A PANEL DATA EXPLORATION OF TRAVEL TO WORK

Transcription

1 A PANEL DATA EXPLORATION OF TRAVEL TO WORK Joyce Dargay and Mark Hanly ESRC Transport Studies Unit Centre for Transport Studies University College London Gower Street London WC1E 6BT 1 INTRODUCTION The objective of this paper is to examine changes in the individual s commuting behaviour over time and to investigate the factors responsible for these changes. Since we are specifically concerned with changes in the behaviour of the individual as their economic and demographic characteristics change rather than in differences between individuals with different characteristics, we require observations of the commuting behaviour of the same individuals over time. Unfortunately, panel surveys are relatively rare, and the majority of those that do exist contain no information about transport or cover limited regions and/or relatively short time periods. An exception is the British Household Panel Survey (BHPS), which has been carried out annually since 1991 with a relatively large sample designed to be representative of all households in Britain. Although not a travel survey per se, along with a large number of socio-economic and demographic variables, the BHPS also provides some information related to transport: car ownership, commuting mode and commuting time. In previous studies, we have mainly exploited the BHPS to study car ownership (Dargay and Hanly 2000, 2001 and Dargay, Hanly, Madre, Hivert and Chlond This study concentrates on an analysis of the information relating to travel to work. It is predominantly concerned with changes in commuting mode and travel time for individuals over time, i.e. the volatility of individual commuting behaviour. It is an extension of a preliminary paper which gave a descriptive account of changes in commuting mode and travel time on the individual level and the impact of gender, the number of employed in the household and changes residential and workplace location (Dargay & Hanly 2003). The primary objective of the current paper is to estimate these relationships along with the impact of a large number of other socio-economic and demographic factors on the basis of a statistical model, thereby controlling for the interaction between different factors. Although there is a wide literature on commuting, there are few studies that examine changes in commuting patterns on an individual level on the basis of panel data and statistical models. Some of the earlier work on the volatility of commuting time and mode over time is summarised by Cairns, Hass-Klau and Goodwin (1998). Of relevance to the present study, they quote a Department of Transport study from 1978 which indicates that over 20% of employees changed their main mode of journey to work from Spring to Autumn, including 10% of car drivers and nearly 40% of car passengers. A recent study by Clark etal (2003) looks the at relationship between changes in residential and workplace location and commuting distance and

2 travel time using data from the Puget Sound Transportation Panel over the years 1989 to Using a descriptive analysis and the estimation of a probability model, they show they show that the likelihood of reducing commuting distance and time increases with greater home-workplace separation. Wachs etal (1993) studied 30,000 employees at five sites of a large employer over 6 years. They found that work trip lengths had not grown in that period. Most of the workers had relatively short commute distances, but those that changed house tended to have longer commutes subsequently. Rouwendal and Rietveld (1994) look at changes in commuting patterns using the Dutch Housing Demand Survey for 1985 and The focus of the survey is lowincome households and is thus not representative of the population as a whole. They find that for one-person households a change of residence is accompanied by increased commuting distance while for heads of households with a partner commuting distances become shorter and particularly when the partner is also employed. A change of employer resulted in longer commuting distances for all household types except for one-person households whose commuting distance shortened on average. The small sub-sample of households which changed both employer and residence experienced longer commuting distances on average, particularly one-person households and dual-earner households. A number of other Dutch papers (van Ommeren etal 1997, van Ommeren 1998 and 1999, and Rouwendal 1999) develop sophisticated theoretical frameworks to examine job search behaviour and job location. They do not, however, specifically look at the behaviour of commuting time and distance over time. Battu and Sloane (2002) and Benito and Oswald (1999) use the BHPS data for the years 1991 and 1995 and for the years 1991 to 1997, respectively, to investigate commuting behaviour and model the determinants of travelling time. However, neither study exploits the panel nature of the data, so differences in commuting time among individuals are examined rather than changes in commuting time for specific individuals. Most other studies of commuting in the UK (see Giuliano and Narayan 2003 for a recent example) are based on cross section data. The following section describes the BHPS survey and compares the data relating to travel with the repeated cross-section data contained in the National Travel Survey and the Labour Force Survey. Section 3 examines changes in commuting mode and travel time on an individual level, thus exploring the volatility of commuting behaviour. Year-to-year changes over the 10-year period, as well as longer-term changes for the individuals remaining in the survey for the entire 10 years are examined. The remainder of the study discusses the factors influencing mode switching and increasing and decreasing travel time on the basis the results of the statistical modelling. 2 THE BHPS DATA ON COMMUTING MODE AND TRAVEL TIME

