Bus Services

This measure was fully updated by INSTITUTE FOR TRANSPORT STUDIES (ITS) in 2014 under the CH4LLENGE project, financed by the European Commission.


Public transport services refer to the entire range of transport services that are available to the public including demand responsive transport, buses, trams, light rail systems, metro (underground) and long distance rail services.  There are a number of broad, and sometimes conflicting, aims which may influence considerations about public transport service levels and patterns.  These include:

  1. To provide a service responding to travel needs of people who do not currently use public transport.
  2. To improve the quality of service for existing passengers - to help maintain the existing passenger base and, in some cases, generate additional trips.
  3. To be cost effective - many private bus operators attempt to tailor the service levels they offer to meet demand, e.g. lower in the off peak period.
  4. To contribute to social inclusion, for instance by providing services meeting needs of people without private vehicles and for whom walking and cycling are not viable.

In considering service levels, patterns, and quality of service, this note mentions financial aspects of service provision. However it does not consider in detail, different models of regulation such as quality contracts, franchising, and public ownership. These are considered in regulation of bus services.

Description

Public transport services refer to the entire range of transport services that are available to the public including demand responsive transport, buses, trams, light rail systems, metro (underground) and long distance rail services.  

Service levels can be defined according to a number of dimensions, the key ones being the frequency of public transport services (i.e. ‘regular interval and clockface timetables’, ‘combined service frequencies’, and reliability), the hours they operate (period of operation), where they operate and the origins and destinations they serve (both related to network coverage). Network coverage is an important issue (the density and extent of the network). If services are concentrated along main corridors then the network coverage is likely to be poor and result in lower levels of accessibility for passengers than when services have a greater spread.

Frequently consideration of means of encouraging modal shift from private vehicles involves a focus on adjusting fares to compete with cost of driving, as discussed under public transport fares. However the quality of the public transport trip can also play a crucial role in determining the entire decision process on whether to use public transport for a particular journey).  Moreover aspects of quality, especially matters of information provision and security, are crucial in enabling passengers to feel confident and safe while using public transport (SEU 2003).

In relation to transport, Metz has stated that the “quality of a journey is a function of comfort, reliability, safety, and security” (2005, p. 357). Therefore service levels are also seen as a factor in determining the quality of public transport services as they influence reliability.  Karu et al (2007) consider quality aspects in their public transport quality evaluation to be public transport reliability, security, frequency, times of operation, schedule and routes, public transport cost as well as the preference for car usage or walking.

Terminology

A change in service frequency will impact upon passenger demand in a number of ways and will have an impact on the quality of public transport provided. However in terms of terminology there are three which perhaps need to be clarified and result from a change in service frequency and impact greatly on the quality element of reliability.

Schedule Delay Time - This is the difference between when a passenger would most like to travel and the scheduled time of travel. This could involve the time spent waiting at home or at work before walking to the bus stop. The estimation of schedule delay time depends upon when passengers would ideally like to travel, the timetable and passengers’ preferences for arriving early or late. As service headway (time between two particular buses normally calculated as 60mins/number of buses per hour) increases it is likely that this element of generalised cost (the monetary and time cost of a journey) will increase in importance relative to scheduled wait time.

Schedule Wait Time - This is the time spent waiting at the stop and is usually taken to be a function of the service headway and service reliability. Where services are frequent and at regular intervals, passengers are assumed to arrive at the bus stop at random and are therefore assumed to wait on average a time equal to half the service headway. Where services are infrequent, it is assumed that passengers time their arrival at the bus stop to coincide with the arrival of the bus.

Excess Wait Time - This is additional time spent at a bus stop/station when the passenger has been unable to board the first bus due to overcrowding. This component is largely beyond the control of the passenger and is a function of the demand compared to capacity of the service.
Without knowing more about the desired departure times of passengers it is difficult to accurately estimate schedule delay time and excess wait time and for this reason it is common to find scheduled wait time as the sole representative of service frequency in the specification of generalised cost, e.g. half of the headway = (30 mins/number of buses per hour). It should be noted that wait time will also be affected by service unreliability and cancelled services. These are considered separately under bus service management.

Technology

It is technically relatively simple for bus operators to implement changes to their current service levels on a daily, weekly or monthly basis although timetables will need to be altered accordingly. A simple light rail network may also be relatively simple technically to amend in terms of service levels. Heavy rail systems with complex networks are far more difficult to amend. Heavy rail operates from highly specialised infrastructure, where safety procedures are strictly adhered to. As such there is a high level of careful co-ordination between all services that mean changes to one service have considerable knock on effects to the network as a whole. For these reasons any changes in service levels have to be co-ordinated at a network level and if there are several operators then they must involve discussions with all affected service operators. As such changes to service levels can take several months to be approved and implemented.

Why introduce changes to service levels?

