locate close to work, where housing prices are presumably high, but transportation costs are relatively low; or it could select a remote location with cheaper land costs but higher expenses for the commute and other trips. The optimal location for the household is the location where the marginal cost of land just equals the marginal savings in transportation costs. In this model, the tradeoff between land costs and transportation costs fundamentally drives residential location. Since land costs are primarily a function of the land's accessibility, it may be said that this model is oriented to the situational characteristics of the location. For the second school, which follows Tiebout, site characteristics such as schools, taxes, crime, and the local environment are the primary forces driving residential choice, with transportation costs playing a smaller role in people's decisions on where to live.See Charles Tiebout, "A Pure Theory of Local Expenditures," Journal of Political Economy 54 (1956):416-424.

The Alonso school perceives a strong transportation-location link, with transportation costs being a primary driver of locational decisions; under the Tiebout worldview, the transportation-location link is weakened, with households choosing residences on the basis of local attributes with little regard to transportation cost. These two schools lead to different conclusions regarding current transportation and land-use policies. Following Alonso's model, if transportation accessibility remains central to locational decisions by residential, commercial, and industrial actors, changes in metropolitan form--that is, the extent of urban sprawl or compact development--will be sensitive to transportation investment decisions. For example, a decision to expand a peripheral highway could trigger auto-oriented development in the territory it serves; conversely, transit investments hold the potential for supporting compact and pedestrian-friendly development in their vicinity. In the Tiebout framework, residential locators are more motivated by amenities of site than by situation-related attributes such as transportation accessibility. If this is the case, processes of sprawl will occur largely independent of transportation investment decisions. Under this theory, low-density, auto-oriented development has attributes that consumers demand; this market interest is strong enough to produce sprawl even without major supportive transportation investments. Transit investments have little power to spur redevelopment at higher densities, since the improved access that they offer is of little importance to people's locational decisions.

Each position enjoys a measure of empirical support. The "weak link" view tends to be supported by studies that compare actual commuting patterns in a region to those patterns that would theoretically occur if residents were reorganized into homes that minimized their commuting distance. The ratio of the minimized aggregate commute distance to actual distance is taken as a measure of the impact of urban form on travel; where the actual distance is close to the minimum, the physical arrangements of homes and workplaces would seem to be a binding constraint on travel behavior.See For a review of this literature, see Daniel A. Rodríguez, "Spatial Choices and Excess Commuting: A Case Study of Bank Tellers in Bogotá, Colombia," Journal of Transport Geography 12 (1) (2004):49-61. By contrast, if actual commutes are much higher than the minimum required--and this tends to be the case in most areas--it would appear that nontransportation factors drive residential choice to a greater extent.See Genevieve Guilano and Keneth A. Small, "Is the Journey to Work Explained by Urban Structure?" Urban Studies 30 (9) (1993): 1485-1500. In addition, survey research that asks people to rank or rate factors influencing their choice of residence tends to reveal relatively low scores for transportation-related factors.See Martin Wachs, Brian Taylor, Ned Levine, and Paul Ong, "The Changing Commute: A Case Study of the Jobs-Housing Relationship Over Time," Urban Studies 30 (10) (1993): 1711-1729.

The "strong link" view draws support from empirical studies of residential location that regularly find commute travel time to be the single most influential factor in residential choice.See David Boyce and Lars-Göran Mattsson, "Modeling Residential Location Choice in Relation to Housing Location and Road Tolls on Congested Urban Highway Networks," Transportation Research, Part B: Methodological 33 (8) (1999): 581-591; Steven R. Lerman, "Location, Housing, Automobile Ownership, and Mode to Work: A Joint Choice Model," Transportation Research Record 610 (1976): 5-11; Jonathan Levine, "Land Use Solutions to Transportation Problems? Rethinking Accessibility and Jobs-Housing Balancing," Journal of the American Planning Association 64 (2) (1998): 133-149; and M. William Sermons and Frank. S. Koppelman, "Representing the Differences Between Female and Male Commute Behavior in Residential Location Choice Model," Journal of Transport Geography 9 (2) (2001): 101-110. In addition, capitalization studies show that urban rapid transit systems tend to increase property values in their vicinity, suggesting that transit access matters to residential and nonresidential locators.See David S. Damm, Steven R. Lerman, Eva Lerner-Lam, and Jeffrey Young, " Response to Urban Real Estate Values in Anticipation of the Washington Metro," Journal of Transportation Economics and Policy 14 (1980): 315-336; A.C. Nelson, "Effects of Elevated Heavy-Rail Transit Stations on House Prices with Respect to Neighborhood Income," Transportation Research Record 1359 (1992): 127-132; Richard Voith, "Transportation, Sorting and House Values," Journal of the American Real Estate & Urban Economics Association 19 (2) (1991): 117-137; and Robert Cervero and John Landis, " Twenty Years of the Bay Area Rapid Transit System: Land Use and Development Impacts," Transportation Research, Part A 31 (4) (1997): 309-333.

