Women’s Issues in Transportation: Summary of the 4th International Conference, Volume 2: Technical Papers (2011)

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143 Effects of Gender on Commuter Behavior Changes in the Context of a Major Freeway Reconstruction Patricia L. Mokhtarian, University of California, Davis Liang Ye, School of Transportation Engineering, Tongji University, China Meiping Yun, School of Transportation Engineering, Tongji University, China To study the commuter travel behavior impacts of a 9-week reconstruction of interstate 5 in downtown Sacramento, California, a series of three internet-based surveys was conducted. This paper offers a preliminary analysis of the first two of those surveys, focusing on the role of gender in commuters’ responses. Avoiding peak hour and changing route were the most common responses, and women were more likely than men to employ them. Among the changes that reduce vehicle miles traveled, increasing transit use and increasing tele- commuting were the most common. Overall, women were 21% more likely to make at least one change than men were. A binary logit model of the choice to increase transit use suggests that persuading current transit users to increase their transit use was easier than convinc- ing nonusers to switch. Respondents who heard about the increased level of transit service were more likely to increase transit use. Employer transit subsidies sup- ported increases in transit use (but only for women), whereas variable work hours (for women) discouraged them. Men in managerial–administrative occupations and women in larger households were also more likely to increase their transit use. in the 9 weeks between May 30 and July 31, 2008, a 1-mi stretch of interstate 5 (i-5) in downtown Sac-ramento, California, was intermittently closed for a reconstruction project (“the Fix i-5 project” or “the Fix”). This portion of i-5 is part of a major north–south conduit for interregional traffic, as well as a key com- muter route serving downtown Sacramento (the state capital) and other job locations in the region. To miti- gate the impact of the Fix, a number of strategies were implemented by the California Department of Trans- portation (Caltrans) and other public agencies, includ- ing providing extensive information on the Fix and commute alternatives, increasing transit service, and offering reduced-rate parking at some facilities. Two weeks before the Fix (May 16), Gov. Schwarzeneg- ger issued Executive Order S-04-08 (http://gov.ca.gov/ index.php?/press-release/9631/, accessed Aug. 31, 2009) directing state executive agencies to promote commute alternatives for their employees to the fullest extent possible and encouraging other public agencies and private companies to do the same. The executive order also authorized a study of the effects of the Fix, with a view to evaluating the effectiveness of the com- mute modification strategies being promoted and the extent to which more sustainable commute patterns would be the longer-term result. As the interstate high- way system and other elements of the transportation infrastructure continue to age, reconstruction projects such as Fix i-5 will occur quite frequently. Therefore, it is important to learn as much as possible about com- muter reactions to such projects, to enable future proj- ects to be implemented in the most effective way. At the same time, such projects offer valuable opportunities to disrupt habitual behavior in a natural way and to use that disruption to motivate shifts toward more sustain- able commute choices (Gardner 2009). For both of those purposes, it is of interest to examine whether there are any significant differences by gender.

144 WOMEN’S iSSUES iN TRANSPORTATiON, vOLUME 2 Do women, with generally more complex activity sched- ules (Bianco and Lawson 2000; Hjorthol 2004), expe- rience adverse impacts of such reconstruction projects more heavily than men? Are they less likely than men to make changes, and if so is it because they have more constraints, or because they have already built in more flexible options such as flextime or telecommuting that allow them to absorb disruptions more robustly? Or are women more likely than men to make changes, and if so, is it because of experiencing more adverse impacts, a generally greater receptivity to change (Dobbs 2007), or a greater environmental sensitivity (Matthies et al. 2002) giving them a stronger internal motivation to change? The present study can provide some preliminary insights into those questions. A series of three internet-based surveys was conducted to evaluate the effects of the Fix on commuter behavior. The present paper offers an initial analysis of the first two of those surveys. Specifically, the active choices made by commuters to cope with the closure (e.g., taking vaca- tion, telecommuting, or changing mode) are addressed, as well as the way those choices may differ by gender. in addition to presenting descriptive statistics, a binary logit model is developed of the most frequently adopted active choice that reduces vehicle miles traveled (vMT), namely the choice to increase the use of transit during the Fix. The rest of the paper is organized as follows. The next section briefly reviews previous related research, and the following