There are a number of broad, and sometimes conflicting, aims which may influence considerations about public transport service levels and patterns.  These include:

  1. To provide a service responding to travel needs of people who do not currently use public transport. There can be several underlying reasons for this, for instance:
      1. to increase passenger base to secure viability of the public transport services;
      2. to reduce congestion by persuading people to switch from private vehicle to public transport;
      3. to support employment by improving connectivity between residential and employment areas (see Marsden et al. 2013);
  1. To improve the quality of service for existing passengers - to help maintain the existing passenger base and, in some cases, generate additional trips.
  2. To be cost effective - many private bus operators attempt to tailor the service levels they offer to meet demand, e.g. lower in the peak period.
  3. To contribute to social inclusion, for instance by providing services meeting needs of people without private vehicles and for whom walking and cycling are not viable.

Demand impacts

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

For example, if the service elasticity of bus demand with respect to service frequency is 0.4, and all service frequencies were to increase by 10% we would expect patronage to increase by 4%. The service elasticity is therefore a measure of the sensitivity of bus passengers to service levels.

The absolute size of the service 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. Service elasticities are defined as inelastic if they are less than 1.0 and elastic if they are greater than 1.0.

A wide range of factors influence the size of service elasticity and these are listed below:

  • Service levels – the lower the current level of service the more sensitive passengers will be to service level changes.
  • Size of service level change – the larger the change in the service level the more sensitive passengers will be to the service level change.
  • Income levels – those on low incomes are less likely to be sensitive to changes in bus service levels and more sensitive to changes in fares.
  • Competition from other modes – strong competition from other public transport operators will make passengers more sensitive to service level changes.
  • Demographic factors – The elderly and school children are more sensitive to changes in service levels.
  • Journey purpose – travellers commuting to work tend to be less sensitive to service level changes, whilst leisure travellers are more sensitive.
  • Urban vs Rural – passengers in rural areas tend to be more sensitive to service changes than passengers in urban areas.
  • Area - passengers tend to be less sensitive to service level changes in large cities compared with smaller conurbations.

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. In general terms TRL (2004) reports a short run service frequency elasticity with respect to bus of around 0.4 and a long run elasticity of 0.7, with slightly higher figures for rail. These figures relate to mainly UK based evidence whereas the TRB report (2003) is based mainly on US evidence. TRB (2003) reports general service elasticities of around 0.5 in the short run, with most recent studies tending to group their observations around 0.3 and 1.0. The former figure tends to represent studies that are based on central city urban locations and the latter on suburban systems that have undergone well thought out expansion in a growing economy where public transport is well regarded. The expected responses and situations to changes in service levels are outlined below.

The extent of modal switch between bus and car will be dependent upon the service cross elasticity between modes. Cross elasticities measure the change in demand for one mode as a result of the change in one of the factors associated with another transport mode (mainly fare/cost or service frequency). The size of the cross elasticity will therefore depend upon how demand for car will alter (and therefore how demand for bus will alter) due to a change in bus service frequencies.

As time spent travelling within the public transport vehicle is only a proportion of the total journey experience faced by the passenger, it is necessary to individually analyse the various component elements of the entire journey made by public transport. Kelly (1996) attempts to do so as follows:

  • An enquiry: Finding out the route of the service and whether it actually serves the desired destination of the user
  • A walk: Walking to the stop/station
  • A wait: Waiting for the vehicle
  • A ride: The actual travel inside the vehicle
  • A walk: Walking to the destination

Various instruments can be employed to improve the quality of public transport. It is not intended to list all instruments here but to give a flavour of how the quality of the entire package of public transport can be improved using this framework of analysis as shown in Table 1.

Table 1: Examples of Instruments that can be used to improve the journey experience

Kelly’s element

Example issues to consider

An enquiry

Is information readily available and accessible for trips to be made by public transport?  Is information available on mobile devices?

A walk

Is it hazardous even for able bodied people?
Is the footpath suitable for use by wheel chair uses or for those with children?

A wait

Are the stops sheltered from the elements? Do passengers waiting face security problems -if so are there security measures. Is real time running information available at the stops?

A  ride

Is the journey time competitive against the private car?
Is the ride comfortable?
Is it safe?

A walk (to destination)

 

The above is a simple framework, with cases where an interchange is involved, this will increase the need for the “walk”, “wait” and “ride” elements.

Kittelson & Associates et al (2003) provides some of these questions under several broad categories as follows:

Availability

  • Spatial availability: Where is service provided, and can one get to it?
  • Temporal availability: When is service provided?
  • Information availability: How does one use the service?
  • Capacity availability: Is passenger space available for the desired trip?

Comfort and Convenience

  • How long is the walk? Can one walk safely along and across the streets to and from transit stops? Is there a functional and continuous accessible path to the stop, and is the stop accessible to people with disabilities?
  • Is the service reliable?
  • How long is the wait? Is shelter available at the stop while waiting?
  • How comfortable is the trip? Will one have to stand? Are there an adequate number of spaces?
  • How much will the trip cost?
  • How many transfers are required?
  • How long will the trip take in total? How long relative to other modes?
  • Are the vehicles and facilities clean?

Service Delivery

  • Reliability: how often is the service provided as promised?
  • Customer service: what is the quality of direct contacts between passengers and staff and more importantly, the customers’ overall perception of service quality?
  • Comfort: what is the passengers’ physical comfort level as they wait for and use transit service?

