SpotlightsTN Glossary and fundamental ideas under discussion

1.0 Generally speaking, a “model” is just an “algorithm” wich predicts unknown data, often “forecasting” uncertain “futures”. A model is an intelligent simplification of reality. The paramount modelling goal to achieve the maximum simplicity representing reality with the minimum error. Science is about discovering the simple laws governing reality.

1.1 Compared with other scientific fields (e.g. natural sciences and physics), transport modelling and social sciences in general (those fields pretending to model human behaviour), are far from having convincing explanatory theories and predictive models, and it is reasonable to doubt that never they will because understanding and predicting human behaviour is for humans themselves an ontological impossibility. For instance, social and personal experimentation involves ethical aspects which are not present in natural sciences and physics. All taken, from many decision-makers point of view, transport models not only provide poor predictions: they use obscure formulations based on oversimplified assumptions and there is no much hope future research may significantly improve the situation.

1.2 Empirical evidence shows that transport model forecasts often turn out from the actual flows, even if good practice is followed (Nielsen mentions Skamris & Flyvberg, 1996). This fact is creating since early sixties doubts of the interest of using transport models for planning purposes. Critics say that transport models use to work with poor data, apply unnecessarily sophisticated and cryptic formulations in order to get wrong results.

1.3 But, however, transport models are being developed and applied to evaluate almost all important transport policies, at urban, regional and continental scales. Most of the mem-bers of the EU have, or are developing, either national or regional transport models. While structures vary, a majority of these take the form of a traditional four stage model based on aggregate data. In some cases these are only road models, and there are often separate freight models. However, the inclusion of more features is increasing with time of day choice, mode choice, elastic trip generation and the use of tours or trip chains becoming more common. A few models, notably the Dutch National Modelling System, use a di-fferent form of transport model, a disaggregate one, which uses information on indivi-dual people and households rather than averages for zones to predict travel behaviour: This type of model is becoming more widely used particularly in Scandinavia and Italy. At the more local level, microsimulation allows individual cars to be modelling travelling in a ‘real world’ traffic environment.

1.4 The analysis of human behaviour gained scientific attention during last decade (see Himanen et alt., 1998). Linear programming models, gravity models, spatial interaction and entropy models, discrete choice models, non-linear dynamics, genetic models, agent-based models and many more. Transportation research in particular has shown the genesis of a fascinating diversity of models (Himanen et alt., 1998). Despite all these variety of paradigms, it seems that there is no alternative to substitute the classic modelling paradigm (the so-called “four steps”) and all recent research developments use to be refinements, improvements, extensions and complements to this classic

approach, or academic work not easily applicable on conventional decision-making processes, despite their value as knowledge effort.

1.5 Shortly speaking, models could be clusted on three major paradigms: statistically-based (then data becomes an indispensable starting point), theoretically-based (then the abstract formulation, e.g. based on scientific analogies is the starting point and data is used mostly to validate) or expert-based (e.g. rules of thumbs, heuristics... and then comparative cases and expert’s pannels are key modelling procedures). In social sciences, almost any model has a component belonging to each one of these paradigms.

1.6 All considered, accurate predictions and transparent meanigful explanations alone, are not the more important model requirements (or at least not the only ones) for using transport models in decision-making processes. If the model has to be used as planning assessment tool (as a decision-making tool) it has to provide for robust results, in the sense that each run with the same input data yields to the same final results, and marginal changes in input variables do not produce huge variation. This has crutial conceptual implications, since it requires models to be deterministic (even if they include internal schocastic mechanisms) and assumes the existance and unicity of an equilibrium point.

1.7 Contrary to intuition, the “predictive” and the “explanatory” attributes of scientific models are not always coincident: Better explanations not necessarily produce more acurate predictions. Outputs from models with wrong explanatory formulations may produce better predictions (e.g. the famous Kepler formulations in relation to Newton gravitatory laws). And the opposite may also be true. Recent developments on non-linear dynamics show the actual limits of any scientific model predicting not just complex human behaviour but even much simpler physical systems. While evolutionary biology is well ranked for providing right explanations and bad ranked for predictions, quantum mechanics is in just in the opposite situation (excellent, amazing predictions but no clear explanation of why Shrodinger equations are so accurate).

1.8 An “acurate prediction” is usually obtained using statistically-based paradigms, supported by large volumes of data (it is even possible “to let the data speak for itself” and give the computer the capacity to “learn by itself”). But accuracy predicting short-term trends based on statistic adjustments not always provide meaningful explanations: There is the misperception to consider that strong correlation implies something about causal connections between the variables correlated. Furthermore, overstimating a model formulation with the available data may reduce the apparent “error” (between model outputs and data samples) but may also increase the “real” error (between model outpus and the evolution of the system being modelled, specially in the long-term).

1.9 On the other hand, a meaningful explanation is usually obtained by applying a formulation derived from a more general theoretical framework which is independent from a particular set of data. A meanigful explanation may provide less accurate predictions that a meaningless explanation. It is fair to say that a number of important policy questions are not yet solved by the transport economic theory. Fundamental behavioural hypotheses, such as considering people as rational agents having perfect information, have not being substituted yet by more explanatory hypotheses such as considering people as adaptative agents using local and temporary information to satisfy (rather than to optimase) utilitie’s threholds. The practical impossibility to consider

fram-equilibrium dynamic stable solutions (instead of a single static equilibrium) restricts available theories to analyse marginal and short-term changes. Radical changes having with long-term structural impacts, such as building transport infrastructures, have unsufficient theoretical support, as well as other related issues such as costs and benefits redistribution in networks, etc. In part resulting from this weak theoretical support, some fundamental transport modelling issues (such as forecasting induced traffic) use to be poorly treated by most models.

1.10 Transport models add a crutial third dimmension to the prediction/explanatory dicotomy: Models have to be robust to be applied in decision-making, in the sense that the model has to produce the same outputs each time runs with the same inputs. This is crutial to make comparisons between alternatives reliable. This requirement implies, for instance, that model’s iterations have to converge towards an unique equilibrium point, and even if there are internal stochastic algorithms, the overall model has to be deterministic. In fact, the whole architecture of the classic “four-steps modelling paradigm” applied in transport modelling, was conceived to assure this goal; different modellers using the same datasets and applying the same modelling technique should get rather similar stable results (avoiding the periodic, complex and chaotic solutions which may happen even in deterministic models with marginal modifications of the initial values). Needless to say, there is a trade-off between the acuracy of preductions, the explanatory character of the formulations and its robustness.

1.11 Transport models may have three types of purposes: Strategic models (e.g. to evaluate large infrastructure projects), Tactic models (e.g. to evaluate new pricing policies), Operational models (e.g. to optimise service logistics). Each type of purpose requires a more or less detailed information, a different complexity of the model formulation applied as well as a different time horizon. Next table (from Nielsen 1999), summarises this point. While Operational models use to produce accurate results and not always convincing explanations since they are supported by large databases (on-line often) and based on statistically advanced formulations, strategic models are expected to be exactely in the contrary situation.

Type of model Detailed information Complex formulation Time horizon

Operational Precise Low Short-termTactical Precise Low Short/medium-termStrategic Rough High Long-term

1.12 Transport models applied on decision-making situations have to be robust (to provide reliable and comparable outputs), and provide reasonable explanations (at least not missleading, supported by a well established general theory) and realistic predictions (according to expert judgement and available comparative cases). However, for short-term operational decisions (e.g. service optimisation), accurate predictions is also a critical goal to be achieve (and then statistically-based models become more advantageous).