3 The data employed in this study are from the British Household Panel Survey (BHPS). The BHPS was established by the ESRC Research Centre on Micro-Social Change at the University of Essex in 1991 and was primarily designed to further understanding of social and economic change at the individual and household level in Great Britain. Because the BHPS is designed to be statistically representative of all households in Britain, it is a stratified sample rather than a random sample. This study is based on ten years or waves, from 1991 to The initial sample contained over 5000 households, and it has not been refreshed over the years. The BHPS was augmented by the addition of the UK European Community Household Panel (UKECHP) households in 1997 and increased Scottish and Welsh samples in These are excluded in the present study as the resulting data is less representative of the population of Great Britain than is the original sample. Although it is not intended as a transport survey, the BHPS contains some information relating to transport: car ownership, access to company cars, main mode for travel to work and commuting travel time. It addition it contains information on a large number of socio-economic and demographic characteristics. It is the only panel data source that covers a large representative sample of the British population over an extended period of time, and is by far a longer panel than those available for most other countries. As with all panel surveys, however, there is a problem of attrition. This occurs because of non-response, unusable response or because the household or individual cannot be contacted. As far as panel surveys go, the attrition rate is lower than most. In the year 2000, 60% of the households initially interviewed in 1991 still remained in the survey, and 54.8% of the original households gave full interviews for all 10 years. As shown in Dargay and Hanly (2001), households who remain in the survey have on average a higher car ownership than those who drop out, so that the sample becomes less representative over time. A comparison of households with regular use of (access to) cars based on the National Travel Survey (NTS) and the BHPS is shown in Table 1. Even for the first year, the BHPS overestimates car ownership, presumably because of a higher response rate for higher car-owning (and presumably higher income) households. The overestimation becomes greater over time as non-car households drop out to a greater degree than car-owning households. By the year 2000, only 21% of BHPS households do not have cars, while for the country as a whole, 27% do not have cars. Table 1 Percent of households with regular use of cars, 1991 and NTS and BHPS NTS BHPS NTS BHPS 0 cars car cars cars The analysis of travel to work is based on the individual, rather than on the household, and only on those individuals who travel to work. This, too, changes over

4 the observation period, because of attrition, non-reporting or because an individual starts or stops working. In 1992, there are 5439 individuals included and in 2000 there are 5026 individuals. All of these individuals are members of the households originally interviewed in A much smaller sample, only 1711 individuals, has information for travel to work for all ten years. Some characteristics of these individuals and the households of which they are members are shown in Table 2. Clearly, car ownership in the households of individuals who work is significantly higher than for all households, as is the proportion of multi-car households (compare Table 1). Individuals remaining in the sample in all 10 waves age over the observation period and have higher than average car ownership and personal and household incomes. Table 2 Sample characteristics. BHPS. all individuals in original sample individuals in all 10 waves number age % male household size household income individual income cars available Most of the analysis considers changes between two consecutive years. Over the ten-year period, we observe a maximum of 37,637 year-to-year changes, although the number is slightly smaller in the case of some variables because of missing values. The survey questions concerning commuting are: About how much time does it usually take for you to get to work each day, door to door? (one-way journey) What usually is your main means of travel to work? (of 9 mode categories) Table 3 shows the main means of commuting for individuals who travel to work for each of the waves. Although the sample includes only households in the original sample (1991), the number of individuals changes from year to year: it will decrease due to the attrition of households, non-response for the individual and when an individual no longer travels to work (for whatever reason) and the number will increase as individuals who did not previously travel to work begin commuting. Over the 10 years there has been a significant increase in the proportion travelling by car, from 56 in 1991 to over 61 in For the other modes, the trend is less clear taken over the period as a whole, with the shares fluctuating slightly for individual years.

