Click for next page ( 62


The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 61
A 1992 study by Commuter Transportation Services reported on the experience of five employers in the Los Angeles area who offered time off with pay as an incentive. As summarized in Table 19-15, there is a relatively modest financial value represented by these incentives. The first program, offered by the bank, seems to have been offering the highest incentive level and--probably not coincidentally--exhibited the highest VTR at 16.7 percent. Correspondingly, the software devel- oper's program appears to have been offering the smallest incentive and had the lowest VTR achieved in the group (Stewart, 1992). Table 19-15 Sample Programs Using Time Off with Pay as a Financial Incentive Type Time Off Policy and Other Cash/Prize Initial Update Employer Size Requirements Incentives AVR AVR VTR Bank 108 10 min. for each non-drive Quarterly $300 cash 1.08 1.26 16.7% day; 40 hour/year max. prize drawings Software 150 1 point for every non-drive $10 monthly cash 1.12 1.12 0% Developer day (1/2 for 1-way rideshar- subsidy plus $100 ing); 100 points = 1 day off; gift certificate annual maximum = 16 hours drawing if rideshare 75% of month Financial 250 Employees not driving alone $20 and $40 cash 1.18 1.28 8.5% Services 3 days per week for a month subsidies and prize get to leave 1 hour earlier on drawings for gift a Friday the following month; certificates annual maximum of 1 full day and 12 single hours Aircraft 300 Employees not driving alone $10 monthly cash 1.08 1.18 9.3% Parts 3 days/week for 1 quarter subsidy if rideshare receive 4 hours; 3 days/week average 3 days per for 4 quarters receive bonus week; monthly $100 day off; annual max. of gift certificate 24 hours (3 days) drawing Mortgage 250 1 point for each non-drive Employees with 1.14 n/a n/a Provider day; 40 points earns 4 hours 40 points in quarter off; annual maximum of enter drawing for 15 24 hours (3 days) $50 checks plus free ice cream party Note: AVR = Average Vehicle Ridership. Source: Stewart (1992). Response to Alternative Work Arrangements Alternative work arrangement actions are strategies that modify the time at which travel occurs or the frequency of travel. From a TDM perspective, their objective is either to reduce the concen- tration of travel in a peak travel period or to reduce the overall number of trips made on a daily or weekly cycle to an employment site. The strategies included in this group were defined in the 19-61

OCR for page 61
"Overview and Summary" section under "Types of TDM Strategies," but a quick review of impor- tant characteristics will assist in interpretation of their effects: Flexible work hours are where employees are given freedom in choosing their starting and quitting times. Flextime programs fall into two important groups, those where employees are allowed to adjust their arrival and departure times in order to avoid traveling during the most congested time periods, and those where employees are explicitly permitted to adjust their schedules to meet transit, carpool, or vanpool scheduling requirements. Most of the programs reviewed here are of the latter type, designed essentially as an incentive (removal of an imped- iment) to use an alternative mode. General shifting of work hours to avoid congestion is less common, although it was an important strategy for meeting the requirements of peak traffic reduction ordinances common in the late 1980s. Staggered work hours are generally unique to large employers, where some departments or employee groups are assigned to a different but set work day schedule to reduce the impact of having all employees arriving at or departing the site at the same time. This is not to be confused with shift schedules, such as are found at large manufacturing establishments or health centers, which are for purposes of evening and night staffing, not travel management. Neither should it be confused with flexible work hours, which are discussed in the preceding paragraph. Compressed work week (CWW) programs enable employees to work fewer days per week (or bi-week) in exchange for working longer hours each day. The two most common arrange- ments are the 9/80 schedule, where employees work a 9-hour day and receive the tenth day of the 2-week cycle off, and the 4/40 arrangement, where employees work four 10-hour days and receive the fifth day off. In both cases, the transportation benefit is that the employee is able to forego physical commutation to the work site one day per cycle. A secondary benefit is that the longer work day pushes the timing of the commute trip outside the standard peak period as defined by 8-hour work days. Telecommuting, also known as telework, allows the employee to work from home some num- ber of days per week or month, usually by virtue of an electronic connection that keeps the employee in continuous real-time contact with the work site. A less common alternative is where the employee works from a special satellite "telework" center.9 Alternative Work Arrangements Insights from the 82-Program Sample A key question regarding alternative work schedule strategies is whether or not they are support- ive of TDM strategies that encourage use of alternative modes and thereby reduce vehicle trip rates and, if not, whether their introduction leads to a net reduction or gain in vehicle trips. Alternative work arrangements that allow employees to synchronize their work schedules with the demands of an alternative mode are designed to explicitly encourage alternative mode use. Conversely, policies 9 Telecommuting evaluation is complicated by the fact that there are three distinct forms of working at home, of which telecommuting is only one. Another is home-based-business (HBB) workers, self-employed indi- viduals who operate or manage their business primarily from the home. HBB workers may or may not be segregated out from telecommuters in surveys, research, and summaries. The third form is overtime home- workers, who go to the formal workplace but bring home work to do, primarily outside of normal working hours. This third kind is of little interest to transportation planning, because it does not have much effect on the occurrence of travel (Tang, Mokhtarian, and Handy, 2007). 19-62

