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5.1 Introduction and Prior Analysis
The safety effects of alternative changes from the long-standing HOS regulations in place prior to 2003 were analyzed in the 2003 RIA. The key features of the analysis approach are described in the following sections.
5.1.1 Literature Search and Model Selection
The analysis started with a comprehensive review of the literature relating to operator cognitive fatigue, effects of sleep deprivation and related issues. The review identified three main factors determining a truck drivers' ability to perform his or her task safely at any point during the work shift.
Circadian cycle effects. People experience a normal cycle in attentiveness and sleepiness through the 24-hour day. People having a conventional sleep pattern (sleeping for 7 or 8 hours overnight) experience maximum sleepiness in the early hours of the morning and a lesser low in the early afternoon. As well as reduced attentiveness during the low points of the cycle, people find it difficult to sleep soundly during high-attentiveness periods. The cycle is anchored in part by the natural sunlight and darkness cycle and in part by an individual's externally imposed pattern of sleep and waking times. It follows that the performance of night shift workers is always somewhat reduced, because of the influence of the natural day-night cycle on the circadian rhythm is not fully displaced by the night-work routine. In addition, circadian rhythms are persistent, and can only be shifted by 1 to 2 hours forward or backward per day by externally imposed changes in work/sleep routines and travel across time zones. Thus, changing the starting time of a work shift by more than these amounts, or the first night shift after a "weekend" break during which conventional sleep times were followed, will also reduce attentiveness.
Sleep deprivation and cumulative fatigue effects. Individuals who fail to have an adequate period of sleep (7-8 hours in 24 hours) or who have been awake longer than the conventional 16-17 hours will suffer sleep deprivation, leading to reduced performance. The deprivation accumulates with successive sleep-deprived days and is superimposed on circadian rhythm effects. Additional sleep deficits may be caused by breaking daily sleep into two shorter periods in place of a single unbroken period of sleep. Finally, unimpaired performance is not restored instantly after resuming a conventional sleep schedule, but may take two or three such sleep cycles to reach normal performance.
Industrial or 'time-on-task" fatigue. This is fatigue that accumulated during the working period, and affects performance at different times during the shift. Generally, performance declines with time-on-task, gradually during the first few hours and more steeply toward the end of a long period at work. Some studies also show reduced performance in the first hour of work as the individual makes the transition from off-duty activities.
5.1.2 Effects of Work and Sleep Schedules on Driver Performance
After reviewing multiple research studies on individual aspects of sleep and fatigue, and the limited number of efforts to integrate the available knowledge into an operator performance model for individuals performing repetitive cognitive tasks, a slightly modified version of the Walter Reed Sleep and Performance Model was selected for the analysis. The model does not include time-on-task effects, and the literature review failed to yield adequate quantitative information on performance in the last hour or two of a full-length 10 or 11 hour shift. Thus the analysis did not include 'time-on-task' effects.
The inputs to this model were representative truck driver schedules developed from a variety of industry surveys and similar sources. Separate schedules were developed for long and short-haul trucking and for multiple operating patterns within each of these broad categories. The metric used to quantify driver performance in the sleep model output was the response time score on the Psychomotor Vigilance Test (PVT), which has been widely used to measure behavioral alertness in a variety of settings. Past research and testing has established the relationships between the PVT scores and sleep histories to provide the core data that drives the model.
This risk model calculates PVT for each 15 minute period during which the operator is driving. These results are used to develop estimates of truck crash risk as described in Section 5.3.
5.1.3 Relationship Between Fatigue and Motor Carrier Crashes
The first step in the analysis is to develop a relationship between PVT values as calculated by the sleep and performance model and performance in the specific task of driving a truck. This was accomplished through a series of tests on a truck driving simulator with volunteer drivers who had been exposed to different sleep and waking routines. Output from the test program was used to develop a spreadsheet model to convert PVT scores into a crash risk increment relative to a fully rested driver. The individual 15-minute crash risk increments calculated from fatigue model outputs were then combined to obtain average crash risk increments for each overall schedule and a weighted average for all long and short-haul schedules.
Finally, the crash risk increments derived from the fatigue model and simulator tests were calibrated against actual truck crash risk data to project the changes in crash risk based on alternative HOS requirements. The first step in this analysis was to estimate the fraction of crashes that appear to be attributable to fatigue. After reviewing the available data, the data from a Fatality Analysis Reporting System (FARS) maintained by the National Highway Traffic Safety Administration was found to be the most credible, although it obviously does not include data on crashes without fatalities but with injuries or property damage. The FARS database provides consistent data on the causes of crashes, while other highway crash databases (such as NHTSA's General Estimates System (GES) and the FMCSA Motor Carrier Management Information System (MCMIS)) contain only limited cause data. In particular, reporting practices varied by state, and data were often missing.
The FARS data was edited to eliminate records on individual crashes where key data were missing, and also where primary fault appeared to lie with other vehicles (not trucks) involved in the crash, and with certain hazardous weather conditions. The net result of this review was that the percentage of all truck-involved crashes where driver fatigue was a factor was 7.25%, taking an average of four years 1997-2000. In addition, there is evidence that a portion of crashes where inattention is cited as a cause were also due to fatigue. Twenty percent of inattention crashes (0.89% of all crashes) was added to the 7.25%, yielding a final estimate of 8.15% for fatigue-caused crashes. This percentage was projected to be reduced after implementation of the 2003 HOS regulations, to 7% for long-haul, and 3.5% for short-haul crashes .
5.1.4 Inexperienced Driver Effects
The 2003 RIA analysis concluded with a discussion of how the different HOS options outlined in the 2003 RIA would affect driver turnover and the need to recruit new and potentially inexperienced drivers into the industry. Research has shown that inexperienced drivers are at higher risk for a crash. The analysis concluded that there were differences in driver populations between the HOS scenarios, but that the differences were small. Because of the small differences and the likelihood that the adverse effects of bringing new drivers into the industry would be offset by slight reductions in VMT (due to mode shift), the inexperienced driver analysis was omitted from the current analysis.
5.2 Research Background on the Safety Effects
New information relevant to the effects of varying work/rest schedules and maximum driving time for truck drivers has become available in the period since completion of the 2003 analysis. This research has enabled refinements in the analysis of the safety effects of driving time and work/rest patterns. Most importantly, with this information, we have attempted to produce an estimate of the safety effects of varying maximum driving time between 10 and 11 hours. Incorporation of this analysis into the RIA is in response to concerns raised by the U.S. Court of Appeals in its dicta regarding maximum allowable daily driving time.
To provide a basis for the revised analyses, FMCSA initiated a supplementary search of the relevant literature. This search was supplemented by supporting analyses of data emerging from current research, including data from the Trucks Involved in Fatal Accidents (TIFA) database. Key points from the literature search as they relate to time-on-task effects, effects of short work/rest cycles, and driver performance/crash risk modeling are discussed below. Further details on the literature search are provided in Appendix (III).
5.2.1 Time-on-Task Effects
The analysis of time-on-task (TOT) effects in the safety analysis relied primarily on recent data from two ongoing research efforts, one by Ken Campbell of Oak Ridge National Laboratory, and one by a team led by Dr. Paul Jovanis at Pennsylvania State University. Both efforts are being undertaken specifically for FMCSA, and both were ongoing at the time of this analysis, so this review relies on interim reports and other material provided to FMCSA (as identified in the footnotes) to date rather than formal final reports or published papers. As such, while the results of these studies are considered preliminary, they were thought to be the most appropriate of the limited data currently available to analyze TOT effects as part of this analysis. While recent, preliminary research findings by Hanowski [Hanowski, R.J., et al. (2005)] for FMCSA were also considered, the Hanowski work was not thought to be appropriate for this particular (TOT-related) analysis, since it limited its examination of commercial driver fatigue and performance to the 10 th and 11 th hours of driving. Despite the fact that the Campbell and Jovanis studies were still in progress at the time of this analysis, these studies do consider fatigue-related crash risk across the spectrum of driving hours (at least hours one through 11) and, given that research and data of this type are relatively limited, they were considered for the TOT analysis being conducted here.
The Campbell analysis uses national level data from the TIFA database for the years 1991-2002, comprising over 50,000 truck-involved crashes. This database was developed from truck crashes in the NHTSA FARS database, with additional data on the driver and the carrier involved, complied by the University of Michigan Transportation Research Institute (UMTRI) after FARS data are published. Most importantly, UMTRI added data on time since the driver's last 8-hour break, the truck and carrier types, and the planned trip length to the FARS data to create the TIFA database. Note that, because this data collection effort predates the 2003 rule change, the results reflect pre-2003 HOS regulations: driving time was limited to 10 hours, the minimum rest time between trips was only 8 hours, and there were no provisions for a restart of the cumulative 7/8 day duty period. Also, the data do not include any information on the driver schedule over a longer period than the shift in which the crash took place. Thus, it is not possible to determine if cumulative fatigue may have been a factor.