5 However, both walk and bus have generally declined over the period, as has motorcycle. Table 3 Commuting trips: main mode only original households Rail Underground/tube Bus or coach Motor cycle/moped Car or van driver Car/van passenger Pedal cycle Walks all the way Other All modes Number of individuals Table 4 shows the mode shares only for individuals who remain in the panel and commute to work in all 10 waves. This results in a sample of 1711 individuals. The reduction in the sample from the 5439 individuals in 1991 is partially due to attrition but also due to the fact that many individuals do not travel to work for the full tenyear period even though they remain in the survey. For all years, the share that goes by car is higher than for the full sample, while a smaller share walk, go by bus or as a car passenger. This is apparently a result of attrition. As shown in the previous section, proportionally more households without cars dropped out of the survey, so that car owners will be over-represented, and the over-representation will increase as time goes on. As a result, individuals commuting by car will probably also be overrepresented, and this will also increase over time. However, there may be another reason. As our sample only contains individuals who travel to work for the 10 consecutive years, it will not be representative of the population as it excludes younger individuals who begin working during the 10-year period, older individuals who retire during the period and those who are not employed for other reasons for part of the period. Because of this, the individuals included in all 10 waves have higher income and car ownership, and thus use cars for commuting more than the average household. Regarding the other modes, there is little consistent difference between the sub sample and all individuals. For the sub sample, there is also a definite trend towards greater reliance on the car for travel to work, from 69% in 1991 to 77% in 2000 for drivers and passengers together, but the share appears to have levelled off in the most recent years. In contrast, bus use declined from 8% to 5% over the period. Walking declined from 12% to around 9% in the early 90s, and has remained more-or-less constant since. With the exception of the decline in motorcycle, the other modes show no consistent trends.

6 Table 4 Commuting trips: main mode, only individuals in all 10 waves Rail Underground/tube Bus or coach Motor cycle/moped Car or van driver Car/van passenger Pedal cycle Walks all the way Other All modes Table 5 shows the share of individuals commuting by the various modes for the full BHPS sample (Table 3) compared with the same information from the NTS, which should be more representative of the population. We compare the BHPS year 1991 with the NTS for 1988 to 1991 (the NTS is reported in 3-year groupings). Even accounting for the difference in time period, there appears to be some discrepancy between the two sources. Particularly, the BHPS shows a lower share for car passenger, and a higher share for car driver and walk. Comparing the BHPS years 1996 with the NTS for , the two sources are much closer. The most substantial difference is the higher share of walking in the BHPS. That the mode shares from in the BHPS agree better with the NTS over time is rather surprising. The original sample of the BHPS in 1991 was designed to be nationally representative of GB, and we would expect the sample to become less representative over time through attrition. As noted earlier, this seemed to be the case when car ownership was compared on the basis of the two surveys. The differences between the BHPS and the NTS require further examination. Table 5 Commuting trips by main mode in %. NTS and BHPS. NTS BHPS 1991 BHPS 1996 NTS BHPS 2000 Rail 4.8* Underground/tube Bus or coach 8.8** Motor cycle/moped Car or van driver Car/van passenger Pedal cycle Walks all the way Other All modes Number of individuals *includes underground, ** only local bus

7 The differences in commuting patterns between men and women as noted in the NTS are confirmed by the BHPS. Men are more likely to be car drivers than women are, but the difference has declined considerably over the past decade. This reflects the increased licence holding by women: in 1990 only less than half the women of driving age held licences; by 2000 the share had risen to 60% (for men the share increased from 80 to 82%). The increased use of the car for commuting by women is matched by a reduction in bus use, car passenger and walking. Because of this, commuting mode shares for men and women are becoming more similar over time. The mean time taken to travel to work by all working individuals in the BHPS, and those that are in all 10 waves are is given in Table 6. It is apparent that this has been pretty stable over the 10 years, ranging from 22.4 to 24 minutes for all individuals in the sample. The travel times based on the BHPS are slightly lower than the 24 minutes given in the NTS for all years during this period. The numbers agree with those obtained on the basis of the Labour Force Survey (LFS): for the year 2000, both the LFS and the BHPS have an average commuting time of 24 minutes, with 26 minutes for men and 22 minutes for women. In addition, both the LFS and the BHPS indicate a slight increase in travel time over the period. In all years, individuals appearing in all 10 waves have a shorter travel time about 1 minute less than all commuting individuals, presumably as a result of a higher use of cars for commuting, which have a lower than average travel time. Table 6 Commuting trips: travel time in minutes by year. BHPS. All commuting individuals Commuting Individuals in all 10 waves mean The distribution of travel times (Table 9) has also been relatively stable over the period. Over a third of individuals have a commuting time of 10 minutes or less, and about 2/3s under 20 minutes, while less than 20% have journey times greater than 30 minutes. Women are more likely to have a short travel time: in 2000, 31% of men and 39% of women had travel times of less than 10 minutes, while men are more likely to have a long travel time: 25% of men and 18% of women had a travel time of 30 minutes or more. The increase in travel time over the period noted previously in Table 6 is also apparent here: in comparison with 1991, in the year 2000, relatively fewer individuals have a travel time of less than 20 minutes and relatively more have a travel time in excess of 30 minutes.