1.13 Because of the required robustness of models, sensitivity analysis is an indispensable part of the modelling process. Given the likely complexity of any advanced model formulation (with continuous feed-backs and iterations of non-linear

mathematic expressions) there is an obvious risk that marginal changes in inputs lead to non-stable solutions (periodic, complex or even chaotic) or simply to very different outputs. Testing the statistical reliablity of more sensitive parameters and variables becomes then a crutial quality control to be carried out. Needless to say, when it takes days (or many hours) to run a model, the capacity to carry on sensitivity analysis is seriously constrained. In this cases, the use of well-known and validated algorithms is almost indispensable. 1.14 Transport models have to be run by computers, so models are software products. It is naïf to consider computers as scientifically or cultural neutral tools: they influence the way models are build and therefore the way real problems are looked (see the evolution of computers during last decades, from main frames only devote to compute large ma-thematic models in isolated laboratories, to personal communication devices integrating all multimedia capabilities; or the development of non-linear dynamic models). As com-puters and associated technologies become ever faster, it can be tempting to suppose that they will eventually become “fast enough” and that the appetite for increased com-puting power will be sated. However, history suggests that as a particular technology sa-tisfies known applications, new applications will arise that are enabled by that technolo-gy and that will demand the development of new technology. This is certainly true for transport modelling where improvements in processing power have been accompanied by large, more complex models.

1.15 Traditionally, transportation analysts have been faced with the problem of utilising many different software packages, all of them having different interfaces. Some of the legacy software long in use for activities such as travel demand forecasting and air emissions forecasting still utilise what are largely script and control file based interfaces. Locally developed software for custom applications often have their own unique interfaces. More up to date packages include Windows or similar GUI based interfaces. Often, complex projects may involve using all of these types of software in combination. From a management perspective, this complicates the issues of staff training and retraining and, with the attendant investment in staff experience in a particular package and interface, limits consideration of new, improved software and related capabilities that may appear in the marketplace. This situation is likely to continue as more specilised software tools will appear, therefore, instead of pretending that a transport modelling tool be, at the same time, a good statistical tool, a good database management tool, a good GIS, a good transport network manager and having user-friendly interface, an “open multi-software system” solution seems indispensable. Bridges research (1997-2000) provide for the harmonisation formats and routines able to support such a open-support systems to be used in transport modelling.

1.16 The advent of the graphical Windows style interface has revolutionised the use of per-sonal computers by making them more accessible and eliminating many of the technical barriers to their use by the less technical user. Similarly the rise of the Internet and the as-sociated remote access capabilities, including easy to use World Wide Web browser soft-ware, email and other facilities, has begun to change the perception of separation and dis-tance. Already, the Internet, particularly in technical and scientific fields, is widely used for co-ordinated far-flung project participants and activities. Using these capabilities, Bridges research aimed to bridge the gap between the policy-maker and the model, by somehow interfacing them. Two tools were developed to make this feasible: in the one hand a tool for developing Expert Systems able to translate policy questions into model’s inputs and

interprete modelling output’s. The second is a tool able to develop powerful user-friendly interfaces, including together with multimedia and Internet browser capabilities routines needed to handle large databases and complex transport topologies.

1.16 In ASSEMBLING a Executive Support System for European policy makers is being developed in Internet. It includes on-line access to transport models (the Dynamic System model developed by IWW for SCENARIOS) as well as a number of knowle-dge-tools (tools with friendly interfaces and interactive modelling capabilities based on results and algorithms previously developed in advanced models). On the other hand, based on the databases and results of the forecast models developed by NEA and Mk-metric/IWW for the Phare countries, a so-called “Toolbox” has been developed follo-wing the same “knowledge-tool” approach and disseminated in CDRom. These expe-riences show the interest and feasibility of the “interfacing” strategy being developed in European transport modelling since early 1995, when the so-called UTS (Mcrit, 1996) was developed as a user-friendly tool for free dissemination providing interactive GIS visualisation and analysis of pre-calculated accessibility models.

1.17 In the project Bridges a software-tool to develop Expert Systems for advanced mo-dels was created with the aim to bridge advanced models to end-users (decision-makers). A similar case of policy-strategic interface (based on a different approach) is the PACE-FORWARD policy-interface developed by RAND for the Dutch Govern-ment.

1.18 The policy relevance of a model is directly linked to the inclusion in the model of key indicators related to both the policy instruments to be evaluated and the goals to be achieved. For instance, to be policy relevant in relation to Kyoto’s agreement, a model has to produce as output the total CO2 emissions (to be compared with the –8% Kyoto’s reduction goal) and give the user the capacity to modify policy instruments such as road pricing, standards for vehicule emissions etc.

1.19At European level, the policy relevance of transport models can be refered to the following set of questions: Construction of new infrastructures, Introducing transport pricing strategies, Changing transport service provision, Spatial Development strategies and Environmental regulations. The key gap between scientists and policy-makers is that both have different starting points: scientists start with a given question and a provisory hypothesis to be validated or rejected, and policy-makers start with many answers each one looking as a definitive position, and have as objective not to validate any one in particular but to negotiate and agreement among the groups lobbying for each one. How to integrate scientific models and rational ways of thinking in a political process requires a kind of inteligent mediation.

ANNEX 1: 100 Q&A / Glossary of terms and concepts

Questions related to transport modelling paradigms

1.20 The classic “four steps” paradigm has proved to provide a framework able to satisfy all explanatory/predictability/robustness requirements. However, transport models based on other paradigms (e.g. non-linear stochastic processes such as Polya, may be extremely useful as “knowledge-tools” helping the user to understand the

complexity –and to some extend essential unpredictability of complex systems). On the other hand, transport models based on advanced statistic technics (e.g. Neural Networks) may be extremely useful to provide the acurate predictions needed for many operational and management decisions (e.g. optimisation of transport services, logistics...).All taken, there is a large variety of models and purposes, and this is expected to grow in the near future, as the transport system complexity also grows. A clear understanding of the characteristics of the inputs, formulation and outputs of each model is then indispensable to evaluate its usefulness for each particular purposes.

1.21 The basic transport modelling theory can be read in the textbook of Ortúzar & Willumsen (1990), demand models in McFadden & Manski (1981) and Ben-Akiva & Lerman (1985) and supply models in Sheffi (1985). A special issue of Transportation from 1996 includes four bids on the future development of traffic models: Ben-Akiva et.al. (1996), Kitamura et.al. (1996), Stopher et.al. (1996), and Slavin (1996). Travel forecasting models currently in use are based on techniques developed 25 years ago. Many of these techniques are still relevant today, but new policy concerns, on road pricing, land use planning and traffic management, have increased the pressure to develop new techniques.

1.22 The Travel Model Improvement Program (TMIP) in the United States provides an interesting example of a project which aims to tackle perceived weaknesses in techniques. The objectives of the program are to: increase policy sensitivity of existing travel forecasting procedures and their ability

of respond to emerging issues to redesign the travel forecasting process to reflect today’s behaviour , to respond to

the greater information needs places on the forecasting process and to take advantage of changes in data collection technology, and

to make travel forecasting model results more useful for decision makers.As such the research being carried out as part of the TMIP will undoubtedly affect the form of transport models in the future. Further details can be found on their web site at www.bts.gov/tmip.

1.23 As noted by M Ben-Akiva at the 1992 annual PTRC conference, it sometimes takes between 10 and 20 years to transform a viable research idea into a routine model application. Some of the “short term” improvements identified by the TMIP in 1994, are now more commonplace and include:

improved data collection & processing power techniques modelling non-motorised travel using land use allocation models interactive transport / land-use models dynamic assignment modelling tour or trip chaining behaviour improved mode choice models modelling parking departure time or time of day choice modelling the inputs to trip generation travel time reliability / level of service

1.24 However, even some of these techniques are still in their infancy and not automatically considered in the development of new models. Looking at the current

research and proposals for longer term improvements should therefore provide a good indication of the most likely form for the next generation of transport models. Despite this some speculation would be required to identify which of the many areas of research is most likely to be adopted for widespread use.

1.25 The development of traffic models started for real in the 1950’ies and 1960’ies with a number of heuristic models such as gravity, discriminant and simultaneous models. These models typically had some analogies to physical laws (such as the law of gravity). Several of the most successful of the 1960’ies models were later deduced by entropy maximisation, and hereby to some extent theoretically justified. Wilson (1971) provides an overview of this theory, including the deduction of gravity and logit-models. These two categories of models - although significantly refined and improved - are still the main model-types for trip distribution (destination choice) and mode choice respectively.