Safety and Security

This relates to the likelihood or more importantly perceived likelihood that one will be involved in an accident (safety) or become the victim of a crime (security) while using public transport. An example question in this category:

  • Are there security concerns—walking, waiting, or in the vehicle?

Maintenance

Are the vehicles and facilities clean?

Other Types of Service Level Change

There are a number of other service level changes which have been identified by TRB (2003) and TRL (2004). These include:

  1. Regular Interval & Clockface Timetables – The former involves the implementation of a timetable that schedules services to arrive at a station or bus stop, at regular intervals, e.g. every 20 mins. The latter schedules services to arrive at the station or bus stop at the same time past the hour, e.g. 10 minutes past and 30 minutes past the hour. The reasoning behind these service patterns is that they should be easy to remember and will, in some cases, help minimise transfer times (Shires et al, 2003). They are very prevalent in Switzerland, the Netherlands, Austria and Germany.
  2. Combined Service Frequencies – This approach involves mixing stopping and express services on the same route to cater for different types of travel needs.
  3. Reliability Improvements – If a service is experiencing reliability problems, the operator may change the service schedule to improve reliability. This may well involve building delay into the timetable.

Mode choice and quality in public transport services

When we consider public transport, except for particular specialised services and first class services on rail networks, passengers do not generally have the option of purchasing an additional quality element. The practice thus far in public transportation planning and management is that conventional planning tends to overlook service quality impacts (Litman, 2008). In particular quality factors are generally not analysed to any large extent in considering public transport costs even if some of them have been valued in monetary terms using techniques such as stated preference modelling to elicit choices of a sample of the population under hypothetical scenarios (Balcombe et al 2004).

Enhancing the quality of public transport broadly targets two groups of public transport users:

  1. “Captive Users”: Those who do not have a car available for the journey desired or are not able to drive
  2. “Choice Users”: Those who have a car available or are able to drive

These terms represent polar ends and it should be emphasised that the “captive market” should not be thought of as a market that will always use public transport. Hence authorities and operators should not be complacent that they will always be there.  This market is not static. It will more often than not shrink with changing socio-economic demographics, rising income levels etc. Experience indicates that these “captive” customers will in due course find alternatives. These “captive customers” are not to be thought as the equivalent of loyal customers. That loyalty can be gained through delivery of a service that meets the user’s satisfaction.

With regards to choice, public transport provision has to be competitive against the private car in meeting travelling needs.  As an example given in Litman (2008), drivers have the option and are often willing to pay considerable sums to include a variety of “optional extras” such as satellite navigation systems and leather interiors in the car.  Whilst the basic need of the car owner is a means of provision of transportation from point A to point B, the quality elements and more importantly the image associated with ownership and use of this carare clearly an element of importance in the decision making process of car ownership and purchase. If more car users are to be persuaded to choose the option of public transport instead, the entire package of public transport options available has to be improved considerably. When approaching this group, the objective of quality is designed to entice users to change their choice from private vehicle by ensuring that public transport caters both to their basic transportation needs and also to their implied needs.

For them, public transport needs to deliver at least a journey experience rivalling, or better than, the private car. Hence measures improving the image of public transport, enhancing safety and personal security, reducing overcrowding are targeted at this segment of the market.

Expected response to increased service levels

The table below shows the expected response to increased service levels, the responses can be assumed to be opposite if service levels are reduced.

Responses and situations
Response Reduction in road traffic Expected in situations
An increase in service levels is likely to reduce peoples’ schedule delay and excess wait time and so departure time. This will represent a better quality service as the reliability element of quality will be improved.
Unlikely to affect people's routes.
Unlikely to affect people’s choice of destination except where improvements in service level make a given destination more attractive.
An increase in service levels is likely to generate more bus trips from existing users and new users.
An increase in service levels is likely to make bus a more attractive mode of travel and so attract car users.
May affect decision on car ownership.
But probably most likely to move house for other reasons such as job change.
= Weakest possible response = Strongest possible positive response
= Weakest possible negative response = Strongest possible negative response
= No response

Short and long run demand responses

If service levels are reduced then responses can be assumed to be diametrically opposite to those presented here.

Demand responses
Response - 1st year 2-4 years 5 years 10+ years
-
  -
  -
  Total number of journeys likely to increase
  Switch to Public Transport
  Possible factor influencing car purchase decision
  -
= Weakest possible response = Strongest possible positive response
= Weakest possible negative response = Strongest possible negative response
= No response

Supply impacts

Increased service frequency increases capacity of public transport, and in addition to having potential for increasing demand, may reduce overcrowding on popular services. Extended network coverage could increase capacity if it is not done at the expense of frequency of services. The feasibility of increased capacity relies on availability of stock (carriages or buses). 

Financing requirements

The financing of an increase in service levels will tend to come from two main sources, the fare box and support from local/transit authorities. The make up of the funding will largely be decided by who makes the decision to increase service levels and the rationale for that increase. If the decision is made by the transport operator on purely commercial grounds then the operator will expect fare box revenue to cover the financing of the service increase. If a decision is taken by a local/transit authority to increase service levels on social grounds (e.g. to reduce social exclusion) then the costs of providing those services are likely to be met by a combination of fare box revenue and operating subsidies from local/transit authorities. These costs are also likely to vary according to the degree and/or type of regulation that is in place.