The Campbell analysis addressed several aspects of the effect of driver fatigue on crash risk, including the fraction of crashes where fatigue was reported as the leading cause in FARS, the prevalence of fatigue by motor carrier industry segment, truck type, time of day, and hours of driving at the time of the crash. For the last of these analyses, a chart was provided of relative crash risk for each successive hour of driving. Relative crash risk for each hour is calculated as a multiple of the crash risk in the first hour. Exhibit 5-1 shows the results.
Exhibit 5-1 Relative Crash Risk by Driving Time Under the Pre-2003 HOS Rule
NOTE: Numbers above each bar chart represent the number of large trucks involved in fatigue crashes and total fatal crashes, respectively.
Data Source: Trucks Involved in Fatal Accidents (TIFA), 1991-2002
For example, for the 10 th hour of driving, Exhibit 5-1 indicates that the relative risk per involvement in a fatigue-related crash is roughly 2.5 times higher than in the first hour of driving (reading across to the vertical axis of the chart). In the 11 th hour of driving, the relative risk per involvement in a fatigue-related crash is roughly five times higher than that in the first hour. The first number above each bar chart represents the number of large trucks involved in fatigue-related fatal crashes between 1991 and 2002 for each driving hour, while the second represents the total number of large trucks involved in all fatal crashes within that same driving hour. For example, within the 11 th hour of driving, there were 9 large trucks involved in fatigue-related fatal crashes between 1991-2002, while there were 94 large trucks involved in all fatal crashes during that same driving hour. The figures above each chart help to provide a better understanding of the prevalence of large truck fatal crashes in each driving hour, in that they reveal that as driving hours increase, the number of fatal crashes, as well as fatigue-related fatal crashes, generally decrease in a steady fashion.
Using the 11 th hour driving data as an example, the relative risk ratios representing each bar chart in Exhibit 5.1 were estimated via the following steps. First, the number of trucks involved in fatigue-related fatal crashes (9) within the 11 th hour of driving were divided by the number of trucks involved in all fatal crashes in the 11 th hour of driving (94). The result, 9.6 percent, represents the percentage of all trucks involved in fatal crashes during the 11 th driving hour where it was determined that the truck driver was fatigued at the time of the crash. Second, the number of trucks involved in fatigue-related fatal crashes between 1991-2002 for all hours of driving (990) were divided by the number of trucks involved in all fatal crashes for all hours of driving (53,249), which yielded an overall ratio of 1.9 percent, or the percent of all large trucks involved in fatal crashes during this time period where the truck driver was determined to be fatigued at the time of the crash. Finally, to estimate the relative risk ratios that appear in Exhibit 5-1, the percent of all trucks where fatigue was present at the crash within each driving hour (i.e., 9.6 percent in the 11 th driving hour) was divided by 1.9 percent, or the percent of all trucks involved in fatal crashes across all driving hours where it was determined that the truck driver was fatigued at the crash. The result is a relative risk estimate per involvement in a fatigue-related crash for each driving hour. In the case of the 11 th driving hour, this estimate is equal to about five (or 9.6% divided by 1.9%), which is represented by the height of the bar chart in Exhibit 5-1 for the 11 th driving hour.
There were some concerns with the TIFA data contained in Exhibit 5-1: first, there are very few crashes for drive times over 10 hours (i.e., only 9 fatigue-related crashes in the 11 th hour, only 10 in the 12 th hour, and only 8 in the 13 th hour). Such limited populations of fatigue-related crashes raises uncertainty with regard to the relative risk ratios associated with the later driving hours, since the misclassification of a single crash as fatigue-related can affect the resulting relative risk ratios quite substantially. For instance, misclassification of a single fatal crash-involved large truck driver as fatigued in the 11 th hour of driving would increase the number of trucks involved in fatigue-related crashes from 8 to 9, thereby increasing the relative risk ratio from 4.57 to 5.15, or 12.5 percent.
Other concerns with TIFA data include the fact that the pre-2003 regulations limited legal driving time to 10 hours, which meant that driving in the 11 th hour was illegal at the time these data were collected. As a result, the data on the frequency of driving 11 hours or more could be underreported. As such, it is unclear whether fatigue-related crashes are over- or under-represented in the TIFA data set, since it is not possible to determine whether any under-reporting involved all fatal crashes during the 11 th hour of driving, or just those where the truck driver was determined to be fatigued. Also, because driving beyond 10 hours was illegal under the pre-2003 rule (or at the time these data were collected), the relative risk of the subpopulation of commercial drivers admitting to illegal driving during the 11 th hour or later may not reflect the relative risk of drivers operating legally under the 2003 rule. Unfortunately, TIFA data for calendar year 2004 (the first year when driving in the 11 th hour was permissible) will not be available until late 2006. Given these uncertainties, FMCSA conducted a sensitivity analysis regarding the estimates used in the RIA for the relative risk of a fatigue-related crash. The results of this and other sensitivity analyses can be found in Chapter 6, Section 8, of this RIA.
Campbell followed this analysis with a similar analysis of preliminary data (as of December 2004) from the Large Truck Crash Causation Study (LTCCS) . These data covered the period April 1, 2001 to December 31, 2003 and contains a sample of approximately 1,000 crashes. The result of the driving time analysis is shown in Exhibit 5-2. The overall result is similar to that derived from the TIFA data, although relative fatigue involvement factors for hours exceeding 10 hours represented by the LTCCS data appear to be lower than from TIFA data. The preliminary LTCCS data include injury crashes as well as fatal crashes, and it is not clear whether or not the relative risk data includes the injury crashes. However, it is important to note that the LTCCS data are still preliminary and have not yet been published in final form.
Exhibit 5-2 Relative Crash Risk by Driving Time ( Campbell - LTCCS Data)
In contrast to the Oak Ridge/Campbell analysis, the Penn State/Jovanis analysis relies on a sample of data obtained from three cooperating LTL carriers, as described in two interim reports to FMCSA , Currently, the sample includes seven-day driver records for 231 crashes and comparable data for 462 similar periods without a crash. The sample periods were randomly selected. All the data obtained to date are for the calendar year 2004 after the introduction of the revised HOS regulations which permitted an 11 th driving hour and required longer breaks between onduty periods. Conversely, the sample of commercial operators driving in the 11 th hour is very small, with the data limited to 34 drivers. TOT task effects were calculated for the entire sample and for different subsets of the data, including operations with team drivers and sleeper berths, and different start times and shift patterns.
The primary result for all industry segments and driving routines combined is shown in Exhibit 5-3. The main limitation with this analysis is that it is representative of only one trucking industry segment (LTL carriers). Additionally, there are very few driver cases involving 11 hours of driving (34, which includes both crash and non-crash cases) are rather low, which is presumably causing the very high variance surrounding the estimated 11 th hour crash risk. The data show an 11 th hour risk factor of about 3.4, which would be substantially higher than the equivalent estimates derived from the Campbell - LTCCS data discussed above. Jovanis also reports that the results are comparable to results obtained from a similar analysis of data gathered in the 1980s.
Exhibit 5-3 Relative Crash Risk with Driving Time
(Jovanis Sample of LTL Operation)
5.2.2 Driver Performance and Crash Risk Modeling
An evaluation of available models to calculate performance as a function of sleep and work schedules was carried out in preparation for the 2003 RIA analysis. At the time, the Sleep Performance Model (SPM) developed by the Walter Reed Army Institute of Research (USAIR) was selected as the most appropriate for this analysis. Appendix E of the 2003 RIA describes two other candidate models that were not selected. Since that time, the SPM was further refined and expanded in a cooperative effort involving multiple research organizations. The model has been adapted and applied to a number of different activities, including military operations and surface and air transportation modes. Several considerations prompted selection of the revised model for this RIA analysis:
Use of a related model should ensure reasonable consistency with the 2003 RIA.
A limited literature search conducted for this analysis failed to identify any comprehensive model that would be a credible candidate for this analysis, and which had not been previously evaluated.
The model had already been adapted and used for performance analysis for different transportation operations.
The model had been incorporated into a commercially-available software package complete with user interfaces, facilitating use of the model.
Details of the model's approach to analyzing the effects of driving schedules on driver performance is documented in a paper by Steven Hursh and his colleagues at the WRAIR, the Naval Health Research Center (NHRC) and the Air Force Research Laboratory (AFRL). After moving to SAIC, Hursh developed a commercial software tool that applies the analysis methodology (called the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) model) to a Fatigue Avoidance Scheduling Tool (FAST). The tool is closely related to the WRAIR SPM used in the 2003 RIA, and is continually being developed and refined as new research data has become available from WRAIR, NHRC and AFRL research programs. Further refinements to the tools were sponsored by AFRL and the US Department of Transportation in air and surface transportation activities. ICF applied the current commercially available version of the FAST/SAFTE tool to this analysis of variations in HOS regulations.