OCR for page 61
that allow employees to shift their travel outside of the peak, CWW, and telecommuting may or may not detract from the appeal of alternative mode use by either reducing the pressure of peak-period congestion or disrupting a routine daily schedule that might better support ridesharing arrange- ments and transit use. Ability to ascertain the net effect of these potentially opposing outcomes from the 82-program sam- ple is impeded by the nature of the data as it pertains to CWW programs and telecommuting. The data limitations involved were highlighted in the "Analytical Considerations" discussion of the "Overview and Summary" and have been referred to in previous "Response by Type of Strategy" subsections. To summarize, in virtually all cases within the 82-program sample, there is only suf- ficient employer survey data to calculate a vehicle trip rate based on modal split reported for com- mute trips which actually occur. To do more, it would be necessary to know exactly what type of CWW arrangement the employee opted for (4/40 or 9/80 arrangement), or how many days per week the employee telecommuted. Although in some cases there may be an indication of the per- centage of employees who are using the measures in question, none of these cases offers the details necessary to calculate the net effect on bottom-line vehicle trip rates. To illustrate the interplay of mode shift and trip elimination effects involved, assume an employee has been commuting 5 days per week in a two-person carpool. He/she has been generating the equivalent of 0.5 vehicle round trips per day. Now the employee is offered a telecommuting choice, and chooses to work from home 2 days per week. That eliminates two commute round trips out- right. However, if that employee now opts to make the three weekly trips to the worksite by driv- ing alone, she now averages 0.6 vehicle trips per day--a 20 percent increase in the daily vehicle trip rate, apart from any increase in non-work travel which may be occurring on telecommute days. However, if the employee were required to remain in the pre-existing two-person carpool arrange- ment, the average daily vehicle trip rate would only be 0.3, which would qualify as a 40 percent trip reduction. Obviously, accounting for the precise combination of strategies and resulting changes in behavior is critical to determining the net effect of CWW and telecommuting on vehi- cle trip making. For analysis it would be necessary to have a record of the commuter's behavior on each day of the travel "cycle" in question. In most cases this would be a 5-day week, though in the case of a 9/80 compressed work week it would be longer. In the 82-program sample, only the average vehicle trip rate for a "typical" day can be ascertained, and not in full consideration of the combined effect as depicted above. Mode shifts are taken into account (such as the shift from two-person carpool to drive alone in the example above) but trips eliminated outright cannot be (such as the 2 telecommute-day trips not made in the example). It is important to be aware of this distinction when reviewing the calculation results provided in Table 19-16 and the following discussion on relationships observed in the 82 example programs. The data limitations lead to an understatement of overall CWW and telecommute vehicle trip travel savings, but they do directly shed light on the issue of variable work hours effects on alternative mode use. Overall Effect of Alternative Work Arrangement Strategies. Table 19-16 summarizes the compar- ative trip reduction performance among the 82 examples from the perspective of alternative work arrangement strategies. Focusing first on the boldface "All" row in the table, the VTR perfor- mance of each of the four alternative work schedule strategies--shown with and without the respec- tive strategy in place--can be compared. What is evident is that only in the case of flexible hours is the calculated VTR substantially greater in the presence of the strategy than without it. Of employers in the 82-program sample, 45 offered flexible hours and averaged 20.1 percent VTR, compared to 13.1 percent VTR for the 37 employers where it was not offered. With staggered hours, 19-63

OCR for page 61
only seven employers offered this program, and they averaged a 15.9 percent VTR compared to 17.0 percent for the 75 who did not. With CWW, the 54 employers who offered the program aver- aged a calculated VTR of 15.8 percent, well below the 19.1 percent VTR for the 28 who did not, but not necessarily less effective given the computational limitations described above. For telecom- muting, the 65 who offered the program had an average calculated VTR of 17.1 percent, barely above the 16.5 percent for the 17 who did not. Again, the CWW and telecommuting calculations reflect only mode shifts and not trip avoidance effects. Alternative Work Arrangements in Relation to Transit Availability. Flexible hours seem to have their positive impact only when applied in the presence of medium or low transit availability. Perhaps this is where employees who would use transit find the transit schedules least accommo- dating, and thus are more substantively aided by work hours flexibility. In high transit availabil- ity areas there is less than a percentage point of difference in average VTR between programs with flexible hours and those without. Programs offering staggered hours have the opposite relationship with transit availability. Staggered hours implemented in the presence of high transit availability exhibit a difference in average VTR of only 1.5 percentage points with versus without staggered hours. However, for those situations where transit availability is medium or low, the programs where staggered hours are not offered have higher average VTRs than those where they are. It should be noted that these latter staggered hours programs are represented by very small sample sizes. Both CWW and telecommuting have marginally better VTR performance, as computed on the basis of mode shifts alone, when implemented with high transit availability. For CWW, programs with high transit availability average a VTR of 27.4 percent, compared to 25.1 percent without. Programs with telecommuting average a VTR of 26.4 percent with high transit availability versus 25.9 percent without. At medium and low transit availability levels the effects vary as to whether the work schedule program is associated with a positive or negative difference. Alternative Work Arrangements in Relation to Level of Employer Program Support and Employer Transportation Services. No clear or intuitive relationships emerge from examining alternative work arrangements in the context of employer support levels. For the most part, the same must be said for combinations involving transportation services. In the case of transporta- tion services, particularly small sample sizes impede the effort to reach substantive conclusions. 19-64

OCR for page 61
Table 19-16 Vehicle Trip Reduction Percentages Related to Alternative Work Schedules VTR by Type of Alternative Work Schedule Offered (Sample Size) Flexible Hours Staggered Hours Compressed Work Week Telecommuting Other Conditions or Programs With W/out With W/out With W/out With W/out All All 20.1% 13.1% 15.9% 17.0% 15.8% 19.1% 17.1% 16.5% 16.9% (45) (37) (7) (75) (54) (28) (65) (17) (82) Transit Availability High 25.8% 26.4% 27.2% 25.7% 27.4% 25.1% 26.4% 25.9% 26.0% (16) (8) (4) (20) (9) (15) (3) (21) (24) Medium 15.4% 8.3% -12.7% 13.4% 2.4% 11.6% 15.1% 10.0% 12.1% (10) (9) (1) (18) (2) (15) (2) (2) (19) Low 17.9% 9.9% 7.7% 14.1% 15.6% 12.6% 12.5% 14.4% 13.8% (19) (20) (2) (37) (15) (24) (11) (28) (39) Level of Support 19-65 High 17.9% 22.6% 18.1% 19.1% 19.7% 18.4% 18.6% 20.1% 19.0% (24) (8) (2) (30) (15) (17) (10) (22) (32) Medium 21.9% 9.5% 22.9% 15.5% 21.5% 13.5% 14.6% 16.2% 15.9% (17) (16) (2) (31) (10) (23) (6) (27) (33) Low 26.3% 11.6% 9.9% 16.1% 8.3% 16.5% 28.2% 14.2% 15.0% (4) (13) (3) (14) (3) (14) (1) (16) (17) Transportation Services Transit 35.3% 2.6% 28.2% 15.8% 20.5% 14.1% 28.2% 15.8% 18.9% (2) (2) (1) (3) (3) (1) (1) (3) (4) Vanpool 18.5% 24.7% n/a 21.3% 22.2% 21.0% 9.6% 23.9% 21.3% (6) (5) (0) (11) (3) (8) (2) (9) (11) Transit & Vanpool 15.6% 22.6% 11.9% 20.3% 15.4% 19.5% 14.5% 20.4% 18.8% (6) (5) (2) (9) (2) (9) (3) (8) (11) Use of Co. Vehicles 28.0% < 0% n/a 24.6% 24.2% 25.4% 16.4% 26.2% 24.6% (10) (1) (0) (11) (7) (4) (4) (7) (11) No Services 17.9% 9.7% 14.9% 13.5% 16.2% 12.3% 14.6% 13.4% 13.6% (25) (25) (4) (44) (16) (32) (8) (40) (48) (continued on next page)