Altering service levels will have a direct effect on the fixed, the semi-variable and the variable costs experienced by transport operators. These are outlined in Whelan et al (2001) below.

  1. Variable costs are costs that vary directly and immediately with output. For example, fuel costs vary directly with vehicle kms operated, crew costs may vary directly with vehicle hours etc.
  2. Semi-variable costs are costs that only vary partially with output. For example, vehicle maintenance is partly related to the extent that vehicles are utilised but there is some element of maintenance that will need to be undertaken irrespective of how intensely the vehicle is utilised. Similar arguments hold true for vehicle depreciation.
  3. Fixed costs are costs that do not vary immediately with output. That is, they cannot be varied in the short run. These costs include buildings and general administration.

Transport services are not highly divisible and so neither are the costs associated with them. For example, if a bus operator wished to increase a bus service from 4 buses per hour to 5 buses per hour in the morning peak period (7am till 9 am) it would not simply be a case of hiring an additional bus and driver for that two hour period. The additional bus would have to be either purchased or leased on a permanent basis and the driver hired as either a part time or full time employee. The fixed cost element of an increase in service would therefore be substantial, i.e. the bus purchase costs, vehicle insurance, vehicle taxes, depot costs, maintenance costs. In practice a bus operator might choose to increase the service level throughout the day on one particular route, or may increase the service level of one route during the peak and another during the off-peak.
The same problem of indivisibility is faced by heavy and light rail operators to an even greater extent because each vehicle is many times more expensive than a bus. This means that the ability of public transport operators to change service levels, in terms of frequency, are restricted. It is much easier to extend hours of operation since existing vehicles can be utilised.

If the quality of public transport is to be improved the availability element of public transport would also increase. Availability is considered under the following (Kittleson Associates et al, 2003):

  • Spatial availability: The network of services would be expanded in terms of coverage.
  • Temporal availability: The services would also run more frequently.
  • Information availability: The quality of information required for making a trip by public transport would improve.
  • Capacity availability: New vehicles would be added to runs so that overcrowding is minimised.

Increase in service levels: Expected impact on key policy objectives

Contribution to objectives

Objective

Scale of contribution

Comment

 

Increase in public transport service levels – reduction in the waiting times & overcrowding experienced by existing passengers and so a reduction in the generalised costs of travel. Public transport becomes a more attractive mode of transport and will encourage car users to switch, helping reduce traffic congestion. Note the degree of mode switch depends upon the service level cross elasticity between car and bus.

 

If the increase in service level does indeed achieve significant mode switch from car this is likely to reduce local air and noise pollution and perceptions of danger.

 

Increase in service levels - will lead to some mode switching from car and so help reduce air and noise pollution. Note the amount of switching depends upon the service cross elasticity between car and bus.

 

Extension of service – allows a wider range of services, goods & opportunities to be accessed. Additional new services may be focused in particular areas currently not served by bus or to new destinations that better meet user’s needs.

 

Will lead to some mode switching from car and so help reduce accidents. Note the amount of switching depends upon the service cross elasticity between car and bus

 

The generalised cost of travel by public transport will be reduced directly by the improved service level. Furthermore, mode switch from car may reduce congestion levels so leading to further reductions in travel time. These two impacts may increase productivity. On the other hand if the improvements require increased subsidy then the necessary increase in local taxes may stifle economic growth.

 

Financial impact on the operator will depend upon the service level elasticity. A service level elasticity greater than one will lead to a net increase in revenue, if it is less than one there will be a net decrease.

= Weakest possible positive contribution = Strongest possible positive contribution
= Weakest possible negative contribution = Strongest possible negative contribution
= No contribution

Increase in service levels: Expected impact on problems

Contribution to alleviation of key problems

Problem

Scale of contribution

Comment

Congestion

Low cross elasticities between changes to service levels and modal switch may limit the impact on congestion from an increase in service levels. An increase in service frequency will help combat unreliability amongst public transport users. Mode switching may also reduce road traffic related unreliability.

Community impacts

Due to possible reduction in traffic levels.

Environmental damage

By reducing/increasing car traffic-related emissions. This is likely to outweigh any increase in public transport emissions.

Poor accessibility

An increase in the service levels will improve accessibility to goods, services, education and employment for people without a car and some with mobility impairments.

Social and geographical disadvantage

An increase in the service levels will improve accessibility to goods, services and employment for the socially excluded with no car available and those that live in the areas served. The effect will be especially important if network coverage is increased for those in areas that had no service previously.

Accidents

By reducing traffic volumes.

Economic growth

The generalised cost of travel by public transport will be reduced directly by the improved service level. Furthermore, mode switch from car may reduce congestion levels so leading to further reductions in travel time. These two impacts may increase productivity. On the other hand if the improvements require increased subsidy then the necessary increase in local taxes may stifle economic growth.

