Influences on commuter trip departure time decisions in Singapore

?-rmup,. ks,-A, Vol. 26AA. No. 5, pp. 321-333, Printed in Great Britain.

1990

0191.2607/W 13.00t.W Q I990 Pcrgamon Press

pie

INFLUENCES ON COMPUTER TRIP DEPARTURE TIME DECISIONS IN SINGAPORE * . .bTHONY T. H. CHQJ Department of Economics and Statistics, National University of Singapore

(Received 11April 1988; in revisedform 8 February 1990) paper provides empirical evidence to support the widely held view that institutional factors such as official work start times and staggered working hours are powerful policy tools in traffic management and in influencing travel behaviour. This approach is to be preferred over continued investment in infrastructure given the scarcity of land in Singapore. A more efficient use of existing infrastructure could be achieved by spreading peak travel. Full utilisation of the Mass Rapid Transit will depend on changing the commuter’s perception on multi mode travel in addition to using public transport. While many studies have been carried out on modal choice, research on commuter trip departure decisions have been few and remain largely least understood. This paper employs multinomial logit and simultaneous nested logit analysis to model the choice of departure time (using household data collected in Singapore in 1983). Preliminary findings show that schedule delay, travel cost, and journey time to be important influences on commuter’s choice of trip departure time to work. Some difficulties are highlighted and suggestions for further research are made. _

Abstract -This

1. INTRODUCTION

Land transportation in Singapore has been and is still dominated by private automobiles, buses, and taxis, (and the Mass Rapid Tmnsit (MRT) by the 1990s). The desire for speed and accessibility has led to the rapid growth in automobife ownership and use (a main contributor to congestion on the roads). Planners and economists are often faced with the challenge of attaining an optimal mix of traffic spread over various modes in a dynamic environment. Singapore’s urbanization policies have been closely related to national development. Transportation has been recognized as an important element in the development of strategies in influencing economic restructuring and growth. The policy of decentralisation of economic activities away from the Central Area (which includes the Central Business District (CBD) and the Golden Shoe Area) was pursued until the mid-seventies with the objective of alleviating traffic congestion. Supermarkets, shops, cinemas, and studio apartments were relocated to the fringe of the central area and new towns. The late seventies however saw a reversal to one of urban renewal and development of the CBD. The strategy was aimed at increasing residential development, improving amenities, and commercial developments. A package of traffic management policies including a bold attempt at road pricing was introduced to alleviate traffic congestion {around 75% of work trips end at the CBD each working day). In 1983184 the decision was made to invest in a Mass Rapid Transit (MRT). Currently 14 stations are in operation covering approximately twenty kilometres in length. Its impact on car use has been minimal as roads have experienced little or no change in road speeds. Integrated landuse models lack rigour in analysnl(A)

24:5-A

ing individual choice and behaviour. Relieving congestion and the maintenance of free flowing traffic will remain real and poignant challenges in Singapore in the future. Travel modes do not cause congestion.

It is the individual behind the wheel that matters and whose behaviour and attitude which is to be influenced. Wilbur Smith and Associates (1981) reports that prior to the introduction of the Area License Scheme (ALS), the traffic flow pattern was typical of any city (with flows peaking in the morning and late afternoons). However as a consequence of the Scheme, the pattern translated itself into four peaks (0730,0900, 1030, and 1730). The shifting peak phenomena has been observed in cities where employment centres adopt flexible or staggered working hours. Workers have been observed to respond accordingly by altering their departure times to work. Travel and the use of alternative transport modes is dependent upon the location of work, leisure, home, and other activities. A more comprehensive and coordinated approach to transport must take into account a strategic urban land use as well as an understanding of travel behaviour. Transportation planning is aimed at reducing the need to travel rather than to produce transportation per se. Urban planning has lacked a strategic approach often influenced by macroeconomic priorities of the country. Transport policy has thus taken a piecemeal and static approach which very seldom is identified with the dynamic processes which make up the city. As travel is seldom desired for its sake but rather a prelude to pa~icipating in an activity, two issues have to be considered. The first falls into the realm of urban planning of activity centres and transportation needs. The second considers the organisation and process of scheduling these activities by the inhabitants. One approach to the first issue is to replicate the

322

A. T.

functions of the urban system with its interacting elements, trying to understand at the same time the inter-relationships between parts of the system, such as land use and transportation. This paper adopts the latter approach which identifies the relationships between decisions which influence the households participation in activities. The need for cross sectional information therefore arises given the nature of this study which focuses on the individual’s activity time schedule. The rest of the paper will take the form of a brief summary of the literature, development of the simultaneous nested logit approach to estimation, some empirical results and policies as well as identification of areas for future research.

2. THE TIMING OF TRIPS AND SCHEDULING OF ACIWITIES

The factors governing the decision to participate in an activity type may differ from those that affect the intensity/level of participation. The timing of an activity could be a result of an objective as well as subjective decision criteria. It would be useful to draw a distinction between activity scheduling and travel time departure. The former involves allocating a block of time to an activity with due consideration to the resources available and constraints facing the individual and/or the family. The timing of trips involves decisions faced by an individual given different sets of situation characteristics. The interest in transport in time varying demand phenomena stems from problems of traffic congestion in urban areas, and the desire to minimise the amount of time spent in congested travel circumstances (subject to the constraints on the timing of such travel). The success of measures to reduce the amount of congestion involves identifying time dependent demand functions of travellers (such as car pooling, flexible working hours, improved public transport). Though much effort has been spent in measuring own and cross price elasticities of modal demand, there is scant evidence on elasticities for temporal travel demand as observed by Small (1984) and by Hendricksen and Plank (1984). Why the concern with trip timing? Intuitively, changes in departure times such as the shifting peak phenomena serve to confound cost benefit computations as congestion effects occur quite independent of supply and demand conditions when schedules are altered. Small concludes that a shift in timing also affects welfare if the value of time savings varies by time of day. This depends on the interaction between scheduling conditions and time varying price of service quality. The transport demand modeller may also gather a few points from numerous studies on the demand for electricity by Granger et al. (1979). Hendricks et al. (1979), Lawrence and Braithwait (1979), Atkinson (1979), and ‘Ihylor (1979) which emphasize the influence of differentiated tariffs imposed at each time of the day and season. In an effort to develop a long run