section describes the data collection. Subse- quently, descriptive statistics for baseline work schedule– commute characteristics, and the active choices made by commuters are discussed. The binary logit model is pre- sented in the penultimate section. The final section sum- marizes the study and suggests future research directions. liTeraTure revieW Numerous studies have addressed gender differences in various indicators of travel behavior and likely reasons for those differences. Some key findings (especially in, although not limited to, the U.S. context) include the following: • Women continue to exhibit shorter commute times and distances than men (Crane 2007). • Nationwide, women are (still) more likely than men to commute by transit (Pisarski 2006) although their transit use has declined during the past 20 years, and the picture is more complex when controlling for race (Crane 2007). in a 1994 stated preference survey of suburban commuters in Montreal, Canada, authors Patterson et al. (2005) found that women were less likely to choose public transit than men, suggesting that their higher revealed preference shares often represent con- strained choices. • Women do more child chauffeuring and make more household-serving trips (such as grocery shopping) than men [Mauch and Taylor (1997) using trip diary data from a 1990 survey of San Francisco Bay Area resi- dents, with similar results found in Norway by Hjorthol (2004)]. • Women are more concerned about safety (Liss et al. 2005) and more likely to take safety considerations into account in making travel choices (Church et al. 2000; Kenyon et al. 2002). Although women’s travel patterns have changed dra- matically during the past few decades, Crane (2007) found persistent gender differences in a nationwide (U.S.) panel study covering the period 1985 to 2005. One Nor- wegian study (Hjorthol 2004) similarly found that gen- der differences did not diminish during the 1990s, noting that women’s travel distances were (still) more local than men’s, men had more work trips, and women had more nonwork trips. in view of these and other differences, decision mak- ers have been advised to consider the distinctive travel needs of women and men during transportation planning and operations (Kerkin 1995; Lu and Pas 1999). Tranter (1994) pointed out that gender-based role differences influence not only daily travel patterns but also economic and social status, which in turn affects the choices avail- able for women, which consequently affect mobility. The Women’s Planning Network, inc. (1995), also indicated that there is an absence of transportation policies directed at women’s transport needs. Law (1999) suggested the need for further research on “gender and daily mobility,” incorporating it within a larger theoretical project. interestingly, no studies were found that focused spe- cifically on the subject of this paper: gender-based effects associated with the execution of a planned freeway recon- struction. However, several (linked) literatures are gen- erally relevant. Many or most strategies associated with mitigating the disruption of a freeway reconstruction fall into the category of transportation demand management (TDM) policies, and a sizable literature exists on the adoption and effectiveness of TDM measures. Although TDM policy seldom addresses gender differences in travel behavior, within that field some gender-based observa- tions have been made. For example, Bianco and Lawson (2000) suggest that TDM strategies may be more bur- densome on women than on men, because women are more likely to be in the lower-income strata. Other ana- lysts also argue that the numerous policies aimed at dis- couraging car use may run counter to the transportation needs of women, particularly in instances in which com- plex, multidestination trip making is required (Women’s Planning Network, inc., 1995).

145EFFECTS OF GENDER ON COMMUTER BEHAviOR CHANGES The disruption occasioned by a freeway reconstruc- tion may be severe enough to trigger the reconsideration of habitual behavior, and thus another literature rele- vant to this study deals with voluntary behavior change and habit disruption. Research on voluntary behavior change is often directed to reducing the use of private motor vehicles in urban areas (Taylor et al. 2003; Fujii and Taniguchi 2005; Rose and Marfurt 2007). in one study that investigated gender differences in travel mode changes, Rose and Marfurt (2007) found (using 2003– 2004 Australia “Ride-to Work” annual event travel survey data) that female first-time transit riders were more likely (30%) than men (22%) to continue riding transit 5 months after the event. Finally, a third relevant literature deals with environmental concern and related behaviors. For example, Matthies et al. (2002), using a survey of 187 inhabitants of a German city, found that women were more likely than men to report an inten- tion to reduce their car use, as a result of their stronger ecological norms and weaker car habits. daTa collecTion Because the study was conducted just a few weeks before the closure, there was not time to draw a rigorous, geo- graphically based random sample and recruit partici- pants by mail. instead, respondents were recruited via e-mail invitations, disseminated through numerous