1.26 The gravity model has been extended to the class of so-called spatial interaction models, described e.g. in Pooler (1994). Their interactions with mode-choice models have been secured by

Types of models

Heuristic gravity, discriminant and simultaneous models

Gravity, logit and simultaneous models

Discrete choice models (logit, cross-nested logit, probit, etc.)

Microeconomic models

Activity based models

Theoretical foundation

A certain degree of analogy to

physical laws

Entropy maximisat

ion

Heuristic utility

function

Indirect utility function

deduced from a direct utility

function based on

microeconomic theory with time- and

budget constraints

The individual units activity

pattern, e.g. a persons travel pattern over a

day

Deduction of the model

Heuristic Analytic Analytic from the heuristic

utility function by

random utility theory

Analytic from the indirect

utility function by random

utility theory

Usual microsimulation of all units, but may also build

on micro economic

frameworkBasic unit Aggregated

, e.g. traffic count on matrix-element

Trip Usually trip, but also trip-

chains

Usually trip or trip-chains, but deduced from

e.g. daily activity patterns

Relations between units, e.g. persons in

households

Development of the paradigm

1950 – 1975

1965-1975

1975 - 1985– (though many earlier works exist)

1990-

aggregating the utility measures in log-sum formulas to so-called composite functions (Williams, 1977 and Williams & Senior, 1997). This secure a theoretical consistence between destination and mode-choice models and the two can be estimated simultaneous from discrete choice data. Daly (1997) describes how this can be extended for trip decisions.

1.27 It is worth noting – that opposite this approach – the early simultaneous models as described in e.g. Manheim (1973) have almost been forgotten. One of the historic reason for this is, that no statistical tools – except least square methods following a logarithmic transformation – was developed at that time, and this highly biased the estimated parameters. With today’s non-linear multiple regression techniques, this is no longer an issue (refer e.g. to Bates & Watts, 1988 and Gallan, 1987). Nielsen (1994) showed that Quandt's model (1965) as used in Sjafruddin (1992) has the most promising features of the early simultaneous models, and developed this further. Sonesson (1998) recently systemised models of the simultaneous type. Simultaneous models are more heuristic in nature than the mainstream development of discrete choice and microeconomic models. However, they can easily be formulated and estimated from aggregated data such trip matrices. Thus, simultaneous models can by rather limited resources be formulated in the initial planning phases prior to the formulation of the far more resource demanding models in the later phases.

1.28 The discrete choice models for e.g. mode-choices have attained much more consideration and development in the literature than the spatial interaction models. McFadden & Manski (1981) and Ben-Akiva & Lerman (1985) describe the basic development, while Bath (1997) and Munizaga & Ortúzar (1997) provides an overview of the later development of new classes of models such as cross-nested logit models and probit models. All these models can typically be calibrated on discrete choice data of either Revealed- or Stated preference type. One of the break-through of estimation techniques was for tree logit models as described in Daly (1987).

1.29 Discrete choice models based on random utility theory can be deduced from a utility function that may be completely heuristic. The development of models based on micro-economic focus mainly on deducing utility functions from basic time- and money constraint - thereby becoming an indirect utility function. One of the advocators of this is Jara-Díaz who has written a large number of papers on the subject (e.g. the references from 1989, 1994 and 1996). It is noted though, that the application of the theory to traffic has evolved over the years with early contributions of Becker (1965), Lancaster (1966), De Serpa (1971), Train & McFadden (1978) and McFadden (1981). As a starting point, microeconomic theory can pinpoint many pitfalls in practitioner’s heuristic utility functions. Considering land-use models

and transport-land use interaction, microeconomic theory is often the core of the theoretical framework.

1.30 The research trend of today points to some extent towards the development of models based on activity theory rather than the theories described above, although many activity-based models contain components deduced from random utility theory or microeconomic theory. One of the corner stone of this development is, that activity theory to a higher degree meet the criticism, that traffic models do not reflect persons decision making (by e.g. Gärling, 1994). Another main issue is that activity-based models better can describe the evolution of traffic and land use over time (the day, year or several years). Due to the complexity, activity based models are often solved by Microsimulation1. This is also one of the main point of criticism: The models must be calibrated by trial-and-error or by learning, and it is accordingly difficult to proof convergence, equilibrium2 and that the calibration does find the global optimum. It is also worth mentioning, that Monte-Carlo simulation techniques will require a lot of iterations to provide the same answer by two runs (Nielsen, 1997c). This is crucial in order to distinguish between random effects in the algorithm and true effects of the plan-proposal under examination.

1.31 The session on ‘Microsimulation of Travel Activities in Networks’ at the 8th Meeting of the International Association for Travel Behaviour Research, Texas 1997, provides several examples on successful activity based models, while the work of Wegener & Spiekermann (1996) is an example of a success-full European application. However, activity based models and principles do not necessarily need to use simulation in application and estimation. As an example, PETRA utilise to some extent principles from activity based models (e.g. elements of trip chaining decision based on daily activity patterns). The activity-based models do not need to be purely simulation models: The theoretical framework and estimation processes can follow random utility theory. A similar approach is used for the later route choice models developed by (Nielsen & Jovicic, 1999 and Nielsen et.al. 2000c), where the estimation is based on stochastic utility theory, while the application have to use Monte Carlo Simulation of the error components and error terms in the utility functions. 1.32 Supply models seek typically equilibrium between road users route choices, the resulting link loads (assignment) and this impact on

1 It is a continuos source of confusion, that researchers in activity based models often consider microsimulation as part of this theory, while many researchers in traffic assignment and traffic technique use the word microsimulation for models that simulate cars movements in road networks (e.g. as many of the models described in Algers et.al. 1997).2 Although equilibrium is a nice theoretical concept, it has debated whether a real traffic system ever finds equilibrium and thus whether it is relevant in practice.

travel times etc. (supply). Route choice models can rarely be simplified to all-or-nothing, as there as usual are alternative routes. This necessitates – as a minimum – a stochastic assignment model. The model should also be able to describe congestion - not only queues but also weaker speed-flow relationships. Finally, the model should have the same level of detail as the demand model. Thus, if the calibration of the demand model shows that there are strong differences of preferences (utility functions) between different trip purposes (user classes), then it is most likely that these purposes will also have different utility functions regarding route choices.

1.33 Although, dynamic models and simulation models have been developed for operational and tactical purposes it is usually not necessary to use such models for more strategic purposes. In the following only static equilibrium models are considered.

1.34 The early logit-based stochastic traffic assignment models (e.g. Dial, 1971) rest on the assumption that different routes are independent. Thus, they lead to problems in networks with overlapping routes (see Sheffi, 1985, pp. 294-297). Nielsen (1994) showed that this could bias the result significantly for urban areas. Daganzo & Sheffi (1977) suggested the use of probit-based models to overcome this problem. Sheffi & Powell (1981) presented an operational solution algorithm that was deduced from these assumptions. As pointed out by Van Vuren (1994), a key aspect is that the cost components at link level are linear additive. A similar Probit-based concept is part of the Stochastic User Equilibrium (SUE), where the travel resistances are flow dependent. Thus, equilibrium is reached where no travellers' perceived travel resistances could be reduced by unilaterally changing routes. SUE was suggested by Daganzo and Sheffi (1977) and operationalised by Sheffi & Powell (1982).

1.35 Although, other solution algorithms have been proposed (e.g. Maher & Hughes, 1997) Sheffi & Powell’s are recommended due to the strong empirical evidence (see Nielsen, 1994, 1996 & 1997b). However, due to problems with truncation of the Normal distribution (Nielsen, 1997) it is recommended to base SUE on either a symmetrical truncated rectangular distribution – or better the Gamma-distribution.

