Nonwork Travel Behavior Changes During Temporary Freeway Closure The Fix I-5 Project in Sacramento, California Meiping Yun, David van Herick, and Patricia L. Mokhtarian average annual person-trips and 28% of average annual persondistance traveled in the United States, according to the 2001 National Household Travel Survey (5). Further, the share of work-related travel is falling over time; for example, it was 25% of person-trips in 1969 (6). Yet, few studies have focused specifically on non-work-related travel during construction or reconstruction. One exception is that Hendrickson et al. presented a few descriptive statistics on nonwork travel (3). Besides the fact that the vast majority of travel is not workrelated, such travel could also be more malleable than workrelated travel (7 ), with greater opportunity to cancel activities or change the time or place at which they occur. This additional flexibility may mean a greater receptivity to travel demand management (TDM) measures intended to promote more sustainable travel choices. Thus, to improve the understanding of the potential effectiveness of TDM measures during construction or reconstruction, it is as important to examine nonwork travel as it is to examine work-related travel. This study focused on nonwork travel changes during a temporary freeway closure caused by reconstruction. The context of the project—also known as “the fix”—is a 1-mi stretch of Interstate 5 (I-5) in downtown Sacramento, California, that was intermittently closed for reconstruction for 9 weeks between May 30 and July 31, 2008 (8). This portion of I-5 is part of a major north–south conduit for interregional traffic (extending from Canada to Mexico) and a key commute route serving downtown Sacramento and other regional job locations. Many TDM strategies were implemented to mitigate congestion during the fix, including providing extensive information on the fix and travel mode alternatives through the media, seminars, and the Internet; funding increased transit service and (in some cases) reducing transit fares and offering free parking at some facilities; reducing off-street parking rates downtown after 5 p.m. to motivate traveling after the evening peak; and providing roving tow truck service to promptly remove disabled vehicles on other portions of the highway network. The fix offered a valuable opportunity to study travel behavior changes in the context of a planned network disruption. To do this study, a series of Internet-based surveys was conducted: two contemporaneously and one 6 months later. This paper uses data from the first two of those surveys to assess the extent to which nonwork travel changes of various kinds were made and the circumstances under which they tended to be made. Earlier papers produced by the project analyzed the commuter impacts of the fix (9, 10). The current study of nonwork behavior changes may shed some light on how nonwork travel behavior changes are similar to and different from those of work-related travel.
A 1-mi stretch of Interstate 5 in downtown Sacramento, California, was intermittently closed for reconstruction in the summer of 2008. Nonwork travel behavior changes during the reconstruction were investigated with the use of data from two contemporaneous Internet surveys. More than half of the sample of 6,362 respondents made at least one studied change during the study period, including changes of route, activity location, time of day, day of an activity, and cancellation of an activity. First, the choice to make any nonwork travel change was modeled with binary probit (the any change model). Respondents were more likely to make nonwork travel changes when traffic conditions were worse than usual and less likely to do so when conditions were much better than usual. Women were more likely to make changes than men were. Those travelers who were not aware of any of the travel demand management measures designed to mitigate the impacts of the reconstruction were less likely to make nonwork changes. Next, the five individual nonwork changes were simultaneously modeled with multivariate probit. Again, women were more likely to make the individual changes studied (except route changes). The impacts of traffic conditions and commute characteristics were consistent with those in the any change model. Unobserved characteristics associated with the two temporal changes (day and time) were highly correlated, as were those associated with the two spatial changes (location and route). Unobserved influences on location, day, and time changes were highly correlated with those affecting activity cancellation.