8 Table 7 Distribution of travel time to work, %. BHPS. Year Total All consecutive years 10 minutes or less to 20 minutes to 30 minutes to 40 minutes to 50 minutes to 60 minutes over 60 minutes From these figures it would appear that both mode and travel time to work change relatively little from year to year. In the next section, we will see how the net changes between years conceal a substantial variation for individual commuters at different points in time. 3 THE VOLATILITY OF COMMUTING BEHAVIOUR OVER TIME Given that we have observations of the same individuals for more than one year, we can examine the changes in commuting mode and time for each individual. From the 1991 to 2000 surveys, we have over 4000 pairs of observations for the same individual for two consecutive years for each of 9 years. For the entire period, there are observations; some of these are repeated observations (in different years) for the same individuals. On the basis of these data, we find that 7.6% of individuals change main commuting mode between any two consecutive years. This percentage is relatively stable over the 10-year period. Although, as seen in the previous section, commuting mode shares vary little between two years, quite a considerable proportion of individuals change their main mode of travel. Table 8 looks at commuting mode in two consecutive years by three main modes: public transport (rail, underground and bus), car (passenger and driver) and walk or cycle. The figures refer to the percentage of all commuters falling in each group. The final column (row) shows the mode shares in year t (year t-1). Clearly, the shares for the three modes are very stable between any two consecutive years: car accounts for 69%, walk or cycle for 17% and PT for 13%. The main diagonal indicates the individuals who do not change mode between the two years: 60.8% of them are car users, 11.1% walk or cycle and 8.5% use public transport. Each year 8.5% of commuters switch from car to other modes while an equal proportion switch from other modes to car. Switching between other modes is marginal accounting for slightly over 1% commuters. Although car drivers are least likely to change mode, they make up such a large group of commuters that even small percent changes within the group have a large influence on overall commuting patterns. In total 17% of commuters either switch to or from the car each year, whereas only 2.7% switch

9 between the other two main mode groups. Although this does not take into account mode changes within each group, we have seen above that such mode changes are marginal. Table 8 Commuting mode in two consecutive years (year t-1 and year t), % of commuters in each group. mode in year t -1 mode in year t public transport car walk or cycle all modes public transport car driver or passenger walk or cycle all modes Mode changes over a longer time period can be examined on the basis of the 1711 individuals who report travel to work in all 10 years. After one year, 17% of these individuals changed mode to work, which is slightly lower than the percentage obtained for all individuals (i.e. between 1991 and 1992). After 2 years, 25% changed mode at least once. The proportion increases over time, as expected, and after 9 years, just over half of all individuals changed commuting mode at least once. The proportion of commuters who switch mode differs for the individual modes. As is shown in Table 9, only about a third of car drivers in 1991 change mode over the 9- year period, while the proportion is between 70% and 87% for the other modes. Still, a third of car drivers is a very large number of commuters. Table 9 Percent of individuals who change main mode at least once over a 9-year period, by mode in 1991 (year 1). Individuals in all 10 waves. Mode Percent Mode Percent Rail 70.0 Car or van driver 31.6 Underground/tube 81.8 Car/van passenger 86.6 Bus or coach 82.2 Pedal cycle 75.4 Motor cycle/moped 86.7 Walks all way 80.6 We next look at changes in commuting time by considering the distribution of the differences from one year to the next and over a longer period. From Table 9, we see that slightly over 50% of individuals have the same commuting time (plus or minus 4 minutes) in any two consecutive years, between 11 and 12% either increase or decrease travel time by 5 to 9 minutes, while 7-8% and 5% of individuals experience larger changes of 10 to 19 minutes and 20 or more minutes respectively. After 5 years, the percentage of individuals with the same travel time decreases to 38.5%, and after 9 years to about 1/3. Clearly, there s a considerable variability in individual travel time over the 10-year period, with over 2/3 of individuals increasing or decreasing travel time by 5 minutes or more and 8.9% decreasing and 12.7% increasing travel time by 20 minutes or more. The changes after 9 years are far less symmetric: nearly 38% increase travel time while only 30% decrease travel time.