1.36 In Nielsen (1996) it was discussed whether the modelling approach in SUE is sufficient to describe road users' behaviour. It was shown that the perceived travel resistances to a certain point could make up for the road users' lack of knowledge of the ‘true’ travel resistances. However, it was also shown that this modelling approach does not consider variations within the road users' utility functions (e.g. the weighting of travel length versus time). To consider this, a modification of SUE was presented in which two types of stochastic components occur - the first considers road users' perception of the

traffic network at link level (as in the traditional SUE) and the second considers differences within the road users' utility functions. This parallel to some extent the relaxation of response homogeneity in discrete choice models (see Bath, 1997). This work has been continued in several projects, incl. the Harbour Tunnel project (Nielsen et.al. 2000c).

1.37 It is also worth noting, that it might be necessary to allow different user classes (e.g. students-, homework, business- and other trips) to have different utility functions with different weights and distributions of length, cost and time. This was the case in both the Harbour Tunnel (Nielsen et.al. 2000c) and Copenhagen Ringsted (Nielsen et.al. 2000b) models. The supply models hereby reflect the complexity of the demand models, which eliminate consistency problems in the equilibrium process between supply and demand.

1.38 Nielsen (1998a) describes such a multi-class assignment model. If the corridor pass urban areas, it might be necessary to include intersection delays. This is tradition ally considered too detailed for corridor models, but Nielsen et.al. (1997b & 1998) showed that excluding intersection delays might lead to biased results at a quite aggregated level. In Nielsen et.al. (2000b) an operational model was needed to interact with the tactical model to address the aim of the study and planning context.

1.39 Organisational network models describe the flow of passengers or goods in the organisational network of routes, terminals and transfers. It is assumed that the ‘units’ in this network follows predetermined lines, that some-how can be related to the physical network of links, nodes and turns (a railway switch may topologically be defined as a turn). Nielsen, et.al. (1997 & 1998) describe ways of handling such topologies in a GIS-context. This have been developed further in the BRIDGES-project under the 4th EU framework programme (to be continued in the SPOTLIGHT project under the 5th

framework programme) and the Copenhagen – Ringsted project.

1.40 Assignment of passengers is more complicated, since the individuals decide their routes in the organisational network according to different principles. This rise the following questions:

Public traffic networks consist often of parallel lines with the same or different frequencies. Thus, it is often a question if two lines should be considered as different or as one line with a bigger frequency. This also raises the question how to weight frequency versus driving time and cost?

Transfers and waiting times are significant factors in public traffic assignment. Some passengers may choose routes in order to minimise the number of transfers, while other minimises travel times (or something between).

Different sub-modes in public traffic mode-chains have different service levels (e.g. buses versus trains). The deterministic travel times can therefore not just be used in the assignment, but must be weighted in some kind of utility function (to generalised travel times). The distribution of different modes' utilities may be

different as well: Trains are e.g. often more precise than buses, people’s preferences towards buses differ more than towards trains.

While perceptions of links in the car-network can be considered rather independent, choices in public traffic are often dependent. This is due to the fact that public traffic assignment conceptually contains elements of mode choice. As peoples’ choices of sub-modes in a public mode-chain depend on their preferences, the choice of the next line at a terminal depends also on the preceding choice.

The public network structure is very complicated. Thus, it is not sure that each passenger is aware of all feasible routes.

1.41 Early models attempted to model public traffic assignment after the principles developed for car assignment. As the points above indicates this is not very proper. In simple corridors, one might reduce the assignment to a finite number of alternatives and then use a discrete choice model. However, usually one has to use assignment models developed specially for public traffic.

1.42 In very detailed modelling networks, all transfers between all lines are described by timetables. Therefore, exact transfer times between each sub-mode in a mode chain can be determined. In this case, a Probit-based assignment can be run directly on the network - or more detailed approaches such as micro simulation can be used. This was the case in the Copenhagen – Ringsted model (Nielsen & Jovicic, 1999a).

1.43 However, most modelling networks contain only travel times and line-frequencies, as exact timetables demand a lot of coding work, computable time and a very complex topologic model (see Nielsen et.al. 1998 and Nielsen, 2000a). In addition, it is often impractical to work with timetables for strategic forecasts, since the timetables should be changed according to each plan-proposal under examination. Thus, more aggregated assignment models are sometime needed.

1.44 The most common approach is to select a number of feasible routes in order to reduce the complexity of the choice problem, and then to choose among the selected routes according to a generalised cost function of driving time and waiting/transfer time. This is typically done after two different strategies: Frequency dependent strategies. An example is the model of Spiess & Florian

(1989) used in the modelling packages EMME/2. Here, it is assumed that the coach that arrives first at a given node among a set of attractive lines is boarded.

Frequency aggregation strategies. Examples are Chriqui & Robillard (1975) and De Cea & Fernandez (1989). Equal principles are used in the modelling package Trips. Here parallel lines between node-pairs are aggregated to single arcs with some aggregated measures of frequency and driving times.

1.45 Not all relevant routes are necessarily found by these two approaches. The selected routes may also be more or less dependent

on each other (e.g. from the case of two completely different routes, to two routes that use different feeder buses to the same main mode-chain). This equals somewhat the problem with overlapping routes in Logit-based car traffic assignment models. To avoid this, Nielsen (1997c) developed a Probit-based framework based on the methodologies from Nielsen (1996).

1.46 Furthermore links and use of time-table based models have been established in the Copenhagen – Ringsted project (Nielsen & Jovicic, 1999) and in a project linking the stochastic framework to the more efficient deterministic solution algorithm implemented in the software EMME/2. 1.47 Finally, it is a precondition that a proper feedback mechanism is implemented from the link load models to prior steps. It is stressed out that a naive iterative approach might not lead to convergence, why this should be tested by statistical measures. If the algorithm does not converge properly, one might adapt the Method of Successive Averages (MSA) for the whole traffic-modelling complex as recommended by Willumsen et.al. (1993). It is also noted, that the final supply matrices (times, lengths and costs between all zone-pairs for all classes for all time-intervals for all modes) defined at the equilibrium condition may not be consistent with the times, length and costs used in the assignment model’s solution algorithm. Thus, a consistent aggregation of supply-variables must be implemented directly as part of the assignment software.

Both the Harbour Tunnel project and the Copenhagen – Ringsted modelling project addressed the issue of feedback between assignment models (at an operational / tactical level) to demand models at a tactical level.

1.48 All the changes in the Transport Semi-circle except car ownership decision happens immediate (that is within few months). The land use impacts happens – on the other hand – at a much slower speed. Referring to Wegener et.al. (1986) and Wegener (1998), land use impacts them-selves can be ordered by the speed by which they change – in the order from slow to fast: Very slow changes: Networks, land use. Urban transport networks are the most

permanent elements of the physical structure of cities. Large infrastructure projects require a decade or more, and once in place are rarely abandoned. The land use distribution is equally stable; it changes only incrementally.

Slow changes: Workplaces, housing. Buildings have a life span of up to hundred years and take several years from planning to completion. Workplaces (non-residential buildings) exist much longer than the firms or institutions that occupy them, just as housing exists longer than the households that live in it do.

Fast changes: Employment, population. Firms are established or closed down, expanded or relocated; this creates new jobs or makes workers absent and so affects employment. Households are created, grow or decline and eventually are dissolved, and in each stage in their life cycle adjust their housing consumption

and location to their changing needs, this determines the distribution of population.

Immediate changes: Goods transport, travel. The location of human activities in space as derived from the employment and population gives rise to a demand for spatial interaction in the form of goods transport or travel.

1.49 Naturally, land use data are essential to estimate activities in space as input to traffic models. But this data is often assumed non-dependent of the traffic infrastructure, and thus estimated from pure macroeconomic, regional economic or population/workplace forecasts. One of the reasons for the scarce use of land use transportation interaction models is the quite vast resources needed to model the very complex relationships compared with the often highly uncertain model outcome. Thus, different scenario techniques are often used instead of models.

1.50 But a thorough review on land-use and transport models has been carried out in the Ph.D.-study of Jeppe Husted Rich at DTU. Thus, it is referred to this review as well as recent articles by Wegener (1998), Handy (1997), Martinez (1997) and Eliasson & Mattsson (1997).

1.51 Future model forms. Two alternative model forms currently in use are disaggregate and aggregate models. There is increasing interest in disaggregate models, particularly in the Netherlands, Scandinavia, Italy and the USA. While this method of modelling is popularly believed to be the way forward, it does have associated problems.