state agencies, the Fix i-5 listserv, transportation manage- ment agencies, and a press release from the University of California, Davis. Given the ad hoc nature of the recruit- ment process, a response rate cannot be computed, nor can a completely representative sample be expected. it is likely that state workers are overrepresented, as well as internet-literate workers, workers with burdensome commutes, those heavily affected by the Fix, and those who made environmentally beneficial behavior changes. However, self-employed and small-business workers are probably underrepresented, some of whom (together with the less computer literate) may also have been heav- ily affected by the Fix, so biases in the sample are coun- teracting each other to an unknown extent. Accordingly, the subsequent descriptive statistics should be viewed with caution. Nevertheless, it is sug- gested that some data are better than no data, and it is also thought that comparisons in the data should be largely robust (particularly the comparison of men and women). Thus, the reader is encouraged to focus on trends, conditional relationships, and the model (which is a specific type of conditional relationship): in these cases, it is less critical to have a sample that is representa- tive of the total population (Babbie 1998). Two internet-based surveys were administered during the period of the Fix: the first, hereafter referred to as the Wave 1 survey, sought information on behavior during the first closure of the freeway, of all lanes in the northbound direction (Monday, June 2 through Sunday, June 8, 2008, for the purposes of this paper). The second survey (Wave 2) addressed the first closure of the freeway in the southbound direction (June 16 through June 22). Another paper (Ye et al. 2009) includes selected comparisons between the two waves, but the present paper will focus on comparisons by gender, without regard to wave. To avoid the complication of having a sizable fraction (about a third) of the sample not being independent across waves, in this analysis all of the Wave 1 cases have been retained (4,520 after screening for completeness), and among the Wave 2 cases, only those who did not complete Wave 1 (2,414) were retained, for a pooled sample of 6,934 cases. The pooled sample contains 2,247 (32%) men, 4,160 (60%) women, and 527 (8%) respondents who declined to state their gender. The siz- able proportion of women in the sample is not as unbal- anced as it may seem, in view of the preponderance of state employees in the study population, and the fact that in gen- eral some 60% of state and local government workers are women (Caldwell 2009). Table 1 presents the sample statistics for some selected characteristics. The “average” characteristics are age 47, female, college graduate, in a household with 2.7 mem- bers, 2.1 cars, and having an annual income of $75,000 to $99,999. Women tend to have lower (household) incomes than men, at least in part because on average they have less education, are slightly younger, are more likely to hold clerical positions, and are more likely to work part time. They also tend to have smaller house- holds (hence, generally fewer wage earners). However, number of vehicles and commute distances and times are relatively similar across gender. Three filters were applied to the data at various points in the analysis: (a) 344 respondents (118 men, 210 women, 16 unknown) who were out of the region for the entire week of the closure were excluded from the rest of the analysis; (b) also excluded from the analysis of commuters’ active choices (see section on commute-related active choices) were 28 respondents (9 men, 19 women) who did not commute to work during the closure week; (c) finally, also excluded from the model of the choice to increase transit use (see section on the “increase transit use” model) were 1,123 respondents (406 men, 646 women) who already used transit for all or almost all of their commuting trips. The remaining sample for the “increase transit use” model is 5,439 cases (1,714 men, 3,285 women). Baseline WorK schedule– commuTe characTerisTics information on respondents’ baseline work schedule– commute patterns was collected through a series of ques-

146 WOMEN’S iSSUES iN TRANSPORTATiON, vOLUME 2 TABLE 1 Selected Characteristics of the Sample, by Gender Pooled Data Men Women Characteristic (sample sizes) Number Percent Number Percent Number Percent Number of cases 6,934 100.0 2,247 32.4 4,160 60.0 Average age (years) (6,053, 2,067, 3,965) 46.5 47.3 46.1 Average educational levela (6,291, 2,192, 4,038) 4.06 4.3 3.9 Annual household income 5,977 100.0 2,112 100.0 3,817 100.0 <$15,000 29 0.5 14 0.7 15 0.4 $15,000–$29,999 106 1.8 25 1.2 80 2.1 $30,000–$44,999 493 8.2 112 5.3 374 9.8 $45,000–$59,999 715 12.0 176 8.3 535 14.0 $60,000–$74,999 1,070 17.9 312 14.8 752 19.7 $75,000–$99,999 1,243 20.8 490 23.2 740 19.4 ≥$100,000 2,321 38.8 983 46.5 1321 34.6 Average household size (6,328, 2,184, 4,092) 2.72 2.83 2.67 Driver’s license possession (6,387, 2,215, 4,109) 6,333 99.2 2,200 99.3 4,071 99.1 Average number of household operational vehicles (5,722, 1,991, 3,683) 2.08 2.17 2.03 Walking time from home to the nearest bus stop or light-rail station (min) 6,356 100.0 2,210 100.0 4,081 100.0 <5 1,680 26.4 676 30.6 992 