2.1 Introduction
This chapter reviews the literature on coach menu, income, service and transverse monetary value snaps based on surveies prior to and after 1990s. Major surveies reviewed have concentrated on the difference between short- and long-run snaps ; fluctuation of menu snaps across countries, by metropolis size and for different trip intents ; and, on the difference in menu snaps for little and big monetary value alterations. Research work on the kineticss of response to menu and other monetary value alterations in the field of bus demand have been scrutinized. It has to be noted that the literature reappraisal exposed here have placed accent on surveies which are partially correspondent in nature to the undertaking in manus. This chapter is organised as follows: a reappraisal of theories underlying bus demand and snaps is conducted in Section 2.2 followed by an overview of the empirical grounds in Section 2.3.
2.2 Theory
A brief overview of bus demand theory and the chief factors impacting demand are discussed in Section 2.2.1. After that, different impressions of snap and the factors impacting their appraisals are presented in Section 2.2.2.
2.2.1 Bus Demand Theory and Factors finding Bus Demand
Demand is defined as the measure of good or service that consumers are willing to purchase at a given monetary value in a given clip period. In our context, the service is bus and the monetary value is fare. Demand for bus conveyance or public conveyance in general is said to be a derived demand i.e. it is seldom in demand for its ain features, therefore derived from other map. For illustration, an single go forthing off from Port-Louis who desires to eat and shop at “ Le Caudan Waterfront ” would see bus conveyance as a agency to acquire at that place to fulfill his desire. So the more people are attracted to shop and eat in Port-Louis, the higher the demand for conveyance installations at that place.
Some factors finding the demand for coach services are outlined below:
Monetary value: the lower the monetary value, the more people are likely to demand the service offered.
Relative monetary values charged by different modes/operators: e.g. the lower the monetary value of cab menus, the more people will switch from coach to taxi travel.
Passenger income: as income rises, so the sum of going for both leisure and concern trips will increase.
Quality of service: the more comfy, safe, dependable and faster it is, the more people are encouraged to utilize coach travel.
2.2.2 Elasticity Concept and Factors impacting Transit Elasticities
Fare is a anchor in the operation of public conveyance as it generates gross to operators. The effects of menu alterations on coach backing, ridership or merely bus demand, can be measured in footings of snaps. Economists define snaps as a measuring of monetary value sensitiveness of the per centum alteration in ingestion of a good caused by a 1 % alteration in its monetary value or other features.
Unit snap refers to snap with an absolute value of 1.0: monetary value alterations cause relative ingestion alteration. Elasticity values less than 1.0 in absolute value are called inelastic: monetary values cause less than relative alteration in ingestion. They are elastic if they are greater than one in absolute value: monetary values cause more than relative alterations in ingestion.
In our context, the value of the menus snap is the ratio of the relative alteration in backing to the relative alteration in menus. By and large, a coach menu addition brings to a autumn in bus backing i.e. they are reciprocally related. A nothing to -1 value implies that a fare addition will take to increased gross. A more than -1 value suggests that a fare addition will be characterized by a diminution in gross.
There be several methods for the computation of snaps ( Pratt, 2003. The shrinking ratio or factor, which is non an snap step, explains the alteration in demand relation to original demand divided by the alteration in monetary value relation to the original monetary value.
The consequence of the costs of alternate manners on coach backing can be interpreted by doing usage of cross-price snaps. These assess the per centum alteration in measure demanded for, say coach travel, in response to a per centum alteration in the monetary value of another service, such as motoring costs. For substitutable goods and services, the cross-price snap is positive while for complementary 1s, it is negative. If the two goods/services are mugwumps, it would be zero.
The income snap, which measures the per centum alteration in measure demanded with regard to a 1 % alteration in income, ceteris paribus, is one manner to analyze the consequence of income on public conveyance demand. A less than one income snap is attributed to a necessity/normal good. A greater than one income snap reflects the feature of a luxury/superior good while a negative snap is associated with inferior goods connoting that a rise in income brings to a autumn in the ingestion of a good. A zero income snap occurs when an addition in demand has no consequence on the demand for a good.