Two further areas of interest identified by the TRL in the UK recently and also being researched in the USA are: the use of activity analysis to look at the interactions between people, household activities and travel and the use of microsimulation to track individuals and their trips through time. To date the two approaches have often been combined in research projects. A major programme is underway in the USA to enhance current models and develop new procedures. Track C of this programme involves research into the development of fundamentally new approaches to travel and land-use forecasting, and involves the development of TRANSIM (TRansportation ANalysis and SIMulation system). This is a set of integrated simulation and analytical models and databases, based on activity and behaviour analysis.

The range of results demanded from future transport models will probably play a significant role in the way they evolve over the next few years. The current interest in the environmental impact of alternative transport policies is leading to increased spatial disaggregation.

1.52 The most common type of transport model in use today is an aggregate model. In such models, individuals are aggregated into groups (eg car owners) and then groups of people make choices on quantity of trips, location of home / workplace, mode and time of travel and route to use based on zonal information. All these choices are calculated within the model based on the average behaviour of the individuals within the group. The number of groups being used within the model and the homogeneity of the individuals within the groups will therefore impact on the behavioural choices made

within the models, and hence the realism of the results obtained, especially through time. Aggregate methods have traditionally been applied in four stage model systems, which are common throughout many countries in Europe.

1.53 Disaggregate models A disaggregate model is based on the observed choices made by individuals. Again the models can be applied to the many different stages of mode, destination location and travel time in the travel decision process. The basis of disaggregate modelling is often one of utility maximisation, taking into account the characteristics of both the individual and the choice alternative. This is often achieved using random utility theory; with the multinomial logit model being the most widely used for modal split modelling. Once the choice probabilities have been calculated for the individuals, there is then usually the requirement to extend the forecast to the entire population for the area of interest.

A direct application of disaggregate models to obtain forecasts is in microsimulation models. In principle this could be carried out for the whole population, however resource requirements make the use of a sample more likely with the results scaled to the population.

Other methods of aggregation, involve the use of alternative sampling techniques. Depending upon the approach followed, in some cases the models developed have been found to give excellent short term predictions. However longer term forecasts require input from users on variations in the population categories being used.The Dutch National Model System (NMS) is primarily based on disaggregate model techniques. Its development aimed to use the strengths of the best available models with any available data.

1.54 Advantages and disadvantages Since the aggregate approach assumes that behaviour can be adequately represented by large groups of travellers, the detail that is required in the data for implementing, calibrating and validating such models is not onerous. Whereas for disaggregate models the opposite is the case, with the cost and reliability of obtaining the large quantities of population data required being one of the main drawbacks.

Contrary to this argument is that disaggregate models can actually make more efficient use of data, since data on individuals is used, the level of aggregation can be varied to allow policy specific variables to be incorporated rather than requiring further data sets.The main benefit of disaggregate modelling is that it is possible to include variations between sub groups of people which would otherwise be lost. However forecasting at this level means that there is a requirement to forecast the same relationships used in the base year, eg between persons within households in the future.

1.55 Most existing transport models use trips as the unit of forecasting and analysis. It has been suggested by various authorities that the next generation of forecasting models should focus on activities, with travel being one of a series of options for satisfying the activity. In 1992, the US Federal Highway Administration (FHWA) awarded four contracts for proposals to redesign travel demand forecasting processes to meet the requirements brought on by recent US legislation. Three out of the four awards explicitly recommended a move from trip (actually often a vehicle) based models to an “activity based framework”.By adopting this approach the models should be able to

address issues related to the substitution of non-travel alternatives and trip chaining and be able to handle choices which are influenced by interpersonal dependencies between household members.

1.56 One of the four research proposals carried out by the Massachusetts Institute of Technology (MIT), suggested that most of the activity based models developed to date are descriptive and not sufficiently developed to be useful. They need to be extended to be sensitive to changes in transportation and information technology, and to be able to incorporate new travel and no travel options to captive effects of changes in household and individual activity patterns. In addition all three proposals recognised the need to reflect changes in family structures, demographic and activity opportunities. 1.57 In both cases the amount of data required to implement, calibrate and validation such models is large. The submission by Resource Decision Consultants for a model framework called SAMS (Sequenced Activity-Mobility System) listed many sources of data, including household activity diaries, longitudinal panel surveys and stated preference data; all of which are currently expensive to collect.

1.58 An activity based approach to travel demand modelling has already been applied using a software package called VISEM in Germany and some other European countries, Fellendorf (1995). VISEM is a tool to estimate and forecast mode specific origin-destination matrices, by classifying the population into behaviourally homogeneous groups and generating trip chains from activity chain information. Much of the data required to develop this model was taken from the national transportation survey of Germany. From the survey the main activity chains, such as Home-Work-Shopping-Home can be identified. Each activity chain then has an associated trip frequency per person. The distribution of these trips is then based on a calibrated deterrence function for each population group and activity.

1.59 Of the approaches recommended in the FHWA study, it is recognised that activity analysis is at a fairly early stage of development, although they have been around in academic institutions for nearly 20 years. This approach is likely to have advantages over the aggregate models that have been around for many years. In some forms it may also offer an improvement on the more recent disaggregate choice models which necessarily operate on a sample of the population and require sophisticated techniques and vast computing power to expand the results to be representative of the population as a whole.

1.60 Microsimulation: A traditional 4-stage traffic model is an idealised representation of reality, which excludes extraordinary events from the modelling process. In most cases these generalisations are acceptable but with a growing demand for accurate and detailed forecasts many city authorities, including Edinburgh, Sheffield, Leeds, Manchester, London, Birmingham, Tokyo, Los Angeles, Singapore and Buenos Aires, are turning to micro-simulation. Assessment of advanced transport telematics (ATT) on traveller behaviour is also increasing demands for more detailed transport models and has helped enforce the use of microsimulation models.

1.61 Micro simulation offers a more accurate representation of traffic flows, by operating in real time and incorporating more normalised behaviour. Drivers within the system perform according their aggression and awareness (TRL, Hardman and Taylor)

incorporating parameters such as propensity to change lane, top speed, weather conditions and gap acceptance. Research has demonstrated that this data need not be collected at a local level, but that national averages are sufficient. Microsimulation also represents a change in direction away from traditional transport models often programmed in FORTRAN and in existence for many years.

1.62 In the past micro-simulation has been restricted by computing technology, but as has been repeatedly mentioned, the recent acceleration in processor speed and data storage capacity has enabled large-scale microsimulation at an affordable price. An example of a microsimulation traffic model is Paramics developed by the Software Company Quadstone and traffic engineers SIAS. Paramics incorporates a sophisticated microscopic model, intelligent routing, the ability to interface with real-time traffic information and the inclusion of public transport data. The movement of individual vehicles is governed by three interacting models representing vehicle following, gap acceptance and lane changing. Vehicle dynamics are based on a combination of driver behaviour and vehicle characteristics (size, acceleration etc.) Paramics has been applied to a range of situations from individual junctions to wide-area problems of rural and urban congestion.

1.63 The TRANSIMS project being carried out as part of the TMIP shows how the two main themes of US transport research (microsimulation and activity models) can be incorporated into mainstream transport modelling. The TRANSIMS Project objective is to develop a set of mutually supporting realistic simulations, models, and databases that employ advanced computational and analytical techniques to create an integrated regional transportation systems analysis environment.

1.64 The TRANSIMS Microsimulation component simulates the movement and interactions of travellers in the transportation system of a metropolitan region. Using a trip plan provided by the route planner, each traveller attempts to execute the plan on the transportation system. The combined traveller interactions produce emergent behaviour such as traffic congestion. Microsimulation models do require powerful systems on which to operate, and the amount of data to be processed does limit their use. TRANSIMS has been developed on Sun Microsystems software, and requires 1.2Gb of disk space for installation and 250Mb of memory per CPU or per workstation depending upon the configuration being used. With five CPUs being the recommended number of processors for running the software.