H. CHIN micro model of hourly demand for electricity (given socio-economic and demographic characteristics of households), &anger regressed each household’s electricity demand against short run predictor variables which explain the nature of short run variations in the time of day load patterns. These short run variables include weather, daily and hourly variables, dummy variables for peak load pricing periods, public and school holidays. Regression coefficients were then used as dependent variables. Results show great sensitivity of household’s electricity demand to peak period prices. The responsiveness of individual household demand to peak period pricing was strongly related to the possession of electric heating, dishwasher, the number of adults in, and the availability of other appliances. Hendricks et al. (1979) and Koenker (1979) modelled time of day load patterns for individual household by incorporating a periodic cubic spline function. Results show considerable shifting of load from the morning, afternoon, and early evening into the late evening period. The Lawrence and Braithwait (1979) study showed considerable responsiveness to differential prices according to time of day. Atkinson (1979) confirms the above findings whilst Taylor (1979) found little or no price induced substitution response. The amount of time budgeted for travel can be viewed as similar in principal to that of electricity consumption except that the decision on the timing of such consumption involves the choice of time from mutually exclusive sets of time and thus is more manageable as a discrete choice problem. Small (1984) studied the scheduling of trips at the individual level and found that commuters would shift their schedules by one or two minutes earlier to save a minute’s travel time (given family status, occupation, mode of transport, and work hour flexibility). An understanding of factors affecting trip timing decisions is thus essential. Previous studies have achieved relatively little. However, McFadden et al. (1977) shed some light on the sensitivity of commuter trips to arriving at work early or late. Small’s study on Singapore on the choice of departure time was carried out for bus commuters on the basis of peak versus off-peak hours rather than choice of times within the peak period. Several issues still remain inadequately dealt with. These include the consideration of the role of socio-demographic factors; the trade off between travel time and work arrival time and the role of uncertainty in arrival time. To appreciate the complexity of trip scheduling, let us consider a policy measure such as the introduction of flexible working hours (practiced in Germany since 1967). Since its introduction, the concept has found favour throughout Europe by 1975. Polit (1979) found that approximately 6COO European firms had adopted the measure. Flexible working hours require an employee to be at his place of work at certain core times. For example between mid or late morning to mid or late afternoon. This policy is usually designed to diffuse peak period congestion

Commuter trip departure time and related inefficiencies associated with the rigidities of fixed working hours. It also allows a commuter greater flexibility in organizing his daily activity time schedule in order to satisfy other demands placed on his time. Perhaps foremost in the minds of planners is to attempt to explain routine behavlour given constraints which are binding on the commuter (official working hours, shop opening hours, public transport timetables or time schedules of other members of the family). The structuring of such behaviour in terms of ritual or inertia should also be of interest. Bolton (1971) reported that Germany’s experiment on flexible working hours was a success. Rigid working hours between 0700-1600 were replaced by conditions requiring employees to arrive at work between 0700 and 0800 and leave between 1530 and 1800. The core time was between 0800 to 1530. Employees can choose the time they wished to begin work provided they complete a certain total number of hours over a prescribed period. A survey conducted among employees showed that two thirds had benefitted from the above scheme. A similar survey of 50 firms conducted by Rousham (1973) in Britain, exhibited considerable variations in achieving the desired objectives (i.e. increased production and efficiency, improvement, and reduction in time keeping, reduction in overtime, and improved staff relations). Of the sampled surveyed, 20% were able to detect improvements in productivity, a decrease in overtime and declining labour turnover. Approximately 40% did not detect any improvement, while 20% to 30% of the firms had just introduced the measure, thus failing to detect any change. However 50% of the firms thought that there had been some improvement in management and staff relations whilst 30% did not notice any difference. In their study of firms in Reading (England), Shapcott and Steadman (1976) suggested that a majority of employees discovered that flexi-time had assisted them to cope with their travel to and from work, but few had altered their departure times to avoid congestion. Saving five minutes seemed hardly worth the effort of rescheduling time departures. The lack of flexibility could partly be due to the presence of other constraints and habits. Rigid time participation in other activities or the limitations of travelling too far given megre savings in time may not enable the individual to attain his desired participation in other activities. In most studies the assumption is made that every traveller desires to avoid traffic congestion without actually questioning the attitudes which govern the traveller’s strategy towards travel changes. Changes in family life, socio-demographic and economic environment should be taken into account. l&nnir and Hartgen (1977) observed that the basic motivation in favouring work schedule changes was flexibility in participation in family, work, social, and leisure activities. The desire to avoid traffic congestion was not a major factor. In this study, 65% of young employ-

323

ees with young and schooling children were responsive to work schedule changes with variable working hours. Singles, older employees, and car poolers were the least responsive. A later study by Neveu and Koeppel (1979) supported the findings of Tannir and Hartgen. More than 50% of employees switched their work schedules to earlier starting times in response to alternative work hour programs. A finding in support of the Reading study was that employees with longer commuting times were more likely to shift their work schedules to earlier hours than those with smaller times. ‘Shifters’ were mainly male, young, from larger households and higher income levels. There will no doubt be considerable variations in departure times depending on mode of travel, profession, family status, and work hour flexibility (see for example Abkowitz (1981), Small (1984)). Uncertainty in work arrival time and the traveller’s perceived loss associated with varying work arrival times and additional socio-economic factors do potentially affect the choice of departure time through travel times and cost. Small found significant shifts in the timing and duration of the peak period occurring in response to factors which substantially affect congestion. An accurate prediction of traffic conditions should take into account the scheduling process. Research into the above area has emphasized commuter departure time decisions, which seeks its justification in the importance of the peak and the desire to diffuse the concentration. The aim is simple, that is, to encourage commuters to alter their departure time to work. Its resolution is however complex. Hendricksen and Plank (1984), reported that departure time decisions are more elastic than mode choice decisions with respect to changes in road conditions. However variations in travel times were low for any given mode, route, and departure time. Traditional transport studies on trip decisions have always taken into account a set of travel characteristics based on zonal averages. The nature and factors leading to the choice of departure time are seldom taken into consideration. We have included time of day variations because it affects the major factors such as commuting time, travel costs, and service reliability. These time dependent variables have direct and indirect influences on mode choice as well as peak/off peak departure times, thus having an important bearing on urban transport policies. Prior knowledge of road congestion conditions may lead a commuter to depart at an earlier time, thus reaping the benefits of a shorter journey time and less stress. However, the absence of flexible working hours and the presence of family obligations may constrain the traveller to a particular departure time. Toll charges such as the Area License Scheme in Singapore and the availability of and the high cost of parking space within the CBD may induce changes in departure times. Company subsidised car commuters would be less likely to alter their times of travel as they do not bear the opportunity costs. They do however incur the relative marginal