The core concept behind the SAFTE model is that of a sleep reservoir which individuals draw upon while awake and replenish by sleep. The overall structure of the SAFTE model is described below.
Alertness or performance is a function of three inputs:
The amount of depletion of the sleep reservoir - research shows performance declines to the low level of 25% of that of a fully rested individual after 72 hours continuous wakefulness. A linear relationship between performance and sleep debt is used, although some research results suggest a non-linear relationship. Both the slope and shape of relationship can vary with the test task used to assess performance.
Sleep inertia effects. Full wakefulness is not attained instantly on awaking. Instead there is a period, typically on the order of 1 to 2 hours, depending on sleep intensity and the current status of the sleep reservoir, during which performance recovers to a peak value, after which the normal decline with reservoir depletion resumes.
Circadian rhythm effects. As discussed in Section 5.1.1, circadian cycles affect alertness as a function of time of day and the individual's sleep/waking cycles over the past several days.
The model applies the results of numerous research studies of each effect to provide a continuous estimate of performance levels over time. In principal, the model is not limited to one specific performance measure (such as the Psychomotor Vigilance Test (PVT) measure used in both this and the original truck HOS analysis), provided there is a sufficient research base to establish the relationships between task performance and the status of the sleep reservoir.
Replenishment of the sleep reservoir also depends on multiple inputs, as follows:
Length of sleep. A non-linear function exists between the length of sleep and the amount of replenishment. The per-hour replenishment is less for the first three hours of sleep than later in the sleep cycle. The model also incorporates a factor for fragmented sleep, to quantify the reduction in sleep effectiveness due to frequent wakening, such as from external noise and vibration or a sleep disorder.
Reservoir depletion level. Generally the lower the sleep reservoir, the greater the restorative effect of sleep. The term sleep intensity is used to quantify this effect.
Circadian rhythm. Sleep is more effective (higher sleep intensity) around the low points in the circadian rhythm.
As with performance estimates, the SAFTE model applies the results of numerous research studies to estimate reservoir replenishment for a given period of sleep.
The authors conclude by identifying a number of areas where there are unexplained contradictions or anomalies in the available data, and where further research would be useful. One key area is the relationship between the performance metric and the actual demands of a specific task. In the HOS analyses, a relationship was developed between PVT and crash risk from truck driving simulator tests. Similar relationships are needed for other tasks, both to identify the most appropriate measure for a specific task and to establish a relationship for use in practical applications of the model.
The procedures for estimating the crash increment as a function of average PVT values and then the variation in estimated fatal and non-fatal truck involved crashes due to driver fatigue effects are unchanged from the 2003 RIA analysis, except that a baseline estimated performance under the 2003 regulations is substituted for the baseline before the 2003 change.
5.2.3 Other Relevant Literature
A broader review of literature, concentrating primarily on material identified since the 2003 RIA is provided in Appendix (III). Some specific research findings of relevance to this analysis included the following:
A detailed laboratory study by Balkin et al. at the WRAIR, sponsored by FMCSA and other transportation interests, in which a sample of truck drivers with different levels of sleep deprivation operated a truck driving simulator, and were evaluated by the number of crashes during simulated driving and using the PVT. An associated field study used wrist actigraphy to determine sleep times and duration for a sample of long and short haul truck drivers. This study provided the key link between PVT as calculated by the FAST/SAFTE model and truck crash risk, used in this analysis (see Section 5.4.4) and also some material to support the analysis of off-duty activities as described in Section 5.4.1.
A field study to compare driving performance and sleep patterns of team and single drivers on long, multi-day trips, sponsored by FMCSA. The most interesting result from this study was that team drivers suffered fewer instances of extreme drowsiness than single drivers, in spite of short and less effective sleep periods in the truck's sleeper berth, while the truck continued traveling with the second driver. Team drivers tended to drive more conservatively that single drivers and would manage fatigue by swapping drivers when the active driver was fatigued. Single drivers tended to push on in spite of fatigue, exposing themselves to greater crash risk. This result can be contrasted with the results from the FAST/SAFTE model that showed team drivers to have a lower performance than a single driver (Section 5.4.1), primarily because the first part of a period of sleep is less effective at replenishing the sleep reservoir than the later part. At least in the observed population, the basic performance disadvantage of a typical team driving schedule was more than offset by an effective fatigue management strategy employed by the drivers . The small sample size (56 drivers, including 26 team drivers), however, and the possibility that the drivers were self-selected and therefore not necessarily representative, means that we may not be able to generalize the study's results.
In another FMCSA-sponsored study, Hanowski et.al. investigated the relationships between fatigue and critical incidents, and between sleep history and fatigue for a sample of short-haul truck drivers. Key findings from the study included the following:
A recently published (2004) survey of truck drivers concentrating on the effects of safety management practices by the trucking firm on crashes and close calls. The study was unable to establish any relationship with crashes (possibly because fewer than 10% of truck crashes are due to fatigue), but showed that good safety management had a significant effect on close calls. Good practices included regular scheduling and careful management of fatiguing loading and unloading activities. The study also observed that some drivers started work fatigued, and that employers could implement policies to encourage sensible off-duty fatigue management in their employees.
5.3 Overview of Crash and Benefit Analysis
In this analysis, the base case is the set of HOS regulations implemented in 2003, and the analysis evaluates the effects of changes from these regulations. Revisions to the 2003 RIA analysis include inclusion of TOT effects and use of the newer FAST/SAFTE model for analysis of driver performance as a function of work and rest schedules.
The overall approach to this analysis is illustrated in the flow diagram, Exhibit 5-4. The crash and benefit analyses use the output of the truck operations simulations as the starting point for the analysis. The operations analyses, described in Chapter 3, provide a series of realistic truck driver schedules for each trucking industry segment, and for each set of HOS regulations being examined in this analysis. The schedules specify driver activity for each half hour (off duty, on-duty driving, and on-duty performing other activities such as loading, unloading, and waiting) over a multi-day period. The outputs of the simulations are used as inputs into cost modeling described in Chapter 4, as well as the crash analysis described in this Chapter.
The one piece of data about driver schedules that the simulation model does not provide is how the driver splits off-duty time between sleep and other personal activities. This information is an essential input to the driver performance model described in Section 5.4.1 A separate analysis to address this question was carried out to add this information to the working schedules, based on sleep pattern surveys and similar research. These analyses led to a set of algorithms for sleep time based on the length of the break and the time of day at the start and end of the break.
Exhibit 5-4 Flow Diagram for Crash Risk Reduction and Benefit Calculations
The driver performance analysis is described in Section 5.4.2. The FAST/SAFTE human performance model, developed in part from research led by the Walter Reed Army Institute of Research, was used for the analysis. The inputs to the model were the driver schedules developed in truck operations simulations described in Chapter 3, supplemented by the analysis of driver off-duty hours described in Section 5.4.1. The model applies a large body of sleep and fatigue research, including circadian rhythms to provide an operator effectiveness percentage relative to a fully rested individual.
Because, as discussed, the FAST/SAFTE model does not take into account TOT effects, a separate analysis of these effects was performed to determine the relationship between TOT and crash risk. This relationship was applied to the driver schedules from the simulation model to estimate the effects of changing the maximum driving time from 11 hours to 10 hours.
5.4 Detailed Description of the Revised Crash and Benefit Analysis
5.4.1 Off-duty Sleep Time Assignment
In order to use the FAST/SAFTE model to process the outputs of the operational model, we needed to determine how much sleep the drivers were getting and when that sleep would occur during a given off-duty period. This procedure is described below.
In the productivity analysis outlined in Chapter 3, we were concerned with the length of the driver's on-duty, off-duty, and driving periods. While the safety model requires the length of the on-duty and off-duty periods, it also requires the amount of sleep taken by the driver, and the placement of that sleep within the off-duty period. These are the two functions of the sleep allocation model. Once the driver's schedule has been separated into on-duty periods, off-duty periods and sleep periods, it is ready for input into the FAST/SAFTE model.
The first step in the sleep allocation process is to determine how much sleep a driver is expected to get based on past work history. This calculation is a decreasing function based on the cumulative amount of on-duty time in the previous 24 hour period. The basic function is identical to the one used in the 2003 RIA. For a driver who works 14 hours a day on a continuous basis, that amounts to 6.57 hours of sleep per 24 hour period. Once the amount of sleep is determined, the model checks to see how much sleep the driver has received over the previous 24 hour period. If the driver has had more sleep than he is expected to get, a sleep surplus is assumed to exist. If the driver requires more sleep than he has received over the last 24 hours, he has a sleep deficit and the model allocates sleep until the driver's deficit has been reduced to zero or until the driver begins his next on-duty period, whichever comes first.