Service snap indicates how transit ridership is affected by theodolite service quality factors such as handiness, convenience, velocity and comfort based on theodolite vehicle milage, hours and frequence ( Kittleson & A ; Associates, 1999 ; Phillips, Karachephone and Landis, 2001 ) . It is defined as the per centum alteration in theodolite ridership ensuing from each 1 % alteration in theodolite service. A negative mark indicates that the consequence operates in the opposite way from the cause ( an addition in monetary value causes a decrease in travel ) .
Taylor and Fink ( 2003 ) classified the factors act uponing theodolite snap analysis into external and internal. External factors such as gasolene monetary values, can non be controlled by theodolite systems while internal 1s like menu degrees are governable. These are summarized below:
User type
Transit dependent travelers and discretional or “ pick ” travelers ( people who may utilize an alternate manner for that trip ) response otherwise to a menu alteration. Because of this difference, we say that there is a crick in the demand curve ( Clements, 1977 ) , as illustrated in Figure 1. By and large, basic theodolite that serves transit dependent users is the less elastic part of the demand curve, while service that attracts discretional theodolite users is the more elastic part of the curve. Non-drivers and college pupils are illustrations of dependent theodolite riders while drivers are considered as independent theodolite users.
Figure 1: A Kink in the Demand Curve
Monetary value
Ridership
Beginning: Todd Litman ( 2007 )
Time period
Fare snaps are dynamic in nature i.e. they vary well with clip as a consequence of menu alterations. It is of import to separate between short-run ( SR ) , medium-run ( MR ) and long-term ( LR ) . Explanations on SR, MR and LR snaps can change among writers. Most writers define SR to be 1 or 2 old ages ; MR to be about 5 to 7 old ages while LR to be 12 to 15 old ages, or even more.
Geography
Residents of big metropoliss are much more dependent on public conveyance than those in smaller metropoliss for the merely ground that the more populated a metropolis is, the greater the demand for coach services.
On the other manus, people in rural countries with low population denseness are likely to trust more on autos and less on public conveyance than urban occupants as they have the option to exchange to auto if menu rises. So, fare snaps may differ by country type.
Trip intent
Peak journeys normally involve work and instruction journeys and are likely to be fixed in clip and infinite while off-peak journeys which include leisure, shopping and personal concern trips, are more flexible in footings of finish and clip. So, off-peak snaps will differ to top out 1s.
Type of monetary value alteration
Fare snap is besides affected by the current degree of the menu. In other words, people will response otherwise to high and low menus imposed.
Increase in fuel monetary values is the chief cause of lifting coach menus. If fuel monetary values lift up, coach menus will increase and cut down, in its bend, bus travel as less people will be given to go. This consequence can be evaluated through the measuring of snaps for coach backing with regard to fuel costs.
Trip coevals
Fare snaps for concessionary menus may non be the same as for full menus. Offering concessionary menus to certain groups of riders will convey to extra trips. Concessionary menus are those menus provided to pupils, aged and handicapped people who are provided with a free coach base on balls entitling the holder to menus half the standard grownup menus, lower or even free depending on the trip intent.
2.3 Empirical Evidence
This subdivision summarizes the chief findings of surveies related to bus menu snaps and underpins some of the theories discussed in the old subdivision. Most writers have used time-series analysis to analyze the impact of menu alterations in European and American states. The major issues considered are approximately the consequence of menu alterations for different countries and trip intents ; by metropolis size ; at different clip periods and menu degrees, and besides the influence of income, auto ownership, service degree, employment and motoring costs on bus demand. It would be noticed that some groundss are non to the full detailed because they have been re-examined from literature reappraisals which do non supply information on the methodological analysiss used.