A. T. H. CHIN

324

disutility of travel. Reserved parking is another contributory factor. Public transport users (bus patrons and car poolers) are dependent on the schedules of their carriers. Blumstein and Miller (1982) observes that service reliability and frequency may thus be important in the decision of such commuters to plan an early or late departure time when journeys are longer at certain times of the day. Commuters have been observed by Hendricksen and Plank (1984) to change their times of departure when changes in the transport network occur, for example, strikes or major construction programs. The methodology applied in the above studies differ. Small (1984) and McCafferty and Hall (1982) based their results on multinomial logit models (MNL) for a single decision trip time. Abkowitz (1981), estimated a joint mode choice/departure time model. Small found that urban commuters were willing to alter their schedules in order to save travel time to work and that demographic and institutional factors do influence the scheduling of activities. Trade offs were only possible if the marginal rate of substitution between journey time for schedule delay was sufficiently large. McCafferty and Hall (1982) concluded that socio-economic variables and journey times were not significantly related to the choice of departure time. Further to this was an acknowledgement to the possibility of uncertainty of journey time. Small and Abkowitz however had little information on the time related impedance of travel. As indicated earlier, time of day variations do affect various aspects of journey conditions. Even so, cost factors vary according to journey conditions. Hendricksen and Plank (1984) estimated simultaneously a logit model of mode choice and departure time. Time dependent road pricing seems a useful tool of traffic management. Preliminary findings by Small and Brownstone (1982) in modelling congestion (given the desire of commuters to avoid arriving too early or too late for work), showed that large shifts in the timing and duration of the peak would occur if congestion was shortened or lengthened. Scheduling and rescheduling flexibility was more pronounced by car poolers.

3. RATIONALISATION

OF EXPLANATORY

VARlAllLES AND

ECONOMETRIC METHODOLOGY

A number of broad conclusions can be drawn from past studies on commuter departure time decision. Several researchers such as McFadden et al. (1977), Small (1984), and McCafferty and Hall (1982) conclude that variables such as the type of mode used, flexibility in work schedule, type of occupation, income, age, and the service levels of transport modes influence the choice of departure time. Hendricksen and Plank (1984) added schedule delay to the above factors. Intuitively, five potential factors can be identified to affect a traveller’s decision to participate in an activity. These are,

(i) road conditions at the time of travel: by avoiding periods of congestion and routes so as to minimize travel time; (ii) institutional constraints: this includes official work start times, the practice of flexible working hours with a core time or penalties/incentives for late/early arrival at work; (iii) quality of service of competing modes: reliability of car pools, comfort, frequency of service and speed of public transport; (iv) pricing of road space: this primarily concerns the use of road space either through the price mechanism such as the Area License Scheme or rational at certain periods of the day; (v) socio-demographic and life-cycle factors: involvement and the intensity of participation in home activities such as household chores and related child care activities which may affect the desire to leave earlier. Sending the spouse to work and child to school would definitely require the commuter to leave at an earlier time. Our analysis of departure times is based on logit choice models which employed the maximum likelihood method of estimation. Two basic structures were examined, the multinomial logit and nested logit structures (see Figs. 1 and 2). The numbers in parenthesis show the sample size of commuters choosing that alternative. An effective sample of 956 commuters from 511 households was obtained. Estimation had to take into account an appropriate number of observations per node per alternative. The probability of an individual choosing a departure time alternative (Pit) can be written as:

exp( vi,)

pil = I

(1)

C ew ( vj,) j=l

where the individual i, chooses the fth departure time from a set a departure times j, vi, is the indirect utility function associated with the tih time (see Chin, 1989 for a development of the models). While the multinomial logit approach is computationally tractable, it is however deficient in the area of theory and empirical foundation as pointed out by McFadden (1974, 1981) and Hensher (1986). The nested logit approach assumes a pattern of equal dependence within a particular level of a nest and at the same time maintains a general pattern of dependence between the alternatives. For example, in this study of departure time, three main periods in the morning have been defined from which the commuter chooses (i.e. very early morning (VEM), early morning (EM), and morning (M)). At the lowest level of the nest are the sets of departure times associated with each of the periods above (see Fig. 2). There are thus two stages. The periods above the probability of choosing a time period, P, (eqn (2)) and the probability of choosing a departure time, Pi,,, is given a time period (eqn (3)).

325

Commuter trip departure time

0614 (63)

0629 (43)

1 Choice

0644 (68)

of Travel

0659 (67)

0714 (128)

0729 (120)

0744 (106)

0759 (92)

0814 (116)

0829 (82)

0844 (71)

Time

Fig. 1. A multinomial logit (BLOGIT) structure of trip time departure.

