CHAPTER NINE CONCLUSIONS
9.1 Whilst there is only a limited availability of literature on the locations of congestion in Scotland, a number of data sources exist that contain information on the impacts of congestion (delay, speed reductions and reliability problems). The information that does exist does not define congestion per se, nor does it define the point at which congestion is perceived to be a problem. On the available evidence therefore it is only possible to describe the locations where the impacts of congestion are greatest.
9.2 From the analysis of the available data a broad picture emerges. Whilst at the national level only a minority of trips (11.5%) are affected by congestion, this figure disguises large geographic, temporal and journey purpose variations. Congestion impacts are largest in the cities of Glasgow, Aberdeen and Edinburgh (where up to 42% of AM peak travellers experience congestion related delay and up to 49% of the AM peak network generates delays). The trunk road network that experiences the most congestion is that in the vicinity of these cities as well as on the approaches to the Forth estuarial crossings. The peak hours are more congested than the off-peak and commuting and business related trips are more affected by congestion than trips for 'other' trip purposes (no data is available on the impact of congestion on freight movements). Congestion is not however just confined to Aberdeen, Glasgow and Edinburgh and their vicinity, as congestion related delays are reported throughout Scotland, it is just that their frequency and incidence is higher in the large cities. Ultimately it only takes one over-capacity junction to impose a congestion related delay on travellers.
9.3 In seeking a definition of congestion in the literature, despite the past research and frequent use of the term, the state of congestion is often understood but not formally defined. Perceived congestion is an important factor alongside more objective definitions in driving the need for policy measures. Definitions vary according to two major dimensions - the traffic engineering perspective and the economic cost driven dimension which in fact relate to two major efficiency objectives i.e. system efficiency and economic efficiency. Users' perceptions are generally consistent with one or other of these dimensions. Congestion in urban areas can be distinguished from that in the interurban context as it can be recognised by the inability to exit a link within a traffic cycle. Congestion in an interurban context may be defined through speed of travel (or ultimately stopping). Both perceived and formalised concepts of congestion lend themselves to more objective measurement and indicators of congestion.
9.5 There are three economic terms that can be rightfully called the cost of congestion:
- marginal external cost of congestion
- total cost of congestion
- excess burden of congestion
9.6 The marginal external cost of congestion relates to the change in total congestion costs as a result of an extra vehicle-kilometre or trip. The total cost of congestion relates to the cost of congestion in relation to a situation with zero congestion, whilst the excess burden of congestion relates to the cost of congestion compared to a situation with optimal prices - optimal from the sense of maximising economic output. Clearly if capacity is also sub-optimal then even at efficient (optimal) prices there maybe too much congestion, therefore there may be an additional cost associated with sub-optimal capacity. Once prices are efficient ( i.e. reflect the full social costs of using the road) it is possible to develop simple investment rules to determine the optimal level of capacity. The total cost of congestion measure is the easiest of the three measures to calculate but it is argued by some authors that it has the least policy relevance. Primarily this is because there is a cost associated with delivering the capacity necessary to alleviate congestion. As such the total cost of congestion measure, whilst being an economically valid measure of the cost of congestion, can never be delivered in its totality by any transport policy as a benefit. On the other hand the excess burden of congestion measure gives a cost estimate that it is possible to address using transport policy. Unfortunately it is more complicated to calculate as it requires variable demand transport models that can model the impacts of road user charging ( i.e. transport models that can model the behavioural responses we would expect to occur as a result of a reform of road prices). Annex 3 contains a description of the data requirements of such models. Deriving the optimal level of capacity adds an additional degree of complexity and to this date we are aware of only one study that has attempted to do this at a national level.
9.7 The appropriate choice of measure of the costs of congestion will vary according to the end use of the data. For example, in cases where the aim is to consider road pricing measures, the marginal cost of congestion is normally calculated. To review the benefits of significant investment decisions, the total or excess burden of congestion may be calculated. The purpose of the research here has been to provide objective evidence on each based on the existing literature. The work will inform subsequent stages of research to be conducted by the Scottish Executive and at this point it is not possible to propose recommended methodologies until the nature of that programme is defined.
9.8 The methods used to measure costs of congestion can be typified as primarily static versus dynamic methods, with some approaches forming a hybrid between these. A dynamic approach iterates between supply, demand and cost whilst a static approach is based upon a 'snapshot' of the system through area-wide supply/demand curves for example. Within a dynamic approach to estimating the costs of congestion, a static or dynamic traffic network model may be utilized.
9.9 In terms of the data requirements, the calculation of all three variants of the cost of congestion require data on user impacts (some form of transport model) and estimates of the other impacts that congestion causes ( e.g. pollution, accidents, etc.). Marginal costs for each of these impacts are also required (time, reliability, climate change, air pollution, noise, accidents). The evidence from empirical work in this area suggests that the results are sensitive to the transport models used and the values used for the costs of the impacts. Clearly the transport models that provide estimates of junction delay will give more robust results than those which exclude junction delay, particularly as congestion costs are most significant in urban areas. Uncertainty in the values to be ascribed to environmental impacts can also significantly affect the final estimates of the costs of congestion.
9.10 The individual nature of different geographic areas makes it difficult to transfer results from one geographic location to another, particularly in the context of urban areas. This stems from the different topologies, historic development of the network, functions of the network and economic activity in different areas. Extrapolating results from one area of the road network to other sections of the network or the whole network would therefore need to take cognisance of these sources of difference. Bespoke research would need to identify areas between which results can be transferred.
9.11 Considering the question of decoupling transport and economic growth, the starting point is the strong empirical evidence that growth in vehicle-kilometres is a function of income and travel impedance or generalised cost as well as 'the need to travel'. Clearly transport policy that increases incomes and reduces travel impedance ( e.g. reducing congestion) has to use other measures to prevent an increase in vehicle demand ( e.g. road pricing can lock in the de-congestion benefits) or has to reduce the need to travel. Some of the measures needed to prevent the increase may be quite difficult to implement politically, such as road pricing. Despite this, evidence at the EU level and internationally has suggested that historically the decoupling of transport growth and economic growth has taken place, with differences seen between the passenger and freight sectors. Whilst the statistical relationship cannot give definitive evidence on causation, research has identified particular instruments which could be implemented to promote decoupling, seeking to maintain economic activity and achieve sustainability goals. These instruments are likely to have a more successful impact if implemented in packages. However, the underlying relationships are complex and further understanding of the demand for travel is needed before drawing firmer conclusions on the functional relationship with the economy.