5.4.2 Modeled Impacts of Individual Schedule Factors on Performance
This section presents the results of investigations of several individual aspects of driver schedules using the FAST/SAFTE model. These investigations cover the effects of lengthening the breaks between multi-day work periods, changing the degree of regularity in schedules, and splitting rest periods into two pieces.
Impact on Driver Effectiveness of Longer Weekly Breaks
One important observation made while using the FAST/SAFTE model was the relatively small improvement in driver effectiveness when shifting from the 34-hour restart to a 58-hour restart. To compare these scenarios, we modeled two drivers on regular schedules and compared their levels of effectiveness. Both drivers had on-duty periods of 14 hours from 7 AM to 9 PM. Off-duty periods of 10 hours made up the remaining portion of the day, from 9 PM to 7 AM the following day. Those drivers with a 34-hour restart worked 6 consecutive days with 1 day off and those drivers with a 58-hour restart worked 6 consecutive days with 2 days off. Because the function used to estimate sleep for drivers is sensitive to differences in time off, the extra day off in the second scenario leads to about 1.6 extra hours of sleep, once per week. Thus, the driver with more time off can be expected to start the work week slightly more rested, and therefore with a lower crash risk. The average driver effectiveness results for the 14-hour on-duty periods are summarized in Exhibit 5-6 below.
Exhibit 5-6 Average Effectiveness during On-Duty Periods
34-Hour Restart
58 Hour-Restart
Average Effectiveness
St. Dev.
Minimum Effectiveness
Thus, an extra day off, and the slight increase in sleep it makes possible, does show an increase in average effectiveness. The change is quite small, however; less than half of one percent, which would translate into a reduction of only a quarter of one percent in crashes. The safety model results based on the outputs from the productivity model described in Chapter 3 generally support this conclusion, as shown in Chapter 6.
Importance of Regularity in Driver Schedules
Another observation from the results of the safety modeling was the importance of maintaining a 'regular' schedule. By 'regular', we are referring to the driver's ability to work and rest in the same general timeframe over consecutive work days. The importance of regularity stems from the effect that circadian rhythm has on driver effectiveness. Those drivers that had substantial shifts in their daily work/rest cycle performed considerably worse than those drivers that maintained a relatively constant schedule. It should also be noted that those drivers that shift to an entirely new schedule and maintain it over a period of weeks will eventually adapt to the new circadian rhythm. It is those drivers that shift to a different schedule on a daily or weekly basis that show substantial drops in effectiveness. As a visual example, compare the two FAST model screen shots below.
Exhibit 5-7 Driver on a 'Regular' Schedule
Exhibit 5-8 Driver on a 'Variable' Schedule
The 'regular' driver, in addition to showing a higher overall effectiveness, also shows much less variability in effectiveness. The large drops in effectiveness shown in the output of the variable-schedule driver are a characteristic of a constantly changing schedule. In the two examples above, the average driver effectiveness over a one-year period for the 'regular' schedule driver was 92.95%. This compares very favorably to an average effectiveness of 77.89% for the driver with the variable schedule.
Driver Effectiveness - Split v. Continuous Sleeper Berth Periods
Another important observation from the FAST/SAFTE model was the difference in driver effectiveness values based on how drivers took their off-duty periods, and specifically their sleep periods. Of particular interest were drivers who split their sleep period as compared to those that chose not to split. To model these two different drivers, we used the FAST/SAFTE model to calculate the effectiveness of drivers that had 10 hours of on-duty time and 14 hours of off-duty time each day. One driver was given the 14 hour off-duty period in one single block and the other driver was given two 7 hour off-duty blocks. Twelve simulations were run for each driver, each offset by 2 hours, to determine the combined effect of splitting and circadian rhythms. Four weeks of driver data were modeled for this particular analysis. In general, drivers who split their sleep period into two, 7-hour blocks had lower levels of effectiveness than those drivers that took one continuous 14-hour break. Two screen shots from the FAST/SAFTE model show the difference in effectiveness for a driver that chooses not to split and one that does split their off-duty period.
Exhibit 5-9 Driver with Continuous Off-duty Periods
Exhibit 5-10 Driver with Split Off-duty Periods
At different start times over the course of a 24-hour period, the driver that chooses not to split generally has a higher average effectiveness than the driver that splits. However, our modeling shows that drivers beginning their shift between the hours of 22:00 and 0:00 show higher levels of effectiveness if they choose to split their rest period. Exhibit 5-11 summarizes our findings.
Exhibit 5-11 Driver Effectiveness at Different Start Times - With and Without Splitting
5.4.3. TOT Analysis
The TOT analysis relies on crash risks by hours driving, derived from Ken Campbell's analysis of Trucks in Fatal Accidents (TIFA) data as described in Section 5.2.1. Exhibit 5-12 takes the fatigue-related crash risk by driving hour from Ken Campbell's research and fits a cubic curve to the data. Note that that the fitted curve suggests that the particularly high risk factor observed by Campbell for the 11 th hour may be an outlier. Relative risk data for the 13 th driving hour and beyond were combined due to the very small number of crashes that occurred in each of these hours (i.e., in some hours, there were no fatigue-related crashes recorded).
Campbell's data express relative risk as a multiplier of the first hour risk. This has been converted into actual percentage crash risk. As noted earlier in Section 5.2.1 of this chapter, the TIFA data compiled by Campbell have several limitations, including: (1) the number of fatigue-related crashes in the 11 th hour and beyond is very small; (2) because driving beyond 10 hours was illegal under the pre-2003 rule, the relative risk of the subpopulation of commercial drivers admitting to illegal driving during the 11 th hour may not reflect the relative risk of drivers operating legally under the new rule; and (3) because the required off-duty period pre-2003 was only 8 hours long, and not the longer 10-hour period off duty currently required, the data reflects drivers who may well have been more fatigued at any given time on task than drivers would have been if the data had been collected under the 2003 rule. Limitations (2) and (3), all other things equal, argue that the Campbell data may over-estimate the true risk of driving during the 11 th hour in the current state of the world. Limitation (1) argues that any result from the Campbell data carries with it a considerable amount of uncertainty. Because of this uncertainty, Section 6 presents a sensitivity analysis to test the robustness of the conclusions of the impact analysis.
Exhibit 5-12 Crash Risk as a Function of Hours of Driving
The curve in Exhibit 5-12 shows that fatigue-related crash risk starts rising after the six or seven hours. Therefore, for the eighth hour and beyond crash risk was increased by a factor equal to the ratio between the estimate for each hour represented by the polynomial and the average fatigue crash risk. These factors were applied in the procedure for estimating crash risk from the performance data provided by the FAST/SAFTE tool, as described in Section 5.4.4, below.
5.4.4. Crash Risk Analysis
This section explains how the FAST/SAFTE tool outputs and the relationship between crash risk and TOT are combined to provide an estimate of crash risk for each HOS option. The steps in the analysis are as follows:
Estimate raw crash risk increment from FAST/SAFTE performance data for each schedule
Apply the TOT adjustments to all schedules that exceed 7 hours driving time in one shift.
Weight and average crash risk increment values for individual schedules (after applying the TOT adjustment), to obtain crash risk increments for each trucking industry segment and for the industry overall.
Adjust and scale the raw crash risk increments so that industry-wide crash risk attributable to fatigue matches the projected real-world fatigue-related crash risk in the baseline (Option 1).
This procedure is identical to that used in the 2003 HOS RIA, except for the addition of the TOT adjustment to crash risk estimates. The following paragraphs summarize the analysis steps. Further details can be found in Appendix G of the 2003 RIA.
Estimate raw crash increment. The primary source of data to form a link between crash risk and PVT scores produced by the FAST/SAFTE tool was a laboratory study carried out by Walter Reed Army Institute of Research, in which driving performance on a truck simulator was compared with PVT measurements for different levels of sleep deprivation (see Section 5.2.3). A robust straight line relationship between the log of crashes during a 45 minute driving session and fatigue level as measured by 100-PVT score was obtained. Exhibit 5-13 shows the scatter plot and the linear relationship. PVT scores were scaled so that a score of 100 indicates a fully rested individual.
Exhibit 5-13 Relationship of PVT to Relative Crash Risks
TOT Adjustment. Based on the discussion in Sections 5.2.1 and 5.4.3, a driving time risk factor was applied to all schedules over 7 hours. The table, Exhibit 5-14, lists the increase factor for average crash risk for drive times of 8 or more hours, calculated from the polynomial shown in Exhibit 5-12.
Exhibit 5-14 TOT Crash Risk Multipliers
Driving Time in One Tour of Duty
Risk Increase Relative to Average Driving Hours
Source: Exhibit 5-12 and ICF calculations
These factors were applied to each crash risk increment calculated by the FAST/SAFTE model.
Calculate Raw Crash Risk Increments by Industry Segment. The crash risk increments as calculated by the FAST/SAFTE model, with TOT adjustment for shifts of 8 hours and over, were averaged for each truck industry segment and for the industry overall.