69 Total cost divided by commuter’s personal in(2)

CexP vile

jt

I

where, a, and a, are a scale vectors (a, > 0; a, > 0). The variables used in modelling departure time are specified as generic or alternate specific. The ge-

neric variables employed are defined below (those which have values specific to each alternative departure (time)): (i) Schedule delay (SCHDL), which is the difference between the actual arrival time at work and the official work start time; (ii) Journey time (JTIME), the difference between arrival and departure times; (iii) Total cost (TCOST), the total sum of out of pocket cost expenses of travel. Two sets of costs were calculated for the operation of the car based on engineering and the simpler out-of-pocket approach. The latter was adopted on the rationale that car drivers misperceive the true costs of operation; (iv) Total cost divided by the household’s income (TCINC). The rationale is to provide a household income effect on travel cost given the relative effects of different departure times;

come, the rationale is as in part (iv), as the relative effects for the commuter earning $500 per month would be different as opposed to one with a salary of $5000. Alternate specific variables (ASV) such as income, profession, age, and marital status have been made specific to one of the alternatives, either through an interaction between an alternate specific dummy variable (ASDV) and a characteristic or as a pure ASV (PASV) where an attribute is contained in a vector set for an individual (for example income, type of employment). We have only chosen to present variables which are statistically significant from BLOGIT estimations. A total of 166 ASVs were computed and tested (the BLOGIT software was developed at Macquarie University through the efforts of Hensher and Johnson (1981), Johnson and Crittle (1981)). Some variables were transformed in logarithmic form for the purpose of testing translog functional forms. This consists of the five generic variables, schedule delay, journey time, total cost of travel, total cost divided by household and personal income respectively. 4. MODELS AND RESULTS

The discussion and implications of the various models of departure time will focus on the multinomial logit and nested logit results (the latter estimated by the simultaneous full information maximum likelihood software recently developed by

CTT2

0614

0629

'Choice 4garly

0644

of Travel Horning;

0659 Time;

0714

0729

0744

0759

3Very Early

0814

Morning;

'Morning.

Fig. 2. A nested logit (FIMLNL) structure of trip time departure.

0829

0844

326

A. T. H.

Hensher (1986)). Two functional forms will be discussed, linear and translog. The traditional criteria of model significance were used in the preliminary screening of both trip departure models. These are overall goodness of fit (rho square, statistical significance (t-value). degree of colinearity between explanatory variables, and finally, with respect to nested logit models, the magnitude of the inclusive value, IV (i.e. 0 c IV < 1). The BLOGIT programme is a maximum likelihood estimation procedure applied to a specific case of multinomial logit model with the embedded assumption of the independence of irrelevant alternatives (see Hensher and Johnson, 1981; McFadden, 1981). The employment of full information maximum likelihood nested logit (FIML-NL) procedure was aimed at avoiding the potential problem of the violation of the Independence of Irrelevant Alternatives (IIA) property which may arise from MNL estimations and the resulting inefficiency of parametric estimates at the lower level of the nested structure. Hensher (1986) concludes that FIML-NL is an improvement on Sequential-NL in- that it “simultaneously estimate a nested logit model using full information maximum likelihood, which directly gives asymptotically efficient estimates.” We have chosen to present and discuss the results of selected models in this paper. The emphasis in the empirical work is on a simplified linear approximation to the theoretical framework developed (see Chin, 1989). The importance of the schedule variables will be tested. Only token findings have been presented with translog specifications, due largely to nonconvergence of and exceptionally high inclusive values of all FIML-NL models. Explanatory variables however displayed the correct signs. A summary of the results of the six preliminary MNL models is given in Table 1. All explanatory variables had the desired signs. The schedule delay variable (SCHDL) measures, ceteris paribus, the degree of disutility of having to arrive early or late for work. Arriving early could mean lack of productive activity or none at all due to wasted time if working hours were rigid. On the other hand, penalties may be imposed on late arrivers. This trend of argument ignores policies such as flexible working hours, the presence of traffic management or road pricing schemes which may cause a discrete jump in the cost of travel. The trade off between the increase in travel cost and schedule delay may be indeterminate given a particular departure time. For example, the utility incurred by the commuter in arriving on time for work may be offset by the disutility incurred through an increased in travel cost if the commuter had to pay the fee during operating hours of ALS. The ASVs, SNCBDL4, SNCBDGE4, SALSEl, and SALSEO were specific to alternative 3 and 4. The positive sign of SNCBDGE4 may be interpreted as schedule delay having a lesser effect on private automobile commuters who travel with 4 persons or more than those which travel with less than 4 persons (automobiles

CHIN

with 4 or more passengers are exempted from paying the ALS fees). This seems to be the case for models 1, 4, 5, and 6. The variable is also statistically significant (f-values of 8.1 across all models). Among other variables which exhibited a positive sign was SALSEO (i.e. commuter entering the CBD when ALS was not in operation). This was true for models 1, 4, 5, and 6. As for SNCBDL4 and SALSEl, the negative signs confirm schedule delay as a source of disutility perhaps by having to pay the fee or bear the discomfort of travelling. Otherwise, all explanatory variables exhibit negative relationships, thus confirming the a priori hypothesis that commuters generally experience disutility of travel. Variables JTIME and TCOST were statistically significant with the t-values ranging between -5.464 to -5.816 and -2.195 to -2.491, respectively. The f-values of the alternative specific variable, TCMALE (cost of travel conditioned upon the commuter being a male made specific to alternative 3 and 4) ranged from between -2.019 to -2.520. The only generic variable which was fairly significant is TCPINC (t-value between - 1.320 to - 1.530). The pseudo R*‘s however range between 0.0308 to 0.0757. Models with occupational and income ASVs (models 2 and 3 respectively) had lower R*. The importance of institutional constraints as represented through the operation of the ALS and work start times (ASVs conditioned on schedule delay), confirm the theoretical results developed (see Chin, 1989) which derived the importance of the schedule elements in determining the choice of trip departure time. An interesting result showed that any policy aimed at shifting commuter time departure must be targeted at specific income or automobile owning groups. Commuters who were most likely to switch times were those belonging to income groups earning between $499 to $899 (model 2) and whose profession is a clerk, sales person, in the manufacturing or construction. Vast differences in the magnitudes of the mean point elasticities were observed when the model is explained with generic variables in contrast with models which had mixture of ASVs and generic variables. The preliminary evidence from the MNL models provided the starting point for the specification of the nested logit models. The morning departure times for work were classified into three main periods; very early morning (VEM =0600-0659), early morning (EM =0700-0759), and morning (M =OSOO-0844). The first period tries to capture (i) those who work in early shift work and (ii) those who travel early SO as to avoid the morning rush. The early morning period is designed to capture rush hour commuters. These are mainly those that have to send children to school, beat the ALS before it comes into operation at 0730 and finally the usual 0800 morning peak. The last period tries to capture the post peak traveller. FIMLNL was then employed to test a whole variety of groups of commuters spread into three main morning periods. Each period is divided into four