2.3.1 Effect of Empirical Elasticity Estimates
Curtin ( 1968 ) developed a simple step to analyze the impact of menu alterations on theodolite ridership known as the Simpson-Curtin expression. The expression is derived from a arrested development analysis of before-and-after consequences of 77 surface theodolite ( coach and street auto ) menu alterations. It is calculated as follows:
Y = 0.80 + 0.30X,
Where:
Y= Percent loss in ridership as compared to prior ( before ) ridership
X= Percent addition in menu as compared to the anterior ( before ) menu
He found an mean menu snap of -0.33 ; i.e. a 10 % addition in menu would ensue into a 3.3 % loss in backing. However, the Simpson-Curtin expression has proved to be inaccurate today and excessively simplistic as it does non see menu impacts between extremum and off-peak hours or between big and little metropoliss.
Webster and Bly ‘s ( 1980 ) reappraisal of public conveyance snaps suggested a sensible regulation of pollex of fare snap of -0.3, which was acceptable in the 1980s but started to look as if there was a impetus upwards in the menu snap to someplace in the scope -0.3 to -0.4 or more, in the 1990s.
Consequence of menus by metropolis size
Unlike the Simpson-Curtin expression, Pham and Linsalata ( 1991 ) evaluated fare snaps of a sample of 52 US theodolite systems for the North America and categorized the snaps of seven systems into peak and off-peak hours in the 1980s by using an ARIMA theoretical account. On the footing of before-and-after time-series informations and with log of rider trips as the regressand and log of chapfallen mean menu among the explanatory variables, the theoretical account was estimated utilizing OLS. Transit systems of different sizes in big and little metropoliss are represented.
Table 1 summarizes the ensuing fare snap estimations. All-hour menu snap for all system averaged at -0.40: a 10 % addition in bus menus, on norm, would take to a 4 % diminution in ridership in little and big metropoliss, which is higher than the Simpson-Curtin expression.
Table 1: Bus Fare Elasticities ( Pham and Linsalata, 1991 )
Large Cities ( More than One Million Population )
Small Cities
( Less than One Million Population )
Average for All hours
-0.36
-0.43
Top out Hour
-0.18
-0.27
Off-Peak
-0.39
-0.46
Off-Peak Average
-0.42
Top out Hour Average
-0.23
Beginning: American Public Transit Association, 1991
Pham and Linsata ( 1991 ) found that occupants of little metropoliss response more to do alterations than those go forthing in big metropoliss, with mean fare snaps of -0.36 and -0.43 severally. Average peak hr snap is estimated at -0.23 while off-peak hr snap is -0.42 connoting that peak-hour riders are more monetary value medium to do fluctuations than those going during off-peak hours. Alternatively, a 10 % addition in menus, on norm, would take to a dual loss of off-peak riders as compared to top out 1s: 4 % against 2 % severally.
Similarly, the ISOTOPE[ 1 ]( 1997 ) survey of fluctuation of snap by metropolis size based on a sample of 89 European metropoliss, postulated that coach menu snaps are greater in little metropoliss ( a population of less than one million ) than in big metropoliss ( a population of more than one million ) : -0.50 compared to -0.34 correspondingly. Harmonizing to ISOTOPE:
“ the lower menu snap in big metropoliss reflects the greater grade of imprisonment to public conveyance due to longer journey distances ( doing walking less attractive ) , a greater congestion and parking jobs ( doing auto less attractive ) . ”
Webster and Bly ( 1980 ) ‘s survey for North American metropoliss disproved this statement by demoing the contrary, i.e. menu snaps for larger metropoliss are higher than for smaller 1s. Dargay and Hanly ( 1999 ) argued that this is because European metropoliss are physically little and more engorged than North American metropoliss.
Consequence of menus by clip period
Goodwin ( 1992 ) conducted a reappraisal of 50 demand snaps for coach usage, derived from surveies for the UK and elsewhere, and calculated a non-weighted norm of -0.41. Table 2 summarises the snap values categorized by type of survey and clip period covered by the surveies ( either explicitly or implicitly ) .