= Weakest possible positive contribution = Strongest possible positive contribution
= Weakest possible negative contribution = Strongest possible negative contribution
= No contribution

Expected winners and losers

Winners and losers

Group

Winners/Losers

Comment

Large scale freight and commercial traffic

High value freight journeys – less time spent in congestion the greater the vehicle utilization, however a relatively small proportion of the journey distance is in urban conditions. Service increase reduces traffic congestion so is beneficial. This depends upon the size of the service cross elasticities between car and bus.

Small businesses

Service increase – encourages trips to non local areas.

Cyclists including children High incomes associated with high value of time and thus continued car use for high value journeys. These journeys will benefit from reduced congestion. A service increase reduces traffic congestion so is beneficial. This depends upon the size of the service cross elasticities between car and bus.
People at higher risk of health problems exacerbated by poor air quality Unlikely to have car access. An extension of the service will increase the range of services, goods and opportunities open to them whilst an increase in frequency will reduce the generalised cost of travel by public transport.

High income car-users

If changes in service levels are restricted to existing services then no impact. However, if new services are implemented serving different areas then a very positive impact.

People with a low income

Service increase – will lead to reduced generalised costs of travel (e.g. reduced waiting and overcrowding) & more opportunities to travel if the service is extended.

People with poor access to public transport

Service increase - they may benefit from reduced congestion and improved or increased public transport supply.

All existing public transport users

Reduced generalised costs of public transport and reduced congestion will result in valuable time savings. A service increase reduces both so is beneficial.

People living adjacent to the area targeted

Reduced congestion will result in valuable time savings. A service increase reduces both so is beneficial. This depends upon the size of the cross elasticities between car and bus.

People making high value, important journeys

 

The average car user  
= Weakest possible benefit = Strongest possible positive benefit
= Weakest possible negative benefit = Strongest possible negative benefit
= Neither wins nor loses

Barriers to implementation

There are a variety of barriers to the implementation of an individualised marketing campaign.

Scale of barriers
Barrier Scale Comment
Legal This depends on the extent to which services are provided by the private sector. In a publicly operated or franchised service, changes are relatively easily made.  In a deregulated environment, legal barriers can limit what can be achieved.
Finance Altering service levels has a large impact on costs. The key issue is whether such changes are self financing or not. In the case of deregulated bus industry such changes will tend to be self financing and the cost implications will fall upon the passenger and not tax payers. With franchised services the financial burden will tend to fall on both passengers and tax payers.
Governance Governance will be more straightforward where services are run by public sector organisations. Where services are franchised, governance arrangements may be complicated. Where services are deregulated governance is straightforward but political bodies have no means of influencing services.
Political acceptability A reduction in service levels will not prove popular amongst the general public, but might be seen as politically necessary for budgetary reasons. Conversely, an increase in service levels would receive support from the general public but not from political parties, particularly if budgets are constrained.
Public and stakeholder acceptability Increased services and extension of network coverage can gain public support.
Feasibility There are few technical limitations on service improvements.
= Minimal barrier = Most significant barrier

Case study 1: Mass Transportation Demonstration Projects in Massachusetts (1962-64)

Context

Between 1962 and 1964 the Mass Transportation Commission of the Commonwealth of Massachusetts (MTC) conducted a number of mass transit service improvement and fare reduction experiments. The experiments centred around Boston and its inner suburbs, and involved bus operators, other than the Massachusetts Transit Authority (MTA), throughout the states and commuter railroads that served Boston.

The bus experiments mostly involved increasing the service frequencies of a number of local bus services, these are reported in the table below. The rail experiments were carried out on the Boston & Maine Railroad (B&M), the New Haven Railroad (NH), with the New York Central Railroad (NYCR) used as a control. The B&M experiment incorporated three phases,
  1. 77% equivalent increase in all services (92% increase in weekday services) & a 28% equivalent reduction in fares (ranging from 12% to 72%).
  2. Retention of phase 1 service improvements and virtual elimination of fares reduction (except for an off-peak reduction).
  3. Service levels adjusted and fare levels remain the same.
The NH experiment had 2 phases,
  1. Total average service level increased by 42% and fares reduced by 10% on average.
  2. Service levels and fares returned to pre-experiment levels, with off-peak fare incentives retained.

Impacts on demand

Bus Company Experiments

The results in terms of revenue and ridership changes are shown in the table below "Massachusetts Bus Headway Changes and Ridership/Revene Results". In all but one case they demonstrated positive increases in revenue and ridership.

Massachusetts Bus Headway Changes and Ridership/Revenue Results

Route

Service Area Population

New Headway

Results & Comments

Implied Service Elasticity

Milford to Downtown Boston

22,000 (suburban area only)

1 hour all day (78% service increase)

12 month revenue up 22% (18% first 3 months; 27% in the last 3 months)

+0.28

Uxbridge to Worcester (pop. 187,000)

28,000 (suburban area only)

Similar to above

9 month revenue up 5% (none in first 3 months, 16% in the last 3 months)