The Gap We Fill

The studies described above focus on the transportation/land-use link as expressed in current behavior. Implicit in these studies is the notion that the strength of the connection between transportation and land use is a reality that is there to be measured and is reasonably fixed at any time. The relationship is conceived of as an input to policy making, not the target of directed policy. By contrast, this study is motivated by the idea that the strength of the relationship between transportation and land use may be, in part, an outcome of governmental action. That is, it is neither inherently strong nor inherently weak, but can be strengthened or weakened by policy. Within this framework, even if individuals' preferences are fixed, their capacity to act on these preferences varies according to the transportation, land-use, and information environments that they face. If directed policy interventions, such as providing targeted and timely information, can facilitate choices different from those that might otherwise be made, they can strengthen the observed transportation/land-use link. Such a strengthening offers the potential for policy-driven reductions in, or moderations of the growth in, vehicle miles traveled.

See Correspondence Between Transportation Information Provision and Land Use/Transportation Behavior Research describes the relationship between research in travel behavior and in transportation information provision; each has short- and long-term dimensions, but research into long-term decision making impacts of transportation information is currently lacking. This study seeks to contribute in this area.

Correspondence Between Transportation Information Provision and Land Use/Transportation Behavior Research
	Land Use/Transportation Behavior Research	Information Provision
Short-term decision making	Travel behavior (trip generation, modal choice, route choice, and so on)	Itinerary planning, real-time information, multimodal traveler information
Long-term decision making	Locational choice and its implications for travel options	Currently lacking information to facilitate the transportation/land-use relationship

THE ROLE OF INFORMATION IN DECISION MAKING

This project considers information provision as a potentially useful policy tool to alter people's residential choices, and, indirectly, their travel behavior. Yet people's use of information in decision making is far from straightforward. Research has considered strategies people use to sift through information, and the conditions under which information is accepted and used. Of particular importance is the use of information in changing people's behavior, which is difficult when that behavior is habitual.

One starting point for imagining an individual's use of information is the "perfect information" assumption: markets in perfect competition are, by definition, characterized by individuals who possess complete and accurate information about all options available to them. This notion is a poor match for many situations, however. Economists generally assume that people will rationally acquire information up to the point that the marginal value of the information is equal to the marginal cost of acquiring it. To put it more simply, people stop collecting information once they realize that the benefits of new information are unlikely to make it worth the effort of searching for that information.

Herbert Simon has argued that even this assumption of rational information acquisition is an inadequate descriptor of people's use of information; instead, their behavior better matches a concept of bounded rationality entailing four components:

The principle of intended rationality says that although people are goal-oriented, they are influenced by a range of thoughts and emotions and the complexity of the environment; the standard assumptions of economic rationality are not supported.

The principle of adaptation says that the "task environment" forms most human behavior. According to Simon, "There are only a few `intrinsic' characteristics of the inner environment of thinking beings that limit the adaptation of thought to the shape of the problem environment. All else in thinking and problem-solving behavior . . . is learned and is subject to improvement."

The principle of uncertainty says that uncertainty is more fundamental to choice than probability calculus implies. If one is not clear about the factors involved in a problem, this uncertainty affects the entire decision making or problem-solving process.

The principle of trade-offs is explained by Simon's idea of satisficing. In contrast to the standard assumption in economics that people thoroughly evaluate all their options and then choose the optimal one, Simon asserts that individuals merely evaluate their options until they find one that is satisfactory to them.See Herbert A. Simon, The Sciences of the Artificial, 3rd ed. (Cambridge, MA: The MIT Press, 1996):54, quoted in B. Jones, "Bounded Rationality and Public Policy: Herbert A. Simon and the Decisional Foundation of Collective Choice," Policy Sciences 35 (3) (2002): 269-284; Herbert A. Simon, Administrative Behavior, 3rd Edition (New York: The Free Press, 1976).