3

Definition

at zero

Variable

*DV = Dummy

Schedule delay Total cost of travel TCOSTlPersonal income Journey time DV*=SCHDL if trip enters CBD with <4 persons DV=SCHDL if trip enters CBD with > 4 persons DV=‘ICOST if Sex=Male DV=SCHDL if Occupation (Occp) = Clerk DV = SCHDL if Occp = Sales DV=SCHDL if Occp=Administrator DV=SCHDL if Occp=Professional DV=SCHDL if Occp=Manufacturing DV=SCHDL if Occp=Construction DV=SCHDL if Occp=Trade DV =SCHDL if Occp =Commerce DV=SCHDL if Occp=Business DV=SCHDL if Occp=Others DV=SCHDL if Income=S200 DV=SCHDL if Income=S299 DV=SCHDL if Income=S499 DV=SCHDL if Income=$699 DV=SCHDL if Income=$WJ DV=SCHDL if Income=fl299 DV=SCHDL if Income=S1799 DV=SCHDL if Income=$2249 DV=SCHDL if Income=$2749 DV=SCHDL if Income=$3500 DV=SCHDL if Area Licensing is not in operation DV=SCHDL if ALS is in operation

Log-Likelihood Pseudo R2 Sample Size

SALSEl

STRADE SCOMM SBUSIN SOTHERS s200 s299 SW9 S699 S899 S1299 s1799 S2249 S2749 s3500 SALSEO

SCONSTR

SMANU

SPROFF

SSALES SADMIN

TCMALE SCLERK

SNCBDGE4

SCHDL TCOST TCPINC JTIME SNCBDL4

Explanatory Variables

-2109.6 0.0746 956

-

-

-

-

-

-

-

-

7.900 -2.371

8.090

-5.576

-

-8.845

0.2311 -0.0033

0.2339

-0.0619

-0.2553

Model 1 f-stat. est. b

-

-

-2109.6 0.0308 956

-

-

-

-

-0.0296 -0.0273 -0.0224 -0.0287 -0.0259

-0.0249

-

-4.779 -4.267 -2.741 -3.987 -6.886

-5.823

-6.266

-4.437

-0.0278 -0.0274

-5.513 -4.490

-0.0257 -0.0243

-

-5.816

-2.305

-0.0674

-0.cil34

using BLOGIT

-2109.6 0.0309 956

-0.0254 -0.0254 -0.0242 -0.0263 -0.0286 -0.0285 -0.2828 -0.0286 -0.0331 -0.0296

-

-

-

-0.0660

-0.0039

-

-

-

-3.015 -6.781 -7.409 -5.824 -5.675 -5.698 -3.742 -2.835 -2.438 -3.371

-2109.6 0.0751 956

-0.2637

0.2555

-

-

-7.775

8.851

-

-

-

-2109.6 0.0739 956

-0.2631

0.2572

-

-

-0.0015 -0.0025 -0.0037 -0.0019 -0.0003 -

-

-

-0.2306 -0.0038

0.2338

-0.0621

-

-

-7.986 -2.013

8.099

-1.530 -5.555

est. b

-

-

-0.2314 -0.0029

0.2342

- 1.6396 -0.0616

-

Model 4 I-stat. est. b

-

-

-

-5.784

-2.497

Model 3 est. b t-stat.

of selected models estimated

Model 2 est. b f-stat.

‘Ilrble 1. Summary

-7.817

8.871

-0.165 -0.547 -0.857 -0.370 -0.011

-

-

-

-7.960 -2.520

8.089

-5.574

Model 5 f-stat.

-2109.6 0.0753 956

-0.2640

0.2556

-

-

-

-

-0.2314

0.2342

-0.0608

-0.0030

-7.784

8.854

-

-

-

-

-7.987

8.099

-5.464

-2.195 -

-

Model 6 est. b r-stat.

328

A.

T. H. CHIN

quarters except for the final morning period, and is well represented in sample size (VEM =241, M =446, M =269). The last quarter of M was omitted as there was only 12 commuters who chose that time of day to travel. There are two subsections to this part; the exploratory and the final versions which includes MNL as well as nested structures (NL). The exploratory models 7 to 12 (see Table 2) considered the role of generic variables in the nested logit structure. The models 13 to 7 in Table 3 include conditioned as well as alternate specific variables in the indirect utility function. The following summarises the 11 exploratory and final models estimated. Statistically significant variables from BLOGIT were used in testing preliminary models (7-12) in FIMLNL. These were the generic variables TCOST, JTIME, and SCHDL, and ASVs conditioned upon SCHDL. Socio-demographic characteristics had low f-values in 90% of all the models tested. ASVs of SCHDL performed generally better, specifically those which were conditioned on income, occupation, and time of departure. The inclusion of the above ASVs with either SNCBDL4, SNCBDGE4, SALSE 1, and SALSLEO however resulted in statistically insignificant estimates. Locational ASVs had no significant bearing upon the choice of trip departure time. Although FIMLNL models fared well with Pseudo R* of between 0.338 to 0.218 compared with BLOGIT results, the resulting inclusive values all exceeded unity. Refer to Tables 2 and 3 for an overview of the performance of FIMLNL and MNL models. Inclusive values greater than unity indicate that the necessary giobal condition for consistency with utility maximisation is not satisfied (see Hensher and Johnson, 1981). Although the inclusive values greater than unity is still acceptable as a test for local sufficiency as pointed out by Boersch-Supan (1985); efforts to redefine time periods by reappropriating or redefining the number of branches per node failed to rectify the above problem. The MNL specifications for models 13 to 17 (see Table 3) generally resulted in significant estimates with the correct signs but at the expense of goodness of fit (0.117 for model 17) decreasing by as much as 70% (compare models IS and 16). Statistical significance of IVs decreased accordingly. It is our strong suspicion that the root of the problem lies with the quality of the data. We have thus to be satisfied with MNL estimates, setting aside the FIML-NL results. An interesting result was obtained from model 17 included among its explanatory variables, Alternate Specific Variables (ASVs) of SCHDL conditioned upon the eleven time periods of departure (SCHDEPTl to SCHDEPTll) and specified to alternatives 3 and 4. It was observed that SCHDEPTI to SCHDEPT7 and SCHDEPT8 to SCHDEPTll displayed positive and negative signs, respectively. It was noted that SCHDEPTS was linked to the time period 0730-0744, the quarter when the ALS comes into operation daily. The standard errors of estimate