Table 2: Bus menu snaps related to clip period
Type of survey
Time period
Average snap
Standard divergence
No. in sample
Before and after
around 6 months
-0.21
0.12
3
Explicit short
0-6 months
-0.28
0.13
8
Unlagged clip series
0-12 months
-0.37
0.18
24
Explicit long
4+ old ages
-0.55
0.20
8
Equilibrium theoretical accounts
5-30
-0.65
0.18
7
Beginning: Goodwin, 1992
He brought about that the inactive snap figure of -0.3 ( Webster and Bly 1980 ) , estimated from unlagged time-series informations, is well-founded for the effects within the first twelvemonth ( SR ) , and that the consequence after four old ages or MR, would be -0.55, lifting to -0.65 over a period of about a decennary. Some surveies have found higher values ( up to -0.98 ) and others lower ( down to -0.45 ) .
By presuming monetary values of all other manners to be held changeless and utilizing complex econometric theoretical accounts, Gilbert and Jalilian ( 1991 ) estimated coach menu snaps in London of -0.8 in the SR and -1.2 to -1.3 in the LR: a well higher absolute degree compared to those of other surveies reviewed in Goodwin ( 1992 ) and Fowkes et Al ( 1992 ) . The LR value of greater than integrity implies that gross would diminish when menus were increased.
2.3.1.1 Effect of other exogenic variables
Kain ( 1997 ) examined the effects of other exogenic variables on coach ridership for the US as a whole for the interval 1972-1974 to 1981-1983 and used a silent person for 1980 to reflect the impact of higher fuel monetary values in 1980. He applied OLS method to an econometric theoretical account associating one-year theodolite trips to existent mean menus ( -0.29 ) ; service stat mis ( 0.56 ) ; vehicle size ( 0.45 ) ; existent fuel monetary values ( 0.05 ) ; employment ( 0.41 ) ; a tendency ( -0.02 ) and a silent person ( 0.05 ) . All explanatory variables except the silent person and the tendency were expressed in natural logarithms. On norm, any 10 % rise in service degree, employment and fuel monetary values will be accompanied by rushs of 5.6, 4 and 0.5 % , severally, on ridership.
De Rus ( 1990 ) derived snap estimations for menu ad service degree for eleven Spanish conveyance operators between 1980/88. . He employed both inactive and dynamic dual log additive demand theoretical accounts utilizing OLS appraisal with rider trips per month as the regressand and the chapfallen monetary value of an ordinary ticket and vehicle kilometers per month as the regressors. These are reported in Table 3.
Table 3: Dynamic versus Static snap estimations
Menu
Service
Inactive
-0.16 to -0.41
0.34 to 1.26
Dynamic SR
Lawrencium
-0.06 to -0.39
-0.09 to -0.49
0.26 to 1.54
0.64 to 1.88
Beginning: Writer
Both specifications rather give similar consequences. However, the inactive menu snaps seems to meet to the SR 1s, as Goodwin ( 1982 ) stated antecedently. Bus demand is found to be ‘service ‘ elastic while ‘fare ‘ inelastic in both SR and LR. Thus, alterations in the quality of service will be given to do greater alterations in backing than fluctuations in menu monetary values.
Effectss of other exogenic variables by country type
In order to look into differences in menu snaps between urban and less urban countries, Dargay and Hanly ( 1999 ) estimated separate partial accommodation theoretical accounts tie ining coach journeys per capita to bus menus ( existent mean gross per journey ) ; existent disposable income and service degree ( bus kilometers ) , for the Shire counties and the Metropolitan countries in UK, based on pooled informations between 1986-1996. The estimated snaps are illustrated in Table 4. OLS methods were run on dual log specifications.
Table 4: Estimated snaps based on pooled informations for English Shire counties
and Metropolitan countries. Partial Adjustment Model.