10 Table 10 Difference in travel time after 1, 5 and 9 years, % of individuals that increase or reduce travel time to work individuals in all 10 waves. Difference in travel time minutes after 1 year after 5 years after 9 years decrease 20 or more decrease 10 to decrease 5 to same ± increase 5 to increase 10 to increase 20 or more It would be expected that changes in travel time are related to changes in mode. Only 31% of individuals who change mode between two years maintain the same travel time, compared to 55% of those who do not change mode. Of those who change mode, a slightly larger proportion of individuals reduce rather than increase travel time, while the opposite is the case for those who do not change mode. As is shown in Table 11, changes in travel time vary considerably by mode travelled. For those who do not change mode between the two years, the greatest proportion of those who walk maintain a similar travel time (73.3%), while the smallest proportion of bus users have the same travel time for two consecutive years (42.8%). More users of all modes except motorcycle increase travel time than reduce it. Of those who change mode far fewer have the same travel time, from only 17.7% of those who initially used the bus to 40.6% of motorcyclists. A much greater proportion of those who switched from public transport experience a reduction in travel time than an increase, while the opposite is the case for all other modes. Table 11 Difference in travel time between two consecutive years by mode in the initial year for those who change and do not change mode, % of individuals who increase or reduce travel time to work. Individuals who do not change mode individuals who change mode decrease same increase decrease same increase Mode in year t-1 5 minutes or ± 4 minutes 5 minutes or 5 minutes ± 4 5 minutes more more or more minutes or more Rail Underground/tube Bus or coach Motor cycle/moped Car or van driver Car/van passenger Pedal cycle Walks all the way All modes

11 As would be expected, individuals with longer commutes are more likely to reduce travel time while those with short commutes are more likely to increase it. This is clearly illustrated in Table 12. The proportion that reduces travel time between two consecutive years increases with commute time in the initial year, while the proportion that increases travel time declines with commute time in the initial year. For commute times of between 11 and 20 minutes, individuals are equally likely to reduce travel time as they are to increase it. The mean change in travel time is positive for commutes up to about 20 minutes after which it becomes negative. The time saving then increases with commute time. It also appears that individuals with longer commutes are more likely to change mode than are those with short commutes, but those with commutes of between 31 and 40 minutes are least likely to change mode. Table 12 Change in travel time and mode change between two consecutive years by travel time in the initial year, % of individuals who decrease or increase travel time, mean change in minutes and % of individuals who change mode. decrease 5 minutes or more change in travel time same increase ± 4 minutes 5 minutes or more mean minutes change mode travel time year t-1 10 minutes or less to 20 minutes to 30 minutes to 40 minutes to 50 minutes to 60 minutes over 60 minutes all FACTORS INFLUENCING CHANGES IN COMMUTING MODE AND TIME In this section, we investigate some of the factors which can be thought to influence changes in commuting mode and time. Three models are estimated: one for mode and two for travel time. The analysis is based on binary choice logit models. In all cases the dependent variables are binary and are derived from changes in the original variables for each individual between two consecutive years (years t and t- 1). For mode we have: MODECH = 1 if the individual changes commuting mode MODECH = 0 otherwise.

12 Changes in travel time are separated into increasing travel time and decreasing travel time. In both cases, a change in travel time is defined as a change of 5 minutes or more between years t and t-1. The dependent variables are: INCTIME = 1 if the individual increases commuting time by 5 minutes or more INCTIME = 0 if the individual maintains the same travel time (± 4 minutes) DECTIME = 1 if the individual decreases commuting time by 5 minutes or more DECTIME = 0 if the individual maintains the same travel time (± 4 minutes). The explanatory variables initially included in the models are listed in Table 13./ The labels in italics are for groups of similar variables and the reference group denotes the case omitted to prevent perfect colinearity of the alternatives. All variables except WAVE are binary variables, which are equal to zero when the condition does not hold. The distance between home and workplace and the availability, cost and convenience of transport alternatives will clearly be primary factors determining commuting mode and time. Many of these are likely to change when individuals move house or change jobs. We would thus expect individuals who make such changes in their home and/or workplace location to be more likely to change commuting mode and time than those individuals who remain in the same homes and job. In any two consecutive years, around 13% of the individuals in our sample move house and a similar proportion change employer. Of those who move house (change employer) about 22% changer employer (move house), so that about 3% both move house and change employer within the same year. Changes in travel time and mode can also be thought to be related to the initial distance between the home and workplace, the gender and age of the individual and the characteristics of the household, its employment and its residential location. Economic factors, such as the individual s and the household s income, may also be of relevance, as will car availability. As shown in the previous section, the mode used in the initial year also appears to be of importance. For the mode change equation, the mode in year t-1 is included, while for the travel time change equations we distinguish between those whose commuting modes remain the same between year t-1 and year t (designated by mode names ending with Z in the estimation result tables) and those who switch mode (designated by mode names ending in X). In addition to the levels of the variables in the initial year, changes in these variables may also be important in determining changes in commuting mode and time. Change variables considered are changes in the individual s income, changes in the number of cars available to the household and changes in the number employed in the household.