As an aging highway network undergoes the ravages of uninterrupted use, freeway reconstruction projects are becoming more common. Although they are necessary in the long run, they disrupt normal travel patterns during construction or reconstruction. Several studies have evaluated the system-level impacts of such reconstruction projects in terms of capacity, traffic flow, and aggregate traveler response (1–3). Other studies have focused on the individual-level impacts, often on commute travel specifically (4). However, commute and work-related travel constitute only a fraction of the total, making up just 19% of
M. Yun, School of Transportation Engineering, Tongji University, 4800 Cao’an Road, Shanghai 201804, China; Institute of Transportation Studies, University of California, Davis, One Shields Avenue, Davis, CA, 95616. D. van Herick and P. L. Mokhtarian, Department of Civil and Environmental Engineering and Institute of Transportation Studies, University of California, Davis, One Shields Avenue, Davis, CA, 95616. Corresponding author: M. Yun,
[email protected]. Transportation Research Record: Journal of the Transportation Research Board, No. 2231, Transportation Research Board of the National Academies, Washington, D.C., 2011, pp. 1–9. DOI: 10.3141/2231-01
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LITERATURE REVIEW Nonwork travel encompasses a wide variety of trip purposes, including social and recreational, personal business, shopping, and serving passengers such as adults transporting children or elderly parents. However, nonwork travel has often received less policy, planning, and scholarly attention than work-related travel. Although there are good reasons for this lack of attention (including the disproportionate contribution of commuting to peak-period congestion and the fact that many nonwork trips are chained to the commute trip), the sheer quantity of nonwork travel (and its growth over time) makes it a compelling subject of study. Indeed, nonwork travel has been modeled for at least the past three decades. For example, Horowitz modeled the demand for multidestination nonwork travel in Washington, D.C., concluding that household size, income, and automobile availability could not explain households’ trip frequencies well (11). This finding reinforces the observation that nonwork travel patterns are far more diverse and far less predictable than work-related ones. Although all studies of nonwork travel examined the influence of sociodemographic variables, several also included variables measuring the built environment or attitudes. Boarnet and Sarmiento modeled the number of nonwork automobile trips using built environment and sociodemographic variables (12). They found that built environment variables were statistically insignificant in all but one of the specifications (in which service employment divided by land area was significant). Chatman found that the number of potential local transportation system users per unit of transportation network capacity was positively correlated with the number of nonwork activities and nonwork vehicle miles traveled (13). Cao et al. found that residential preferences, travel attitudes, and built environment significantly influenced nonwork trip making (14). Meurs and Haaijer using built environment variables (15), and Scheiner using lifestyle as well as built environment variables (16), concluded that such variables were more important to explaining maintenance and leisure trips than work trips. Regarding the impact of TDM measures on nonwork travel, Meyer summarized ranges of travel impact estimates from various traffic control measures (17 ). He reported that the estimated percentage reduction from implementing parking pricing is much higher for nonwork than for work purposes, in terms of both distance traveled (3.1% to 4.2% versus 0.5% to 4.0%) and daily trips (3.9% to 5.4% versus 0.4% to 4.0%). Thus, it is quite possible that during a reconstruction project like the fix, nonwork travel will be more heavily affected than work-related travel, both because it is more prevalent and because it is more flexible. In brief, although research on nonwork travel does exist, there is little study of changes in nonwork travel behavior, and no studies were found that model changes in nonwork travel behavior in the context of freeway reconstruction, as the current paper does.
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surveys. Respondents were recruited through e-mail invitations delivered by cooperative organizations: 1. Numerous state agencies, which broadcast the invitation to their staff in the Sacramento area; 2. The fix I-5 listserv (which included approximately 6,000 people who signed up to receive daily updates on the fix); 3. Transportation management associations (TMAs), which transmitted the invitation to their members—generally employer-based transportation coordinators—who, in turn, could have transmitted it to the employees of their organization; 4. The Commuter Club of the Sacramento TMA, comprising approximately 25,000 people who signed up to receive commuteoriented information and services; and 5. The University of California, Davis, which issued a press release publicizing the study and the link at which the survey could be taken. Given the ad hoc nature of the recruitment process, a completely representative sample could not be obtained, nor could a response rate be computed. However, because the main purpose of this study was to model relationships among variables rather than to estimate population means of individual variables, it is less critical that the sample be strictly representative.
Survey Contents The contents of the Wave 1 and Wave 2 surveys were nearly identical (aside from referring to different dates and directions of closure). There were four parts in each survey: • Part A collected information on normal (pre-fix) work and commute patterns. Some of these were used as potential explanatory variables in the models because nonwork trips are frequently chained to the commute or at least strongly influenced by commute patterns. • Part B sought information about travel changes made during the target week and whether those changes were made because of the fix. This part of the survey included all the dependent variables for the models (see sections on nonwork travel changes and probit models) and some explanatory variables measuring fix-related traffic conditions. • Part C explored the commute-related programs that were available to respondents, possible facilitators and barriers to changing commuting habits, and sources of information on the fix. This section included variables related to respondents’ knowledge of TDM strategies. • Part D collected information on sociodemographic characteristics, including age, gender, household size, auto ownership, income, and education.