1.65 Fuzzy set theory was originally developed to deal with problems that are characterised by uncertainty. As outlined in Vythoulkas (1994), many of the choices made in relation to travel are based on how the user perceives the travel conditions, eg congested, or dangerous. These perceptions do not fall into neat sets with clearly defined boundaries where it is clear that the level of congestion at a certain point goes from medium to high. Vythoulkas outlines how this vagueness in human perceptions can be easily modelled using the theory of fuzzy sets.

A fuzzy set allows each element to belong to a set with a grade of membership in the range [0,1] where 0 indicates the element is not compatible with the concept represented by the fuzzy set, and 1 is fully compatible. Fuzzy sets can overlap and therefore an element can belong to more than one set. The model framework proposed by Vythoulkas represented travellers perceptions and preferences using fuzzy sets such that

decisions are based on simple rules such as “if..(perceptions)..then..(preferences)..” rather than utility maximising formulae. However as the input perceptions can be similar to several rules, the rules may all be processed simultaneously and the result composited to obtain the final preference. However the rules need to be weighted before composition takes place so that the importance of say travel time over cost is taken into account.

1.66 This approach to modelling also has a similar problem to that of disaggregate modelling, where at the end of the estimation process some further processing is required to predict as set of clearly defined actions which will occur for the population as a whole under the situation modelled. This process sometimes referred to a defuzzification, is a mapping from the fuzzy sets of actions to non fuzzy or crisp ones. Fuzzy set theory was also recommended by the MIT as a methodological advance in travel demand forecasting which requires further research and testing. However this concept is not new. In 1978 Zimmermann developed a fuzzy multi-objective programming model which combined fuzzy set theory and compromise programming to deal with the vague relationships which exist between conflicting objectives. Although the use of this approach has been studied further to work out transit plans in Taiwan, it does not appear to have attracted sufficient interest to make it a standard feature of future transport models.

1.67 Neural Networks use a set of processing elements (or nodes) analogous to neurons in the brain. (Hence the name, neural networks.) These processing elements are interconnected in a network whose purpose is to identify patterns in data as it is exposed to the data. In a sense, the network learns from experience just as people do. As such artificial neural networks can be seen as highly parallel dynamic systems consisting of multiple simple units that can perform transformations by means of their state response to their input information. This distinguishes neural networks from traditional computer programs that simply follow instructions in a fixed sequential order.Neural network models can be characterised into different classes according to the following features. The behaviour, and hence validity, of NN models depends mainly on the learning algorithms adopted. Two different standard classes of learning methodologies have been developed: Supervised and Unsupervised (or self-organisation). Supervised learning implies that the network learns by example from a training set of inputs with the required target outputs for each. Alternative algorithms are then used to adjust network weights to minimise the difference between the target and actual output values. The most common training technique is the backpropagation method. Unsupervised or self-organising algorithms require only input data, with weights being adjusted so that similar inputs produce similar outputs. In practice they have been found to be both more computationally complex and produce inferior results to those obtained from supervised learning techniques. However, this technique has been successfully applied.

1.68 The way forward - a combined approach? In practice, as demonstrated by the Dutch Zuidvleugelstudie, it is possible for disaggregate models to replace aggregate models within the conventional four stage model system to form a hybrid approach which combines the elements of both types of model into a single framework. Thus the distinction between the two becomes blurred.

Adopting this approach has the advantage of controlling the scale and cost of data collection exercises in relation to the nature of the study and the ability to take the best aspects of the two systems. For example modal choice models are now more commonly based on both zonal (and interzonal) information, such as travel times and parking provision; and household / individual data such as whether the person making the trip has a car available or not.

1.69 Using the Finnish nationwide travel surveys, a SOM based model was developed (Himanen) as a means of explaining daily travel behaviour. The Self-Organising map (SOM) algorithm is similar to any vector quantization algorithm. In vector quantization the problem is to represent an input space by a fixed number of vectors called codewords or reference vectors. Each codeword represents the points in the input space that are closer to that codeword than to any other codeword. The SOM determines codewords for a given input space, and it also tries to preserve topological relationships between the inputs. Therefore any two units that are close in the map correspond to two points in the input space that are close together. The closeness between the units in the map is described by defining the map as a one, two or multi-dimensional discrete lattice of units. This kind of feature mapping is a form of non-linear dimensionality reduction that has no exact statistical analogue. However a trained SOM has similarities with non-parametric regression models. The results obtained in Finland using the SOMs mirrored the results from aggregate methods, showing that average daily travel distance and speed per person are closely related. The novel methodology adopted did appear to capture part of the complex structure of daily travel behaviour.

1.70 Systems Dynamics (SD), originally called industrial dynamics was developed at the Massachusetts Institute of Technology (MIT) in the 1960’s as an investigation into some of the important characteristics of the behaviour of complex social systems. Forrester, the original thinker behind systems dynamics, realised that these systems behave counter intuitively which means that direct or short run solutions for problems in these systems are ineffective or even an adverse reflection of people’s way of thinking.The methodology draws its roots from a number of other fields including system theory, cybernetics, information science, organisational theory, feedback control theory, military games and tactical decision making. The main function of SD is to construct models of complex problems and to experiment with them on computers. SD is a methodology designed to help in understanding the dynamics of different real-world systems. It is based on control theory (servomechanisms). This presents a procedure for investigating and understanding a system that is in the form of causal feedback relations. A SD model is a mapping of a system’s important stock-flow structure, where the stocks and flows are embedded in feedback loops. It is not mathematically necessary for the feedback to have dynamic behaviour (the only pre-requisites are stocks and flows), but that most actual social and economic systems possess feedback loops. Computer simulation models based on SD provide controlled experimental environments. The results from the models are arrived at through a feedback framework. Variables are linked in closed chains of causal relationships forming a feedback loop. The models are made up of many such loops linked and interrelated together. Positive feedback loops generate amplified growth or decline i.e. they feed back upon themselves e.g. - births causing population growth. Negative feedback loops generate goal-seeking growth or decline i.e. they feed back toward achieving a stable target e.g. a thermostat which adjusts the temperature of a room to a

given specified value The combination and the strength of the feedback loops that form the structure of the system generally govern the behaviour of a system. Non-linear couplings often join feedback loops together in a SD model and cause the strength and dominance of the loops to change over time. This is important for understanding the behaviour of a SD model.

Other studies that applied SD methodology to different transport related issued include Drew (1989, 1991), Kuroda and Tsaur (1990), Pujantiyo et al (1992), Al-Dawood (1993) and Verroen and Jansen (1991, 1993). SD modelling is also being applied in the Fourth Framework research projects ASTRA, SCENARIOS, and ASSEMBLING.

Questions in relation to the policy needs for EU Transport modelling

The European Transport System is becoming a “Complex System”: It involves an increasingly large number of heterogeneous agents (EU, national, regional and local administrations, carriers, users...), which are both intelligent and adaptive and make their decisions based on local rather than global information. This “complexity” produces unexpected behaviour and surprises, often against common sense and intuition. This complexity challenges traditional transport and economic models applied with success in “business-as-usual” situations (e.g. the classic “4-step” transport model formulation or the Welfare Theory –Microeconomics-, applied to analyse the short-term impact of marginal changes in a well-known system). In a complex system such as the European transport system, under non-marginal changes (e.g. air liberalisation, development of large transport infrastructure projects...) stability patterns may happen far a way from the equilibrium hypotheses of traditional models.

The world is more complex than expected, but the explanations may be simpler; the process of mutation, variation and selection (so-called “cumulative selection”) has generated life and nature as we see it without the need of any previous plan. The spontaneous evolution of complex systems does not guarantee self-organisation, however, even when they operate under global constraints (e.g. the “carrying capacity” of their environment).

The main surprise—generating mechanisms of Complex Systems in European Transport:

Mechanism Surprise effect European Transport SampleParadoxes Inconsistent

phenomenaRoad pricing (tolls) may provoke higher environmental costs if there is no congestion. Car restrictions may induce more pollution (depending on “gross-polluters”) Infrastructure investments may reduce economic development,etc..