The four principles imply that the link between providing people with information about travel options and changes in their travel behavior may not be straightforward. There is only modest evidence that people change their travel behavior as a result of receiving new information. It has been found that transit users tend to make scant use of information resources such as maps and schedules when they are available.See Richard J. Balcombe and Claire E. Vance, "Information for Bus Passengers: A Study of Needs and Priorities" (Crowthorne, UK: Transportation Research Library, 1996); Asad J. Khattak and André de Palma, "The Impact of Adverse Weather Conditions on the Propensity to Change Travel Decisions: A Survey of Brussels Commuters," Transportation Research Part A 31 (3) (1997):181-203; and Asad J. Khattak, Youngbin Yim, and Linda Stalker, "Does Travel Information Equally Influence Commuter and Non-Commuter Behavior? Results from the San Francisco Bay Area TRAVINFO Project," Transportation Research Record 1694 (1999): 48-58. In general, research shows that regular users of transit prefer reliability over real-time information; they are reluctant to invest the time to access information when it is available, preferring to rely on established habits that require little thought.See Thomas Reed and Jonathan Levine, "Changes in Traveler Stated Preference for Bus and Car Modes Due to Real-Time Schedule Information: A Conjoint Analysis," Journal of Public Transportation 1 (2) (1997): 25-47. Nonregular users of transit may be even more impervious to efforts at information dissemination. One approach to this question is to characterize travelers, both those who accept and those who ignore proffered transportation information, with an eye toward developing information provision strategies for each. Colorfully, Mehndiratta, et al., labeled the various groups as "control seekers," "webheads," and "low-tech information seekers."See Shomik R. Mehndiratta, Michael A. Kemp, Jane E. Lappin and Eric Nierenberg, "Likely Users of Advanced Traveler Information: Evidence from the Seattle Area," Transportation Research Record 1739 (2000): 15-24.

Information use in transportation has been the subject of experimental research designs as well. For example, Kitamura, et al., used laboratory interviews to assess the types of information that are important to travelers, the impact of exposure of information on attitudes, and user receptiveness.See Ryuichi Kitamura, Prasuna Reddy, Kenneth M. Vaughn, and Paul P. Jovanis, "Transit Pre-Trip Information Systems: An Experimental Analysis of Information Acquisition and Its Impacts on Mode Use," paper read at Proceedings of the Second World Congress on Intelligent Transportation Systems (1995). Abdel-Aty surveyed travelers in San José and Sacramento to implement a stated preference experiment on the impact of information provision on modal choice.See Mahmoud A. Abdel-Aty, "Using Ordered Probit Modeling to Study the Effect of ATIS on Transit Ridership," Transportation Research Part C 9 (2001):165-277. Published also as Mahmoud A. Abdel-Aty, Paul P. Jovanis, and Ryuchi Kitamura, "The Impact of Advanced Transit Information on Commuters' Mode Changing," ITS Quarterly 3 (2)(1996):129-146; and Mahmoud A. Abdel-Aty, Paul P. Jovanis, and Ryuchi Kitamura, "Investigating Effect of Advanced Traveler Information on Commuter Tendency to Use Transit," Transportation Research Record 1550 (2) (1996):65-72. Mahmassani and Liu employed an experimental simulation design to test factors affecting decisions on when to switch departure times and routes, with an eye to the design of Advanced Traveler Information Systems (ATIS).See Hani S. Mahmassani and Yu-Hsin Liu, "Dynamics of Commuting Decision Behaviour Under Advanced Traveller Information Systems," Transportation Research Part C: Emerging Technologies 7 (2-3) (1999):91-107. Other evaluations of Advanced Public Transportation System (APTS) and ATIS deployments have tended to use quasiexperimental evaluation research designs.See For example, Jonathan Levine, Qiang Hong, Edward G. Hug, and Daniel A. Rodriguez, "Impacts of an Advanced Public Transportation System Demonstration Project," Transportation Research Record 1735 (2000):169-177; Asad J. Khattak and John Polak, "Effect of Parking Information on Travellers' Knowledge and Behavior," Transportation 20 (1993):373-393; Asad J. Khattak, Joseph Schofer, and Frank Koppelman, "Commuters' En-Route Diversion and Return Decisions: Analysis and Implications for Advanced Traveler Information Systems," Transportation Research Part A 27 (2) (1991):101-111. The strength of these designs is in their realism; because they are based on actual choices, they eliminate the possibility of strategic or otherwise erroneous statements of preference. While these studies have the advantage of being carried out under real-world conditions, the vagaries of APTS and ATIS deployment, coupled with the countless other uncontrolled changes affecting travel patterns, have frequently led to ambiguous outcomes.

An overall theme emerging from studies of information use in transportation is the importance of the habitual, satisficing, or otherwise less-than-optimizing behavior of travelers. While standard models assume that people rationally evaluate travel options for each trip, as a practical matter, several factors lead to a fair degree of inertia in travel behavior; travelers tend to rely on established behavioral patterns, and it is hard to budge people from these habits. Under ordinary circumstances, travelers make occasional "strategic" choices to change mode over the long term, but day-to-day modal choice tends to be "tactical" (that is, derivative from their long-term behavior); therefore, people are unlikely to change their patterns just because they are given new information.See Reed and Levine 1997.