DESCRIPTION OF THE DATA
Filtering the Sample
Sample Recruitment
The full data set pools, Wave 1 and Wave 2, contained 9,496 observations. To preserve the independence of observations, all Wave 2 cases who indicated they had taken Wave 1 were removed. Thus, all respondents were first-time survey takers. This process left an initial data set of 6,934 cases, 4,520 from Wave 1 and 2,414 from Wave 2. Four additional filters were applied to obtain the final data set. The first filter screened out 47 individuals who did not live, work in,
The data used in this study were collected from two Internet-based surveys. The first (Wave 1) corresponded to the first closure of all northbound lanes (June 2, 2008, to June 8, 2008), whereas the second (Wave 2) corresponded to the first closure of all southbound lanes (June 16 to June 22). Participants may have taken either or both
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TABLE 1
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Selected Characteristics of the Sample
Characteristic (sample size) Number of cases Number of women (6,039) Average age (decades) (5,704) Average household size (5,950) Annual household income ($) (5,621) Less than 15,000 15,000–29,999 30,000–44,999 45,000–59,999 60,000–74,999 75,000–99,999 100,000 or more Perceived traffic conditions on scale of 1 to 7 (6,266) 1 = much worse 2 [no label] 3 [no label] 4 = about the same 5 [no label] 6 [no label] 7 = much better Average number of times delayed (6,270) Average number of times arrived more quickly (6,214) Average number of times forced to detour (6,269) Average commute time (hours) one way (6,317) Commute by drive alone for most of trip at least once a month (6,083) Commute by light rail for part of trip at least once a month (6,087) Commute by walking for all of trip at least once a month (6,088) Did not hear about any TDM measures targeting the fix (6,168) Distance from home to nearest bus stop or light rail station (5,978) Less than 5-min walk 5- to 10-min walk 10- to 20-min walk More than 20-min walk Don’t know
N (%)
Avg. (SD)
6,362 (100.0) 3,924 (65.0) — —
— — 4.63 (1.09) 2.71 (1.36)
26 (0.5) 99 (1.8) 462 (8.2) 683 (12.2) 1,014 (18.0) 1,170 (20.8) 2,167 (38.6)
— — — — — — —
468 (7.5) 898 (14.3) 1,596 (25.5) 2,127 (33.9) 449 (7.2) 314 (5.0) 414 (6.6) — — — — 4,436 (72.9) 906 (14.9) 131 (2.2) 1,152 (18.7)
— — — — — — — 1.40 (2.04) 0.93 (2.05) 1.69 (2.44) 0.53 (0.32) — — — —
1,569 (26.2) 1,632 (27.3) 994 (16.6) 1,398 (23.4) 385 (6.4)
— — — — —
NOTE: Avg. = average; SD = standard deviation; — = not applicable.
or commute through the affected area; who reported travel conditions being “about the same”; and who made no changes during the fix. The second filter was based on the question “When traveling in the affected area, which do you generally use?” (“freeways and surface streets” or “surface streets only”). This question eliminated another 47 individuals (coincidentally) who used surface streets only, who reported conditions “about the same,” and who made no changes during the fix. Third, 284 individuals were omitted who were out of the area during the fix but not because of the fix. Finally, 194 cases were filtered out for which all dependent variables were missing, thus maintaining only the subset of cases that could possibly be present in any of the models. This filtering left 6,362 cases as the final sample of interest. Selected characteristics of the sample, including all variables significant in any of the models, are summarized in Table 1.
DESCRIPTIVE ANALYSIS OF NONWORK TRAVEL IMPACTS Preliminary to the modeling process, a descriptive analysis of the impacts of the fix was conducted.