Instability Large effects from small and local

Congestion in a hub airport do to bad whether conditions or traffic controllers strike may

causes create chaos in much other transport terminals.

Incompatibility

Behaviour transcends rules

Users wrong perception of their actual costs travelling by car. Public investments based on “prestige” and/or political territorial equilibrium

Interdependence

Behaviour can not be decomposed into parts

Cost-Benefit redistribution’s on networks. Who should pay to solve a missing connection?

Macro-economic-locational-communications-logistic-transport-environmental-social-political interdependencies.

Emergence Self-organised patterns

No more than 30 minutes in average for daily trips to work. Around 10% of revenue invested in transport.

Complex Systems tends to generate “networked” structures of relations between all the agents. Therefore, there are “connections that count”, between different transport modes (road, rail, airports, sea ports...) and connections between networks at different scale (local, regional, international). From independent lines serving few origins and destinations, transport systems have become “networks of networks” at local and global scale, integrated into wider information and communication networks.The following are the more important Treaty Headings:

Community Competition PolicyTrans-European Networks (TEN)Structural FundsCommon Agricultural Policy (CAP)Environmental PolicyResearch, Technology and Development (RTD)Loan Activities of the European Investment Bank

These policies respond to the global goals of improving European main goals:

-Economic and technological growth-Social and political cohesion and well as cultural diversity-Environmental sustainability, quality and safety.

The impact and the level of development of EU policies in the stated goals are unequal. In terms of budget distribution, nowadays (1999) approximately 50% of expenditures cover agriculture and EAGGF guarantee, 30% Structural measures and fisheries, 5% research and development and 15% others.

Some of the previous headings are proper “policies” (e.g. Community Competition, Environmental Policy...) while others are programmes of subsidies to regions and sectors lacking a proper policy framework, with clear goals, objectives and alternative instruments (Structural Funds, Common Agricultural Policy). EC/DGXVI efforts to

develop Europe 2000, Europe 2000+ and currently the European Spatial Development Prospective (ESDP) represent an attempt to get such a “policy umbrella”. Transport Trans-European Networks represent an attempt to create a common infrastructure policy based on pre-defined outline networks (the experience has been called a “half-missed” opportunity by many experts, e.g. Turró, 1999).

Transport policies are not only related to TENs (where represent 80% of total budget, being high-speed rail investment the larger area with 25%).

The partial deregulation and liberalisation of the transport market, resulting from the Competition Policy, is producing important impacts in transport operators and service provision (e.g. Air regulations from 1995,1996 and COM in Air transport and environment and Airline Industry). In particular, “fair pricing” (making each transport mode paying its actual costs, including externalities) is a major transport policy aim (e.g. WP on Fair payment for infrastructure use, 1998).

Transport is also embedded within the Environmental Policy, since it is a major contribution to the emission of primary pollutants (e.g. CO2), land-taking, energy consumption and so on (e.g. Kyoto’s summit agreements, COM Common Transport Policy Sustainable Mobility: Perspective, 1998).

Transport infrastructure still remains as an important expenditure within the Structural and Cohesion Funds, since assuring a minimum level of transport endowment is considered a pre-condition for economic development.

Needless to say, the more difficult problem to overcome is modelling the impact of policy actions in these (and other) indicators, as well establishing clear boundaries between policy goals, main policies, policy aims and policy actions. Documents, such as the White Book on Transport Policy (1992) or the TEN Guidelines (1992, 1994) (see a complete list of relevant transport policy milestones in Turró, 1999) give weight and precision to some of the global objectives of the Common Transport Policy. Similarly, “Towards Sustainability”(1993, 1999) and Europe 2000, 2000+ and currently ESDP (1998, expected revision 2000) provide reference for environmental and spatial development policies. “Towards Sustainability” (1993) contains detailed tables listing specific policy aims, actions and available instruments to be carried out by precise actors in pre-defined time frames. ESDP existing draft document present as well lists of policy aims, but no much detail on concrete instruments neither indicators to monitor the accomplishment of the aims.

“Policy indicators” are understood as: "Quantitative measures obtained from scientific analysis which are relevant to assess specific policy questions".

Different from an “index” (which is a ratio made by combining variables, e.g. the GDP per capita), many contradictory “indicators” can be defined to assess the same policy question. There is a “choice” behind the formulation each indicator, as there is a choice behind each model formulation. Each indicator “points out” in a particular direction, it is a way of looking, so their scientific consistency and policy relevance depends on the choice, it is a matter of “focus”. A scientifically consistent indicator “flashes” or “highlights” the system without introducing distortions, using a validated method;

policy relevant indicators “flash” those questions policy-makers should pay more attention since they are the keys to govern the whole system.

Often, indicators are "model-centric", coming from more or less complex models (e.g. emissions of a primary pollutant in Europe in year 2010), but they can be “data-centric” as well, being “just” the result of data-mining techniques (e.g. the Gross Domestic Product growth ratio for a "meaningful economic period", e.g. calculated using statistical time series). Indicators scientific reliability depends, all considered, on the reliability of the models and data behind them.

Policy indicators should be specially “sensitive to policy actions impacts" in the sense of being able to show the impact of the policy actions in the achievement of specific policy aims and more general policy goals. Indicators applicability in real decision-making processes depends, at the end, on their policy relevance.

In other fields (e.g. macroeconomic policy), the use of formal decision-making processes seems much more advanced that in the transport sector. Based on the Keynesian paradigm, there is an overall agreement on which indicators (integrated as "key variables" in macroeconomic models) best explain the economic system at macro-level (e.g. GPD growth, private and public consumption, inflation, unemployment, interest rates, public debt, private investment, trade...).

Based on Keynesian indicators, EU countries agreed upon the specific indicators to be used as “convergence criteria” to implement the process towards achieving a common currency. There are a number of macro-models at national (e.g. MOISES in Spain) and at international level (e.g. OCDE...) which monitor the system and provide both short-term and long-term predictions. Last but not least, they are a number of statistic criteria and quality controls to survey those variables needed to calculate the previously mentioned indicators.

None of these elements is free from being controversial. Economic forecast models are ranked as the worse of scientific fields, both in terms of predictability (e.g. compared with Quantum Mechanics) and explanation (e.g. compared with Evolutionary Biology). Transport modelling, as any other modelling activity involving human behaviour, share the same difficulty of economic modelling.

Models supporting indicators can be classified into three main types: Statistics (based on Data Mining techniques), Forecasts (based on theories such as scientific analogies, agent-based simulation...) and Evaluation (e.g. Cost-Benefit, Multicriteria...). Typically, Evaluation models integrate both statistic and forecast models and produce the more policy-sensitive indicators (e.g. Internal return rates...). However, statistic and forecast models can produce their own meaningful indicators as well:

Table: Main characteristics of policy indicators

DescriptionPolicy relevance Direct relation to specific policiesScientific consistency The models producing the indicators have to be

scientifically reliable (objective...)Sensitive to policy actions Indicators should highlight the impacts of policies, in

terms of goal achievementMeaningful to policy makers

Indicators have to be transparent, easy-to-understand and communicate by policy-makers.

Applicable Data and models needed to compute the indicator must be available

The US EPA Office for Planning, Evaluation and Policies (“Indicators of the Environmental Impacts of Transport”, 1996) provides a logic framework based on the following taxonomy of indicators:

According to US/EPA, the mentioned “taxonomy of indicators” can be defined as follows:

-“Activity Indicators” (e.g. road mileage, number of vehicles scrapped, energy consumed, vehicle-miles travellers...): Provide information on infrastructure, travel and other transport-related activities, such as vehicle and parts, manufacture, maintenance and disposal.

-“Output indicators” (e.g. typical noise emissions for trains). They are measures of the direct impacts of the transport activity. In relation to environment, they include emissions, ambient concentrations, land take and exposure.

-“Outcome indicators”(e.g. percent of population exposed to levels of roadway noise associated with health and other effects). They are measures of end-results. They provide information on health, environmental and welfare effects resulting from transport (sustainability), on economic growth due to transport (growth) and cohesion overall impacts (cohesion).