Travel behavior has been characterized as a habit, an observation with significant implications for an information-based strategy aimed at encouraging people to change their travel behavior.See Henk Aarts, Bas Verplanken, and Ad van Knippenberg, "Habit and Information Use in Travel Mode Choices," Acta Psychologica 96 (1-2) (1997):1-14. Habitual behavior is characterized by lack of awareness, in that people do not think about their actions; by efficiency, in that actions are carried out with little effort; and in some cases, by lack of control. Because habitual behaviors are taken without conscious thought, it is hard to change them by providing information, since most people are unlikely to pay attention. The more habitual the behavior, the less the actor seeks or is even amenable to new information that might lead to altered behavior. In one study, drivers with stronger habits systematically sought out less travel information than those whose habits were weaker.See Bas H. Verplanken, Henk Aarts, and Ad van Knippenberg, "Habit, Information Acquisition, and the Process of Making Travel Mode Choices," European Journal of Social Psychology 27 (1997):539-560.

On the surface, the habitual nature of most daily travel seems to imply that it is useless to try to encourage people to change their travel behavior habits by giving them information; people who habitually rely on their cars will be relatively impervious to information on alternative travel options. This conclusion implies that short-run information-provision strategies aimed at influencing people's daily choice of travel modes are likely to have only limited effect. However, psychological research offers some hope for information provision as an effective policy to promote behavioral change, at least under certain circumstances. Habits tend to be environmentally conditioned: the immediate surroundings send cues that trigger habitual behaviors. Thus, a particular social environment might trigger smoking or drinking; habitual consumption of fast food might be stimulated by the proximity of outlets. It stands to reason that travel behavior also is triggered by environmental cues. One might expect the environmental dimension to trigger travel behavior more strongly than other habits, since the purpose of transportation is connected to the physical environments of one's origin and destination.

The observation that habits are largely environmentally triggered offers an insight into how information can stimulate behavioral change: information that comes at the moment of a change in one's environment is likely to be received better than at other times. The time when people move to a new home should be a moment when they are more susceptible to behavioral change in general, including information-induced behavioral change. For example, Verplanken and Wood documented a change in students' exercise habits associated with a shift to a different college campus.See Bas H. Verplanken and Wendy Wood, "Interventions to Break and Create Consumer Habits," Journal of Public Policy and Marketing. In press, 2005. Heatherton and Nichols found that, in general, moving to a new location increased the likelihood that people successfully translated a desired shift in some aspect of their lives to an actual change.See Todd Heatherton and Patricia A. Nichols, "Personal Accounts of Successful Versus Failed Attempts at Life Change," Personality and Social Psychology Bulletin 20 (1994):664-675. Bamberg, Rölle, and Weber examined the effects of offering a free bus ticket to people who had just moved, demonstrating that recipients responded with increased transit riding.See Sebastian Bamber, Daniel Rölle, and Christoph Weber, "Does Habitual Car Use Lead to More Resistance to Change of Travel Mode?" Transportation 30 (2003): 97-198. A period of shift in one's environment can be thought of as a special window of opportunity when a number of rigid habits become temporarily looser, until a new set of environmentally conditioned habits sets in.

This notion that people are more open to changing pre-established habits during times of change has clear implications for travel behavior; the time when an individual or household relocates to a new environment is a particularly opportune moment during which new travel behavior is formed. Thus, providing people with information about their travel choices during this window of time is more likely to stimulate them to use the information than providing the same information at other times.

However, psychological research into the modification of habitual behavior suggests a second principle: intervention should occur "upstream" of the behaviors that it is trying to affect.See Verplanken et al.,539-560. That is, treating the behavior at its point of expression may be less effective than altering some precursor trigger to the behavior. In the case of transportation, residential location can be seen as a precursor behavior that is "upstream" in the causal chain from travel behavior. In this sense, providing people with information about their travel choices before they choose a new home may be more influential than providing travel mode information at the time of the trip itself.

None of the above is to imply that driving is a bad habit that is analogous in its health or moral implications to smoking or overeating. Driving is frequently the most rational travel choice for individuals in the environments of U.S. metropolitan areas. Nevertheless, it may have some habitual aspects that render travel decisions less than optimal, even for the individual. Transportation planners are regularly frustrated by the sight of people driving distances that they could easily have walked, or drivers circling for parking when a short bus hop would have saved them time. The natural inclination in these instances is to redouble efforts at information provision. A focus on the habitual nature of driving behavior can assist in developing a program of information that has a greater likelihood of success. The current study is structured around this notion.