OCR for page 61
Table 19-16 (Continued) VTR by Type of Alternative Work Schedule Offered (Sample Size) Other Conditions Flexible Hours Staggered Hours Compressed Work Week Telecommuting or Programs With W/out With W/out With W/out With W/out All Incentives Restricted Parking 28.5% 19.2% 19.2% 25.4% 29.1% 22.5% 26.5% 24.3% 24.6% (20) (15) (5) (30) (11) (24) (5) (30) (35) Parking Fees 25.2% 23.4% 27.2% 24.2% 28.2% 22.3% 24.7% 24.6% 24.6% (21) (10) (4) (27) (11) (20) (6) (25) (31) HOV Discounts 24.0% 30.2% 21.1% 26.1% 29.0% 23.7% 29.5% 25.0% 25.7% (16) (6) (2) (20) (8) (14) (4) (18) (22) Transit Subsidy 23.9% 15.2% 16.2% 21.3% 24.7% 18.3% 24.4% 19.9% 20.6% (26) (16) (6) (36) (15) (27) (6) (36) (42) Vanpool Subsidy 11.2% 19.5% 7.7% 17.2% 8.3% 15.5% 8.3% 17.7% 15.3% 19-66 (6) (6) (2) (10) (3) (9) (3) (9) (12) Travel Allowance 23.9% 12.8% 21.1% 19.1% 24.4% 16.2% 17.8% 20.1% 19.3% (14) (10) (2) (22) (9) (15) (8) (16) (24) Other Monetary 24.8% 15.4% n/a 23.1% 29.2% 18.0% 12.9% 25.3% 23.1% (9) (2) (0) (11) (5) (6) (2) (9) (11) In Combination with Other Work Hours Programs Flexible Hours 27.2% 19.5% 24.2% 16.6% 19.1% 20.5% 20.1% (4) (41) (21) (24) (12) (33) (45) Staggered Hours 27.2% 0.9% 17.5% 11.9% 16.9% 15.5% 15.9% (4) (3) (5) (2) (2) (5) (7) Comp. Work Week 24.2% 4.1% 17.5% 19.5% 17.1% 20.9% 19.1% (21) (7) (5) (23) (13) (15) (28) Telecommuting 19.1% 10.4% 16.9% 16.5% 17.1% 14.6% 16.5% (12) (5) (2) (15) (13) (4) (17) Note: Compressed work week and telecommuting VTRs as calculated reflect only mode shift effects and not trip elimination benefits. Sources: Derived (see Appendix Table 19-A) from Comsis (1994), Comsis and ITE (1993), Rutherford et al. (1994), and Comsis et al. (1996).

OCR for page 61
It can be said, however, that programs offering vanpool or vanpool-plus-transit services perform better overall without work hours strategies than in their presence. Employer programs without any transportation services are the ones that appear to do better with alternative work arrange- ments strategies. Among programs without services the highest calculated VTRs are found in the presence of flexible hours (17.9 percent VTR with versus 9.7 percent without) and CWW (16.2 per- cent VTR with versus 12.3 percent without). Alternative Work Arrangements in Relation to Parking Pricing and Management. The top por- tion of the second page of Table 19-16 highlights the effects of alternative work hours strategies when combined with restricted parking, parking fees, and HOV parking discounts. Of the 35 pro- grams with restricted parking, 20 also featured flexible hours, five had staggered hours, 11 offered CWW, and five had telecommute programs. In all cases except staggered hours, the work hours strategy is associated with a greater VTR when it is included than when it is not. Restricted park- ing with flexible hours exhibits an average VTR of 28.5 percent compared to 19.2 percent without flexible hours; restricted parking with CWW has a 29.1 percent VTR versus 22.5 percent without; and restricted parking with telecommuting added has a slightly higher VTR with (26.5 percent) versus without (24.3 percent). As a reminder, these VTRs are computed with reference to mode shifts alone. Where staggered hours are found with restricted parking the VTR is 19.2 percent com- pared to 25.4 percent without. The apparent effect of linking work hours strategies with parking fees is similar in pattern to that of restricted parking, but less pronounced. Flexible hours found in the presence of parking pricing yields an average VTR of 25.2 percent, versus 23.4 percent without flexible hours. Staggered hours in this case exhibits a positive association, showing an average VTR of 27.2 percent with staggered hours and 24.2 percent without. CWW teamed with parking fees has a VTR of 28.2 percent, com- pared to 22.3 percent without CWW. Telecommuting has the most neutral association of the work hours strategies, showing essentially no difference between programs with versus without park- ing pricing when examined on the basis of mode shifts alone. Finally, variable hours in relation to HOV parking discounts present a mixed bag. Table 19-16 indi- cates that HOV parking discounts exhibit higher VTRs in the presence of CWW and telecommut- ing than without, while the opposite occurs in the presence of flexible and staggered hours. Exactly why this may be is unclear. With CWW and telecommuting, the direction of the effect is consistent with the restricted parking and priced parking relationships, but with staggered hours and partic- ularly flexible hours, it is not. Alternative Work Arrangements in Relation to Modal Subsidies. Flexible hours appear to com- bine well with transit subsidies, travel allowances, and other monetary incentives, but not with vanpool subsidies. This parallels the earlier observation that flexible hours seem to have a posi- tive relationship with employer-provided transit service, but a negative effect with vanpool and transit/vanpool. The same is seen for CWW, with VTR impacts complementary for all incentive strategies but vanpool subsidies. Telecommuting shows a positive effect only with transit subsidies, as calculated on the basis of mode shifts alone. Finally, staggered work hours show a positive effect only with the travel allowance incentive. Alternative Work Arrangements in Relation to Each Other. The final comparison in Table 19-16 examines the outcome when different alternative work hours strategies are combined with each other. Note that to read and interpret this table, the same rules of order apply as in the preceding comparisons: For a program with the strategy listed as a row, each VTR number in the row cor- responds to the average observed when the strategy listed as the corresponding column is or is not applied. To illustrate, of the 45 employer programs offering flexible hours, four of those programs 19-67