+0.06

Amesbury –Newburyport 25,000 Half hourly in the peak; hourly in the base (67% service increase) 8 month revenue up 19% (route through depressed industrial areas) +0.28
Adams – Williamstown 40,000 Better that hourly frequency (100% service increase) 3 month ridership up 48% +0.48
Pittsfield 74,000 (SMSA*) Service increased to 8 round trips (16% service increase) 3 month ridership up 87% (3 mile long radial route) +5.44
Pittsfield 74,000 (SMSA) Service increased to 15 round trips (50% service increase) 3 month ridership up 30% (3 mile long radical route) +0.6
Fitchburg –
Leominster
72,000 (SMSA) 1:40pm to 6.00pm bus trips doubled to give 10 min. headway all day; minor route extension 8 month revenue up 8% (high density service area; fare increase from 20 cents to 25 cents in 9th month) n/a

Fall River

124,000 (SMSA)

Service increase of 20%

Halted but did not reverse ridership decline (high unemployment and disruptive construction)

n/a

* SMSA - US Census Standard Metropolitan Statistical Area (1960); n/a - not applicable

Source: Adapted from TRB (2003)

The implied service elasticities have a wide range, however two thirds of the elasticities appear in the +0.28 to +0.6 range.

Rail Experiments

The results in terms of revenue and ridership changes are shown in the table below,"Masachusetts Rail Headway Changes and Ridership Results". The key figures to note in terms of service elasticities are those presented for phase 2 when fares were increased. The increase in patronage was taken by MTC to infer that improvements in service levels were more effective at increasing ridership than were fare reductions. Overall the additional revenues covered the full incremental cost of the experiment.

Massachusetts Rail Headway Changes and Ridership Results

Rail System Phase 1 Phase 2 Phase 3
B&M +27% increase in ridership +37.5% increase in ridership +44% increase in ridership
NH +10% increase in ridership +11.5% increase in ridership n/a
NYCR -5.9% decrease in ridership -5.9% decrease in ridership -5.9% decrease in ridership

Source: Adapted from TRB (2003)

Surveys indicated that the majority of passengers on the commuting trains used to travel by the following modes, own car (63.6%), carpool member (16.9%), and bus (19.5%).

Impacts on Supply

No cost figures were reported in the TRB publication (2003), however the increase in service levels is likely to have resulted in additional costs from the purchasing/leasing of additional vehicles and hiring of additional operating staff.

Contributions to Objectives

Contribution to objectives
Objective Contribution Comment
  Evidence indicated that a reduction in car use is likely to have contributed to an efficiency improvement by reducing road congestion.
  The reductions in car use are likely to have contributed to a liveability improvement.
  The reductions in car use will have contributed to a reduction in environmental impacts.
  Whilst no direct evidence was presented the increase in services is assumed to have had a sizeable impact upon equity and social inclusion.
  The reduction in car use will have contributed to a reduction in accidents.
  The generalised cost of travel by public transport will be reduced directly by the improved service level. Furthermore, mode switch from car may reduce congestion levels so leading to further reductions in travel time. These two impacts may increase productivity. On the other hand increased subsidy was necessary and the requisite increase in local taxes may stifle economic growth.
  No information given for the bus elements of the experiment but the low elasticities suggest that costs will not have been covered. The higher elasticities for the rail services meant that increased revenue covered the cost of the increased services.
= Weakest possible positive contribution = Strongest possible positive contribution
= Weakest possible negative contribution = Strongest possible negative contribution
= No contribution

Case Study 2: Frequency and Service Hours Enhancements in Santa Clarita, California

Context

Santa Clarita is an outlying suburb of Los Angeles in California (U.S.) with a population of around 150,000 that is served by a metrolink commuter rail service and local bus coverage. Between 1992 and 1998 there were significant extensions to public transport service hours and service frequencies. These are outlined below:
  • 1992 - Saturday services expanded by 3 hours, i.e. larger operating period.
  • 1992 - Weekday service hours expanded by 2 hours, i.e. larger operating period.
  • 1994/5 - New express commuter bus services added.
  • 1995 - Weekday services expanded on three routes.
  • 1995/8 - 30 minute all day headways introduced on 4 routes (including 2 on a weekend) and 15 minute peak headways on two routes.
In addition a 90 minute pass was introduced in 1992, fares raised by 33% in 1993, youth passes rose to $15 from $10 in 1996, and Sunday services introduced on about two thirds of local routes.

Impacts on demand

The increase in both bus miles and bus hours over the five years in question has seen a greater than proportionate rise in ridership. The lack of statistical smoothing of short run anomalies however, means that not much weight can be placed upon the yearly elasticities. More reliance can be placed on the long run elasticities which are +1.14 for bus miles and +1.55 for bus hours.

Santa Clarita, CA Local Fixed Route Performance and Log Arc Service Elasticities

Local Fixed
Routes-Year

City
Population

Annual
Bus Hours

Annual
Bus Miles

Annual Bus
Rides

Bus Hours
Elasticity

Bus Miles
Elasticity

FY 1992-93
1993-94
1994-95
1995-96
1996-97
1997-98
123,400
124,000
124,300
124,800
n/a
n/a
48,778
53,391
60,028
62,750
66,947
81,216
787,807
1,018,021
1,163,607
1,179,140
1,389,082
1,569,891
769,137
915,869
1,107,587
1,366,537
1,527,253
1,693,173
-
+1.93
+1.62
+4.74
+1.72
+0.53
-
+0.68
+1.42
+15.84
+0.68
+0.84
5 fiscal years +2% (4 yrs*) +66% +99% +120% +1.55 +1.14
FY - Financial Year: * - Calendar years 1992 (122,949 population) through to 1996 (125,153 population)

Source: TRB (2003)

Impacts on Supply

No cost figures were reported, however the increase in service levels is likely to have resulted in additional costs from the purchasing/leasing of additional vehicles and hiring of additional operating staff.