TRANSPORTATION CHOICE AND RESIDENTIAL CHOICE

A policy designed to increase the use of transportation alternatives should be based in a theory of people's choice of transportation modes. Since the development of McFadden's discrete choice framework for transportation demand analysis,See Daniel McFadden, "Conditional Logit Analysis of Qualitative Choice Behavior," in Frontiers in Econometrics, edited by P. Zarembka (New York: Academic Press, 1974). the determinants of modal choice have been described with great consistency; as such, they underpin the analysis described here. People consider total trip times when choosing among travel modes, but they find the time spent outside the vehicle (for example, in walking, waiting, or transferring) several times more onerous than that spent in the vehicle itself. In addition, transfers between vehicles carry a utility penalty beyond the time involved in switching between vehicles. Service cross-elasticities between competing modes--for example, the effect of an improvement in auto service on transit usage--tend to be greater than price cross-elasticities (for example, the impact of transit subsidy on use of the drive-alone mode).See Phil B. Goodwin, "A Review of New Demand Elasticities with Special Reference to Short and Long Run Effects of Price Changes," Journal of Transport Economics and Policy 26 (1993):155-163; Frank S. Koppelman and Chester G. Wilmot, "Transferability Analysis of Disaggregate Choice Models," Transportation Research Record 895 (1982):18-24. The availability of free parking is among the most significant determinants of use of the drive-alone mode.See Donald Shoup, "Evaluating the Effects of Cashing Out Employer-Paid Parking: Eight Case Studies," Transport Policy 4 (4) (1997):201-216; Donald Shoup and Richard W. Willson, "Employer-Paid Parking: The Problem and Proposed Solutions," Transportation Quarterly 46 (2) (1992):169-192.

For policymakers interested in improving the market share of nonautomotive modes, the problem is that much of the above is effectively predetermined by patterns of land use. With significant investment, a high level of transit service can be deployed in limited corridors. However, where development is not concentrated in those corridors, use will be slight because the physical environment presents the opposite conditions from those that support transit: walk time is great, transfers are many, highway level of service is high, and free parking is plentiful. In this environment, transit can attract people without cars, but faces nearly insurmountable barriers in attracting riders who have the option to drive. Pedestrian trips are even more spatially conditioned, with the majority under one-half mile in distance. Thus a policy of enhancing transportation options is intimately related to the locational choices people make, and these depend in part on the information that they can assimilate.

Information about residential location opportunities tends to deteriorate with distance; these spatially determined limitations on information availability have been shown to influence the patterns of residential relocation within metropolitan areas. Most people move relatively short distances. They also tend to move within the same sector of the metropolitan area; for example, a resident of the northeast corner of the city is much more likely to move to a northeastern suburb than a northwestern suburb. Palm and Davis explored the effect of the less spatially conditioned Internet-based information on this pattern: Would Internet users exhibit a less constrained pattern of relocation?See Risa Palm and Michelle A. Davis, "The Impacts of Web-Based Information on the Search Process and Spatial Housing Choice Patterns," Urban Geography 22 (7) (2001):641-655. In fact, Internet use was associated with neither greater distance nor more sectoral switching. The mere fact of an information-rich environment does not necessarily affect housing-search patterns. The question of whether a targeted policy of information provision can affect households' locational decisions remains. Because such decisions effectively determine the relative attractiveness of the modes--for example, by placing the individual within or beyond walking access to a direct transit trip to his or her destination--they underpin the location-transportation relationship and appear to be a natural target for information dissemination.

Study Methods

To test the hypothesis that individuals choosing where to live are more likely to prefer a rental unit with good transit and/or walking access to destinations if they are provided with this kind of accessibility information as part of their initial housing search, we followed a classical experimental research design in a laboratory setting. We brought 236 University of Michigan (UM) graduate students into a computer laboratory, where they were asked to select their top five choices of where to live after reviewing a database of residential properties that was custom designed for the study. The study used data from actual rental properties gleaned from the UM off-campus housing database. To assess the influence of the transit information, we divided study participants into two groups. The "control" group received information about each rental unit, in table form, that included only the attributes currently standard in most private and university housing databases, such as price, the number of bedrooms, and the availability of off-street parking. In different laboratory sessions, the "experimental" group received the same information, plus additional information about how far the unit is from a transit stop, the transit service frequency and directness, and distance to the part of the university campus that the student visits most often. Data on housing opportunities were presented to the experimental group in map form, with each property classified according to its accessibility to the individual's campus (See Definitions of Accessibility Ratings As Presented in the Information System). After choosing their desired rental properties, all participants filled out a survey that asked about their current travel behavior patterns, desired features in housing, and sociodemographic characteristics.

