First principles assessment

Why introduce different fare levels

Fare levels tend to reflect the costs facing operators. As such fare levels will tend to differ between modes and also within mode between different operators. Over time fare levels will tend to rise to reflect the rise in costs facing operators, however fare levels can also differ in the short run for the same service, for example peak and off-peak fares. This reflects a desire of the operator to maximise his profits by introducing price segmentation into the market place and charging what the market will bear. It also reflects a desire on behalf of the operator to spread their passenger loading throughout the day to ensure that every passenger who wishes to travel can do so, and that the level of service offered to those passengers is at least perceived to be of an acceptable standard.

In countries and regions were fares are regulated and controlled by central or local government, fares may be changed to reflect a desire to improve accessibility/equity throughout the general population. A desire to reduce car use to reduce environmental externalities and improve efficiency are other possible policy aims which can aided by reducing fare levels.

Demand impacts

When fare levels change they influence the level of demand for public transport. In general, all other things being equal, an increase in fares will reduce patronage, whilst a decrease in fares will increase patronage. The size and direction of the change in demand following a change in fares can be expressed in terms of a fare elasticity and is defined as,

For example, if the fare elasticity of bus demand with respect to bus fares is –0.4, and all fares were to increase by 10% we would expect patronage to decrease by 4%. The fare elasticity is therefore a measure of the price sensitivity of bus passengers.

The absolute size of the fare elasticity conveys information on the sensitivity of demand to changes in the factor affecting demand and its sign conveys information on the direction of the change. Fare elasticities are defined as inelastic if they are less than 1.0 and elastic if they are greater than 1.0. The larger the fare elasticity the more sensitive passengers are to changes in the fare.

A wide range of factors influence the size of fare elasticities, and are considered in the following sections, e.g. current fare levels, size of fare change, service quality etc. Whilst these factors can be discussed in isolation it is likely that more than one of them will exert an influence at the same time.

There are a number of factors that will influence the size of the fare elasticity and these are listed below:

• Fare levels – the higher the current fare the more sensitive passengers will be to fare changes.
  
• Size of fare changes – the larger the change in the fare the more sensitive passengers will be to the fare change.
  
• Income levels – those on high incomes are less likely to be sensitive to changes in bus fares, whilst those on low incomes more sensitive.
  
• Service quality – passengers may be less sensitive to fare changes if the quality of service is high.
  
• Competition from other modes – strong competition from other bus operators and from other modes of transport will make passengers more sensitive to fare changes.
  
• Socio factors – Males tend to be more sensitive to fare changes than females. The elderly and school children are also more sensitive to fare changes.
  
• Journey purpose – travellers commuting to work or school tend to be less sensitive to fare changes, whilst leisure travellers are more sensitive.
  
• Distance – passengers will be more sensitive to changes in fare if they are only travelling short distances since walking is always an option.
  
• Urban vs Rural – passengers tend to more sensitive to fare changes in rural areas compared to passengers in urban areas.
  
• Area - passengers tend to be less sensitive to fare changes in metropolitan areas compared with non- metropolitan areas.
  
• Peak vs Off Peak – passengers tend to be less sensitive during peak periods of travel, compared with off-peak periods of travel.

The recent publication, Demand for Public Transport Publication (TRL, 2004) compares short run bus, metro and suburban rail fare elasticities for both the UK and non-UK systems.

Table 1 Public Transport Fare Elasticities (short run)

(TRL, 2004)

The UK fare elasticities are higher than the non-UK values, which may reflect the lower fare levels (due to higher levels of subsidy) and better quality of service found in many non-UK countries. Metro (underground) has the lowest fare elasticities, hence metro passengers are least sensitive about a change in price. So for example a 10% increase in fare levels would reduce patronage on the metro by 3.1% as compared with a !0% increase in fare levels for suburban rail which would reduce patronage on the railway by 5.8%. Metro’s low elasticity reflects the main advantage it has over other modes in a major city environment, namely its ability to offer a fast method of travel between the city centre and outer urban areas. Road congestion prevents the bus or car from offering a comparable service, whilst suburban rail does not have the same network penetration and walking takes too long. Suburban rail has the highest elasticity and this may reflect the fact that the car is the next most preferred mode. The cost of suburban rail is quite high when compared with the car and a moderate change in rail costs might be enough to persuade a rail user to switch to the car.

This last point illustrates the interaction between modes and the choices faced by passengers when experiencing an increase in costs for the mode they currently use. The impact on the demand of one mode as a result of competition from another mode is measured by cross elasticities. These elasticities measure the change in demand for a given mode as a result of the change in one of the factors associated with another transport mode (mainly fare or service frequency). Cross elasticities tend to be very specific to the relative market share they are estimated from and so are not easily transferred across time and space.