OCR for page 61
also included staggered hours (presumably for different employees). They have an average VTR of 27.2 percent, while those without staggered hours average 19.5 percent. (One might well sur- mise that the reason for the significantly enhanced performance of these particular four programs is something more than just inclusion of a staggered work hours strategy.) Reviewing the table, flexible hours programs exhibit a higher calculated VTR when combined with staggered hours (27.2 versus 19.5 percent VTR) and CWW (24.2 versus 16.6 percent), but not when telecommuting is offered (19.1 versus 20.5 percent). Staggered work hours programs are appar- ently enhanced by the addition of each of the other work hours strategies. CWW programs are found to perform better when combined with flexible work hours (24.2 versus 4.1 percent). The sampled CWW programs exhibit somewhat lower efficacy in combination with staggered hours and telecommuting. Like staggered hours, telecommuting programs appear to be enhanced with the addition of any of the other work hours programs. They exhibit higher VTRs in the presence of flexible hours (19.1 versus 10.4 percent), staggered hours (16.9 versus 16.5 percent), and CWW (17.1 versus 14.6 percent). As stressed in the introduction to this discussion, all combinations involving compressed work hours and telecommuting would be associated with higher VTRs than reported above and in Table 19-16 were it possible to include the effect of trips eliminated outright in computations made with the 82-program sample. It is thus particularly important to make full use of such addi- tional research evidence as it is available, which is the subject of the next subsection. Additional Research Evidence on Alternative Work Arrangements While alternative work arrangement strategies like flexible work hours, CWW, and telecommut- ing are widely applied as TDM strategies, the amount of quality public information providing a quantitative insight into the effectiveness of these programs is surprisingly limited--most partic- ularly for recent years. This research shortcoming notwithstanding, this subsection supplements the preceding descriptive analysis of the 82-program sample with a selection of individual studies out of the literature that offer additional information on this important category of TDM strategies. While some of these examples are rather dated, several represent carefully constructed experiments, and their value--as should be evident--is not necessarily diminished by time. The one major question with respect to the older studies, especially those from the 1970s, is whether and to what extent the fresh ground plowed by the early experiments and demonstrations focused on mitigating traffic peaking still exists. The U.S. Department of Labor data reviewed immediately below suggests that the baseline ambient peak-spreading effects of flextime in particular must now be more extensive than earlier. The further peak-spreading possibilities for alternative work hours may thus be diminished by the smaller remaining increment of potential. The degree to which this may pertain can be assessed in individual cases by determining the amount of travel peaking found in the "before" condition. Note that the concern expressed here is much less likely to be of impor- tance with regard to either mode shift or trip reduction findings/outcomes. Flexible Work Hours. The U.S. Department of Labor has reported that, in 2002, almost 29 percent of the U.S. workforce of full-time wage earners and salaried employees had schedules permitting them to vary the time they begin or end their day. Interestingly, only about one-third of those employees worked for companies with official flextime policies. The proportion of such workers has grown slightly since the U.S. Department of Labor's previous survey in 1997, when 26.6 per- cent reported working flexible schedules. More striking is the comparison with 1991, when just 15 percent of workers had flexible hours options. Flexible schedules were found, in 2002, to be most 19-68

OCR for page 61
common among executives, administrators and managers, with 45.5 percent able to vary their schedules. Almost 41 percent of sales personnel were also able to adjust their schedules (Associated Press, 2003). At Bishop Ranch in exurban San Francisco, flextime policies were a major part of employee com- mute assistance programs for major employers relocating to this business park from downtown San Francisco. Faced with a local traffic management ordinance requiring a reduction not only in vehicle trips, but also in the number of those trips occurring in the peak hour, flextime was found to be very effective in shifting employee arrival times to less congested periods. A survey of 14,800 employees 2 years after the opening of Bishop Ranch showed an increase from 8 percent to 17 percent in the percentage of employees starting work before 7:00 AM. The percentage start- ing after 9:00 AM increased from 1 percent to 9 percent. Departure peaking also was reduced, with the percentage of workers leaving before 4:00 PM increasing from 12 percent to 17 percent (Beraldo, 1990). About 6,000 employees from 23 San Francisco employers participated in a broad-scale trial offer- ing of flextime in the 1980s. Post-implementation surveys showed at least one-half of the partici- pants arriving to work 30 or more minutes earlier than before flextime, with many arriving before 7:00 AM. By traveling before the main peak period, those arriving by car or by carpool saved an average of 9 minutes each trip, with over 60 percent reporting much less congestion on the way to work (Jones, 1983). The larger trial in San Francisco was preceded by the Downtown San Francisco Flextime Demonstration Project. Through surveys of participating employees, researchers found that the start-time peaks of three employers offering flextime were significantly smaller than the peak for downtown employees as a whole and/or they occurred before the downtown peak. Employers surveyed included the California State Automobile Association (CSAA), Metropolitan Life, and Fireman's Fund, with findings as illustrated in Table 19-17. The peak 30-minute arrival period for downtown employees as a whole was 8:00 to 8:30 AM. Of all employee arrivals, 61 percent occurred during that period. Among variable work hours participants, the CSAA employee arrival peak was 40 percent, during the same time period; the Metropolitan Life peak was 53 per- cent, occurring between 7:00 and 7:30 AM; and the Fireman's Fund peak was 34 percent between 7:30 and 8:00 AM (Harrison, Jones and Jovanis, 1979). This experience provided early corrobora- tion with other evidence that typical flextime employees in the United States, as well as in Germany's extensive programs, select earlier arrival schedules than the pre-existing norm (Pratt and Copple, 1981). In 1978, the U.S. Department of Transportation's Transportation System Center (now Volpe Center) in Cambridge, Massachusetts, conducted a 1-year experiment of flextime offered to its 600-person staff. It was able to stage an evaluation of the test using the monitoring resources of its Service and Methods Demonstration program. 19-69