Definitions of Accessibility Ratings As Presented in the Information System
Level of Accessibility	Description
Excellent	Walking distance from campus (0.5 mile) or bus route (0.33 mile) with no transfers and less than or equal to 15 minutes frequency
High	Walking distance to bus route (0.33 mile) with no transfers and greater than 15 minutes frequency
Medium	Walking distance to bus route with one transfer, regardless of frequency
Low	None of the above

The strength of this research design lies in its ability to overcome sample selection bias and provide an unambiguous indication of causality. These advantages are particularly useful for research on information and travel behavior. Although some researchers have argued that experimental designs are limited in their ability to predict effects to the general population or effects that can be translated to other populations,See Arnold Zellner and Peter E. Rossi, "Evaluating the Methodology of Social Experiments," in Lessons from the Income Maintenance Experiments, edited by A. Munnell (Boston: Federal Reserve Bank of Boston, 1986). others maintain that it is of critical interest to understand the effects of policy interventions at the individual level before attempting generalizations.See Gary Burtless, "The Case for Randomized Field Trials in Economic and Policy Research," Journal of Economic Perspectives 9 (2) (1995):63-84. As a stated-preference study, this research was able to present the "treatment" of information provision that is unavailable in the real world, yet its findings are subject to the caveat that we did not establish with certainty people's actual residential-choice behavior if faced with these information systems.

HYPOTHESES AND OUTCOME VARIABLES

The main expectation of our study is that integrated accessibility and housing information presented in the context of a residential relocation will influence people to select residences with higher accessibility than they would have without the integrated information.

One dimension of increased accessibility is proximity to destinations. We hypothesize that individuals exposed to integrated transportation-housing information will choose residences that are closer to major destinations relative to individuals not receiving the information.

A second dimension is proximity to transit lines. We hypothesize that individuals exposed to the integrated information will choose housing closer to transit lines in general, and to those serving their destinations in particular.

A third dimension is accessibility provided by transit to regional destinations. We hypothesize that individuals exposed to the integrated information will select properties that are served by transit routes with higher frequency and route diversity than the properties selected by participants not exposed to the information.

Fourth, we expect that the above effects are likely to be more pronounced among some sociodemographic groups than others. These are expected to be groups that are "swayable"--they do not have strong a priori commitments to the auto, transit, or walking modes. Groups determined to walk would be expected to seek residences close to their destinations with or without an information system; groups of regular bus riders already know where the routes are and need no information system to help guide them in their residential choices. If transportation "fence-sitters" can be identified, these are likely to be the groups most amenable to behavioral change through information provision.

Given our hypotheses, See Outcome Variables identifies four categories of dependent or outcome variables, which are used in our analysis. They include local access to transit; access provided by transit in terms of service frequency and destination diversity; and the nonmotorized accessibility to major destinations in the area. The transit variables are key characteristics of transit service.See Alan T. Murray, Rex Davis, Robert J. Stimson, and Luis Ferreira, "Public transport access," Transportation Research Part D 3 (5) (1998):319-328. The accessibility variables correspond to research suggesting that proximity to destinations is critical for the viability of nonmotorized transportation modes.See For a review, see Brian E. Saelens, James F. Sallis, and Lawrence D. Frank, "Environmental Correlates of Walking and Cycling: Findings from the Transportation, Urban Design, and Planning Literatures," Annals of Behavioral Medicine 25 (2) (2003):80-91.

Comparisons of the average value of each outcome variable between experiment and control groups will identify the desired effects. To determine the statistical significance of continuous outcome variables between the experimental and the control groups, we use analysis of variance (ANOVA). For proportion-type variables, such as the percentage of units selected within one-quarter mile of a bus stop, we used ordered logistic regression with the proportion as the dependent variable and a dummy variable identifying group membership as the independent variable. See For example, the outcome variable "% of most preferred within 1/4 mile of transit" had four possible values: 0, 33, 66 or 100%. Treating these outcomes as continuous would be inappropriate; therefore, we decided to use ordered logistic regression instead. The significance of the dummy variable coefficient reveals statistically significant differences between the two groups.