The impact of cross elasticities can be complex as a change in one factor can impact very differently across different modes of transport. The bulk of the evidence suggests that the cross elasticity of car with respect to changes in public transport characteristics is low. For public transport demand with respect to car characteristics the evidence suggests a somewhat larger elasticity, whilst the cross elasticities between public transport modes are also more substantial. The factors influencing cross elasticities are listed and outlined below,

• Relative Market Shares - this has already been touched upon but in practice if bus has a major share of an existing market then an improvement in the competition will have a smaller impact than if bus’s share was minor. That is to say that a large market share for bus would indicate that other modes are not perceived to be good substitutes to bus for a variety of reasons, quality of current service, price of current services, range of services etc.
  
• Own Mode Elasticity – the higher the own mode elasticity the greater the scope for passengers to substitute it for other transport modes. Again this is related to the current price and service quality of a mode and would indicate that other modes are seen as good substitutes.
  
• Substitution – Bus and underground (in London) and inter-urban coach and rail (in non-London areas) are seen as close substitutes for each other and have similar sized cross elasticities. This contrasts with bus and car in both London and non-London areas, which are not seen as close substitutes.

Cross elasticities of demand are difficult to interpret, as they are partly dependent on modal shares. Dargay & Hanly (1999) suggest an elasticity of + 0.02 for car use with respect to bus fare. An earlier review by Dodgson (1990) found the most convincing elasticities for car used with respect to bus fare to be + 0.03 in London and + 0.01 in provincial UK cities. He reports that the low value outside London reflects the low modal share of public transport in non-London regions. Grayling and Glaister (2000) use a cross elasticity of + 0.09 for London, whilst London Transport

We now present the demand impacts on car kilometres from both an increase and a decrease in fare levels for public transport. It should be noted that the biggest impact on car travel would come from a change in the fare for rail travel since rail users are more likely to switch to car than bus users, who are less likely to have access to a car, and metro users for whom the car is not a viable option due to congestion levels in the cities.

Table 3 Public Transport Fare Increase  - Demand Impacts

	= Weakest possible response,		= strongest possible positive response
	= Weakest possible negative response,		= strongest possible negative response

= No response

Table 4 Public Transport Fare Decrease  - Demand Impacts

Response	Reduction in road traffic	Expected in situations
	-	May switch from off-peak period travel to peak travel for non-commuting trips.
	-	Possibly change route as car users switch to public transport.
		Some local journeys might be replaced with longer journeys , e.g. shopping trips.
		Decrease as some car passengers switch to public transport service.
		Some passengers with car access shift to public transport.
	-	Unlikely.
	-	Highly unlikely

	= Weakest possible response,		= strongest possible positive response
	= Weakest possible negative response,		= strongest possible negative response

= No response

Level of Response

In the long run we would expect more public transport users to purchase a car or to move jobs/house in order to reduce the distance they have to travel, following an increase in public transport fares.

Table 5 Public Transport Fare Increase  - Demand Impacts

	= Weakest possible response,		= strongest possible positive response
	= Weakest possible negative response,		= strongest possible negative response

= No response

In the long run we would expect more public transport users to make additional trips and for some car users to switch to public transport for some trips.

Table 6 Public Transport Fare Decrease  - Demand Impacts

	= Weakest possible response,		= strongest possible positive response
	= Weakest possible negative response,		= strongest possible negative response

= No response

Short and long run demand responses Supply impacts

In the short term a change in public transport fares and the change in patronage it triggers is unlikely to have any impact upon the level of service provided by operators unless a large increase in passengers, following a reduction in fares, led to severe overloading.  Over the long-term operators are more likely to reconfigure their services to take into accounts overloading, different movements in populations and changes in land use. In reality any such changes would not be driven solely by changes in fares, but would a contribution of factors of which fares would be one. 

Expected impact on key policy objectives

Table 7 Fare Levels : Expected Impacts - of an Increase in Fares

Objective	Scale of contribution	Comment
		Increases congestion and delays due to public transport users switching to car.
		Increases community severance
		Increases air and noise pollution.
		Low income users cannot afford to travel as often.
		Additional accidents from more traffic.
		Higher congestion reduces time for more productive work
		Increased revenue for public transport operators.

	= Weakest possible positive contribution,		= strongest possible positive contribution
	= Weakest possible negative contribution		= strongest possible negative contribution

= No contribution

= Unknown contribution

Table 8: Increase in Fare Levels, Expected Impact on Problems

Contribution to alleviation of key problems

Problem	Scale of contribution	Comment
Congestion-related delay		By increasing traffic volumes
Congestion-related unreliability		By increasing traffic volumes
Community severance		By increasing traffic volumes
Visual intrusion		By increasing traffic volumes
Lack of amenity		By increasing traffic volumes
Global warming		By increasing traffic volumes
Local air pollution		By increasing traffic volumes
Noise		By increasing traffic volumes
Reduction of green space		By increasing pressure for new road building and less dense car orientated development
Damage to environmentally sensitive sites		By increasing traffic volumes
Poor accessibility for those without a car and those with mobility impairments		Increasing cost of travel and therefore reduced accessibility
Disproportionate disadvantaging of particular social or geographic groups		Increasing cost of travel will disproportionately affect the socially excluded with no car available.
Number, severity and risk of accidents		By increasing traffic volumes
Suppression of the potential for economic activity in the area		Higher congestion may reduce productivity and, along with the increased cost of public transport, may deter people and businesses from locating in the area. On the other hand, a possible reduction in subsidy requirement and therefore taxes may stimulate economic growth.