Estimate Actual Fatigue-Related Crash Risk. The final stem in the analysis is to convert the raw crash risk increments to an estimate of the actual variation of crash risk for different HOS alternatives and industry segments. This is achieved by calibrating the crash risk increments for a base case to real-world fatigue related crash data. The procedure is identical to that described in Chapter 8 of the 2003 RIA report. The raw crash risk increments are the percentage increase in crash risk over the crash risk for a fully rested driver. Thus the proportional change in fatigue-related crashes between two HOS scenarios is represented by the ratio:
[100+crash increment for HOS option A]/[100+crash increment for HOS option B]
The base case for this analysis is the fatigue-related crash risk for LH truck operations under the 2003 HOS regulations, estimated at 7% of all truck involved crashes. The fatigue-related crash risk percentage for each of the HOS scenarios analyzed in this analysis is then as shown below:
7.0 x [100+crash increment for revised HOS option]
[100+crash risk increment for 2003 HOS option]
The calculation is repeated for each HOS option analyzed.
Foot Notes


text:
A collection of news and information related to Turkey published by this site and its partners.
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Displaying items 34-44
Ukraine seeks new markets as its steel rebar prices plummet
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Turkey's Gul says Facebook, YouTube ban out of question
Story | Mar 7, 2014 | 6:49 AMISTANBUL (Reuters) - Turkey's president on Friday ruled out a ban on Facebook and YouTube after Prime Minister Tayyip Erdogan threatened such a move to stop political foes posting audio recordings purportedly exposing government corruption. "The... -
Turkey local election holds key to Kurdish peace talks
Story | Mar 7, 2014 | 4:06 AMBy Daren Butler DIYARBAKIR, Turkey, March 7 (Reuters) - The face of jailed Kurdish rebel chief Abdullah Ocalan briefly beamed down from billboards in southeast Turkey until police tore down the posters, a mark of official unease over his enduring... -
US STOCKS-Futures edge up ahead of jobs data, Ukraine eyed
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EMERGING MARKETS-India leads emerging stocks higher; Ukraine fears ease
Story | Mar 7, 2014 | 2:44 AMBy Sujata Rao LONDON, March 7 (Reuters) - Indian shares rallied to record highs on Friday on hopes of a market-friendly outcome to upcoming elections, leading broader emerging equities to six-week highs ahead of key U.S. jobs data. Investors are... -
Erdogan says Turkey could ban Facebook and YouTube
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subset: commoncrawl


text:
Credits & Info
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Author Comments
Feel the Beat (F2Jam v4): http://www.newgrounds.com /portal/view/451339
Rated 4 / 5 stars April 11, 2008
ermm nice
ermm iam kinda new to the o2jam/o2mania stuff but it whasnt hard at all srry dude but it whas funn to play good job
Rated 3.5 / 5 stars February 29, 2008
Good but not great
I believe Markypoops had a very good argument. He wasn't flaming or bashing the author or game, just giving his opinion. I like how people give negative votes on opinions..Nice.
Anyway, this game wasn't the greatest. Honestly, if you took your skill in flash and AS and put it together with a really good flash artist, you'd have a great game. Also, better music and possibly someone who knows how to make a decent chart. Not to say that the charts were too easy..Wait no, that's exactly what I'm saying. Team up with someone who can make charts and someone who is really good at flash art and a great game will be created. :)
Well, this game was from around two years ago... you should check out what i've been working on...
and yeah, I made this game in 6 hrs.
And I am teaming up with people on my latest f2jam game :D.
Rated 5 / 5 stars February 28, 2008
whoooo hoooooo!!!!!
i love this game it reminds me of o2jam!!!!!
Rated 5 / 5 stars February 22, 2008
Sensational =D
This game is probably the best rythm game on NG, and maybe even the best game on NG ALLTOGETHER :)
you did a fantastic job there dude :)


text:
Dirty Dancing Boys.
These 2 boys & their "Dirty Dancing" are my "Question Authority" heroes.
From the Department of don't lose your power (or humor) because you're questioned by power:
Dirty Dancing Bonus:
If you liked this, you might like these:
Cinema's 10 Sexiest Dances.
Jon Stewart: A moving interview with Tony Bennett re: Nat King Cole, Sinatra, Ella Fitzgerald, Louis Armstrong…& Amy Winehouse.
Britney Spears predicts date of End of the World; dances.
ExxonMobil cares about (making big money at the expense of) you.
"Who knew pole dancing could be like this?" {reddit}
One Response to "Dirty Dancing Boys."
No one puts Logan and Brandon in the corner! teehee


text:
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Very Rare 1965 " Buick Sport Wagon", cousin to the Olds Vista cruiser. Check out the cool glass roof! Buick V-8 auto. pwr. steering, factory... More
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Good top, nice leather seats, cold a/c, pw and seats, newer tires, alum whls, 113,xxx mi, V6, at, runs and drives great.... More
V-8 Automatic Power Steering Power Convertible Top All new brakes New motor and transmission seals Excellent driver, very smooth ride... More
1969 BUICK GRAN SPORT GS400 CONVERTIBLEExotic Classics is proud to present this impeccable 1969 Buick Gran Sport GS400 convertible. Having spent its life in... More
Custom, nice paint, new int, top, new tires, V8, fact a/c, ps, runs,drives, and looks great... More
1972 Buick "Gran Sport" Stage I "Lemans Blue" Arrival Blue GSX Stripes, Stunning White Bucket Seats w/Console & White Vinyl Top, Factory Rear Wing, "Ram Air"... More
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text:
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.
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OCR for page 46
The published report on the 1/1,000-sample survey of
fertility in 1982 includes data on age-specific fertility
rates by single years of age from 1950 through 1981. The
published tables also include single-year rates for the
rural and urban populations for these years and a
separately tabulated set of rates for the total population
extending back to 1940. The detailed fertility histories
were obtained by interviewing women aged 15-67. Because
of the upper age limit, the age-specific fertility rates
presented for women aged 49 are derived from actual
responses of the women interviewed only for years after
1964, rates for women aged 45 are derived from responses
only after 1959, etc. The upper age limit of the women
interviewed also means that all rates above age 25 in
1940 were estimated by methods not explicitly described.
As noted above, comparison of the annual number of
births thus calculated with the annual number from
official sources indicates that the official number of
births is understated. The comparison also shows sys-
tematically lower ratios of official numbers of births to
calculated numbers of births for years that have 13 lunar
months. It is thus clear that the number of births
reported in the fertility survey in those years was too
high. Table 5 shows birth rates and total fertility
rates (TFRs) based on the estimated annual number of
births calculated from the fertility rates reported from
the survey. Also shown in Table 5 are birth rates and
TFRS adjusted for overstatement of births in years with
13 lunar months and for understatement in other years.
If all births were reported by the lunar calendar,
13-month years would have 1.05 times the number of births
OCR for page 46
TABLE 5 Birth Rate and Total Fertility Rate Derived
from Fertility Survey: China
~ .
Ad justeda
Birth Rate Birth Rate
Year (per 1,000) TFR (per 1,000) TFR
. . .
1950 46 e 5 5 ~ 81 41e 3 5 e 93
1951 39 ~ 8 5 ~ 70 40 ~ 6 5 ~ 82
1952 45 ~ 1 6 ~ 47 43 ~ 7 6 ~ 26
1953 42 ~ 2 6 ~ 05 43 ~ 1 6 ~ 17
1954 43.5 6~28 44~4 6~41
1955 42 ~ 7 6 ~ 26 41 ~ 3 6 ~ 06
1956 39 ~ 4 5 ~ 85 40 ~ 2 5 ~ 97
1957 42 ~ 5 6 ~ 41 41 ~ 1 6 ~ 21
1958 36 ~ 9 5 ~ 68 37 ~ 7 5 ~ 80
1959 27 ~ 7 4 ~ 30 28 ~ 3 4 ~ 39
1960 26~0 4~02 25~2 3~89
1961 21.9 3 ~ 29 22 ~ 3 3 ~ 36
1962 40~1 6~02 40~9 6~14
1963 48 ~ 9 7 ~ 50 47 ~ 3 7 ~ 26
1964 39~9 6~18 40~7 6~31
1965 38 ~ 9 6 ~ 08 39 ~ 7 6 ~ 20
1966 39~6 6~26 38~3 6~06
1967 33 ~ 4 5 ~ 31 34 ~ 1 5 ~ 42
1968 40~4 6~45 39~1 6~24
1969 35 ~ 8 5 ~ 72 36 ~ 5 5 ~ 84
1970 36 ~ 5 5 ~ 81 37 ~ 2 5~93
1971 34 ~ 6 5 ~ 44 33 ~ 5 5 ~ 27
1972 31~8 4~98 32~4 5~08
1973 29 ~ 5 4 ~ 54 30.1 4 ~ 63
1974 28~0 4~17 27~1 4~04
1975 24 ~ 8 3 ~ 57 25 ~ 3 3 ~ 64
1976 23 ~ 2 3 ~ 24 22 ~ 5 3 ~ 14
1977 21e 1 2 ~ 84 21e 5 2 ~ 90
1978 20~8 2~72 21~2 2~78
1979 21.6 2 ~ 75 20 ~ 9 2 ~ 66
1980 18~1 2~24 18~5 2~29
1981 21~2 2~63 (21~2) (2~63)
1982 21~3 2~66 (21~3) (2~66)
aAdjusted to correct for effect of lunar calendar
and for understatement in other years.