24.3 5–10 1,729 27.2 592 26.8 1,123 27.5 10–20 1,042 16.4 352 15.9 674 16.5 >20 1,501 23.6 482 21.8 1,002 24.6 Don’t know 404 6.4 108 4.9 290 7.1 Average commute (min) (6,878, 2,231, 4,134) 31.9 32.8 31.4 Average commute (mi) (6,872, 2,231, 4,130) 17.8 18.4 17.4 Primary job work schedules 6,920 100.0 2,245 100.0 4,152 100.0 Part timeb 388 5.6 84 3.7 274 6.6 Conventionalc 3,221 46.5 1,065 47.4 1,899 45.7 variabled 1,461 21.1 509 22.7 858 20.7 Compressed 9–80 work weeke 1,504 21.7 470 20.9 921 22.2 Compressed 4–40 work weekf 261 3.8 82 3.7 158 3.8 Other 85 1.2 35 1.6 42 1.0 Occupation 6,914 100.0 2,242 100.0 4,150 100.0 Manager or administration 1,591 23.0 541 24.1 929 22.4 Professional or technical 3,929 56.8 1,432 63.9 2,211 53.3 Services or repair 47 0.7 29 1.3 13 0.3 Clerical or administrative support 1,184 17.1 175 7.8 916 22.1 Sales or marketing 71 1.0 20 0.9 43 1.0 Production, construction, crafts 42 0.6 27 1.2 12 0.3 Other 50 0.7 18 0.8 26 0.6 Number using transit as primary commute mode (6,595, 2,160, 3,945) 1,183 17.9 484 21.5 609 14.6 Number currently using transit but not primary commute mode (6,595, 2,160, 3,954) 1,098 16.6 368 16.4 654 15.7 Awareness of Fix strategy that increases number of buses (6,544, 2,228, 4,116) 3,798 58.0 1,291 57.9 2,397 58.2 Employer-provided reduced-rate transit passes (6,575, 2,236, 4,139) 4,122 62.7 1,383 61.9 2,636 63.7 Employer-provided variable start and end times (6,575, 2,236, 4,139) 4,109 62.5 1,376 61.5 2,617 63.2 Note: Boldface designates traits that differ significantly by gender (at the .05 level or better). Detail may not add to total because of rounding. a 1 = some grade school or high school; 2 = high school graduate; 3 = some college or technical school; 4 = four-year college, university, or tech- nical school graduate; 5 = some graduate school; 6 = completed graduate degree(s). b Less than 35 hours per week. c 7.5 to 8 hours per day, with a start time between 8:00 a.m. and 9:00 a.m. d 7.5 to 8 hours per day, with a variable start time. e Nine 9-hour days in 2 weeks. f Four 10-hour days per week.

147EFFECTS OF GENDER ON COMMUTER BEHAviOR CHANGES tions focusing on a “typical 28-day (4-week) period” “before Fix i-5 began.” Respondents were asked for the number of days out of 28 on which they • Worked at home as the regular location of their job, • Worked at home instead of commuting to their regular workplace, and • Physically traveled to a regular workplace outside their home. Those with a nonzero answer to the last question were asked how many days out of that number they • Drove alone for most of the commute, • Carpooled or vanpooled for most of the com- mute, • Rode a bus for any portion of the commute, • Rode light rail for any portion of the commute, • Rode Amtrak (commuter train) for any portion of the commute, • Walked for the entire commute, and • Rode a bicycle for any portion of the commute. Thus, only the drive-alone, car–vanpool, and walk modes are mutually exclusive, and walking (for the entire commute) in principle excludes any other mode. The transit and bicycle modes can occur in any combination with each other or with the car modes. Accordingly, the sum of the mode-specific answers can exceed the total number of commute days for a given respondent. The sample’s baseline participation was analyzed in each of these commute options, together with compressed work schedules, because they also reduce the number of vehicle commute miles traveled (for more details, see Mokhtarian et al., in press). Engagement (at least 1 day out of 28) in compressed work schedules (26%), both types of working at home (5% as regular workplace; 9% telecommuting), and physical commuting (97%) is relatively similar across gender; the main differences appear with respect to mode choice. Women are considerably more likely than men to drive alone (75% versus 68%). Women are also slightly more likely to car–vanpool. But [in contrast to Pisarski (2006)] they are substantially less likely to use transit (30% versus 34%), as well as less likely to bicycle (6% versus 18%) or walk (2% versus 3%). Computing the average share of physical commute days on which each mode is used—the measure closest to a true commute mode split available in the sample— reveals that the drive-alone share is relatively small at 55%, with car–vanpooling at 18%, transit about 25%, and walking and biking about 7% (combined). These shares are quite different from those in the region as a whole, which are 82%, 10%, 3%, and 5%, respec- tively (Sacramento Area Council of Governments 2008). However, the latter set of shares is for all passenger trips during the weekday peak, whereas the authors’ is for commuting only (and disproportionately downtown ori- ented), but without regard to time of day. commuTe-relaTed acTive choices The literature identifies a number of possible active choices that can be made in these circumstances (Giu- liano 1992; Möser and Bamberg 2007; Shiftan and Suhrbier 2002). Making fewer commute trips (via any or all of the following: telecommuting, alternative work schedules, and vacation), changing mode, and changing departure time or route are discussed in the subsections below. An analysis of teleconferencing and non-work- related trips can be found in Mokhtarian et al. (in press). As discussed above, this