ranged from between 0.1429 for SCHDEPTll to 0.0102 for SCHDEPTZ. The pseudo R2 was relatively much better than other previous models (0.117). We tested for the nested structure but this did not result in convergence. The results seem to suggest that commuters who depart before ALS becomes operational do not mind arriving early for work and either do not experience any disutility of schedule delay or that the disutility of travel and inconvenience of getting up early is over-ridden by the utility derived from less congestion and stress on the road and working early. Another possible explanation is that the commuter may not experience any disutility by arriving early to work simply because of the presence of flexitime. Furthermore it is the common practice of office workers to arrive early for breakfast. It is also possible that many commuters may just simply be passing through the CBD in a car pool or have work places at the fringe of the ALS zone. One final suggestion could be that automobile drivers would not mind incurring some schedule delay as a trade off for an easy flow of traffic, predictable travel time and avoidance of the ALS fee. Changes in trip departure time had been found to be pronounced for car drivers when ALS was first introduced. Watson and Holland (1978) found that the proportion of trips which began before 0730 increased from 28% (before the introduction of ALS) to 42% afterwards while bus passenger trips declined by only 6% from 38% to 32%. Table 4 summarises the results from translog models estimated with multinomial logit specifications. All nested logit specifications failed to converge. However MNL specifications fared much better than linear models with an improvement in the goodness of fit (0.179, 0.174, and 0.183). The models estimated with BLGGIT gave a goodness of fit of 0.075 (not presented here). A number of specifications were estimated. No convergence was possible when the number of explanatory variables was greater than five. The schedule delay variable had the desired signs with high t-values (-2.936, -3.653, and -3.623). The rest of the explanatory variables remained statistically significant.

5. POLICY IMPLICATIONS

The implications of the above findings on the travel to work and the subsequent effects on the individual’s activity time schedule is tremendous. Most working individuals do not have flexible work start times and thus, have their activity schedule pegged on to working hours and its associated travel. This is borne out by the low elasticity between home activities with respect to travel to work (0.009-0.026 (see Chin, 1989). However, inducing commuters to shift departure times to work should be such that any time saved traveling to work is worth his while spent in some other activity. For example, leaving at a latter time may take five minutes off travel but may not be

g

Schedule delay Journey time Total cost Total cost/personal income Alternate specific constant (ASC) for very early morning ASC for early morning ASC for morning

Definition

Log-Likelihood at zero Log-Likelihood at convergence Pseudo R2 Inclusive Value (I-statistic) Sample Size VEM: very early morning EM: early morning M: morning

ASCM

ASCEM

ASCVEM

SCHDL JTIME TCOSITCPINC

Explanatory Variables

-1518.7 0.338 21.438 (7.045) 956 369 587 -

-2292.4

-

0.0149 -

8.490

-1.782

-0.0151

-

-13.173 -

-4.5802

Model 7 (NL) est. b l-stat.

-

(Iti:

956

956

-2292.4 - 1834.8 0.200

-

-2.4968

-4.7118

- 10.488

-15.154

Model 8 (MNL) I-stat. est. b

-2292.4 - 1745.4 0.239 1.440 (9.945) 956 241 354 269

-0.0262 0.0086

- 1.0893

7

-3.4755

-3.431 1.404

-

-6.203

-9.052

Model 9 (NL) I-stat. est. b

-2292.4 - 1503.7 0.344 16.789 (6.955) 956 489 467

-

16.7887

-6.180

-1505.6 0.345 17.111 (6.866) 956 489 467

-2292.4

-0.0282

- 1.6200

-6.061

-1.589

-5.828 -10.811

-0.0513 -3.8728

-0.0522 -3.8412 -5.937 - 10.667 -

Model 11 (NL) est. b I-stat.

Model 10 (NL) I-stat. est. b

Table 2. Summary of some FIMLNL and MNL results

,:.i: 956

-2321.2 - 1989.7 0.143

-

-

-0.0282 - 1.0287 -2.1476

-

-

-

-3.037 -2.909 -2.222

Model 12 (MNL) I-stat. est. b

z O

1

Schedule delay Total cost Journey time Total cost/personal income DV=SCHDL if income is $200 DV=SCHDL if income is $299 DV=SCHDL if income is $499 DV=SCHDL if income is $699 DV=SCHDL if income is $a99 DV=SCHDL if income is 51299 DV=SCHDL if income is $1799 DV =SCHDL if income is $2249 DV=SCHDL if income is $2749 DV=SCHDL if income is $3500 DV=SCHDL if departure time: 0600-0614 DV=SCHDL if departure time: 0615-0629 DV=SCHDL if departure time: 0630-0644 DV=SCHDL if departure time: 0645-0659 DV=SCHDL if departure time: 0700-0714 DV=SCHDL if departure time: 0715-0729 DV=SCHDL if departure time: 0730-0744 DV=SCHDL if departure time: 0745-0759 DV=SCHDL if departure time: 0800-0814 DV =SCHDL if departure time: 08 15-0829 DV=SCHDL if departure time: 0830-0844 DV=SCHDL if there are<4 persons entering CBD DV=SCHDL if there are>4 persons entering CBD DV=SCHDL if Area License is in operation DV=SCHDL if ALS is not in operation DV=TCOST if commuter=male Alternate specific constant (ASC) for very early morning ASC for early morning ASC for morning