OCR for page 46
in a solar calendar year, and 12-month years would have
.97 times the number in a solar year. The adjusted birth
rates and TFRs are calculated on the assumption that
two-thirds of the reported births are based on lunar
years: leap-year (13-months) rates were divided by 1.033
and non-leap-year (12-months) rates by 0.98.
The unadjusted total fertility rates in Table 5 are
taken directly from the report on the fertility survey,
which also contains partially estimated TFRS for 1940-49,
with an average value of 5.44 for that decade. This rate
differs very little from the total fertility rate of 5.50
estimated by Barclay and others (1976) for the Chinese
farm population in 1929-31. The total fertility rate
from the survey rises to about 6.0 through the 1950s
until 1958, when a dramatic decline begins. An increase
in fertility from 5.5 to 6.0 as part of the first impact
of modernizing forces is not unusual. It frequently
occurs because of the diminished effect of various
customs and practices that restrict fertility below its
potential maximum in almost every less-developed
country. These customs and practices, which do not vary
according to the number of children already born and
hence are not intended to impose a direct limit on the
size of the family, include prolonged breastfeeding,
periodic separation of spouses because of seasonal
migration, prohibition of intercourse during lactation,
The most striking features of the sequence of TFRS are
the dramatic reduction from 1956 to 1961 (the TFR in 1961
is only a little more than half of the TFR in 1957); the
recovery in 1962 to a TFR similar to the 1950s; and the
unique, very high TFR of 7.5 in 1963. The TFR in the
mid-to-late 1960s was comparable to the mid-1950s except
for a temporary drop in 1967, coinciding with the Cultural
Revolution. A sustained decline began after 1970,
reaching a low in 1980 that was more than 60 percent
below the level in 1970; there was a modest recovery in
1981, but only to a fertility level still well below half
the level in 1970. These episodes of a large reduction,
an extraordinary recovery, and a subsequent major decline
are analyzed further below.
Changes in age at marriage have a different effect on
fertility in populations in which married couples
OCR for page 46
practice little contraception and therefore have similar
age-specific marital fertility rates whatever the age at
marriage and in populations in which married couples
effectively control fertility and attain fixed targets of
completed size of family. In noncontracepting popula-
tions, increases in age at marriage reduce the number of
children born to each cohort of women by reducing the
number of younger women exposed to the risk of child-
bearing. The reduced number of younger women who are
currently married causes lower fertility in each time
period as well as for each cohort. In populations
practicing effective contraception, changes in age at
marriage alter the timing of births for each cohort of
women without necessarily alterina the final average size
of family achieved.
_ ~ _ , _ _ ,
A postponement of childbearing
caused by later marriage produces a temporary reduction
in period fertility even if cohort total fertility is not
changed. When age at marriage stops increasing, this
temporary depression of fertility ends.
The effect of increasing age at marriage on fertility
even when each cohort achieves the same family size is
not generally noticed nor well understood. Imagine, for
example, that all women marry at the mean age of marriage
and bear only one child, one year after marriage. Suppose
that for a long period the age at marriage is 22 years
and that both the total first-marriage rate and the total
fertility rate are 1.0.
Then suppose that at a certain
moment the mean age at marriage rises to 23 years. In
the year following this shift, there would be no marriages
because the cohort that was 22 in the preceding year
would all have already married and the cohort becoming 22
in the given year would not marry until reaching 23 a
year later. In the ensuing year there would be no births
for a similar reason. So from a one-year increase in age
at marriage (and age at childbearing) there would be a
loss of a full year's quota of marriages and a full
year's quota of births even though the proportion ever
marrying and the completed family size for every cohort
remained fixed. In a more complex change of the sort
that actually occurs, a rise in the mean age at marriage
within a period of time means that one year's quota of
marriages is lost, not in a single year, but during a
scan of several vears.
A rise by one year in the mean
age of childbearing also means the loss, over a span of
years, of one year's quota of births, even though
completed size of family of cohorts does not change.
OCR for page 46
The effect of changes in age at marriage on changes in
the total fertility rate can be evaluated under two quite
different assumptions about the fertility of the married
women when age at marriage is changing. One assumption
is that the fertility of married women at each age remains
constant as age of entry into marriage changes. This
assumption is logically tenable if there is very little
contraceptive practice. The second assumption is that
the fertility of married women at each duration of mar-
riage remains fixed, with a duration-specific fertility
schedule that declines quite steeply because each
marriage cohort is curtailing its childbearing after the
early attainment of desired family size.
The change in fertility associated with changing nup-
tiality in China can be estimated under both of these
assumptions. First the effect of changes in age at
marriage on total fertility is determined when the
schedule of age-specific marital fertility is fixed. The
proportion of ever-married women at each age in selected
years is combined with the age-specific fertility of the
ever-married women in 1956. The age pattern of marital
fertility in the 1950s (see below) differs little from
the age pattern characteristic of populations that do not
deliberately control fertility (do not try to reduce
childbearing when desired family size has been reached)
by contraception or abortion. The first calculation
shows the reduction in the total fertility rate that
would have occurred if the virtual absence of contra-
ception and abortion of the 1950s had continued (and if
there had been no change in factors, such as duration and
intensity of breastfeeding, that affect marital fertility
in the absence of contraception). The data provided in
the report of the 1982 fertility survey are the basis for
this calculation. The proportion of ever-married women
at each age in each year since 1950 is estimated by the
cumulation of first-marriage rates for each cohort (see
Table A-4).8 Division of the tabulated overall age-
specific fertility rate at a given age by the proportion
ever married at that age yields an age-specific fertility
rate of ever-married women. The next step is to multiply
the proportion of ever-married women at each age in
different years by the age-specific ever-married fertility
rates of 1956. The results are shown in Table 6. The
total fertility rate would have fallen by a little more
than 20 percent from 1950 to 1980 and then risen by
several percent to 1982 if the ever-married fertility
rates of 1956 had been in effect. Most of the decline in
OCR for page 46
TABLE 6 Total Fertility Rate Calculated for Selected
Years, from Proportion of Women Ever Married and
Age-Specific Marital Fertility Rates, 1956: China
1950 1955 1960 1965 1970 1975 1980 1982
Calculated TRE 5.97 5.87 5.68 5.62 5.42 4.96 4.71 4.88
TFR of 1950=100 100 98.3 95.1 94.1 90.8 83.1 78.9 81.7
total fertility rate would have occurred after 1970, when
the greater part of the rise in age at marriage occurred.
The TFR in 1980 was 61 percent lower than the TFR in
1950; the decline that would have occurred from changes
in marriage alone, with constant marital fertility rates,
is thus about one-third of the actual decline.
The second calculation of the effects of changing
nuptiality is limited to the years after 1970, when the
change in nuptiality was greatest and when there was a
major increase in the deliberate restriction of marital
fertility by contraception and abortion. The rationale
of this calculation is different from the first.
Duration-specific marital fertility rates have been
calculated for 1970, 1977, and 1981 from data in the 1982
fertility survey that give the number of births to women
at marriage durations of 0-1, 1-2, .
., 19-20, and 20+
years in those 3 years and from estimates of the number
of ever-married women classified by duration of marriage
(Song et al., 1983). The number of women by age in each
year~have been determined by intracohort interpolation:
multiplication by the proportion ever married provides
the number of ever-married women by age, and the
distribution of the ever-married at each age by duration
can then be ascertained from the sequence of first-
marriage rates in the cohort for earlier years and
younger ages. (The estimated number of ever-married
women by duration of marriage from 1970 to 1982 is shown
in Table A-5.) The ratio of births by duration since
first marriage to number of ever-married women by
duration provides a set of duration-specific ever-married
fertility rates for 1970, 1977, and 1981 (see Table
A-6). The next step is to assume that the married women
in each year from 1970 on were subject to 1981 duration-
OCR for page 46
specific fertility rates. This assumption is based on
the hypothesis that women during the 1970s were as
effective in limiting marital fertility at every stage of
marriage as were women at the corresponding stage of
marriage in 1981. Such a hypothesis is acceptable (even
as an hypothesis) only when it incorporates a schedule of
duration-specific fertility that falls off rapidly after
relatively high values at early durations, because only
then would it be possible for duration-specific fertility
to remain unchanged when mean age at marriage increases.