Travel Conditions During the Fix In a previous paper based on the same data set, Mokhtarian et al. focused on the gender-related impacts of the fix with respect to workrelated travel (9). Because gender is consistently significant across the nonwork travel models of this study as well (with the exception of route change), and because it is the only sociodemographic characteristic in any of the equations of the multivariate probit (MVP) model, a review of the gender differences in perceived travel conditions is presented. One qualitative measure of conditions during the fix was the question “How were travel conditions in the affected area for you?” Approximately one-third of respondents felt the conditions were “about the same.” Women (32.7%) were somewhat less likely than men (36.4%) were to feel that way, however. Overall, more respondents felt the conditions were worse to some degree (47.4%) than better to some degree (18.6%), and women were more strongly represented at both extremes (23.2% for the worst two categories combined, and 12.7% for the best two) than were men (18.7% and 9.7%, respectively). Three quantitative measures of travel conditions were the number of times delayed, the number of times a trip was quicker than
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normal, and the number of times forced to make a detour. The percentage of men experiencing no delay (56.6%) was higher than that of women (52.6%), whereas the percentage of men experiencing quicker trips through the affected area (at all) was lower (21.6%) than for women (25.1%). These results are consistent with the gender differences in perceived travel conditions. The percentage of men who were forced to make unplanned detours was similar to that of women (43.8% versus 44.9%).
Nonwork Travel Changes Respondents were requested to “consider travel to and from nonwork activities during the week” and were asked a series of questions about travel changes “because of fix I-5,” specifically, “How many times did you” 1. Travel using a different means of transportation than you normally would? 2. Change the location of an activity (location change)? 3. Choose to change your route but not your destination (route change)? 4. Change the day you did something (day change)? 5. Change the time of day you did an activity (time change)? 6. Use the Internet to do an activity instead of traveling to it (asked only in Wave 2)? 7. Not do an activity at all (activity forgone)? In addition to these specific changes, the study modeled whether respondents made any change at all (any change, at least one of the previously mentioned choices, except for Internet substitution), which allowed researchers to better understand what kinds of people are susceptible to travel behavior change in general, in the context of a planned extreme event such as highway reconstruction. The shares of people making each nonwork change at least once during the study week are shown in Figure 1, where the bottom row of numbers represents the denominator of the fraction.
More than half (58.4%) the respondents made at least one nonwork travel change, which is very similar to the proportion (60.0%) making commute changes (10). Taking a different route was the most common individual change (44.0%), suggesting that it is the least disruptive adaptation behavior, whereas changing the activity time was next most common (21.8%). These results are roughly consistent with the previous findings for commute travel (with 48.0% avoiding peak hour and 45.1% changing route), except that the order of the two most common strategies is reversed, as is sometimes the case in other studies as well (10). The comparison suggests that avoiding peak hour by arriving at work early or departing late is less of a problem than changing the times of nonwork activities—somewhat contrary to the expectation of greater flexibility for nonwork activities. It may be that many nonwork activities are infrequent or irregular events involving other people and, therefore, are not subject to having their times changed, whereas for the white-collar jobs characterizing much of the sample, some flexibility in work schedules is generally possible, especially on a short-term basis. Alternatively, perhaps because nonwork activities are often more flexible, they were already scheduled at times (and in places) that would minimize the impact of peak-period commuting (and thence of the fix). It is likely that both explanations hold, to some extent. By contrast, activity cancellation is another measure of flexibility, and, in this respect, nonwork travel appears more flexible: 21.3% of the sample canceled at least one nonwork activity, which is higher than the percentage (14.1%) of respondents making fewer commuting trips [and only 3.1% actually canceled the work activity through taking vacation; the remainder simply worked at home, or a compressed schedule (10)]. In the current study, similar shares of respondents (approximately one-fifth in each case) canceled a nonwork activity altogether, changed its location, or changed the day on which it was conducted. Thus, although forgoing the activity might have been thought to be the most disruptive choice, at least the comparative frequencies of each choice do not support that expectation. Approximately 8% of individuals changed mode, similar to the share (7.8%) doing so for commute travel during the fix (10), whereas the least
FIGURE 1 Respondents’ reported nonwork travel changes during the fix (*asked only in Wave 2; **excluding Internet substitutions).