OCR for page 61
Table 19-17 Employee Arrival Times at Three San Francisco Employers Adopting Variable Work Hours Fireman's Fund (Self-Staggered Metropolitan All Downtown Arrival Time Start) CSAA (Flextime) Life (Flextime) Employees 7:00 7:30 AM 31% 16% 53% 8% 7:30 8:00 AM 34% 31% 24% 13% 8:00 8:30 AM 20% 40% 14% 61% 8:30 9:00 AM 10% 7% 6% 1% After 9:00 AM 5% 6% 3% 17% Total 100% 100% 100% 100% Note: Earliest sanctioned arrival time at CSAA was 7:30 AM. Source: Harrison, Jones, and Jovanis (1979). Beginning in March 1978, Transportation System Center employees were given the flexibility to shift their time of arrival from the existing 8:15 AM to a larger period extending from 7:00 AM to 9:30 AM. A core workday of 9:30 AM to 4:45 PM was maintained, and employees could arrive and depart when they chose around these hours so long as they worked a full 8-hour day with a mandatory 30-minute lunch period. The majority of employees opted to adjust their schedules under this arrangement, shifting the mean arrival time to 7:55 AM. The distribution of arrival times around this mean was fairly sym- metrical, with a 32-minute standard deviation. About 56 percent arrived at or before 8:00 AM, and 14 percent at or after 8:30, meaning that 30 percent arrived in the 8:00 to 8:30 time frame. In this program, employees were also allowed to vary their schedules from day to day without prior approval. This feature showed surprising variation, with more than one-half of all workers devi- ating more than 10 minutes from their mean arrival time more than one-half of the time. Among the reasons given for adjusting their schedules, the top two reasons were "to accommodate after-work activities" (72 percent of all responding) and "avoiding traffic congestion" (cited by 69 percent of respondents) (Ott, Slavin, and Ward, 1980). In September 1974, the Port Authority of New York and New Jersey began a flexible hours experiment that lasted 8 months and involved about 850 headquarters staff. Those involved in the experiment included employees previously on staggered hours and also those on a normal work schedule. The basic 5-day work week remained unchanged for flexible hours program participants. The total expanded day covered the period between 8:00 AM and 5:30 PM, during which time employees were required to be at work for a core period between 9:30 AM to 4:00 PM. Workers were given 45 minutes for lunch. The 1-1/2 hour periods preceding and following the core period were flexi- ble periods within which the employee could vary time to any extent, as long as a 40-hour work week requirement was fulfilled. To support evaluation, arrival and departure counts were made before and after the experiment, for both participants and also two control groups of employees whose work schedule (floating day and normal) did not change. Because the different participating and control groups also worked on separate floors, arrival and departure times by floor could be meaningfully com- 19-70

OCR for page 61
pared, as illustrated in Table 19-18. Fifteen-minute work floor arrival and departure peaks were decreased by 13 percentage points (from 31 percent to 18 percent) and 10 percentage points (from 35 percent to 25 percent), respectively, on floor A, where the majority of employees changed from a conventional fixed schedule to flextime. Meanwhile, peaking changes were insignificant on the floors that changed from staggered hours to flextime (Port Authority of New York and New Jersey, 1975). Table 19-18 Port Authority of New York/New Jersey Flextime Experiment-- 15-Minute Peaking Before and After Flexible Work Hours Peak 15-Minute Arrivals Peak 15-Minute Departures Percent of Percent of Work Floor / Work 7:30-10:00 AM Peak AM 3:30-6:00 PM Peak PM Hours Programs Arrivals Time Period Departures Time Period Floor "A" Before (Conventional Hours) 31% 8:45-9:00 35% 4:30-4:45 After (Flexible Hours) 18% 8:45-9:00 25% 4:30-4:45 Floor "B" Before (Staggered Hours) 20% 8:15-8:30 26% 4:00-4:15 After (Flexible Hours) 20% 8:15-8:30 27% 4:00-4:15 Floor "C" Before (Staggered Hours) 28% 8:15-8:30 25% 4:00-4:15 After (Flexible Hours) 24% 8:30-8:45 26% 4:15-4:30 Floor "D" (control) Before (Floating Day) 24% 8:15-8:30 30% 4:00-4:15 After (Floating Day) 29% 8:15-8:30 25% 4:00-4:15 Floor "E" (control) Before (Conventional Hours) 27% 8:30-8:45 30% 4:15-4:30 After (Conventional Hours) 27% 8:15-8:30 28% 4:15-4:30 Note: Arrivals and departures were surveyed on the individual work floors. The surveys included some employees not participating in the flexible work hours experiment. Source: Port Authority of New York and New Jersey (1975). The association between allowing flexible arrival/departure times and employee choice of mode is somewhat unclear, and may depend significantly on the conditions placed on the flextime pol- icy by the employer or institution. In many of the examples within the 82-program sample, flex- time was a special privilege made available to employees using alternative modes, with flextime intended to provide additional latitude for accommodating the schedules of transit service or of a ridesharing unit. Indeed, the regional rideshare agency in the San Francisco Bay area found the placement rate among its rideshare applicants on flextime to be 30 percent compared to 16 percent for applicants not on flextime (Burch, 1988). 19-71

