تجزیه و تحلیل حساسیت از هزینه های تراکم ترافیک در شبکه تحت مقررات حقوقی

The costs of congestion can be measured using three approaches: the total costs, the marginal costs and the ‘excess burden’. Understanding variation in these measures with particular policies is important for planning and resource management. Assessing the cost distribution (e.g. according to priority routes or urban segments) is key to assessing the delivery of both transport objectives and wider social objectives. The aim of this research is to illustrate how the costs of congestion vary with policy-related demand changes around the city of Milan. The case study used is the “Cerchia dei Bastioni” (called for administrative purposes Area C). This is an old urban area within the inner centre of City of Milan network, with a ‘real life’ charging policy that is applied to private vehicles. A large number of scenarios with differing demand levels and elasticities by vehicle classes were explored and equilibrium assignment used to assign demand to the network. Alternative measures for congestion costs were calculated along with other link parameters. Further data collection, including a parallel field survey of changes in PT speed, was also undertaken. The results indicate a high degree of correlation between changes in the different measures of congestion and changes in vehicle speed (at different levels of demand). Changes in the total cost of congestion are, however, more marked than changes in the excess burden of congestion. Sub-optimal conditions appear to exist in certain parts of the network which (it is conjectured) arise as a consequence of the configuration of the network i.e. the presence of one way streets and vehicle restrictions. Identifying a more optimal network is left for further research, as is identifying the precise conditions for which vehicle speeds can be used as a proxy for changes in congestion.

Congestion is seen as an issue in urban networks as well as inter-urban environments and as such it features heavily in regional, national and supra-national transport policies. The European Commission white paper (2011) proposed that congestion in the European Union (EU) is often located in and around urban areas and costs nearly 100 billion Euro (or 1% of the EU's GDP) annually. Congestion is invariably regarded negatively and it is seen as a limiting factor on economic efficiency as well as a source of pollution. One common policy approach associated with the costs of congestion is that of road charging schemes (for example in Stockholm (City of Stockholm, 2005)), where an understanding of the costs of congestion may create a more conducive public-acceptance of the scheme and also set an economic framework within which charges may be set. This research is concerned with an investigation around the sensitivity of traffic congestion costs in Milan. In particular, how these costs vary with a charging policy specifically introduced to reduce congestion but with a secondary goal to achieve environmental improvements. The starting point is to consider the definition of congestion and how the costs of congestion may be measured. The calculation of congestion costs requires the use of a transport model and as such it is resource intensive for city authorities to monitor. The paper then continues to consider whether vehicle speeds can act as a proxy for congestion costs for the purposes of monitoring. A specific evaluation concerning speed changes for public transport after the charge is presented. The principal contribution of this paper therefore is to illustrate how the different costs of congestion vary with policy-related demand changes around the city of Milan and how they also relate to vehicle speeds. The findings have particular relevance and implications for city policy makers by illustrating the methodology used to measure the different congestion costs in a practical and real environment, given what tools may be readily available to them. Due to the complexities of measuring the costs of congestion, the examination of the changes in vehicle speeds as a proxy for congestion costs has a strong policy interest – particularly where congestion reduction targets have been set. The analysis presented here has also highlighted specific outcomes that would be of interest to policy makers wishing to build a case for charging schemes in particular contexts, for example, changes in bus speeds following the introduction of charges. Whilst based on a firm existing theoretical foundation, the essence of the paper is as a case study rather than a theoretical exposition. The aggregate picture is made up of a number of disaggregate calculations, a sample of which are presented here. Our goal in the paper is to present information at a level that might be of interested to policy makers and as such is likely to be of interest to a broad range of transport sector stakeholders. This paper has the following structure. Following this introductory section, the second section describes the underlying causes to congestion, what congestion is, its relevance to policy and the different methods used to measure it, as well as providing empirical estimates of the costs of congestion found in the literature. Section three sets out the modelling methods used in this paper to calculate the costs of congestion, whilst the fourth section introduces the City of Milan and the demand management schemes being analyzed. Results are presented and discussed in the fifth section and conclusions are set out in the final, sixth, section.