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popular option was to conduct an activity online instead of making a trip (6.8%). It is of interest to analyze how nonwork travel modes changed during the fix, but because many such adjustments are possible and the share of any given one is small, this paper will not model mode changes separately. However, mode alterations of any kind are included in the any change model.
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model. Many possible explanatory variables were considered for each model, including gender, age, work schedule, occupation, household size, income, presence of children, vehicle ownership, neighborhood density, baseline commute mode, availability of transit near home, travel conditions, and whether or not the respondent had heard about different TDM strategies for the fix. The following two sections discuss the final any change model and the final MVP model for the five individual changes, which are shown in Tables 2, 3, and 4.
PROBIT MODELS OF NONWORK TRAVEL CHANGES Given the goal of modeling individual nonwork travel changes, several different model structures are possible. As indicated in the section on nonwork travel changes, respondents reported counts of the number of times each change was made, so Poisson–negative binomial or ordinal response models could potentially be used for each change separately. Disadvantages to these approaches include (a) the large proportion of zero cases for each variable, which adds a layer of complexity to the modeling; (b) likely inaccuracies in reported counts; (c) generally small shares of counts of two or more, meaning that the models would largely be making a binary distinction anyway (i.e., between making the change zero or one times); (d) an inability to allow the impact of an explanatory variable to differ depending on the number of times a choice was made; and (e) an inability to consider multiple choices separately but simultaneously. Among binary model structures, a multinomial or nested logit approach could be adopted, but doing so is complicated by the fact that more than one change among the five of interest could be made (33.6% did so). In fact, for five binary choices (to make a given change or not) there are 25 or 32 possible outcomes, all of which appear in the sample (with the two most common outcomes being no change at all, 43.0%, and route change only, 16.3%). A 32-alternative model would be extremely cumbersome to estimate and interpret, yet the best way to group alternatives into a tractable number of mutually exclusive and collectively exhaustive combinations is not readily apparent. For situations such as this, in which each choice could be made separately from the others, yet many variables are likely to influence more than one choice, MVP is a natural model selection. In MVP, the coefficients of the utility functions for the five individual choices are estimated simultaneously, which is a parsimonious model structure (five utility functions instead of 31 plus a reference alternative). The error terms (unobserved influences) are allowed to be correlated across choices, which (by allowing the information from one choice to influence the parameter estimates for correlated choices) results in more efficient estimation (i.e., smaller standard errors of estimators). This means that some variables could be found to be significant in the MVP model that did not achieve significance in an isolated binary model. Interpreting the estimated error term correlations also provides useful insight for future model improvements. Accordingly, first a binary probit model for any change (i.e., whether the respondent made at least one of the studied changes, including mode but excluding Internet substitutions) was estimated. Then separate binary probit models for each of the changes were estimated (not shown). Using those specifications as a starting point, the five most common nonwork travel changes (i.e., excluding mode and Internet substitutions) were simultaneously modeled with MVP (using Limdep 9.0, each run took 27 h on a 2.2-GHz central processing unit, 2-GB random-access memory computer). Specifications were fine tuned to test previously excluded variables (none entered) and ultimately to exclude all insignificant variables from the final
Binary Probit Model for Making Any Nonwork Travel Behavior Change The adjusted ρ2 goodness-of-fit measure (18) for the any change model (equally likely model as base) is 0.098. Although this is within a typical range for disaggregate discrete choice models, it is on the low side, which is perhaps not surprising in view of the heterogeneity of the multiple alternatives reflected in the binary dependent variable. With respect to sociodemographics, several variables are significant, including gender, age, and income. Women were more likely to make at least one nonwork travel change during the fix. This may be partially because women work part time (6.2%) more often than men do (3.6%), which offers women more flexibility to make a nonwork travel change. Other speculations based on related research (10) include the following: • As shown in the section on travel conditions during the fix, women were apparently more strongly affected by the fix, and may, therefore, have been more intrinsically motivated to make both work and nonwork changes. • Women with families still spend more time on domestic duties than men do (19, 20) and, therefore, may have more complex activity schedules that are more vulnerable to disruption. • Women tend to have more proenvironmental attitudes than men do (21) and, therefore, may be more susceptible to external promotions of more sustainable choices. The older the respondents were, the less likely they were to make any nonwork travel change, perhaps because their nonwork activity patterns tend to be simpler (e.g., not involving children), less focused on the downtown area affected by the fix, and more focused on their residential neighborhoods, where they are likely to have lived longer than younger workers. Another possibility is that older respondents may be less amenable to change in their routines. Similarly, the lower the income reported was, the less likely the respondent was to make any nonwork travel change—perhaps because lower incomes are associated with less travel in general, and therefore lower-income individuals may have fewer nonwork trips to think about changing. As to commute condition variables, the longer the commute time reported, the less likely a respondent was to make any nonwork travel change, possibly because the commute occupies more of her or his nonwork time, leaving less time for additional activities and making it more likely for those activities to be occurring outside (or on the shoulders of ) the peak (and so less affected by the fix). This interpretation is supported by the 2001 National Household Travel Survey, in which workers with short commutes ( .05; unmarked MVP p-value remains the same as that of the corresponding binary model (to 4 decimal places); superscript + indicates that MVP p-value improves; superscript − indicates that MVP p-value gets worse.