OCR for page 61
In the Transportation Systems Center flextime study described above, 9 percent of workers were found to have changed modes due to flextime, with a small net change in favor of ridesharing (+2.0 percent) and transit (+1.0 percent) and a reduction in driving alone (-3 percent), primarily attributed to savings in travel time offered by a schedule shift (Ott, Slavin, and Ward, 1980). Employee surveys in Pleasanton, California, suggested that only 7.6 percent of workers under flextime also rideshare, compared to 11.4 percent of the entire Pleasanton workforce (Cervero, 1988). Another study covering flextime, as introduced at the Tennessee Valley Authority (TVA), suggests a 2 percent loss in vanpool ridership paired with a much larger loss in bus ridership. Vanpoolers adjusted vanpool schedules to meet rider preferences for earlier arrivals, but bus schedules were not changed in a similar way, and bus ridership fell by 21 percent (Wegmann and Stokey, 1983). In-depth survey results for the CSAA organization (see Table 19-17 for arrival times) showed that flextime had especially aided work trip ridesharing among friends and family, a particularly resilient form of carpooling (Harrison, Jones, and Jovanis, 1979; Pratt, Pedersen, and Mather, 1977). Among six early implementers of flextime in the San Francisco Bay Area, ride-sharing was the big beneficiary, with shared-ride increases ranging from 1 percent to 28 percent.10 Drive-alone activity decreased in all cases, with declines between 3 and 26 percent for the three employers with significant drive-alone activity. Transit mode share impacts were mixed, with shifts ranging from -22 percent to +8 percent, with three losers and three gainers (Harrison, Jones, and Jovanis, 1979; Jovanis and May, 1979). The early observation that "The majority of actual before and after survey data . . . indicates at worst an insignificant or neutral effect on single occupant auto usage and gives some evidence of a predominance of mode shifting to carpools" (Pratt and Copple, 1981) seems borne out by more recent information. However, there is one caveat: Particular circumstances, as in the TVA exam- ple above, may cause atypical and undesirable shifts. Staggered Work Hours. The state of Hawaii, as a demonstration project to determine whether spreading arrival times of downtown workers would relieve peak-period congestion, changed offi- cial office hours for state, city, and county employees from 7:45 AM4:30 PM to 8:30 AM5:15 PM for a test period covering February 22 through March 18, 1988. Private sector participation was encouraged but not required. About one-half of all public sector employees shifted their work hours to the prescribed later schedule, while only 8.4 percent of private employees shifted sched- ule (Giuliano, 1992). The net effect was that about 4,000 workers, or 6 to 7 percent of the downtown workforce, partic- ipated in the project. The shift in start times was judged to have had a significant positive effect on traffic conditions. Average estimated time savings for commuters were 3 to 4 minutes, or 7 to 9 per- cent of the average 45-minute commute time. Travel-time savings differed by route and time of day. The project did spread out peak travel, which improved conditions for those traveling dur- ing the most-congested time periods, but made conditions slightly worse for those already travel- ing during the less-congested time periods. Most project participants experienced little or no significant change in travel conditions during the project, with some important exceptions. Participants from the most distant suburbs who had pre- viously worked the 7:45 AM4:30 PM schedule saved 9 to 15 minutes (15 to 25 percent), while those 10 The percents reported here are not percentage point changes but are relative percentage shifts. 19-72

OCR for page 61
who had been starting work prior to 7:30 AM experienced increased travel time of up to 10 minutes (30 percent relative to their originally shorter travel times). Demographic characteristics of partic- ipants and non-participants were quite different. Non-participants had more children, used child care services, were younger, and a higher percentage were female. Participants were more likely to be in professional or technical occupations and to be from households with fewer numbers of workers. Participants were also more likely to be car drivers, and reported more problems with time pressures or schedule constraints (Giuliano, 1992). Flexible and Staggered Hours Employee Involvement. An important consideration in any vari- able work hours program, be it flexible/staggered work hours or CWW, is that a sufficiently high percentage of the employment base needs to participate in the program. If peak-spreading and con- gestion reduction are the primary objectives (as opposed to mode shifts and vehicle trip reduction), then enough employees must be involved in order to have a tangible effect on total travel volumes. In Ottawa, for example, where the Canadian government is the dominant employer, it was possi- ble to include almost all government workers--and thus almost 50 percent of the central area workforce--in a variable work hours program. Peak hour to peak period ratios for transit rider loads were reduced by 8 to 19 percent at the downtown Ottawa cordon and even more as mea- sured at the workplace (Safavian and McLean, 1975). (See "Site- Versus System-Level Impacts" under "Related Information and Impacts" for additional detail on Ottawa highway and transit impacts.) About 100,000 out of some 480,000 Lower Manhattan employees eventually became involved in a program jointly sponsored by the Port Authority of New York/New Jersey and the Downtown-Lower Manhattan Association. Transit passenger counts at key subway stations were lowered by 18 to 26 percent (O'Malley and Selinger, 1973). (See the case study "Staggered Work Hours in Manhattan--New York, New York" for more.) Thanks to U.S. federal government support, circa 1980 variable work hours programs in Washington, DC, and Denver likewise involved substan- tial portions of both the federal, as well as the overall, employment base (Pratt and Copple, 1981). Given a choice, uninitiated employers--at least prior to more experience with flextime--have pre- ferred the concept of staggered hours to flexible hours, since flexible hours puts considerably more discretion in the hands of the individual employees, making the number of employees who will show up at any given time less certain. However, evidence from the 1970s experiments in Manhattan and San Francisco suggested that employees on flexible hours do tend to stagger their own work hours. Between 73 percent and 75 percent of employees involved in those flextime programs reported selecting work arrival and departure times designed to avoid traffic and transit congestion. The trip timing results were comparable to those achieved with typical staggered hours schedules (Pratt and Copple, 1981). It should be noted, however, that certain more recent suburban applications (Bishop Ranch and Pleasanton in California) stand as exceptions. Compressed Work Week. In the early 1980s, a carefully controlled experiment involving CWW schedules was staged in Denver, involving about 9,000 federal employees in 42 separate agencies. The participation within federal agencies exposed to the program was 65 percent. The most pop- ular schedules were the 9/80 and the 4/40, which resulted in participants arriving at work an hour earlier and departing about an hour later than before. The program was shown to flatten the peak, with the maximum percentage of total arrivals in the peak half hour declining from 56 percent to 42 percent, and the equivalent percentage of departures dropping from 47 percent to 34 percent. Evaluations suggested that the CWW strategy had only small effects on aggregate ridesharing and transit mode shares. Within the aggregate figures, matched data did show that non-participating employees showed decreased ridesharing and increased solo driving and transit use, largely counter- balanced by increased ridesharing by participants. It was inferred that ridesharing arrangements had been disrupted for non-participants, but it was also evident that those electing CWW had been able 19-73