portion of the analysis excludes respondents who did not commute at all during the clo- sure week. Making Fewer Commute Trips Respondents were asked whether during the closure week they made “fewer commute trips than you nor- mally would,” with response options “Yes, because of Fix i-5,” “Yes, for some other reason,” and “No.” Only the changes made because of the Fix are discussed. Some 14.1% of respondents made fewer commute trips (because of the Fix) during the closure week (see Table 2). it was estimated [see Mokhtarian et al. (in press) for details] that increased telecommuting, compressed work schedules, and vacation days were adopted by about 5.6%, 3.1%, and 3.1% of the sample, respectively. Women were significantly more likely than men to reduce their commute travel because of the Fix (15% versus 11%); interestingly, however, they were about 16% less likely than men to do so by compressing their work schedules [in contrast to Mokhtarian et al. (1997)]. instead, women were more likely than men to reduce their commute travel by taking some vacation days (28% versus 23%). Changing Modes Respondents were asked whether during the closure week they traveled “to or from work using a different means of transportation than you normally would,” with the same response options listed in the subsection above on mak- ing fewer commute trips. Again, only the changes made because of the Fix were analyzed. Respondents reporting such changes were then asked which mode(s) they used on more occasions than normal and what they would

148 WOMEN’S iSSUES iN TRANSPORTATiON, vOLUME 2 have ordinarily done on those days (multiple answers were possible for both questions). The vast majority (at least 93%) of those making com- mute mode changes increased their use of more sustain- able modes, most often at the expense of driving alone. increasing transit use was the most common change: 64.9% of those who altered their commute mode patterns made that choice (5.3% of the total eligible respondents), among which 70.1% would otherwise have driven alone to work. About 2.5% of eligible respondents increased their walking or biking, and 1.4% increased ridesharing; only 0.6% increased driving alone. There are substantial differences in these patterns by gender, however. Taking all modes together, women were 16% more likely to change modes (8.1%) than men (7.0%). Among changers, women were consider- ably more likely than men to increase their use of tran- sit (73% versus 58%) and car–vanpooling (20% versus 12%), and much less likely to increase bicycling or walk- ing (21% versus 49%). Changing Departure Time or Route The active choices discussed so far are considered to be potentially somewhat costly, in that they may require lifestyle adjustments, affect other people, or both (Salo- mon and Mokhtarian 1997). By contrast, the remain- ing changes shown in Table 2 are considered relatively low cost, entailing minimal change to established pat- terns. Accordingly, a number of studies have found such changes to be the most common responses to increasing congestion or disrupted commute patterns (Mokhtarian et al. 1997), and the present analysis is no exception. TABLE 2 Commuters’ Work-Trip-Related Active Choices, by Gender Average Number of Times (by those Number Making Choicea who made this choice) Choice (sample sizes) Pooled data Men Women variable (sample sizes) Pooled Data Men Women Made fewer commute trips (6,552, 2,118, 3,925) 923 (14.1) 240 (11.3) 578 (14.8) Made fewer work-related trips (6,552, 2,118, 3,925)b 3 (0.1) 1 (0.0) 2 (0.1) Made fewer trips; trip purpose not specified (6,552, 2,118, 3,925)c 36 (0.5) 10 (0.5) 24 (0.6) Means of avoiding the Means of avoiding the commute trips (627, commute trips (433, 125, 180, 391)d 627 (100.0) 180 (100.0) 391 (100.0) 271) 2.63 2.56 2.59 Telecommuted 297 (47.4) 85 (47.2) 186 (47.6) Days avoided commuting Adopted compressed by telecommuting (290, 84, 182) 2.17 2.24 2.04 workweek 163 (26.0) 52 (28.9) 95 (24.3) Days avoided commuting Took some vacation 165 (26.3) 42 (23.3) 109 (27.9) by compressed workweek (157, Other 2 (0.3) 1 (0.6) 1 (0.3) 51, 93) 2.92e 2.63e 3.01e Days avoided commuting by Missing responses for means taking vacation (99, 26, 63) 1.8 1.73 1.83 of avoiding the commute tripsf (923, 240, 578) 315 (34.1) 75 (31.3) 192 (33.2) Changed mode (6,356, 2,073, 3,837) 493 (7.8) 148 (7.0) 318 (8.1) Avoided peak hour (5,506, Days avoided peak hour 3.90 3.87 3.91 1,745, 3,428) 2,642 (48.0) 751 (43.0) 1,725 (50.3) Changed route (5,254, Days changed route 3.44 3.40 3.45 1,650, 3,282) 2,371 (45.1) 681 (41.3) 1,540 (46.9) Note: Boldface designates traits that differ significantly by gender (at the .05 level or better). Detail may not add to total because of rounding. a Percent is given in parentheses. b The survey directly asked about changes to commute trips and to non-work trips, but not about changes to (noncommute) work trips (such as business meetings). The responses reported here were obtained from write-in descriptions to the “other (please specify)” option on the commute reduction question described in Footnote b. As such, they certainly understate the actual level of changes made to noncommute work trips in the sample and are included here only for completeness. c These responses were also write-ins to the “other (please specify)” option on the commute reduction question. d Question: “During the week of [June 2–8 or June 16–22] (Mon–Sun) . . . , how did you avoid the commute trips that you would have other- wise made?” Options: telecommuted to work; adopted a compressed work week; took vacation time; other. Categories are not mutually exclu- sive. e This unexpectedly high average suggests that a number of respondents failed to read the word “avoid” in the question (“how many days did you avoid physically travelling to and from work by . . .”) and responded with the number of days they commuted to work. The same could be true of the telecommuting and vacation strategies as well. f Cases that reported they made fewer commute trips but did not indicate how they avoided the commute trips.