perhaps because they probably drive for nonwork travel as well, and driving is obviously likely to be affected by a major highway reconstruction. By contrast, light rail commuters are also more likely than others are to make a nonwork change. Because the fixed schedules and limited routes of light rail impose more constraints on activity patterns than do other commute alternatives, perhaps disruptions to that “brittle” alternative are more likely to force changes to those patterns. In addition, several variables capturing travel conditions during the fix are significant. Generally speaking, respondents have a greater tendency to make a change when perceived conditions are worse than the base case (which includes “as normal” to “somewhat better” responses) and a lesser tendency when conditions are much better. Both results are natural. Similarly, a worsening of actual conditions, measured by the number of times delayed and number of times forced to make a detour when traveling through the affected area, also increases the likelihood of making a nonwork travel change, which is consistent with the results from perceived traffic conditions. Conversely, respondents arriving more quickly when traveling through the affected area are also more likely to make a change. It is possible
that this finding reflects the opposite direction of causality—the traveler arrived more quickly because he or she changed departure time, or route, and so forth—but it is also plausible that a successful outcome for one travel change would likely lead to further changes. Several TDM measures were promoted to reduce the impact of fix I-5, as described earlier. The model shows that those who were aware of any of those measures were more likely to make at least one nonwork travel change. It is reasonable that respondents who heard about TDM measures were more likely to adjust their nonwork travel in the direction to which those measures are pointing (e.g., changing to transit, staying downtown until after the evening peak). If so, that finding supports the value of implementing and publicizing TDM measures during the reconstruction. It further suggests that the TDM strategies helped modify nonwork travel as well as commuting, but the relationship could be one of association instead of (or as well as) causality. Specifically, people who expected to be more heavily affected by the fix may simply have been both more attentive to possible mitigation strategies, and more likely to have made changes.
TABLE 3 Correlation of Unobserved Variables for Any Change and MVP Model for Individual Changes
MVP Model for Five Individual Nonwork Travel Changes
Unobserved Variable
The adjusted ρ2 for the MVP (equally likely model as base) is 0.361, which is considered relatively good. Because shares across the 32 alternatives are quite unbalanced, the ρ2 for the market-share (constants-only) model is quite high in its own right. However, temporarily removing the constant terms from the final model shows that the real variables account for 96% of the improvement in log-likelihood from the equally likely model to the final model [essentially, 96% of
Route change Location change Time change Day change
Route Change
Location Change
Time Change
Day Change
Activity Forgone
— — — —
.570 — — —
.451 .491 — —
.369 .580 .673 —
.344 .590 .514 .674
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TABLE 4
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Indicators for Any Change and MVP Model
Indicator No. of cases, N Final log likelihood, LL(β) Log likelihood without constants, LL(βK − c)a Log likelihood for MS model, LL(MS) Log likelihood for EL model, LL(0) No. of estimated parameters, K (without constants, K − c) ρ 2ELbase = 1 − LL(β)/LL(0) –ρ 2 ELbase = 1 − [LL(β) − K]/LL(0) ρ 2MSbase = 1 − LL(β)/LL(MS) –ρ 2 MSbase = 1 − [LL(β) − K + c]/LL(MS) ρ 2MS = 1 − LL(MS)/LL(0) χ2 for final model versus the EL model χ2 for final model versus the MS model
Any Change 4,934 −3,071.28 −3,075.64 −3,358.39 −3,419.99 15 (14) 0.102 0.098 0.085 0.081 0.018 χ2 = 697.41 p = .000 df = 15 χ2 = 574.22 p = .000 df = 14
MVP 5,603 −12,346.22 −12,650.69 −12,875.42 −19,418.52 66 (61) 0.364 0.361 0.041 0.036 0.337 χ2 = 14,144.60; p = .000, df = 66 χ2 = 1,058.40, p = .000, df = 61
NOTE: For any change model, 57% made a change. For MVP model, 43.9% made route change, 20.3% location change, 21.7% time change, 18.1% day change, and 21% canceled activity. No. = number; MS = market share; EL = equally likely; df = degrees of freedom. a Final model specification, excluding constants, to ascertain explanatory power of real variables. Unmarked MVP p-value remains the same as corresponding binary model (to four decimal places); positive MVP p-value increases; negative MVP p-value decreases.