OCR for page 61
to form carpools and to do so to a greater degree than before (Atherton, Scheuernstuhl, and Hawkins, 1982; Cambridge Systematics, 1980). The Denver Federal Employee CWW experiment found, with regard to travel mitigation, that net household VMT reductions for both work and non-work travel together averaged 14 to 15 percent for participating employees. This determination took into account not only commute VMT savings from the 1 or 2 days per week where travel to work was not required, but also household travel effects for the full 7-day week. The strong implication from the Denver evaluations is that CWW allowed more efficient accomplishment of non-work travel objectives, may have discouraged travel on the inherently longer workdays, and did not induce expanded weekend travel. Similarly, no expansion of out-of-area travel and no offsetting of work trip VMT savings were observed in Washington, DC, federal CWW applications (Cambridge Systematics, 1980; Skinner and Shea, 1981; Atherton, Scheuernstuhl, and Hawkins, 1982). As part of its effort to meet the trip reduction requirements of the Air Pollution Control District's Rule 210 (predecessor to Regulation XV), Ventura County tested a variable work hours program consisting of flextime and both 9/80 and 4/40 work weeks. Commuter Transportation Services, Inc. (CTS) conducted a 6-month pilot project to determine the impact on ridesharing and organizational effectiveness. A total of 367 employees were involved, with 172 adopting a 9/80 schedule, 76 on flex-time, and 33 on a 4/40 schedule. The remaining 86 either did not opt for one of the variable work hours offerings or chose to discontinue participation somewhere in the 6-month program. Survey information revealed that drive-alone rates in the sample of 367 declined from 82.2 percent to 76.6 percent over the course of the project, while ridesharing rates increased from 8.0 percent to 12.8 percent, and use of "other" methods increased from 9.8 percent to 10.6 percent. While the CTS evaluation also determined that commute time decreased for persons participating in one of the variable work hours programs, the actual amount of time saved was not determined, nor was a link drawn between the specific variable work hours program and the pattern of mode shifts. The time savings was believed to be related to shifting out of peak period traffic and possibly also to the ability of ridesharing participants to use carpool lanes (Freas and Anderson, 1991). A 1995 study of the effects of CWW on employee travel by the California Air Resources Board found that 2,600 Southern California employees on CWW schedules reduced their net number of trips by an average of 0.5 per week. Those employees working a 9/80 schedule drove an average of 13 fewer miles per week, while those working a 4/40 schedule drove an average of 20 fewer miles per week (Association for Commuter Transportation et al., 2004). Analysis of Washington State CTR Program data indicates that employee participation in CWW schedules at CTR-covered employers grew steadily from 14.5 percent in 1993 to 20.0 percent in 2005. The 2005 rate was in a context where roughly 2/3 of CTR Program employees were appar- ently eligible to choose CWW. Participation in 9/80 schedules doubled between 1993 and 2005, to 5.8 percent. Participation in 4/40 schedules grew more slowly, but still remained most preva- lent at 7.3 percent. The 3/36 variation, common in health care facilities, attracted 2.3 percent of employees covered by the CTR program in 2005, with a health care worker participation rate of 33.6 percent. A related notable statistic was the craft/production/labor employee CWW partici- pation rate of around 24 percent, actually a shade higher than the rate for professional/technical employees (Zhou and Winters, 2008). More information on relationships between CWW involve- ment and worker characteristics is found in the upcoming "Underlying Traveler Response Factors" section, under "Individual Behavioral and Awareness Considerations"--"Alternative Work Arrangements Considerations." 19-74

OCR for page 61
Telecommuting. A 1992 national telecommuting survey obtained information on telecommuting behavior from 16 organizations representing almost 5,000 telecommuters. The organizations con- sisted primarily of government agencies (13) and telecommunications companies (2). One of the 16 was a telework center. While the overall size of these organizations is not known, the number of telecommuters ranged from seven to 2,600, with a mean of 310 and a median number of partici- pants of 82. The great majority of employees taking part in these telecommute programs were found to be in professional (61 percent) or managerial (23 percent) occupations, with clerical and data entry (14 percent) and other classes of employees (2 percent) making up the remainder (Rathbone, 1992). (More information on telecommuter demographics, job types, and workplace characteristics is found in the above-mentioned "Individual Behavioral and Awareness Con- siderations" discussion.) The most common telecommute schedule found in the 1992 survey was 1 day per week, represent- ing 55 to 59 percent of the sample (depending on interpretation), followed by 18 percent who telecommuted 2 days per week. On the other hand, 12 percent were found to telecommute 5 days per week. The organization with 2,600 participants, a county government, reported telecommut- ing frequency distributions of 5 days per week (1 percent), 4.5 days (6 percent), 3 days (14 percent), 2 days (53 percent), 1 day (8 percent), 1 day per 2 weeks (13 percent), and 1 day or so per month (5 percent) (Rathbone, 1992), for an average of 1.96 days per week. Most of the 16 organizations surveyed reported telecommuting rates that average 1 to 2.3 days per week. The 2 outliers are the telework center with 24 participants averaging 4.8 days a week and a transit agency with 10 cleri- cal workers telecommuting 5 days a week (clearly a telephone service-information operation). The average for the overall sample is 1.8 days per week and the median is 1.6. It is important to note that only a fraction of responding organizations in the 1992 survey reported telecommuting at frequencies of less than 1 day per week, creating some ambiguity. A 2002 Southern California Association of Governments (SCAG) home-based survey of about 5,000 Southern California residents does fully address infrequent telecommuting, having first identi- fied persons in the workforce, and then the telecommuters among them, and finally those who telecommuted in the previous week. A strict definition of telecommuting was utilized, eliminat- ing home-based-business workers and overtime home-workers. Out of 2,766 workers, 24.6 per- cent were employees reporting at least 1 day of teleworking in the last 2 months, 7.0 percent were home-based-business workers, and 68.4 percent were non-teleworking employees. Table 19-19 provides a telecommuting frequency analysis derived using a final frequency analysis sample of 499 telecommuters (Walls, Safirova, and Jiang, 2007). 19-75