13 Table 13 Definitions of the explanatory variables used in the analysis. WAVE = wave (1 to 10) Binary variables = 0 otherwise Moving house & job L1OMOVEH =1 if individual moves house but does not change job between year t-1 and t L1OJOBCH = 1 if individual changes job but does not move house between year t-1 and t L1BOTHJH = 1 if individual both moves house & changes job between year t-1 and t OMOVEH =1 if individual moves house but does not change job between year t-2 and t-1 OJOBCH = 1 if individual changes job but does not move house between year t-2 and t-1 BOTHJH = 1 if individual both moves house & changes job between year t-2 and t-1 initial travel time: reference between 11 and 25 minutes T10L = 1 if travel time in year t-1 is < 10 minutes T26M = 1 if travel time in year t-1 is > 26 minutes Mode (ends with X switches mode; ends with Z: does not switch mode DRIVER = 1 if car driver in year t-1 RAIL = 1 if uses rail in year t-1 TUBE = 1 if uses tube in year t-1 BUS = 1 if uses bus in year t-1 MCYC = 1 if uses motorcycle in year t-1 CARPASS = 1 if car passenger in year t-1 CYC = 1 if uses bicycle in year t-1 WALK = 1 if walks in year t-1 OTHERM = 1 if uses other mode in year t-1 Car ownership variables COCAR = 1 if household has access to a company car NOCAR = 1 if household does not have regular access to a car CAR1MA =1 if household has 1 car & more than 1 adults of driving age L1INCCAR = 1 if the number of cars in the household increases between year t-1 and t L1DECCAR = 1 if the number of cars in the household decreases between year t-1 and t Gender, age (reference group years) & household type variables WOMAN = if individual a woman A34 = if age < 35 in year t-1 A64 = if age > 64 in year t-1 SINGLE = 1 if single person household in year t-1 CHILDREN = 1 if household has children in year t-1 Income: Individual s real income quintile (reference group 3 rd quintile) & change IINC1 =1 if real individual income in 1 st quintile in year t-1 IINC2 =1 if real individual income in 2 nd quintile in year t-1 IINC4 =1 if real individual income in 4 th quintile in year t-1 IINC5 =1 if real individual income in 5 th quintile in year t-1 L1INCIIN =1 if real individual income increases by 1000 or more between year t-1 and t L1DECIIN =1 if real individual income decreases by 1000 or more between year t-1 and t Household s real income quintile: reference group 3 rd quintile HINC1 =1 if real household income in 1 st quintile in year t-1 HINC2 =1 if real household income in 2 nd quintile in year t-1 HINC4 =1 if real household income in 4 th quintile in year t-1 HINC5 =1 if real household income in 5 th quintile in year t-1 continued on next page