the information in the final model (22)]; that is, they are helping to explain why and how the market shares are so unbalanced. There is considerable stability in the set of variables appearing in the five equations of the MVP: among the eight variables significant anywhere, all appear in three or more equations, and five appear in all five equations, always with the same signs. Women are more likely than men are to make each of the nonwork travel changes, except route changes, which is consistent with the result for any change. Commute time (negative) and drivealone commuting (positive) are significant across all changes. Interestingly, commuting by light rail is significant (positive) for three of the five changes—two of those involving spatial changes (location and route)—but not significant to the two temporal changes (day and time). This finding suggests that the spatial constraints of the rail system may be more binding in the context of a network disruption than its temporal constraints. Perceived travel conditions during the fix significantly influence all the individual changes. Overall, the worse the perceived conditions are, the more likely respondents are to make nonwork travel changes. This pattern is consistent across all models. The number of times delayed and number of times forced to detour are positively linked with all five changes, supporting the idea that the worse the travel conditions are, the greater the motivation (and perhaps the necessity) is to make changes. By contrast, the number of times arriving more quickly occurs is also positively correlated with all the strategies, except route change. The correlations of the error terms across equations of the MVP can be interpreted as follows: All are positive—indicating that unobserved variables influencing a person to make one type of nonwork change also tend to influence making others—and moderate to strong (ranging from .34 to .67). Logically enough, the error terms for the two temporal changes—day and time—are strongly correlated (.67), as are those for the two spatial changes—location and route (.57). In
addition, unobserved variables influencing location, time, and day changes are all strongly correlated with those influencing activity cancellation (.59, .51, and .67, respectively). The three lowest correlations all involve route change: with forgoing the activity (.34) and with day (.37) and time (.45) changes. The route change binary probit model had the lowest ρ2 (equally likely base) of any of the individual models (.09), indicating that the observed variables also do not explain much of the information in the choices. Route changes are extremely common (see the section on nonwork travel changes) and may involve very little disruption or advance planning, so it is not surprising that such decisions are heavily idiosyncratic and hence neither easily predictable with the observed variables available nor a consequence of the same unobserved variables influencing other choices. Compared with the variables significant in the any change model, income, age, or awareness of TDM do not appear in the MVP model, reinforcing the separate value of the any change model. Conversely, all variables significant in any of the MVP equations also appear in the any change model (reinforcing its robustness). In one MVP specification, awareness of TDM measures was actually significant in both the day change ( p = .033) and the activity forgone (p = .045) equations (showing that respondents who did not hear about any TDM measures were less likely to make day changes or cancel activities). However, given that there was a loss of 51 cases and that it might not represent a truly causal relationship (see the section on the binary probit model), it was excluded in the final model. However, its presence reinforces the idea that awareness of TDM strategies at least has some correlation with nonwork travel changes, if not a causal effect. Compared with the binary probit models for the individual choices, generally, coefficient magnitudes do not change drastically in the MVP model. Significance levels diminish for a few variables but markedly improve for many more variables, as shown in Tables 2,
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3, and 4. This finding is an expected manifestation of the greater efficiency of estimators derived from allowing the unobserved variables in each equation to be correlated. The overall goodness of fit of the MVP model is considerably stronger than that of the individual binary probit models, comparing its ρ2 of .364 to those of the individual models (ranging from .088 to .363, with an average of .273).