6. Conclusions The objectives of the study were to consider how the costs of congestion may vary with policy-related demand changes around the city of Milan. The demand change scenarios effectively represent hypothetical variations in the charge within the so-called Area C scheme – a subarea at the heart of the Bastioni sector, which is itself a part of the wider Milan city. The demand variations were introduced within two main scenarios, representing charging variations for a subset of vehicles and for the whole traffic respectively. The levels of demand change were set with consideration to the size of demand change observed when the Area C scheme was first introduced. In summary, these represented a marginal further demand change (+ or − further 10%), a further equivalent decrease in demand (−40%, roughly comparable with the −34% observed) and finally a significant demand reduction (−70%). Two measures for the costs of congestion were calculated – one being an estimate of the total cost of congestion (TCC) and the second being the excess burden of congestion (EBC). These were calculated for both the immediate Area C region and the wider Bastioni sector in order to explore possible shifts in costs. Other traffic related measures relating to speeds were also calculated. The study has generated some interesting insights and has produced a series of questions for further study, with the main findings as follows: • A strong correlation is seen between the cost of congestion measures and vehicle speeds (r = 0.98); this is not surprising since speed (v) and cost (c) are related by a relationship of the form of v ≈ 1/c for each link. This does, however, lead to the conjecture that speeds may be used as a proxy for the costs of congestion, a phenomena that is worth further future study. • From the two measures for the costs of congestion considered, it can be seen that the total cost of congestion is much higher than EBC (EBC is between 13% and 18% of TCC for main scenarios). However the Total cost falls more quickly than EBC as the cordon charges increase (demand reduces). At low levels of demand EBC is almost one fifth of TCC, whilst at higher demand levels it is closer to a tenth. This raises the possibility of value in further research into the non-linear relationship between the two measures and the need for careful policy interpretation of each of the two measures in practice. • Sub-optimal conditions can occur on certain parts of the network even though the network is moving towards a more optimal position (from a congestion perspective). This is evidenced by the fact that for some links EBC can be negative. It is attributed to particular characteristics of cordon charges, one way systems and PT only links. It is worth noting that what may be viewed as sub-optimal conditions in terms of congestion and system efficiency may be perceived as very acceptable and even positive conditions from the perspective of some stakeholder groups (for example residents or regular commuters with ‘rat-running’ behaviours). • Finally, a travel time (speed) analysis was carried out by way of ex-post analysis of the impact of introducing the Area C scheme (representing the change in demand of −34% compared with the previous charging scheme, Ecopass). The changes in demand in Area C are clearly not entirely independent of the whole city, although the conditions at the whole city level could be considered as an approximate comparison group. For the whole city, an increase in traffic speeds is seen for both links and lines (PT). However, the increase in speed is more marked for Area C than for the whole city, reflecting the immediacy of the impacts around the direct locality of the charging policy. The effect is more evident at 9:00 than 8:00, and for links than for lines. A number of topics for further research have arisen alongside the main research findings: • A more elaborate set of scenarios could (in principle) be explored to look at the impact of re-investing congestion charges back into the transport network (through improved PT or better circumferential road routes around a cordon, or a form of active traffic management using ‘intelligent transport’ schemes. • Further analyses that separates the data into city segments or main route roads vs the remainder would be interesting in order to calculate some simple measures around equity in terms of distribution of impacts. Research (in collaboration with the city authority) could involve identification of which particular areas, routes and ‘critical links’ are known to have policy issues or are otherwise of priority. Critical links may also emerge from further research concerning the influence of particular links on the overall indexes. As can be seen from Fig. 7, not many critical links may be present in the network though. These could then be studied in more detail for either an equity analysis or for more in-depth knowledge of other impacts of the charging policy. • A more in-depth study should consider the network design issue – this relates to the presence of one way streets, regulatory restrictions on traffic in particular areas and possibly planning/engineering issues around road width or quality that impact on route choices and traffic flow. It is conjectured that these types of factors may be underlying the presence of some negative EBCs in the cost calculation. A set of wider considerations may be included in such a study such as the impacts on particular sub-groups or sub-areas of the study region, who may perceive particular positive advantages from the current network design. • As mentioned above, further research is needed to better define the relationship between changes in vehicle speed and EBC/TTC also at microscopic level.