149EFFECTS OF GENDER ON COMMUTER BEHAviOR CHANGES Nearly half the respondents reported making a special effort to avoid peak hour during the closure week (on an average of 4 days out of the week), and 44% of those driving or carpooling to work that week reported mak- ing planned changes to their commute route (on an aver- age of 3.4 days). Women are considerably more likely than men to use each of those strategies, consistent with the findings of Mokhtarian et al. (1997) for changing departure time. Altogether, about 60% of respondents adopted at least one of the active changes to the commute, which have been analyzed in the section on commute-related active choices. However, women were 21% more likely to make at least one change than men were (64% versus 53%). At least two explanations for this greater activism on the part of women present themselves. The first is that women were more adversely affected by the Fix and thus had a greater need to make a change. Mokhtarian et al. (in press) found that women were more likely than men to report passive impacts, both good and bad, resulting from the Fix. The greater reported incidence of negative impacts on women lends credence to the idea that they had a greater external motivation to make changes. The second explanation is that women may have a greater internal motivation to make changes, that is, that they may be more inclined to make socially or environmen- tally beneficial changes to their commute in general. This interpretation is consistent with the independent findings of Matthies et al. (2002) and Rose and Marfurt (2007) discussed in the literature review section. The “increase TransiT use” model Although it is helpful to see descriptive statistics on how commonly various changes were made, it is also desir- able to better understand the types of people who make a given change. This is perhaps best done in the context of a model in which multiple covariates can be controlled for simultaneously. Space does not permit providing models for all the changes seen in the sample, but a model is presented for the most commonly chosen change that reduces vehicle travel, namely, the choice to increase the use of transit for commuting. The dependent variable is created from the survey question that asks [of those who previously indicated “travel(ing) to or from work using a different means of transportation (during the closure week) than you nor- mally would,” because of the Fix], “During the week of …, which did you use on more occasions than you normally would have?” with seven possible response options: “carpool or vanpool,” “bus,” “light rail,” “Amtrak train,” “walking or biking,” “driving alone,” and “none of the above.” in the model, “bus,” “light rail,” and “Amtrak train” are combined into a single “transit” variable, equal to 1 if respondents selected any of those modes, and 0 otherwise. Among the “0” respondents are included those who did not change modes because of the Fix, on the basis of the previous question, but exclude those who (a) were out of the region the entire week, (b) did not travel to work any days that week, or (c) already take transit for all or almost all of their commute trips, because in none of those cases would increasing transit use be a feasible option. This left 5,439 cases, including 249 (4.6%) who increased their transit use as a result of the Fix. A number of potential explanatory variables are available, including sociodemographic traits and the availability of various employer-based commute modifi- cation instruments. To identify gender-related differences in the choice to increase transit use, first, best models for each gender were developed separately, and for the pooled sample. On the basis of those models, a fully gender-specific single model was created (i.e., a single equation in which every variable was interacted with gender, so that coefficients of each variable could differ by gender). Then, as indi- cated by statistical tests and conceptual considerations, insignificant variables were deleted and coefficients were constrained to be equal across genders, resulting in a final hybrid model in which some coefficients are gender- specific and others are based on the pooled sample. The final sample necessarily excludes cases in which gender was not reported. Because of missing data on gender and other variables, the preferred model (Table 3) has 214 (4.6%) respon- dents who increased their transit use during the closure week and 4,422 who did not. Overall, as mentioned