Outcome Variables (Continued)
Outcome	Units	Statistical test
Access to transit
Average network distance to transit from top three	Miles	ANOVA
Average network distance to transit from top five	Miles	ANOVA
Average crow-fly distance to transit from top three	Miles	ANOVA
Average crow-fly distance to transit from top five	Miles	ANOVA
Average network distance to high-quality transit from top five. (This is defined as a bus route with no-transfer service to the destination of interest--central campus or one's home campus--and a service frequency of 15 minutes or better.)	Miles	ANOVA
Percent of three most preferred properties within 1/4 mile of transit route	%	Ordered logistic
Percent of five most preferred properties within 1/4 mile of transit route	%	Ordered logistic
Distribution of choices between properties by accessibility ranking	%	Chi-Square, Kendall's tau-b
Service frequency
Total buses per hour within 1/4 mile of top three	# of buses/hr	ANOVA
Total buses per hour within 1/4 mile of top five	# of buses/hr	ANOVA
Destination diversity
Sum of the number of routes within 1/4 mile of top three	# of routes	ANOVA
Sum of the number of routes within 1/4 mile of top five	# of routes	ANOVA
Pedestrian accessibility
Average network distance to closest destination from top three	Miles	ANOVA
Average network distance to closest destination from top five	Miles	ANOVA
Average sum of network distance to main destinations from top three	Miles	ANOVA
Average sum of network distance to main destinations from top five	Miles	ANOVA

DETAILED DESCRIPTION OF THE SIMULATION TOOL

We developed a Geographic Information Systems-based housing search application using the ESRI software package ArcGIS and Visual Basic Programming language. The housing search tool the control group used mimicked currently available on-line housing search applications; the experimental group received a version that had transit and pedestrian accessibility information added. The experimental group also saw the available units on a map, instead of merely in a text list as the control group saw them.

To create a realistic set of rental units for study participants to look at, we based the simulation tool on real properties listed in UM's on-line database of available housing. This database, maintained by the University Housing office, is the system most UM graduate students use to find living arrangements. The 286 properties included in the study came from the 9,324 units retrieved on July 28, 2004.

The specific properties and the total number were carefully selected to provide study participants with a diverse set of choices. First, we enumerated the key attributes that we wanted to be represented in the rental units, including accessibility, price, unit type, and number of bedrooms. The accessibility-price combination was assumed to be the central attribute pair, and rental prices and access to campus were each recoded into four categories. Units were selected to ensure that a choice of units was available at each price-accessibility combination. Attention was paid to other attributes, including bedrooms, furnished versus unfurnished, on-street versus off-street parking, and in-building laundry facilities, to offer a range of housing choices to participants. As a check, both the geographical distribution of samples and summary statistics for attributes was analyzed, and some outliers were removed. With this design, we ended up with 286 properties. See Study Area, Transit Routes, and Distribution of Sampled Properties shows the study area and a distribution of sampled properties, and See Description of Variables and Summary Statistics presents summary statistics on the chosen property set with respect to several key attributes.

Study Area, Transit Routes, and Distribution of Sampled Properties

Description of Variables and Summary Statistics (Continued)
Description of variables	Mean	S.D.	Min	Max	Obs.
Monthly rent ($)	880.46	637.74	250	5800	286
Bedrooms* (number)	1.59	1.22	0.5	6	286
Furnished (1=yes, 0=no)	0.23	0.42	0	1	286
Pets allowed (1=yes, 0=no)	0.37	0.48	0	1	279
Parking (1=on premises, 0=no parking or on street)	0.78	0.42	0	1	286
Laundry (1=yes, on premises 0=no)	0.83	0.38	0	1	286
Smoking (1=yes, 0=no)	0.34	0.48	0	1	286
Wheelchair accessible (1=yes, 0=no)	0.05	0.21	0	1	263
Unit type (1=apartment, 2=house, and 3=room)	1.67	0.80	1	3	286
Distance to closest transit stop (miles)	0.47	0.53	0.01	4.19	286
Distance to Central Campus (miles)	2.09	2.19	0.20	14.50	286
Distance to North Campus (miles)	3.33	2.00	0.62	15.04	286
Distance to Medical Campus (miles)	2.34	2.11	0.20	14.80	286
Distance to downtown Ann Arbor (miles)	2.08	2.11	0.17	15.58	286
* Studio apartments and single rooms were recoded as 0.5.

The housing search tool comprised four main pages: the search page, which allowed participants to search for units according to various criteria; the search results page, which showed participants all the properties that met their search criteria; the unit details page, which showed all the attributes about any unit of interest; and the comparison page, which showed, in table format, the key attributes of up to ten properties that interested the participant. The experimental group also saw a fifth page, the transit detail page, which provided detailed information about bus routes and schedules. A more detailed description of each page follows.

Search Page

The search page allowed participants to search the housing database according to various criteria. For the control group, these were unit type, rent price, number of bedrooms, laundry on premises, furnished, off-street parking, and within 1/3 mile of campus (See Search Page for the Control Group). For the last variable, distance to campus, participants were asked which of the three main university campus sites they visited most often, the Central, Medical, or North Campus, and distance was calculated from that site.

Participants in the experimental group received the same search criteria, with the additional option of searching for units "close to bus," defined for them as within 1/3 mile from a bus stop (See Search Page for the Experimental Group).