14 Table 23 (continued) Definitions of the explanatory variables used in the analysis. Binary variables = 0 otherwise Regional variables LONDON = if lives in London in year t-1 METS = if lives in other Metropolitan area in year t-1 Population density of local authority district of residence: 500 < reference <3000 DENS1 = if population density < 100 persons per sq km in year t-1 DENS2 = if 100 < density population < 500 persons per sq km in year t-1 DENS4 = if 3000 persons per sq km < population density in year t-1 Employment variables EMP1 = 1 if only 1 person employed in household in year t-1 PTEMP = if part time worker in year t-1 SEMP = if self employed in year t-1 L1INCEMP = 1 if the number employed in the household increases between year t-1 and t L1DECEMP = 1 if the number employed in the household decreases between year t-1 and t The estimated model for travel mode is presented in Table 14 and those for travel time are presented in Table 15. Although all variables mentioned above were included in the initial specifications, the final models presented here omit those variables shown to be of very low statistical significance. The variable names are defined as in the previous table. The estimated coefficients, standard errors (S.E.), Wald statistics for the significance of the estimates and the probability values are presented. We see that in all three cases a large number of the explanatory variables are statistically significant at high significance levels (P-values well below 0.000). Goodness-of-fit measures are shown at the foot of each table. The Chisquare statistics refer to the Likelihood ratio test for the significance of the explanatory variables. In both cases, the relationship is strongly supported. Also shown is a comparison of the observed and predicted values of the dependent variable. Overall, the mode model predicts the data better, with 84.6% correct predictions compared to 70.3 and 75.1% for the travel time models. However, changes in travel time are more often correctly predicted: 25.5% of the increases in travel time and 38.7% of the decreases in travel time, compared to 22.6% of the changes in mode. It is notable that decreases in travel time are so much more often correctly predicted than increases in travel time, but there is no apparent reason for this difference. The interpretation of the coefficients of estimated models is given below for the different grouping of variables. Moving house and job Moving house and changing job are important determinants of both changing mode and travel time, and the effects are similar. Changing job alone (L1JOBCH) has a greater impact than moving house on its own (L1MOVEH), and the effect of moving house and changing job in the same year (L1BOTHJH) is smaller than the sum of the effects of the individual move/change variables. Moving house and changing job

15 lead to both increases and decreases in travel time. These are more-or-less symmetric in the case of changing job, but moving house leads significantly more often to an increase in travel time than to a decrease. The effects of moving house and changing jobs appear to take more than one year, since the lagged values of these variables are also significant at the 5 or 10% level. In all cases the lagged effects are considerably smaller, but also positive, suggesting that the initial effect is smaller than the total effect. Initial travel time The commuting time in the initial year does not appear to affect the probability of changing mode, but it is highly relevant in determining changes in travel time. Those with low travel time (< 10 minutes) and those with high travel time (> 26 minutes) are less likely to increase travel time than those with a travel time between these values; i.e. those with commutes of minutes are most likely to increase travel time. The impact on decreasing travel time is rather different: those with a low travel time are least likely to reduce it and those with a long commute are most likely to reduce it. In agreement with other evidence, those with short commutes are more likely to increase travel time than reduce it, while the opposite is the case for those with long commutes. Mode Changes in mode and travel time are strongly related to the mode used in the initial year. In the mode change model, car driver is the reference mode, so that the impacts of the other modes are related to car driver. Users of all other modes are far more likely to change mode than are car drivers (significantly positive coefficients). Of these, users of other (non-defined) modes, car passengers, motorcyclists and cyclists are most likely to change mode and rail and tube users and those who walk are least likely to change modes. For the travel time change equations we distinguish between those whose commuting modes remain the same between year t-1 and year t (designated by mode names ending with Z in the estimation result table) and those who switch mode (designated by mode names ending in X). Here, car driver (DRIVERX and DRIVERZ) are also included (as we have omitted the constant) so the parameters are not relative to car driver. First considering those who do not change mode between the two years, we see that bus users are significantly most likely to increase travel time, while cyclists and walkers are least likely to increase travel time. On the other hand, car passengers are most likely in increase travel time while public transport (rail, tube & bus) users and walkers are least likely to decrease travel time. Car users fall in between in both cases. The impact of mode on changes in travel time is rather different for those who switch mode (mode variables ending with X). In general we see that the probability of either increasing or decreasing commuting time is far higher for those who switch modes (Z coefficients are smaller than X coefficients). Of these individuals, those who switch from car passenger and walker are most likely to increase travel time, while those who switch from rail and tube are