previously, women were considerably (45%) more likely than men to increase their use of transit (6.1% versus 4.2% in the final estimation sample), perhaps because, using it less than men to start with (see section on base- line work schedule–commute characteristics), they had more room to do so. The r2 goodness-of-fit measure (Ben-Akiva and Ler- man 1985), with the equally likely model as base, is 0.766, which, taken at face value, is considered quite good in the context of disaggregate discrete choice models. With shares this unbalanced, however, the market-share model alone (the model containing just the constant term) has a r2 value of 0.730, initially suggest- ing that the true explanatory variables add only 0.036 to the goodness of fit. However, the final model is sig- nificantly better than the market-share model (c2 = 232 with 7 degrees of freedom, p = .000). Further, when the same model is reestimated except without the constant term (not shown), a r2 value of 0.499 is found, which indicates that most (65%) of the explanatory power of the full model lies in the “true” variables. That is, the true variables are substantively helping to explain why the shares are so unbalanced.

150 WOMEN’S ISSUES IN TRANSPORTATION, VOLUME 2 Seven variables besides the constant are retained in the model: two mode usage variables, one awareness variable, two employer strategies (gender-specific), and two (gender-specific) sociodemographic variables. Each of these is discussed in turn. It is hypothesized that people who (had) already used transit to some extent would be more likely to increase their use of it than others would be to start using it. Accordingly, several indicators of transit use were tested. Two dummy variables, marking those who use transit as their primary commute mode and those who currently use transit but do not have it as their primary commute mode, were strongly significant and positive, as expected. That is interpreted to mean that people who currently use transit are familiar with the schedule, stop–station locations, and riding experience and would therefore find it easier to step up their use. Those who already use transit as their primary commute mode, however, have less room to increase their use than respondents who currently use transit but do not have it as their primary commute mode. By contrast, nonusers may have already firmly concluded that transit is not suitable for their needs, whether based on erroneous impressions [e.g., an overestimation of travel time by transit, as found by Fujii et al. (2001) and van Exel and Rietveld (2010)] or on accurate ones. It is valuable to realize that it may be more effective to try to persuade current transit users to increase their use than to try to convince nonusers to switch—although it is recognized that both potential markets are impor- tant. However, it could also be argued that including user history variables is not as insightful as identifying the “first principles” that influence whether one does currently use transit or not, and including those in the model instead. Thus, excluding these two variables from the model was also tested (results not shown). In the best alternative model, the only difference is that the female- specific household size variable also dropped out, and the r2 value fell to 0.73. This indicates that the transit experience variables are not dominating the explanatory power of the model. One awareness variable is significant in the model, with the expected sign: it is unsurprising that respon- dents who had heard about the increased number of buses were more likely to increase their transit use. Two employer strategies were significant in the pre- ferred model, with expected signs. Not surprisingly, the availability of reduced-rate transit passes substantially increased the probability of using transit more, but it is interesting that the case was only for women, whereas the availability of reduced fares had no significant effect on men. It is possible that men’s higher incomes make them less sensitive to transit costs. Similarly, the avail- ability of flextime decreased the propensity to increase transit use, but again only for women. The interpretation of the latter result is that if people could change their departure times to avoid congestion caused by the Fix, then their current commute mode might remain viable, TABLE 3 Binary Logit Model of Increased Transit Use, Pooled Data Variable Coefficient P-value Constant –4.302 0.000 Sociodemographics Household size (female) 0.120 0.019 Manager/administration occupation (male) 0.644 0.022 Transit usage Use transit as primary mode 1.350 0.000 Currently use transit but not primary mode 2.229 0.000 Awareness of Fix impact mitigation strategies Increased number of buses 0.378 0.027 Employer-provided commute strategies Reduced-rate transit passes (female) 0.569 0.002 Variable start or end times (female) –0.434 0.013 Note: 1 = increased transit use; 0 = did not increase transit use. Summary statistics: Valid number of cases, N 4,636 (yes: 214; no: 4,422) Final log-likelihood, LL(b) –751.21 Log-likelihood for market share model, LL(MS) –867.168 Log-likelihood for equally likely (EL) model, LL(0) –3,213.43 Number of explanatory variables, K (including constant) 8 r2ELbase = 1 – LL(b)/LL(0) 0.766 Adjusted r2ELbase = 1 – [LL(b) – K]/LL(0) 0.764 r2ELbase = 1 – LL(b)/LL(MS) 0.134 r2MS = 1 – LL(MS)/LL(0) 0.730 c2 (between the final model and the EL model) 4,924.44 c2 (between the final model and the MS model) 231.916