Log-Likelihood at zero Log-Likelihood at convergence Pseudo R2 inclusive Value (f-statistic) Sample Size VEM: very early morning EM: early morning M: morning

SNCBDL4 SNCBDGE4 SALSE I SALSEO TCMALE ASCVEM ASCEM ASCM

SCHDEPTl

SCHDL TCOST JTIME TCPINC s200 s299 s499 S699 sa99 S1299 s1799 S2249 S2749 s3500 SCHDEPTl SCHDEPT2 SCHDEPT3 SCHDEPT4 SCHDEPTS SCHDEPT6 SCHDEPT7 SCHDEPTB SCHDEPT9 SCHDEPTIO

Explanatory Variables

- 1643.1 - 1076.4 0.345 12.330 (9.101) 956 241 446 290

- I .a352 -3.5408 2.6168 -1.1766 -0.0548

I .a260

-

-

-

-

-0.0871 -0.0223

-

-5.603 -4.966 -

5.540 -5.589 -5.482 10.181

-

- 1.786 -3.144

Model 13 (NL) est. b I-stat.

- 1457.8 - 1079.3 0.260 10.945 (20.732) 956 369 587

4.2270 -4.3329 -2.4691 3.1330 0.0034 0.03 19 2.OwO

-3.7558 -0.0120

-

15.344 - 15.994 4.383 12.648 -0.780 3.501 3.509

-

-

-

-8.612 -0.726

-

Model 14 (NL) est. b r-stat.

Table 3. Summary of some FIML-NL and MNL results

- 1238.4 -968.1 0.218 19.669 (3.232) 956 241 354 290

0.0234 0.0491 0.0365

-

-

-0.0486 -0.0428 -0.0520 -0.0518 -0.0546 -0.0495 -0.0574 -0.0534 -0.0634 -0.0669

-0.0187 -

-

1.477 2.222 I .2aa

-

-

-

- 1.923 - 1.786 -2.109 -2.083 -2.154 -2.003 -2.223 -2.027 -2.212 -2.401

-0.788 -

Model 15 (NL) est. b f-stat.

Model 16

- 1238.4 -1149.4 0.072 1.000 (2.039) 956

-4.1693 -7.3027 -0.0715 -0.0951 - I.3978 - 1.5350 -1.6251 - 1.9377 - 1.9775 -1 .a845 - 1.94547 -2.2375

est. b

-5.683

-2.467 -4.817 - I .959 -2.240 -4.239 -3.506 -3.385 -3.911 -2.817 - I .985 -1.859 -2.665

(MNL) f-stat.

(3:;; 956 -

- 1595.7 - 1408.3 0.117

-2.4824 -0.030

-

7.2329 4.4363 2.4957 I .3937 1.4562 1.6444 0.0430 -0.0580 -0.0476 -2.3154 -5.9450

-

-

-1.6404 - 1.5499

-9.777 - i .92a

-

7.435 4.33 I 5.996 2.988 3.473 3.547 2.102 -2.137 -2.116 -3.671 -6.451

-

-

-

-2.156 -2.157

-

Model 17 (MNL) est. b I-stat.

331

Commuter trip departure time Table 4. Summary of translog models (MNL) Explanatory

Variables

Definition

LNSCHSQ LNPNCSQ

Square of log (schedule delay) Square of log (total cost/personal income) Square of log (journey time) Square of log (total cost/household income) Log (schedule delay) Log (total cost/personal income) Log (journey time) Log (total cost/household income) Log (Schedule delay) x Log (personal income) Log (schedule delay) x Log (journey time) Log (schedule delay) x Log (household income)

LNJTMSQ LNHNCSQ LNSCH LNPNCM LNJTM LNHNCM LNSP LNSJ LNSH

Log-Likelihood at zero Log-Likelihood at convergence Pseudo R2 Inclusive Value (t-statistic) Sample Size

Model 18 est. b t-stat.

Model 19 est. b

t-stat. -3.653

-0.0910

-2.936

-0.0936

-1.124

7.1621 -

29.556

-0.0319

-0.0470 -1.8070

-6.771

-0.0329 3.4618 -

-4.703 13.487

-13.064

-3.5221 -4.1640

-16.066

-30.032

Model 20 est. b t-stat. -0.0959 -

9.2595 -0.0175 -

37.092 -2.650

-4.6328

- 17.479

-

-4.8070

-2635.0 -2163.0 0.1791

-2635.0 -2176.2 0.1741

-2635.0 -2152.8 0.1830

(3.16:) 956

(3.827; 956

(3.71; 956

long enough for him to time with the family at home. One important observation is that 5 1.2% of com-

muters depart before 0730 (with the majority leaving between 0700 to 0730) and 48.9% leaving after 0830. Of these 16.6% had official work times between 0700 to 0730, 56.9% between 0800 to 0830 and 24.3% between 0900 to 0930 (refer to Table 5). Tables 6 and 7 show the sensitivity in travel times (TRVACT), if STARTIM (trip departure time) and OFFWS (official work start time) were to be altered at 15 and 30 minute intervals. The imposition of the ALS in 1975 altered the departure times of car drivers by 24% and noncar poolers by 19%, while car poolers and bus riders remained fairly constant as noted by Watson and Holland (1978). The pre-ALS morning single peak for inner corridor trips going into the CBD was observed to form three peaks (i.e. just before 0730, around 0830, and just after 1015 with 6800,710O and 6900 trips, respectively). Changes in travel times had been small, +2 minutes for car drivers, + 1.4 for car passengers, -0.5 minutes for bus riders, and - 1.2 minutes for non car owning households. Our findings show that staggering official work start time by 30 minutes would decrease travelling time by 8 to 10 minutes. Adjusting work start time to 0800 from 0830 and from 0830 to 0730/0800 would decrease travel time by as much as 8 to 10 minutes. Official working hours are generally fixed at 0830 for public workers, 0900-0930 for commercial and services and 1000-1030 for other business. Work start times of some public officers and commercial/service workers should be staggered to 0730/0800 and 1000 respectively. Shops and retail centres begin business trans-