When fertility is little controlled (as in 1956) women
marrying at age 25 might have about the same fertility at
each duration during the first 10 years of marriage as
women marrying at 21, but not in the 20th year, when
reduced capacity for reproduction would cause lower
fertility among those who had married at 25.
In short, the calculation of the births that would
have occurred from 1970 to 1981 with the duration-
specific fertility rates of 1981 illustrates the change
in total fertility rate that would have resulted from
changes in age at marriage, even if each cohort had
maintained lifetime fertility approximately constant but
had shifted the time of childbearing by marrying later.
Table 7 shows the results. There were almost 30 million
births in 1970; with 1981 duration-specific fertility
there would have been only about 16 million. The TFR in
1970 would have been only 3.09 with 1981 duration-
specific rates; it was 5.81. Had the rates remained
unchanged at the low level, the TFR would have declined
from 3.09 in 1970 to a low of 2.41 in 1979 and then risen
to 2.85 in 1981. With no change in duration-specific
fertility rates (and therefore approximately constant
cohort total fertility), there would have been a decline
to only 78 percent of the 1970 TFR in 1979 and then a
recovery to 92 percent in 1982.
The two calculations--with constant age-specific
fertility rates (as of 1956) and constant duration-
specific rates (as of 1981)--illustrate different sorts
of influence that changes in age at marriage can have on
overall fertility. The first calculation shows that had
there been no increased use of contraception (and no
change in "natural" fertility from reduced breastfeeding),
the TFR would have fallen by about 20 percent from 1950
to 1980 because rising age at marriage would have reduced
the average exposure to married life and the attendant
risk of childbearing. The second calculation shows that
even with fixed, highly controlled marital fertility in
OCR for page 46
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OCR for page 46
which childbearing beyond the early durations of marriage
is severely limited, the TFR would have declined by about
20 percent from 1970 to 1979 because rising age at mar-
riage during these years would have reduced the number of
married women at early durations. Recall that the total
first-marriage rate was low during most of the 1970s (as
low as 0.64) despite the continuation of universal entry
into marriage cohort by cohort. The increase in mean age
at marriage that was the principal source of low total
marriage rates also produced a reduced number of women in
the early durations of marriage. The cessation and
slight reversal of the increase in age at marriage, plus
the marriage boom associated with the new marriage law of
1980, produced a phenomenal increase in the total
marriage rate and a commensurate increase in the number
of marriages of short duration. A consequence is that a
rise in overall fertility by 18 percent (from a TFR of
2.41 in 1979 to one of 2.85 in 1982) would have occurred
with constant fertility by duration of marriage.
The report of the 1982 fertility survey does not
provide a time series of age-specific rates of fertility
by order of birth, but it does show such rates for 1980
and 1981. The total first-birth rate (the sum over all
childbearing ages of age-specific rates of bearing a
first child) rose from 0.869 in 1980 to 1.162 in 1981--
nearly three-fourths of the increase in the total fer-
tility rate. (Of the increase in the TFR, 90 percent is
in the total first-birth rate plus the total second-birth
rate, both increases largely the result of compression in
1981 of first and second births by women at different
ages into the same time period because of the marriage
boom and the reduction in age at marriage.) Some increase
in fertility would occur with constant duration-specific
fertility even if age at marriage merely stopped rising.
If the continued increase in age at marriage had ceased
and not reversed, the total first- marriage rate would
have returned to about 1.0; the persistent shortage in
the annual number of first marriages caused by rising
mean age at marriage would have ended.
One of the benefits of the detailed information that
appears in the report of the 1982 fertility survey is the
feasibility of calculating age-specific marital fertility
schedules. To do so, the proportion of ever-married
OCR for page 46
women by age is determined from the data on first
marriages; a fertility schedule of the ever-married women
is then obtained by dividing the overall fertility of
women at each age by the proportion ever married.
Finally, the marital fertility schedule is derived by a
further division of the ever-married rate at each age by
the estimated proportion of currently married to ever-
married women. This last proportion can be estimated as
having approximately the same values in different calendar
years because of the surprisingly little difference
between it in i982 and in the Chinese farm population in
1930. The proprortion of currently married to ever-
married women at the two dates are as follows:
Year 20-24 25-29 30-34 35-39 40-44
1982 .986 .977 .960 .933 .888
1929-31 .981 .968 .953 .916 .860
The very slight difference in these ratios despite the
very substantial difference in mortality implies that the
higher incidence of widowhood at the earlier date must
have been offset by a high rate of remarriage of widows.
Approximate age-specific marital fertility schedules by
single years of age from 20 through 39 have been
calculated for selected years. In every year the ratio
of currently married to ever-married women was assumed
equal to the average of the ratios for 1929-31 and 1982.
In Figure 13, marital fertility rates are shown
relative to a schedule of "naturals fertility, with the
ratio to natural fertility at ages 20-24 set equal to
1.00. The comparison with natural fertility provides
indirect evidence of the extent to which marital
fertility is affected by deliberate control through the
use of contraception and abortion. Louis Henry (1961)
was the first to note that the age pattern of marital
fertility was similar in different populations in which
couples do not practice contraception or take other
measures to reduce fertility after a certain family size
is reached; he called such fertility "natural." In
OCR for page 46
1 .2
[~< =~ ~~ ~ 6
__ 1 970
1 980
1 1 1
20 25 30 35 40
FIGURE 13 Ratio of Age-Specific Marital Fertility to
Natural Fertility (with ratio at 20-24 set at 1.00),
1956, 1961, 1970, 1975, and 1980: China
Figure 13, the calculated marital fertility rates are
divided by a set of natural fertility rates, with the two
schedules--a schedule for China and an average of natural
fertility schedules selected for good quality of data
from those cited by Henry (Coale and Trussell, 1974)--
brought to the same average at ages 20-24. In 1956 the
ratio of marital fertility to natural fertility remains
above 0.9 except for two points that are probably the
result of sampling variation or slightly inexact
reporting by respondents who were over age 60 in 1982.
In 1961 the TFR was only 56 percent of the TFR in 1956;
the reduction in marital fertility was large at younger
ages (a 34 percent reduction at 20-24) as well as at
older ages (a 49 percent reduction at 35-39). To achieve
the modestly increased departure as age increases from
the low level of natural fertility schedule in 1961 would
entail only a modest use of deliberate limitation by
older women.
Low fertility among young as well as older married
women in 1961 is consistent with restriction of child-
bearing caused not only by contraception but by social
disruption and the famine conditions in 1960. As noted
below, a very high peak in the death rate occurred
OCR for page 46
in 1960, the result of a famine associated with crop
failures induced by a combination of chaotic economic
conditions, drought, and floods. Famine is known to
prevent ovulation and to reduce male libido. Disruption
of normal family life while the population was mobilized
for various projects and engaged in searches for food,
like the direct effects of famine on reproductive ca-
pacity, would cause fertility to fall at all ages. The
death-rate peak in 1960 implies that 1960 was the worst
of the crisis; reduced conceptions in 1960 would produce
a very low TFR in 1961. The slightly steeper reduction
in marital fertility relative to natural fertility as age
increases in 1961 compared with 1956 may have been caused
by the greater susceptibility of older women to fertility
impairment during a famine. A further indication of a
quasi-biological basis for the very low marital fertility
in 1961 is the height of the peak in fertility reached in
1963. The TFR of 7.50 in 1963 is well above the highest
TFR during the 1950s (6.41 in 1957). The age structure
of marital fertility in 1963 is very similar to 1956, but
at a much higher level. The 1963/1956 ratio of marital
fertility rates by five-year intervals for women aged
20-24 to 35-39 varies only from 1.33 to 1.38. The
1963/1961 ratio of marital fertility rates increases from
2.06 at 20-24 to 2.70 at 35-39, possibly because of the
cessation of whatever slight degree of contraceptive
practice there may have been in 1960-61 or possibly
because the catastrophic situation in 1960-61 impaired
the fertility of older women more than the fertility of
younger women. Marital fertility that is at least
one-third higher in 1963 than in l9S6 at every five-year
age interval from 20 to 40 implies a much higher than
normal susceptibility to pregnancy. The source of such
above-normal susceptibility was doubtless an abnormally
low proportion of women who had recently given birth (who
experience many months without ovulation if they breast-
feed) and also an abnormally high number of newlyweds
(the total first-marriage rate in 1962 was 1.19).