Menu
Income
Service
SR LR
SR LR
SR LR
Shire counties
-0.51 -0.70
-0.64 -0.87
0.64 0.87
Metropolitan countries
-0.21 -0.43
-1.02 -2.08
0.35 0.71
Beginning: Dargay and Hanly, 1999
They found that travelers from less urban countries are more sensitive to do alterations than those from urban 1s. This is because people have the option of traveling by auto in rural countries ( due to higher auto ownership, less route congestion ) . White ( 2002 ) argued that short-term snaps may be highest in suburban countries due to bing wider modal picks. Further, LR income elasticties for urban countries are two times the snaps for less-urban countries. Quality of service turns up to impact more positively in less urban countries. The coefficients of accommodation ( 1-I? ) for the urban and less-urban countries amounted to 0.49 and 0.74, severally. To sum up, less urban people tend to set quicker to internal and external alterations.
Effectss of menus and other exogenic variables at different menu degree
Dargay and Hanly ( 2002 ) , still utilizing partial accommodation attacks, specified two different theoretical accounts: a changeless menu snap model/log-log specification and a variable menu snap model/semi log specification where merely the menu variable is expressed in flat term, leting the menu snap to change at different menu degrees. The theoretical account estimations are summarized in Table 5.
They noticed that high menus ( a‚¤ 1 in 1995 monetary values ) were associated with higher menu snaps than with low menus ( 27p in 1995 monetary values ) in UK. For case, when the get downing point of a fare addition is comparatively high, the loss in ridership would be six times higher than with the low menu addition in the LR: -0.26 against -1.54 severally.
Table 5: Changeless versus Variable Fare Elasticities
Menu
Income
Service
Driving costs
% Pensioners
Specification
SR LR
SR LR
SR LR
SR LR
SR LR
Changeless
-0.33 -0.7
-0.39 -0.81
0.49 1.02
0.32 0.67
( -0.08 ) ( -0.17 )
Variable
Minimum = 17p
Average = 56p
Maximum = a‚¤ 1
-0.13 -0.26
-0.41 -0.86
-0.74 -1.54
-0.39 -0.81
0.47 1.0
0.35 0.73
( -0.01 ) ( -0.02 )
Beginning: Dargay and Hanly, 1999
Note: snaps in parentheses are non statistically different from nothing.
The other estimations from the changeless theoretical account are practically the same as those derived from the semi log specification. Driving costs ( auto purchase and running costs ) show a positive influence on demand in both SR and LR, bespeaking the average permutation consequence from auto to bus travel. Although insignificant in values, the consequence of pensionaries is negative on backing. The coefficient of accommodation estimated at 0.48 in both specifications, bespeaking that accommodations in UK coach backing with regard to alterations in controlled and uncontrolled factors, are rather low. This explains the big disagreements between the SR and LR snaps.
Romilly ( 2000 ) and Oxera ( 2003 ) employed mistake rectification theoretical accounts for the appraisal of bus demand snaps for the British Industry. Romilly based his survey on one-year informations for the British coach industry outside London 1953-1997 while OXERA had used informations for the Government office parts ( excepting London and Northern Ireland ) between 1985/6 and 1999/2000. The ensuing estimations are illustrated in Table 6.
Table 6: Mistake rectification theoretical account estimations of bus demand snaps: Passenger Journeies
Bus Fare
Bus Service
Income
Car-price
Survey
SR LR
SR LR
SR LR
SR LR
Romilly ( 2001 )
-0.38 -1.03
0.11 0.30
0.23 0.61
0.17 0.45
OXERA ( 2003 )
-0.63 -1.08
0.38 0.37
0.60 -0.56
Beginning: TRL Report 593, 2004
While Oxera did non describe the feedback parametric quantity ( I?-1 ) , in Romilly model it was estimated at -0.37, connoting that rider journeys adjust in a given clip period by 37 % . Since Romilly excluded motoring costs in his theoretical account, the estimated snaps seem somewhat higher. Demand for local coach came up to be ‘fare ‘ elastic in the LR.
In line with old surveies reviewed, OXERA found negative LR income snaps whereas Romilly revealed positive 1s. This is because he included a clip tendency among the explanatory variables.