OCR for page 61
Table 19-19 Telecommuting Frequency Analysis of 2002 Southern California Workers Telecommuting at Least Once in Two Months Days Telecommuted in Number of As a Percentage of As a Pct. of Workers Telecom- the Previous Week Telecommuters Telecommuters muting in the Previous Week 0 258 52% -- 1 54 11% 22% 2 45 9% 19% 3 27 5% 11% 4 18 4% 8% 5 97 19% 40% Subtotal telecommuting 241 48% 100% in previous week Total telecommuting in 499 100% -- previous 2 months Source: Derived from Walls, Safirova, and Jiang (2007). From Table 19-19 it may be calculated that workers who had telecommuted the previous week averaged 3.2 days per week of telecommuting. If one makes an assumption that the remaining telecommuters averaged 0.25 telecommutes per week, then the average per week for all persons in the telecommuter sample was 1.7 telecommutes per week. Applying this all-telecommuter rate back to the full sample suggests an all-worker rate of 0.4 telecommutes per week for the 2002 Southern California working population as a whole. There are contemporary estimates of telecommuting that may appear to be, but are not necessar- ily, higher. For example, it has been concluded that data collected in national workplace surveys suggest that "the incidence of work at home at least one day per week ranges from 8.9 percent of all employed persons in Ireland to 11.1 in the UK and 15 percent in the US" (J. H. Pratt as quoted in Lyons, Farag, and Haddad, 2008). From the SCAG survey findings for telecommuters, it may be computed that 12 percent of Southern California employees telecommute at least 1 day per week (48 percent of 24.6 percent). However, since the quoted international comparison appears to include home-based-business workers and may even include overtime home-workers, which the SCAG analysis does not, the Southern California rate is most likely highest when placed on equal footing. A definitively higher estimate of telecommuting comes from Washington State, although this estimate is based on surveys limited to firms in the state's CTR program (generally firms of over 100 employees). In 2007, telecommuting displaced about 2 percent of commute trips among the cov- ered firms. Periodic surveys have showed a slow but steady increase in telecommuting (Hillsman, 2009). Quite possibly increasing gasoline prices were a factor up through the latest available sur- vey dates. Increases in telecommuting have also been reported from the United Kingdom, which lags a little behind the United States in adoption (Lyons, Farag, and Haddad, 2008). Turning to observed commuter response to telecommuting, there are several demonstration pro- grams of interest. Beginning in 1990, the Washington State Energy Office staged a 2-year Tele- commuting Demonstration project in the Puget Sound area involving 25 public and private organizations and 280 participants. In structuring the monitoring and evaluation of the experi- 19-76

OCR for page 61
ment, the researchers obtained information through surveys, travel logs, and related media using four groups of people: telecommuters, their supervisors and co-workers, and a comparison group. Most of the telecommuters were home-based, but 24 individuals from nine organizations used the Energy Office's new State Telework Center. Pre-program surveys revealed that 61 per- cent of the telecommuters previously drove alone to work, 18 percent used carpools, 17 percent used transit, and the remainder walked, biked, or were dropped off. The comparison group was similar, but with lower use of carpools (12 percent). These proportions did not change significantly over the demonstration, with 63 percent of telecommuters and 64 percent of the controls reporting drive alone as their usual method of reaching work. Average commute trip length for telecom- muters was 18 miles, compared to 8 miles for the comparison group and 10 miles on average in the Puget Sound region, suggesting that telecommuting is particularly attractive to employees living a long distance from work. The survey data indicated that by the end of the demonstration, telecommuters had reduced their total commute trips by about 26 trips per year, while members of the comparison group reduced their trips by 10 per year, primarily as a result of shifts in mode motivated by the employers' com- mute assistance programs. A travel log analysis revealed that telecommuters were saving trips and miles traveled on their telecommute day, and not making up for lost trips on their non-telecommute days. Before the program began, the telecommute participants made 4.3 trips per day, correspond- ing to 52 miles in 101 minutes. One year later, they made 2.6 trips per day on telecommute days, traveling 13 miles in 35 minutes, and on non-telecommute days made 3.9 trips, going 49 miles in 91 minutes. Sample size limitations prevented an analysis of use by other household members of the telecommuter's car on telecommuting days. Telecommuting was estimated to have reduced average commute VMT per telecommuter on telecommute days by 36 miles, netting out to 29 miles after accounting for mode shifts. In the special case of the telework center, travel data indicated that vehicle trips did not decrease for these participants, since they still had to commute to the center. Furthermore, while only 56 per- cent of these telecommuters drove alone to the main work site, 83 percent drove alone to the tele- work center (Washington State Energy Office, 1992). A Southern California telecommuting test among SCAG employees in 1988 showed a reduction in person trip miles as a result of work trips avoided and shorter trips to telework satellite centers. The average net person trip distance reduction was 46 miles for each telecommute occasion. Allowing for the usual mode of travel for telecommuters--accounting for the fact that some nor- mally used transit or carpool--31 vehicle miles of travel were saved per telecommute. Fourteen percent of SCAG's employees participated in the experiment, with average participation being once every 9 days. Most worked from home, and one worked at a satellite work center. The SCAG experiment showed some increase in non-work trips due to telecommuting. It was estimated that VMT "created" as a result of working at home amounted to 14 percent of the miles saved; there- fore, the net savings in VMT per telecommute was 26 miles, not 31 (SCAG, 1988). The state of California also conducted a pilot telecommuting project in the late 1980s, involving over 400 state employees across 13 agencies (both participants and non-participating "controls"). The findings suggested that physical trips to work by telecommuters decreased by 30 percent, from 0.90 to 0.63 trips per day, compared with non-telecommuters whose rates did not change. Those participating were found to telecommute 1 to 2 days per week. Preliminary findings showed no increase in non-work trips by telecommuters, but instead found a reduction in non-work trips for other household members. Non-work person trips fell by 35 percent, from 3.6 to 2.3 per day (Kitamura et al., 1990). 19-77