In sum, the very low fertility of 1961 was probably
caused by the disruption of normal married life and by
famine-induced subfecundity; the unequaled high fertility
of 1963 resulted from the restoration of normal marital
life, from an abnormally large number of marriages, and
from the unusually small fraction of married women who
were infertile because of nursing a recently born
infant. The age structure of marital fertility was
essentially that of natural fertility, unaffected by
OCR for page 46
deliberate restriction, in 1956 and again in 1963. At
the low point in 1961, marital fertility fell with age
slightly more steeply than does natural fertility. This
pattern may have resulted either from some practice of
folk methods of contraception or abortion or from a
greater biological effect of the crisis on older women.
In 1970 the decline of marital fertility with age
relative to natural fertility closely paralleled the
corresponding curve for 1961 (see Figure 13 above); but
in 1970 this decline was almost certainly the result of
an increase in deliberate control with age and not to the
biological factors that may have affected older women
disproportionately in 1960-61. In 1975 the steep fall of
fertility relative to natural fertility clearly shows the
much greater effect of fertility restriction among older
women; by 1980 the decline in marital fertility with age
is comparable to the structure of marital fertility in
highly developed societies with total fertility rates
below replacement levels.
Urban/Rural Differences
The 1982 sample fertility survey provides annual data on
fertility and nuptiality in the detail already described
for the rural and urban populations as well as for the
total. The TFRs for the rural and urban populations are
given in Table 8 and shown graphically in Figure 14. In
the early 1950s average overall urban fertility increased
relative to rural fertility from about 80 to about 90
percent, and it remained at a constant ratio of about 90
percent until 1959 and 1960; evidently, rural fertility
was more affected by the Great Leap Forward than was
urban fertility. From 1960 to 1966 the ratio of rural to
urban TFR fell to about 50 percent, and since then the
ratio has been nearly constant. In absolute terms, rural
fertility in 1964 returned (after the crisis deficit and
the postcrisis peak) to about the level of the 1950s,
while urban fertility barely surpassed its 1957 level
even in 1963, fell steeply for a few years after 1963,
recovered somewhat in 1968, and then fell at the same
relative rate as did rural fertility. The decline of
urban fertility from 90 percent of rural fertility in the
late 1950s to about 50 percent in the late 1960s (while
rural fertility remained little changed) appears to
OCR for page 46
TABLE 8 Total Fertility Rates, Rural and
Urban Populations, 1950-81: China
Total Fertility Rate
Year Rural Urban Urban/Rural
1950 5.963 5.001 .839
1951 5.904 4.719 .799
1952 6.667 5.521 .828
1953 6.183 5.402 .874
1954 6.390 5.732 .897
1955 6.391 5.665 .886
1956 5.974 5.333 .893
1957 6.504 5.943 .914
1958 5.775 5.253 .910
1959 4.323 4.172 .965
1960 3.996 4.057 1.015
1961 3.349 2.982 .890
1962 6.303 4.789 .760
1963 7.784 6.207 .797
1964 6.567 4.395 .669
1965 6.597 3.749 .568
1966 6.958 3.104 .446
1967 5.847 2.905 .497
1968 7.025 3.872 .551
1969 6.263 3.299 .527
1970 6.379 3.267 .512
1971 6.011 2.882 .479
1972 5.503 2.637 .479
1973 5.008 2.387 .477
1974 4.642 1.982 .427
1975 3.951 1.782 .451
1976 3.582 1.608 .449
1977 3.116 1.574 .505
1978 2.968 1.551 .523
1979 3.045 1.373 .451
1980 2.480 1.147 .463
1981 2.910 1.390 .478
OCR for page 46
l _
. � ~
~ i,;:
1950 1955 1960 1965 1970 1975 1980 1985
~ �.
� . Rural
FIGURE 14 Total Fertility Rates, Rural and Urban
Populations, 1950-81: China
conform to the classic picture often ascribed to (but not
always followed by) European fertility during the
so-called demographic transition: the urban population
has a higher age at marriage; it starts deliberate family
limitation before the rural population does; and after
the transition starts in the rural population, the urban
population continues to have lower rates of childbearing.
The early timing of the urban decline has a simpler
explanation, however: the antinatalist program was
initiated earlier and pursued more vigorously in the
Age-specific fertility rates of the rural and urban
populations in less, 1968, and 1980 are compared in
Figure 15. In 195S the difference between rural and
urban TFRs is composed almost equally of lower fertility
below age 25 in the cities (caused by later marriage) and
lower fertility above age 31 in the cities (caused by a
modest prevalence of contraception and abortion or
possibly by a higher proportion of widows). In 1968, the
rural fertility schedule looks much like the schedule in
1955 though slightly higher, except at the younger ages,
where slightly later marriage reduced fertility. The
early part of the urban schedule in 1968 shows the strong
effect of later marriage and the later part the strong
OCR for page 46
0.5 _
0.4 _
0.3 _ ..
E .j~ I
0.2 _ ./ ~ :
:/ w
in O ~ ~ ~ ~ ~ ~ IN .. ~
6 _
~ 0.5
~ 0.5
~ 0.3
. . ~
~ fN
� � .
/ ~ ..
/ v~ ..
1�;1~ . . . 1
40 45 50
_ ,
15 20 25 30 35
15 20 25 30 35 40 45 50
FIGURE 15 Age-Specific Fertility Rates, Rural (dotted
line) and Urban (solid line) Populations, 1955, 1968, and
1980: China
OCR for page 46
effect of deliberate birth control. By 1980 these
effects are evident in the rural fertility schedule but
are still stronger for the urban schedule.
Han and Minority Group Differences
Another major difference in fertility in China is
revealed by data on the childbearing rates of the rural
Han population (the dominant Chinese ethnic group) and
those of the ethnic minorities, with a total population
of some 60 million persons (6 percent of the population).
(Li et al., 1983). The minorities have been exempt from
most of the pressures of the official antinatalist
program, are generally more isolated and less educated,
have strong pronatalist traditions, and have much higher
fertility. In 1981 the TFR of the minority populations
was 5.05 compared with 2.76 for the rural Han. The
age-specific fertility schedule of the minorities shows
the effect of earlier marriage up to age 25 and less
effective control of fertility at higher ages (Figure 16).
Other Fertility Determinants
There are also differences in fertility for women with
different levels of education and with differences in
occupation. In 1982 the average number of children ever
born to women aged 35-49 was 4.74 for illiterate women,
3.81 for women with primary school education, 3.08 for
women finishing junior high school, 2.41 for women
finishing senior high school, and 1.94 for university-
educated women. (These figures, taken from the report of
the survey, are subject to the following bias: the women
at higher levels of education are more concentrated near
age 35 in the 35-49 age interval because of rapid change
in education in China; their average parity is lowered by
this compositional feature.) At age 50 farmers had an
average parity of 5.95, workers of 4.27, and cadres of
3.10 (Li and Zhang, 1983; Zhao and Sun, 1983).
In a chapter entitled "Birth Control of Women of
Reproductive Age" in the special issue of Population and
Economics (Qui et al., 1983) devoted to the large-scale
OCR for page 46
,,, 0. 1
,~,./- 1
15 20 25 30
~ . .
-. ~
35 40 45 50
FIGURE 16 Age-Specific Fertility Rates, Ethnic Minority
Women (solid line) and Rural Han Women (dotted line),
1981: China
fertility survey, it is reported that 69.5 percent of
married women aged 15-49 were practicing contraception in
1982. Of these, 25 percent used female sterilization, 10
percent male sterilization, 50 percent IUDs, 8 percent
the pill, and 6 percent condom and other (Qui et al.,
1983). The overall rate of use is not very different
from Taiwan (65 percent), Hong Kong (72 percent), or
Japan (69 percent), but the use of sterilization was more
frequent in China than in those neighboring populations
(Population Reference Bureau, 1983).


text:
Stocks fell, ending the longest winning streak for the Dow average in nearly 17 years. Trading Friday was tentative because investors fear that rising inflation will cause the Federal Reserve to back off from policies designed to boost markets. A rise in consumer prices in February also weighed on indexes. 'It's real and it's a drag, and I think people are growing concerned that it can get out of control quickly,' one fund manager said. Cruise ship operator Carnival fell as passengers have been booking vacations at a slower pace after a series of mishaps. And Krispy Kreme fell after the doughnut maker reported a drop in fourth-quarter net income.
US stocks close lower, ending Dow's 10-day rally
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This page contains Cheats for Shrek 2 organized by sections for Mobile. Shrek 2 is a Miscellaneous Board Games game, made by Skyworks Technologies, released on May 31, 2004 and published by Activision. If you can't find a hint or secret in our list, then please check this page periodically for the latest updates.
Shrek 2 Mobile Cheats
- Developer: Skyworks Technologies
- Publisher: Activision
- Genre: Miscellaneous Board Games
- Release Date: May 31, 2004
- ESRB: Everyone
Don't forget to Bookmark this page for the latest Shrek 2 Cheats. At this moment there are no cheats or walkthroughs available for Shrek 2. If you know cheats, secrets, hints or glitches for this game then please Submit them.
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