	= Weakest possible positive contribution,		= strongest possible positive contribution
	= Weakest possible negative contribution		= strongest possible negative contribution

= No contribution

= Unknown contribution

Table 9: Decrease in Fare Levels, expected impacts on objectives

Objective	Scale of contribution	Comment
		Reduces congestion and delays due to public transport users switching to car.
		Decrease in community severance
		Reduction air and noise pollution.
		Low income users can afford to travel more often.
		Reduction in accidents from reduced traffic.
		Lower congestion reduces time for more productive work
		Reduced revenue for public transport operators.

	= Weakest possible positive contribution,		= strongest possible positive contribution
	= Weakest possible negative contribution		= strongest possible negative contribution

= No contribution

= Unknown contribution

Table 10: Decrease in Fare Levels, Expected impact on problems

Contribution to alleviation of key problems

Problem	Scale of contribution	Comment
Congestion-related delay		By decreasing traffic volumes
Congestion-related unreliability		By decreasing traffic volumes
Community severance		By decreasing traffic volumes
Visual intrusion		By decreasing traffic volumes
Lack of amenity		By decreasing traffic volumes
Global warming		By decreasing traffic volumes
Local air pollution		By decreasing traffic volumes
Noise		By decreasing traffic volumes
Reduction of green space		By decreasing pressure for new road building less dense car orientated development
Damage to environmentally sensitive sites		By decreasing traffic volumes
Poor accessibility for those without a car and those with mobility impairments		Decreasing cost of travel and therefore increased accessibility
Disproportionate disadvantaging of particular social or geographic groups		Cost of travel increase will disproportionately affect the socially excluded with no car.
Number, severity and risk of accidents		By decreasing traffic volumes
Suppression of the potential for economic activity in the area		Reduced congestion may increase productivity and along with the reduced cost of public transport may encourage people and businesses to locate in the area. On the other hand a possible increase in subsidy requirement and therefore taxes may stimulate economic growth.

	= Weakest possible positive contribution,		= strongest possible positive contribution
	= Weakest possible negative contribution		= strongest possible negative contribution

= No contribution

= Unknown contribution

Expected winners and losers

The previous sections have already emphasised the fact that an increase in fare levels is likely to have a larger impact than a reduction in fare levels.  The main losers from an increase in fare levels will be people on low incomes and other road users in general.  The former will have reduced access to public transport whilst the latter are likely to experience an increase in traffic levels and so journey times.   The main winners will be the transport operators who will see an increase in revenue, despite experiencing a fall in patronage.

Table 11: Increase in Fare Levels: Winners/Losers

Winners and losers

Group	Winners / losers	Comment
Large scale freight and commercial traffic		More traffic and congestion on roads, increases journey times.
Small businesses	-	No change
High income car-users		More congestion on the roads increases journey times.
People with a low income		Reduces amount of travel they can afford.
People with poor access to public transport	-	No change.
All existing public transport users		An increase in costs. Tempered somewhat by a reduction in journey times (reduced boarding/alighting) and overcrowding.
People living adjacent to the area targeted	-	No change.
People making high value, important journeys		More traffic and congestion on roads, so an increase in journey times.
The average car user		More traffic on roads, so an increase in journey times.

	= weakest possible benefit,		= strongest benefit
	= weakest possible disbenefet,		= strongest possible disbenefit

= neither wins nor loses

The main winners from a decrease in the fare level will be people with low incomes and road users in general.  The former will be able to afford to travel more often and so access a wider range of goods, services and employment opportunity.  The latter will experience a small reduction in traffic levels and so an improvement in travel times.  The main losers will be transport operators who will experience a reduction in revenues despite an increase in patronage.

Table 12: Decrease in Fare Levels: Winners/Losers

Winners and losers

Group	Winners / losers	Comment
Large scale freight and commercial traffic		Less traffic and congestion on roads, very slight reduction in journey times.
Small businesses	-	No change
High income car-users		Less congestion on the roads so a very slight reduction in journey times.
People with a low income		Increases the amount of travel they can afford.
People with poor access to public transport	-	No change.
All existing public transport users		A reduction in costs. Tempered somewhat by an increase in journey times (increased boarding/alighting) and overcrowding.
People living adjacent to the area targeted	-	No change.
People making high value, important journeys		Less traffic and congestion on roads, so a very slight reduction in journey times.
The average car user		Less traffic on roads, so a very slight reduction in journey times.

	= weakest possible benefit,		= strongest benefit
	= weakest possible disbenefet,		= strongest possible disbenefit

= neither wins nor loses

= minimal barrier,

= most significant barrier