Search Page for the Control Group

Search Page for the Experimental Group

Search Results Page

Once participants selected their search criteria, they were shown all units that matched. For the control group, this was presented as a simple table showing the address and rental price for each unit (See Sample Search Results Page for the Control Group). For the experimental group, the search results appeared in map format (See Sample Search Results Page for the Experimental Group); each available unit appeared as a dot on the map, color coded by its accessibility rating. The three campuses and all bus routes also were shown on the map, to help participants orient themselves.

Sample Search Results Page for the Control Group

Sample Search Results Page for the Experimental Group

A sidebar (See Instructions for Using the Map Shown to the Experimental Group) explained to participants how to interpret the special features of the map. The map displayed the units as different colored dots, where the colors represented how accessible the unit was to the respondent's selected campus. This measure of accessibility incorporated the distance from the unit to the campus that the participant visited most often, and the quality of bus service nearby.See A composite of pedestrian and transit accessibility, available only in the experimental group's version, was measured as follows: (1) Excellent accessibility: walking distance from campus (0.5 mile) or bus route (0.33 mile) with zero transfer and less than or equal to 15 minutes frequency; (2) High accessibility: walking distance to bus route (0.33 mile) with zero transfer and greater than 15 minutes frequency; (3) Medium accessibility: walking distance to bus route with 1 transfer, regardless of frequency; and (4) Low accessibility: none of the above Bus routes also were color coded according to service frequency, with the dark green routes having the most frequent service.

Instructions for Using the Map Shown to the Experimental Group

Unit Details Page

For the experimental group, detailed information about a unit or a bus route was provided on a unit detail page. This page showed property information, address, proximity to campus and transit, and transit frequency and transfers. For all properties, the page also displayed photos of the property and its neighborhood. The version of the unit details page shown to the control group was similar, except that it did not provide information about transit frequency and transfer (See Sample Unit Details Page for the Control Group and See Sample Unit Details Page for the Experimental Group).

Sample Unit Details Page for the Control Group

Sample Unit Details Page for the Experimental Group

Transit Details Page (Experimental Group Only)

The experimental group also had the option to see detailed information about the service on any bus line, as explained in the instructions for using the map (See Instructions for Using the Map Shown to the Experimental Group). Clicking on any bus line brought up a page with the route and schedule information for that bus line (See Sample Transit Details Page for the Experimental Group).

Sample Transit Details Page for the Experimental Group

Results Comparison Page

The results comparison pages allowed participants to see the detailed unit information for multiple rentals of interest, all in one table, facilitating comparison of properties. The criteria were displayed as rows, and the data on each unit showed up in a column. The comparison pages were similar for the control and experimental group, except that only the latter received information on the variables "Close to Transit," "Frequency of Bus to Campus," and "Bus Transfers to Campus" (See Results Comparison Page for the Control Group and See Results Comparison Page for the Experimental Group).

Results Comparison Page for the Control Group

Results Comparison Page for the Experimental Group

THE PARTICIPANT SURVEY

After participants used the housing search tool to select the five rental properties that most interested them, they completed an on-line survey conducted through the Website SurveyMonkey.com. The survey was organized into five sections. The first section asked participants to indicate the five rental units they had selected from the simulation exercise, by typing into the survey the "Property Identification Number" (PIN) for the five housing units that they identified as most interesting. The second section collected information about the factors that participants value when choosing a place to live. The third section asked participants about their attitudes, preferences, and behavior related to daily travel and housing. The fourth section collected demographic and background information about both the participants and their household members, including age, income, and current home location. The last section asked participants to evaluate how easy the housing search tool was for them to use. (See See for a copy of the full set of survey questions.)

Summary of Simulation and Survey Results

This chapter and See Summary Statistics of Selected Survey Responses summarize the results for the four sections of the survey that collected information on the characteristics of respondents (See ). It presents summary statistics for participants' sociodemographic characteristics (survey section IV), current travel patterns (survey section III), preferences for selecting where to live (survey section II), degree of exposure to transit service (survey section II), self-assessed inconvenience of using transit (survey section II), and ease of using the simulation tool described in the previous chapter (survey section V).

Summary Statistics of Selected Survey Responses (Continued)
Description	Mean	S.D.	Min.	Max.	N
Sociodemographic characteristics
Age (yrs.)	25.7	4.0	20	52	235
Sex (1=Female)	0.56	0.5	0	1	236
Midpoint of yearly income range ($1,000s)	22.7	17.1	5	75	225
Current monthly rent or mortgage ($1,000s)	0.70	0.50	0	5	232
Affordability (% of monthly income for housing)	60.1

MTI Report 05-03