Contribution to Objectives

Contribution to objectives
Objective Contribution Comment
  No direct evidence was provided, however it is likely that some modal shift has occurred, reducing congestion costs and improving efficiency. The generalised cost of bus travel will have also declined.
  No direct evidence was provided, however it is likely that some modal shift has occurred and that this has led to an improvement in liveable streets.
  No direct evidence was provided, however it is likely that some modal shift has occurred, leading to a reduction in environmental externalities.
  The extension of service will have opened up a wider range of services, goods and opportunities to those on low incomes.
  No direct evidence was provided, however it is likely that some modal shift has occurred, which is likely to have lead to a reduction in accident rates.
  No direct evidence was provided but it is likely that the improved service levels with no increased subsidy requirement will have been beneficial for the local economy.
  The high service level elasticities suggest that increased fare revenue will have more than covered the cost of increased service provision.
= Weakest possible positive contribution = Strongest possible positive contribution
= Weakest possible negative contribution = Strongest possible negative contribution
= No contribution

Further Evidence on service level Elasticities

Public Transport Elasticities: Time For A Re-Think (Preston, 1998)

Preston (1998) used evidence taken from an English Metropolitan area to estimate short and long run bus service elasticities over various time periods. The data had been collected by the area Passenger Transport Executive (PTE) using continuous on-vehicle surveys and consisted of 4 weekly bus usage data by ticket type and time of day (3,822 observations) covering the periods 1987/88 to 1992/93.

The estimated service elasticities are presented in the table below “Elasticity Estimates for Adult Bus Users in a Metropolitan Area".

Elasticity Estimates for Adult Bus Users in a Metropolitan Area

Time Period Service Short Run Service Long Run
Early morning +0.38 +0.56
Peak am +0.36 +0.58
Inter-peak +0.17 +0.30
Peak pm +0.32 +0.42
Late +0.35 +1.95
Saturday +0.52 +0.67
Sunday +1.05 +1.67
Source: Preston (1998)

The service elasticities appear to be intuitively correct for the peaks (inelastic), and the inter-peak, which is more elastic. Early morning trips are also very inelastic, which probably reflects the dominance of work trips at that time of day. The service elasticities on Sundays and late at night are very high in the long run, however, the confidence interval around these estimates is also very large, suggesting they are unreliable.

Further evidence on service level elasticities (The Demand for Public Transport: a practical guide, 2004)

The service level elasticities presented in the two tables below have been derived from a number of studies conducted throughout the UK for bus services.

Service elasticities, with the range and standard deviation according to average values – bus (TRL 2004)

Time period Elasticity Range Standard deviation Number of measurements
Short run 0.38 0.10 to 0.74 0.135 27
Long run 0.66 0.22 to 1.04 0.275 23

Service elasticities, with the range and standard deviation according to average values – rail (TRL 2004)

Time period Elasticity Range Standard deviation Number of measurements
Time period not stated 0.49 0.33 to 0.65 0.23 2
Short run 0.75 0.65 to 0.90 0.13 3

The table below presents price and service level elasticities for rail demand in Spanish cities. It is clear that customers are more responsive to service level changes than price changes.

Price and service level elasticities for Spanish cities - bus (Arsenio 2000) (taken from TRL 2004)

Elsaticity Large cities Small cities
Price -0.3 -0.32
Service quantity (train km) 0.78 0.39

The table below presents elasticities with respect to wait time (a function of headway) for bus based on analysis of data in 23 UK towns.

Elasticities with respect to wait time – bus

Dependent variable Time period/destination Elasticity with respect to wait time
Total trips   -0.64
Adult trips   -0.74
Adult trips Peak/town centre -0.65
Adult trips Off-peak/town centre -0.85
Adult trips Peak/other -0.39
Adult trips Peak/town centre -1.17
Total trips Peak/town centre -0.64
Total trips Off-peak/town centre -0.64
Total trips Peak/other -0.50
Total trips Peak/town centre -1.05

The Transportation Research board interim handbook (Pratt et al, 2000) found the following fare and service elasticities in the period 1985 to 87 when fare and service changes were introduced.

Fare and service elasticities – Dallas

  Fare elasticity Service elasticity
Urban bus -0.35 0.32
Suburban express bus -0.26 0.38
Suburban local -0.25 0.36

The isotope research study (European commission, 1997) reported Service elasticity is for bus in a number of European cities by city sides (small: population less than 500,000)

Service elasticities for bus in European cities

  Small city Large city
Service elasticity 0.33 0.49

Gaps and Weaknesses in the Evidence

Changes in service levels are likely to impact upon motorists and other travel and even location decisions in the very long-term. The difficulty is that the greater the length of time period that I studied them the greater the number and magnitude of confounding factors. Long-term impacts are therefore very difficult to derive despite the fact that they may well be very much greater than short to medium-term impacts.