SUMMARY AND FUTURE RESEARCH This study analyzed nonwork travel changes during the I-5 freeway reconstruction, on the basis of contemporaneous Internet surveys of 6,362 workers who traveled in the affected area. Overall, more respondents felt the conditions were worse to some degree (47.4%) than better to some degree (18.6%), and women were more strongly represented at both worse and better. They were also more likely to make nonwork changes than men were. Nearly 60% of the sample made at least one of the following nonwork travel changes: changing route, location time, day, mode, or canceling an activity. Changing route (44.0%) was the most common individual adjustment, whereas changing mode (8.4%) was the least common among those studied. Contrary to the expectation that nonwork travel might be more heavily affected by the fix because it is more discretionary and ostensibly more flexible than commute travel, these shares are similar to those for the comparable changes to commuting (10). Either nonwork travel is less flexible than surmised (involving, as it may, other people and predetermined arrangements), or precisely because such travel can be more flexible, nonwork activities had already been arranged to minimize exposure to peak-period congestion (i.e., by choosing less congested times and locations), and thus experienced less of an impact from the fix than commute travel. Both explanations could well be true. However, with respect to the most extreme change, that of activity cancellation, far more (21.3%) respondents canceled nonwork activities than reduced commute trips (14.1%) or work activities (3.1%). Overall, these results offer an important reminder that (a) on-the-ground changes in link volumes and travel times during a reconstruction project may be proportionally caused by changes in nonwork travel as well as in commuting, and (b) TDM-oriented mitigation strategies should be designed and promoted just as much for nonwork trips as for commutes. Binary probit was used to model whether respondents made at least one nonwork travel change, followed by a MVP model of the five individual nonwork travel changes. There was considerable commonality of significant variables across all models. The results prompt several observations. First, the changes studied here all represent a loss of utility to the traveler, from the relatively minor inconvenience of changing the route, to the potentially drastic cost of canceling the activity altogether. Some of the activities in question may be entirely discretionary (although even so, “dispensable” is another matter altogether, and quite debatable), but others are doubtless important to maintenance of personal and household well-being. Accordingly, it is appropriate to consider the equity implications of a differential incidence of impacts of the fix on nonwork travel. From that perspective, the fact that women were more likely to report more negative travel conditions (as well as more positive ones), and more likely to make each of the changes studied (except for route changes), is noteworthy. As discussed in the section on the binary probit model, however, the reasons for the latter result may be complex and not readily amenable to policy manipulation.
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Along the same lines, the fact that drive-alone commuters were more likely than others to make each of the nonwork changes studied is not surprising, in view of the fix’s impact on a major highway segment. But, the fact that light rail commuters were also more likely than others to make route and location changes or to cancel a nonwork activity, may be an indication of the relative inflexibility of the rail system and the adaptations that inflexibility requires of those who depend on it. Several directions for future research are indicated for the same data set. A more detailed comparison of commute and nonwork travel changes can be undertaken, while also investigating more extensively the two nonwork changes not studied carefully here: mode changes and Internet substitution. Using the subset of cases with both Wave 1 and Wave 2 responses, it would be of interest to examine the dynamic aspects of choice behavior: How do conditions in Wave 1 influence behavior in Wave 2? Finally, several findings of this study are intriguing enough to invite replication or contradiction in independent studies of the nonwork travel impacts of future reconstruction projects.
ACKNOWLEDGMENTS Data collection was funded by a grant from the California Air Resources Board. The first author was supported by a fellowship under the State Scholarship Fund in China and Key Project of National Natural Science Foundation of China. The second author was partly supported by funding from the Sustainable Transportation Center at the University of California, Davis. Thanks to Kristin Lovejoy, Laura Poff, and Julia Silvis for their invaluable involvement in the survey design, data collection, and initial data cleaning process, as well as to Giovanni Circella and several undergraduate students for assistance with data cleaning and management. Comments of the anonymous reviewers have improved this paper.
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