Clark ( 1997 ) reported auto ownership snaps of -1.04 ( SR ) and -1.43 ( LR ) for Great Britain as a whole by utilizing a lagged dependant silent person variable arrested development. Separate snaps were besides derived for London ( -0.70 ) , the Metropolitan countries ( -1.04 ) , Wales ( -2.01 ) and Scotland ( -1.35 ) by running simple separate arrested developments. On the whole, a individual in a auto owning family is likely to do important fewer coach trips in both SR and LR. On the other manus, Dargay and Hanly ( 1999 ) found negligible consequence of auto ownership on backing in the SR by utilizing a structural attack. The LR snap amounted to -0.73. The kineticss were represented by an mistake rectification theoretical account and leaden 3-stage least squares for the appraisal.
Meta-analysis of menu snaps
Meta-analysis is a statistical process for pooling together the research findings from different empirical surveies based on similar facts and developing a quantitative theoretical account explicating fluctuations in consequences across surveies.
Holmgren ( 2007 ) used meta-regression to lucubrate on the differences of snap estimations obtained in old surveies. He estimated short-term U.S snaps with regard to do monetary value ( -0.59 ) , degree of service ( 1.05 ) , income ( -0.62 ) , monetary value of gasoline ( 0.4 ) and auto ownership ( -1.48 ) .
2.4 Decision
A most noticeable characteristic of the reappraisal is the difference in the snaps obtained for the different surveies. This is chiefly due to the unsimilarities in informations, methodological analysis used and state of affairss analyzed. We found that menu snaps differ in footings of trip intent, clip of the twenty-four hours, clip period, menu degrees and by countries and metropolis size, in the developed states. Despite these fluctuations, some general decisions sing the snaps can be drawn on the footing of some “ likely ” values and scopes.
Elasticity values were found to change at different clip positions. The grounds suggests that LR snaps are from 1.5 to over 10 times the SR snaps. The SR menu snaps range from -0.06 to -0.6 while the LR values diverge from -0.09 to -1.08. Therefore, alterations in menu would convey to more than relative alterations in coach backing in the LR instead than the SR. There is grounds that bus demand is more antiphonal at higher menu degrees in both SR and LR than at low menu degrees in the UK.
Peak travel is found to be less price-sensitive than off-peak travel. Top out hr snap is likely to be around -0.2 while off-peak one is -0.4 for the US. Both peak and off-peak travels are inelastic. The empirical grounds besides suggests that all SR and LR snaps are higher in rural than in non-rural countries.
Fare snaps for smaller metropoliss ( less than one million populations ) are found to be greater than for big metropoliss ( more than one million population. In the US, the snaps for little and big metropoliss are around -0.4 and -0.3 severally.
There is a high grade of uncertainness about the expected marks on income snaps. As a consequence of the different methodological analysiss used, the income snaps appear to be either positive or negative in both SR and LR. The SR snaps range from 0.2 to -1.0 while in the LR they vary from 0.6 to -2.1. The negative estimations reflect the impact of income through its positive consequence on auto usage and their negative effects on coach backing. The grounds recommends that the negative mark will go positive as auto ownership reaches impregnation. Therefore, the demand for public coach service is likely to be income elastic in both SR and LR. Car ownership snaps range from 0 to -1.5 in the SR while the LR estimations diverge from -0.7 to -1.4. Hence, auto ownership is likely to hold no influence on backing in the SR.
Driving costs have a positive impact on coach usage, therefore bespeaking the price-substitution between coach and auto usage. The grounds show that LR car-price snaps are 2 to over 3 times the SR values. The SR estimates lie between 0.2 and 0.4 piece in longer periods they range between 0.5 to 0.7.
Service and employment degrees are found to impact positively bus demand. In the SR, the service elasticities range between 0.1 and 1.3 piece in longer periods they range from 0. 3 to 1.9.
The grounds from the UK states was unable to observe any impact of the per centum of pensionaries on coach demand, although the relevant snaps were found negative.