It was difficult to find completed studies looking at the quality aspect of public transport services. There was information readily available about various schemes such as quality bus partnerships; however results on the impacts proved to be limited.

Contribution to objectives and alleviation of problems
Objective Massachusetts Santa Clarita TRL study
 
 
 
 
 
 
 
= Weakest possible positive contribution = Strongest possible positive contribution
= Weakest possible negative contribution = Strongest possible negative contribution
= No contribution

 

Contribution to alleviation of key problems
Objective Scale of contribution Comment
Congestion Low cross elasticities between changes to service levels and modal switch may limit the impact on congestion from an increase in service levels. An increase in service frequency will help combat unreliability amongst public transport users. Mode switching may also reduce road traffic related unreliability.
Community impacts Due to possible reduction in traffic levels.
Environmental damage By reducing/increasing car traffic-related emissions. This is likely to outweigh any increase in public transport emissions.
Poor accessibility An increase in the service levels will improve accessibility to goods, services, education and employment for people without a car and some with mobility impairments.
Social and geographical disadvantage An increase in the service levels will improve accessibility to goods, services and employment for the socially excluded with no car available and those that live in the areas served. The effect will be especially important if network coverage is increased for those in areas that had no service previously.
Accidents By reducing traffic volumes.
Economic growth The generalised cost of travel by public transport will be reduced directly by the improved service level. Furthermore, mode switch from car may reduce congestion levels so leading to further reductions in travel time. These two impacts may increase productivity. On the other hand if the improvements require increased subsidy then the necessary increase in local taxes may stifle economic growth.
= Weakest possible positive contribution = Strongest possible positive contribution
= Weakest possible negative contribution = Strongest possible negative contribution
= No contribution

 

Appropriate area-types
Area type Suitability
City centre
Dense inner suburb
Medium density outer suburb
Less dense outer suburb
District centre
Corridor
Small town
Tourist town
= Least suitable area type = Most suitable area type

Relevant links

Further information on service level elasticities can be found in: The Demand for Public Transport: a Practical Guide

References

Balcombe R, Mackett R,  Paulley N, Preston J, Shires J, Titheridge H, Wardman, M White P,(2004)  The Demand for Public Transport: A Practical Guide, Transport Research Laboratory Report TRL 593

Kittelson & Associates et al (2003) “Transit Capacity and Quality of Service Manual” 2nd Edition, Transit Cooperative Research Program Report 100, Transportation Research Board, Washington DC

Karu K, Rõivas T, Antov D, Oja T and Mander Ü (2007) The quality of public transport as a determinant of the number of car commuters, Urban Transport XIII: Urban Transport and the Environment in the 21st Century:  http://library.witpress.com/pages/PaperInfo.asp?PaperID=18129

Kelly P (1996) “Quality buses – Buses for Car Owners” Proceedings of the PTRC/ETC Conference

Litman T (2008)   "Valuing Transit Service Quality Improvements"   Journal of Public Transportation,11(2) 43-63, The original file is here: http://www.nctr.usf.edu/jpt/pdf/JPT11-2Litman.pdf

Mackie, P. and Preston, J. (1996) "The Local Bus Market. A Case Study in Regulatory Changes". Avebury.

Mass Transportation Commission, MA, McKinsey & Co., Systems Analysis and Research Corp. and Joseph Napolitan & Associates (1964) "Mass Transportation in Massachusetts". U.S. Department of Transportation, Washington, DC (July 1964).

Metz, D. (2005) Journey quality as the focus of future transport policy. Transport Policy, 12(4), 353-359

Oxley, P.R. (1982) "The Effects of the Withdrawal and Reduction of Rural Bus Services". Transport and Road Research Laboratory, Supplementary Report 719.

Pratt, R. H., Turnbull, K. F., Evans IV, J. E., McCollom, B. E. Spielberg, F. Vaca, E. Kuzmyak, J. R. 2000. TRAVELER RESPONSE TO TRANSPORTATION SYSTEM CHANGES: INTERIM HANDBOOK, TCRP Project B-12, Transportation Research Board

Preston J.M. (1998) Public Transport Elasticities: Time for a Re-think? paper presented to the Universities Transport Studies Group Conference, Dublin, January, 1998.

Shires, J.D., Johnson, D., Nash, C.A. and Tyler, J.T. (2003) "Appraisal Framework and Results for Testing A Regular Interval Rail Timetable". ITS Working Paper 578, University of Leeds.

Social Exclusion Unit (SEU) (2003) Making the Connections. Final Report on Transport and Social Exclusion.  Office of the Deputy Prime Minister/ SEU.

Transportation Research Board (2003) "Traveller Response to Transportation System Changes". Chapter Nine, Transit Scheduling and Frequency, TCRP Report 95.

Transportation Research Board (2004) The Demand for Public Transport: a practical guide, Balcombe et al.

Whelan, G.A., Shires,. J.D, Toner, J.P. and Preston, J.M. (2001) "Modelling Quality Bus Partnerships". Report for DETR.