Questions in relation to decision-makers policy needs

Broadly speaking, the increasing difficulties of western societies to plan join transportation and development projects as well as any other long-term comprehensive strategy, is the result of the break in the social consensus towards economic development. In some South-Asiatic countries (Taiwan, South-Korea, Singapore, with some delay in China as well), this break has not happened yet. Under authoritarian rulers fast economic development has been the result of export-oriented economic policies supported by long-term and comprehensive public investment policies (in transportation and urban development along with other social and economic infrastructures), not always with high environmental costs (as the Singapore planning experience shows). This situation was similar in some south-European countries during the sixties, and, with some differences, in northern-European countries during the early stages of their industrial development. However, the social consensus towards a model of economic development is today broken in Western

developed countries. To explore the reasons of this change is beyond the objectives of this report, but it will be interesting to briefly explore the implications it has in the transportation and development decision-making process. The increase of the functional urban space due to the transportation systems (mostly highways and railway systems) and the rigidity of territorial administrations (Municipalities, States...) has produced functional distortions, that make the government of territorial issues increasingly difficult. The incoming non-territorial logic of the communication networks crashes against the territorial logic of traditional political jurisdictions. The fragmentation of land-use planning following municipal boundaries and the increase in scale of economic activities is at the origin of the weakness of local authorities to manage the process.

Developed societies seem to go toward pluralistic democracies where the central political power is partially dissolved and decentralised. The future appears unpredictable. Fragmented public "authorities" have less capacity to manage and to solve the existing problems from a comprehensive, integrated and long-term view. In a context with complex transfers among different institutions and private groups, with high uncertainties concerning the behaviour and strategies of the other groups, comprehensive long-term agreement seems hardly feasible. The discredit of long-term planning is due to both operational as cultural reasons. Without a central authority able to impose comprehensive and long-term plans to the society, it is needed to reinvent both, the role of public authorities and the role planning.

On the other hand, in mature democratic societies it seems necessary to facilitate the pro-active participation of all the actors involved in the transportation and development fields: Without social mobilisation towards development and towards and early control of its environmental impacts, development would be hardly possible and, if it came up, it could produce unnecessary external costs. As a result, it seems convenient to implement decision-making processes more open to negotiation, where both political and bureaucratic-rational approaches can interact. The increasing public referendum practice to approve (or to reject) land-use and transportation policies (traditional in US cities and now beginning to be used in some European countries as well, like Switzerland or the Netherlands) requires much more open and complex analysis when planning transport investments. In the the present day complex and dynamic society, centralist planning strategies based on the imposition of an "optimum evolution" or an "ideal form", can produce unpredictable results. The most useful contemporary planning paradigms seem those to be focused on the control of only a few strategic elements, the elements which are more likely able to induce sustainable self-organisation in

the others. For instance, the infrastructure networks long-term configuration and the overall environmental conditions that assure both the permanence of minimum urban qualities and allow for a maximum of development flexibility and continuous self-organisation opportunities. This implies a conception of order and disorder as interpenetrating rather than opposed, an interest in relating local sites to global structures, an awareness that in complex systems small causes can lead to massive effects and an understanding that complex systems can be both deterministic and unpredictable.

Instead of only complex, short-term, functionally oriented strategies, probably future planning strategies will also require few simple and fixed rules encouraging de-centralised processes of self-adaptation suited to every scale and assuring permanent structures of minimum order. The plan of the chess-board (networks of infrastructures - transportation and other- and spaces with highest and lowest urban density) and the definition of the rules of the game (land-use laws based on environmental aspects) should provide for the most useful methodological approach to introduce basic spatial controls (in the mobility and location aspects), as a sort of overall guidelines preventing non-convenient future evolution.

According to every specific geographical context and scale, these general strategies should be implemented stimulating the self-organisation of the system (facilitating the feasibility of strategic projects through public-private partnerships, increasing the transfer of information and technology to the actors) and encouraging a permanent multiparty negotiation.

As a result, the planning activity should be focused on avoiding the possibility of inconvenient configurations and undesirable evolution, rather than pretending to impose the "ideal" or the "optimum" one.

The following tables try to summarise these considerations in a simple way:

FUNCTIONAL-ECONOMIC APPROACH

STRATEGIC-POLITICAL APPROACH

stakes economic efficiency allocating scarce resources

political interest

paramount objective

maximum marginal socialbenefit-cost

create social consensus andsocial mobilization

goals maximise users (travellers) utility and minimise externalities

maximise voters perception

issues sectorial multi-sectorial

timing basically short-term

long-term in theory, short-term in most cases

framework cost-benefit quantitative results

multi-party negotiation

Any analytic methodology pretending to become useful, should be formulated in order to avoid unnecessary mathematics complexity as well as to provide results easy to read, really understandable by all decision-makers. Instead of being a black-box, models should be simple and open, easy to adapt to specific cases to and even, with the support of an adequate user-friendly software, easy to be personalised to any potential user.

Policy makers receive from experts both qualitative and quantitative analysis (which often can be “summarised” into indicators). According to a “rational” decision-making approach, politicians should base their final decision upon these objective analysis, and any decision-making aspect should be quantified or, at least, make objective.

However, the relevance of rational decision-making process in democratic societies (where decisions have to be negotiated, and agreement between contradictory goals is needed) personal and non-rational elements are almost unavoidable. While rational and scientific analysis (often providing objective quantitative measures) are able to get “universal” validity, emotions and intuitions are just personal, often non-transferable at all. In past, removal of subjective and qualitative emotions by objective and quantitative indicators was the key strategy to improve decision-making processes. Nowadays, it is assumed that, all considered at the end the final decision corresponds to a human being (so to a “subject” which is unavoidable “subjective”) and no to a computer (an “object” which can be “objective”). A proper dialogue an interaction between both is the key to improve decision-making processes.

Increasing awareness towards intuitive, even emotional aspects (so-called “emotional intelligence”) demonstrates that, all taken, human experience is needed to cover the whole range of implications of a given decision (at all geographic scales and time

horizons, for all different groups, across sectors). Moreover, human capacity identifying “patters” is nowadays impossible to reproduce by artificial intelligence.

Scientific decision-makers (“computers”)

Emotional decision-makers (“people”)

Start with the data: Almost free from prejudices.

Start with a provisory solution to be validated and modified

Rational formulation Adaptative behaviourLarge volumes of information Limited access to informationInductive in-depth analysis Intuitive-deductive search for "patterns"Specialised: Sectorial analysis Global: Multisectorial synthesisLarge volumes of calculations, slow conclusions

Rapid expression of personal perceptions

Optimisation strategy from all possible solutions

Satisfying strategy based on "acceptable" possibilities

All taken, a balance between a rational approach (based on quantitative and qualitative analysis) and an emotional approach is needed. The paramount goal is therefore not just providing decision-makers results from scientific analysis but empowering decision-makers, developing a mechanism helping them to confront their intuitions with scientific analysis.

In order to achieve this goal, indicators (data and models behind them) have to be accessible to decision-makers and integrated into a so-called “Executive or Policy Support System”, a knowledge-tool in hands on policy-makers.

Questions in relation models usability

The ESS is understood as a computer system to be used first for rapid consultation and then for interactive analysis. It will enjoy a number of basic modelling, GIS and mapping capabilities, have links to information sources (e.g. ASSEMBLING Observatories), in-house databases and models (e.g. those developed for ASSEMBLING based on Bridges software). Therefore, the ESS is understood as a friendly and interactive system to be used by top decision-makers for on-line consultation and analysis, as friendly and interactive as the Observatories Internet sites should be.

An Expert Support System is therefore a particular case of a Decision Support System. It emphasises the top decision-maker point of view (so it gives more priority to give them productive access to policy-relevant indicators and assessment tools). Links to complex databases and advanced non-interactive models behind those indicators (the paramount interest of expert users) are considered as external elements to ESS; they are components or modules of of a larger Decision-Support System where ASSEMBLING ESS should be somehow linked.

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