16 least likely to increase travel time. Rail, bus users, cyclists and walkers who switch mode are most likely to reduce travel time, whereas car users (drivers and passengers), tube users and motorcyclists are least likely to reduce travel time. As shown earlier, apart from the tube patrons, the majority of these switch to car which generally results in a shorter travelling time. Car accessibility The car variables are more influential in determining mode change than changes in travel time. Individuals in households having access to a company car are less likely to change mode but more likely to increase commuting time than those without a company car. The same is true for those in households without cars in comparison to those with cars. Those living in a household with one car and more than one adult of driving age are, however, slightly more likely to switch modes, presumably as the single household car must be shared. This variable has no significant effect on changes in travel time. Both an increase and a decrease in the number of household cars over the same period increase the probability of switching mode, presumably as the addition (disposal) of a household car permits switching from other modes to car (encourages switching from car to other modes). Regarding the effect on commuting time, a reduction in the number of cars tends to increase travel time while an increase has no significant effect. Surprisingly, both an increase and reduction in household cars tend to reduce commuting time. However, the overall effect is that an increase in car ownership leads to a reduction in commuting time, while a decrease leads to longer commutes. Gender, age, household characteristics Women are less likely to change mode and to increase commuting time than men are, while both genders are equally likely to reduce travel time. Those in singleperson households change mode more frequently, but the number of household members does not effect changes in travel time. Commuters in households with children are more likely to switch mode and more likely to reduce travel time than to increase it. The only effect of age concerns commuters over 65 who are less likely to change mode or travel time than are younger individuals. Income Both the individual s income and the total income of the household can be thought to affect mode choice and commuting time. However, we find that neither income variable is relevant for mode change, but both show some significant influence on changes in travel time. Those with higher incomes are less likely to reduce commuting time than those with low incomes. As expected, individual income has a stronger effect (larger spread between the coefficients) than household income. Changes in income have a significant effect on both mode switching and changing travel time. Although both an increase and reduction in income increase the probability of changing mode, the impact of a reduction in income is far more

17 substantial. Regarding the effects on travel time, a decline in income tends on average to reduce travel time. Regional variables & population density Commuters living in London and the other Metropolitan areas switch mode more frequently than those in other areas, but the propensity to change travel time is significantly different only in London, where commuters are more likely to increase commuting time. Population density of the local authority district has no relevance for mode switching, but it does for travel time. Those living in low-density areas are least likely to change travel time. Given our density measure relates to a rather large area, and thus is very approximate, much weight cannot be placed on this result. Employment variables Whether the individual is part-time employed or self-employed is found to have no significant effect in any of the models. Individuals who are the sole employed in the household tend to be less likely to change mode and slightly less likely to change travel time than those in households with other workers. This is in agreement with other results, but the evidence is rather week. Changes in the number of employed in the household are also important: individuals in households where the number of workers increases are more likely to switch mode, while a decline in the number of employed has no significant effect on mode, but tends to reduce travel time. Along with our conclusions concerning the determinants of mode switching and changes in travel time, we can summarise the most important observations about commuting volatility. Nearly 18% of commuters change main commuting mode between any two consecutive years. The proportion of individuals switching mode increases over a longer time horizon: after 5 years, 40% of individuals change main commuting mode at least once and after 9 years this increases to 50%; the proportion changing after 9 years ranges from 32% for car drivers to between 70% and 87% for the other modes. Nearly 50% of commuters change travel time by ± 5 minutes or more between 2 consecutive years; about 8% increase or decrease travel time by 10 to 20 minutes. After 5 years, 61% of individuals change travel time and after 9 years this increases to 67%. Our conclusions are based on a first attempt to model changes in commuting mode and travel time on the basis the BHPS data and further work is needed to confirm and strengthen the evidence presented here. However, the results confirm that the BHPS provides a valuable source of data for analysing commuting behaviour.

18 Table 14 Change in commuting mode: estimation results. Coefficient S.E. Wald stat. P-value. L1OMOVEH L1OJOBCH L1BOTHJH OMOVEH OJOBCH BOTHJH T10L T26M RAIL TUBE BUS MCYC CARPASS CYC WALK OTHERM COCAR NOCAR CAR1MA L1INCCAR L1DECCAR WOMAN A SINGLE CHILDREN L1INCIIN L1DECIIN LONDON METS EMP L1INCEMP L1DECEMP Constant Chi-square df = 32 P = Predicted % correct Observed All 84.6

19 Table 15 Change in commuting time: estimation results. Increase in time of 5 minutes or more Decrease in time of 5 minutes or more Coefficient S.E. Wald stat. P-value Coefficient S.E. Wald stat. P-value L1OMOVEH L1OJOBCH L1BOTHJH OMOVEH OJOBCH BOTHJH T10L T26M WAVE DRIVERZ RAILZ TUBEZ BUSZ MCYCZ CARPASSZ CYCZ WALKZ OTHERMZ DRIVERX RAILX TUBEX BUSX MCYCX CARPASSX CYCX WALKX OTHERMX COCAR NOCAR L1INCCAR L1DECCAR WOMAN A SINGLE CHILDREN IINC IINC IINC IINC HINC HINC HINC HINC continued on next page