151EFFECTS OF GENDER ON COMMUTER BEHAviOR CHANGES giving them less incentive to switch to transit. This may be true for women but not for men for the same reasons, leading fewer women than men to use transit in normal times. Finally, two gender-specific sociodemographic traits were also significant. Men in managerial–administrative occupations were substantially more likely to increase their use of transit than other groups. it is possible that men in high-end, white-collar occupations are more likely to live in locations better-served by commuter train, light rail, and express bus service. The positive coeffi- cient of household size (for women only) was initially unexpected, but is also saying something meaningful. it is assumed that people in larger households have more complex activity-travel patterns, and therefore the origi- nal expectation was that commuters in such households would be more likely to find the car to be most practical and would be more likely to stick with the car. Further reflection and comments on open-ended questions of the survey suggest another possibility, however. it may be that the more complex patterns of larger households are more vulnerable to disruption and have less ability to absorb a disruption without much change. Thus, com- muters in larger households may have a greater need to change than those in single- or two-adult households, and, at the same time, there might be more limitations on the ability of such commuters to choose other actions such as changing route or departure time. The fact that the variable is significant only for women suggests that it may also reflect an income effect, because women in smaller households are more likely to be single mothers. Besides the seven variables just discussed, a number of other variables were tested, including work schedule type, commute time and distance, the distance of the nearest bus stop or light rail station from home, income, number of vehicles per household member and per licensed driver, and a geographically based indicator of how strongly the respondent’s commute might have been affected by the Fix. However, none of these variables were significant in the final model. summary and suGGesTions for fuTure research This study offers a preliminary analysis of commuters’ responses to the Fix i-5 reconstruction project, focusing on gender differences. The easiest options (such as avoid- ing peak hour, adopted by 48%, and changing route, 45%) were the most common responses, and women were considerably more likely than men to use each of those strategies. About 8% of eligible respondents altered their commute mode choices during the closure week. The vast majority (93%) of those increased their use of more sustainable modes, most often at the expense of driving alone. increasing transit use was the most com- mon change (made by 5.3% of the eligible respondents), and there were substantial differences in mode change patterns by gender (with women considerably more likely than men to increase their use of transit and car– vanpooling). Overall, women were more likely to make at least one change (64%) than men were (53%). A binary logit model was built to better understand the choice to increase transit use (the most popular vMT- reducing option) during the Fix. Two mode usage vari- ables, one awareness variable, two employer strategies (gender-specific), and two (gender-specific) sociodemo- graphic variables were significant. Being a current transit commuter positively influenced the propensity to increase transit use, suggesting that persuading current transit users to increase their use may be easier than convincing nonusers to switch to transit. The availability of reduced- rate transit passes and flextime decreased the propensity to increase transit use, but only for women. The latter result in particular illustrates the conflict that sometimes arises among policy instruments: Making work hours more flexible is considered beneficial for reducing con- gestion and for balancing work and family needs, but the increased flexibility in avoiding peak-period congestion may enhance the appeal of continuing to commute by driving alone. Finally, men in managerial–administrative occupations and women in larger households were more likely to increase their use of transit. The latter result is interpreted to reflect a greater vulnerability to disruption of the complex activity patterns in larger households, with the result that commuters in such households have a stronger impetus to make a change when disruption occurs. Several directions for future research are indicated. Using the same data set, one can model not only other behavioral changes beyond the one explored here (increased transit use), but also the reported likelihood of continuing to use a strategy adopted (or increased) during the Fix. With the addition of geocoded home and work location information (for those who reported nearby street intersections), one can explore a number of geographic relationships with observed outcomes. 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