-3.623

-40.484

actions between 1030 and 1100. This may help to ‘fill’ the troughs (0730-0745 and 0930-0945) between the peaks thus enabling a freer flow of traffic and more efficient use of road space. Subsequently changing the work ‘end time’ could ‘flatten’ the 1700-1815 peak which totalled approximately 39,800 vehicle trips out of the city. An evening ALS is thus recommended to help ease afternoon congestion. Intuitively changes in the work start times would affect commuter departure times. As we have shown, a large part of those leaving before 0730 contribute to the 0730 and 0845 peaks. This study has shown that those leaving at 0630 as opposed to 0700 save as much as 7 minutes. It should be noted that traffic congestion is not an acute problem in Singapore. Traffic flows could thus be maintained and improved in the future without excessive investment in road infrastructure. A profitable area of analysis is to consider the influence of location and socio-economic characteristics on the Activity Time Schedule of individuals. A policy response to activity specific policies would be useful. Debates in the past on the stability of time spent in travelling to work seem academic as concern should be focused on identifying factors which affect peak travel. The success or failure of any policy depends on the response of heterogeneous commuters. lXvo important outcomes of the logit results were the statistical significance of journey time and schedule delay Alternate Specific Variables in explaining the choice of trip departure time. A 1% increase in journey time would ceteris paribus, decrease the overall probability of the choice of departure time by between 1.55 to 1.73%. This important implication shows the sensitivity of departure times to changes in

332

A. T. H. CKIN Table 5. Breakdown of commuter departure time by different modes

Time Mode*

Car Bus Motorcycle (MC) Van Car/MC Xssenger Walk Taxi Others

06000614

06300644

06450659

07000714

0 9

7 18 1 0 7

11 27 1 1 10

11 27 1 2 10

21 51 2 0 20

21 49 2 1 19

22 82 3 1 7

19 71 3 0 7

24 86 3 0 8

19 64 2 0 6

15 54 2 0 5

3 0 2

3 1 1

4 0 2

4 2 2

7 2 4

7 0 4

6 0 4

5 0 4

6 1 5

4 1 3

3 0 2

10 23

1

06150629

07150729

07300744

07450759

OSOO0814

08150829

08300844

*Number travel times. This is contrary to the findings by Mc-

Cafferty and Hall (1982) which showed the insignificance of journey times. Occupational factors are also important. For example, persons engaged in business, trade, construction, administration, sales, and clerical were less likely to switch departure times (see model 2, BLGGIT results). The results are similar for higher income groups. Commuters earning less than $200 and above $1799 seem less likely to switch than those between $899 to $1299 (the elasticity range is between -0.28 to -0.29). Groups who are likely to switch are those between $299 to $699 (elasticity: -0.33 and -0.65) (see BLGGIT results, model 3). The seemingly reluctance of the ~$200 group of commuters to switch may be explained by the fact that most are factory/ production workers whose working hours are rigid, and transport to work is provided by employers. There is thus no flexibility in choice of departure time. The relatively low elasticity of higher income groups could be explained by social/habitual factors, a result which has been observed by Clarke (1985) and Shapcott and Steadman (1976). However, those within the former and latter groups make up only 3 and 12%) respectively of all commuters. This leaves 85% with some probability of being affected by any policy changes. Any change in work start time would alter schedule delays through travel times. Out of pocket cost and the ASV (TCMALE) did not exert an important influence on departure time. One interesting result is the disutility experienced by those who have to pay a fee to enter the Restricted Zone (the area covered by the ALS is referred to as the Restricted Zone) or by having to enter it early (before 0730, the time ALS is in operation). Indeed there is a group of commuters which have a high probability of changing departure times (23% in-

crease in probability for every 1% decrease in schedule delay: see logit results). As for the majority (approximately 70% take a bus, ride in cars, walk or cycle) the response to any change is small (0.15). This can be explained by rigidness of bus schedules, or dependance on the schedules of others (for example car pool). The indications are that those least likely to switch departure times, ceteris >aribus, are the lowest and higher income groups as well as those who a lack choice of mode of travel to work. The introduction of Mass Rapid ‘Bansit would most likely move commuters off buses to rail rather than from cars. To date the Singapore Bus System aims to reduce services by 35 A over the next five years through natural attrition. There is a possibility of bus commuters switching to other modes of transport and with it a change in departure times (for given mode characteristics such as speeds and comfort) but such conclusions remain premature. Finally, an interesting result from model 17 (Table 3) is the manner in which schedule delay is perceived by commuters. The results suggest that those who depart before 0730 do not seem to experience any disutility as opposed to those who do after 0730. This may be due to the presence of flexible working hours or from relatively less congestion on the road before 0730. 6. CONCLUSION In conclusion we suggest that traffic management policies should focus more on mode and user specific measures in order to ensure their success. The importance of institutional constraints such as work start time as a policy tool cannot be over emphasized in the absence of other elements such as traffic management, road pricing as well as other tax measures. It is

Table 6. Variations in travel activity given changes in departure times STARTIM

0630

0645

0700

0715

0730

0745

TRVACT: model 23* TRVACT. model 25* TRVACT. (actual)**

69.2 66.9 65.0

72.1 70.9 71.8

75.0 75.0 75.0

77.9 79.1 79.2

80.8 83.1 84.5

83.8 87.2 90.5

*predicted **mean

Commuter trip departure time

333

Table 7. Variations in travel activity given changes in work start time OFFWS

0730

0800

0830

0830

0900

0930

TRVACT: model 23 * TRVACT?model 25 * TRVACT: (actual)**

65.6 67.9 69.6

75.5 75.5 75.5

85.4 83.6 81.4

78.3 80.2 81.4

88.7 88.7 88.7

99.1 97.2 93.6

*predicted **mean

also paramount

for public transport operators to set fare structures right. The issue does not only concern the roles of public and private transport but also the efficient use of roads and transport facilities at all times of the day. To this end future studies should

focus on factors which not only affect travel characteristics such as travel cost and time but also factors which influence the commuter’s activity time schedule. The study of scheduling of activities and its related costs is an under researched area.

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