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This paper proposes an in-depth analysis of Ro-Ro/Ro-Pax services in the Western Mediterranean, with a focus on Italy. Following an already consolidated research track, a database of Ro-Ro/Ro-Pax services has been built and analysed, enabling policy insights on port connectivity, market positioning of shipping companies across ports and routes, and congestion of Ro-Ro/Ro-Pax port terminals. Furthermore, an adaptation of the well-known GLS-based procedure that updates/estimates o-d flows from traffic measurements has been proposed, to estimate Ro-Ro/Ro-Pax freight flows between ports in a study area, given the total inbound/outbound port throughput and the total weekly capacity of port-to-port services. Application to both a laboratory experiment and to a real case study yielded very effective results. Overall, the presented analyses update earlier contributions in the literature and set the basis for an observatory on Ro-Ro/Ro-Pax services that might be regularly brought up to date and applied also to other countries.

Highlights • HSR had impacts on socio-economic, territorial and transport systems in several countries. • The paper analyses the impacts induced by HSR services in Italy after ten years of operation. • Italy represents the first case of competitive HSR market without public subsidies. • Competition between HSR operators brought a significant decrease in ticket prices (-40%) • HSR passengers/year passed from 15 to 45 millions (+200%) in 10 years. • HSR contributed, in 10 years, to an increase of the rail-based transport accessibility up to + 30% • HSR contributed to an extra growth of per capita GDP of + 2.6–5.6% in 10 years. • Regional equity in terms of travel time accessibility decreased by 11% in 10 years. • Extension of the HSR network would increase accessibility (+18%), economic growth (+3.6%) and equity (+49%), The deployment of HSR services in the recent decades has been, arguably, the most significant innovation for intercity travel around the globe. HSR has brought impacts which have been widely studied in different countries in relation to the different socioeconomic, territorial and transport characteristics. This paper analyses the economic growth, the transport accessibility and the social impacts observed in Italy after ten years of HSR operation, as well as the estimated impacts of the system completion. The Italian case study is of particular interest since along the 1,467 km of new high speed line (300 km/h), a combination of major cities distances and a unique HSR competitive market, producing prices reductions and more daily trains, brought a 200% increase of HSR demand (from 15 to 45 millions of passengers/year). Estimations results show that, on average, HSR in Italy contributed to a significant increase in transport accessibility (+32%) for the zones along the HSR network, while only marginal for the others (+6%). Impacts on the economic growth show that HSR has contributed to an extra growth of per capita GDP of + 2.6% in 10 years and would have contributed to a further increase of 3.6% if the final project scenario (HSR_N) would had been completed by 2018. Regional (horizontal) equity impacts were evaluated in terms of the Gini indexes variations with respect to the distribution of the transport accessibility. It results that HSR in Italy has decreased equity in terms of users’ travel time accessibility of 11%, increasing the differences between the zones served by HSR and those not. If the HSR_N scenario would have been completed equity indices would have increased of 29% with respect to the pre-HSR 2008 scenario, thus reducing regional inequalities in the country. Results show that the HSR project was a country-level “game changer” in Italy, suggesting that the wider economic benefits, the assumptions on market regulation, the effects on regional disparities and the compensatory measures should be included in the ex ante and ex post evaluation of similar projects.

Decision-making processes related to transportation systems are often very complex, belonging to the class of “wicked problems” in social studies. The literature and the media abound in examples of transportation planning “failures” including decisions made, delayed or withdrawn without any traceable motivations, public opposition, public opposition, extra costs/implementation times and outright cancellation, wrong traffic/revenue forecasts. Planning failures stem from several causes, including technical errors in forecasting predictable effects, lack of consensus, new governance cycles and inability to recognize the intrinsic uncertainty affecting some key variables. The underlying assumption of this paper is that the quality of the decision-making process critically depends on how the process is structured and managed. The paper proposes a conceptual model to represent and organize transport planning as a cognitive decision-making process with strong involvement of different stakeholders in various stages, accounting for different levels of uncertainty and including short- and long-term decisions options. The result is a planning process that allows for changes in objectives, strategies and decisions during different stages of the process itself, as is usually the case when different governance and socio-economic cycles succeed over time. However, the process maintains a form of procedural consistency and is intended to counteract the “Penelope syndrome”, whereby decisions made under previous governance cycles are undone regardless of their merits and costs. The real-life implementation of the proposed model requires strong “political commitment” to an open and accountable decision-making process, a technical structure able to act as “process owner” coordinating technical and engagement activities, and a general societal attitude towards organized participation to public decision making. The proposed model was applied to the Regional Transportation Plan of Veneto in Italy where the above conditions applied. A number of lessons were learnt from that exercise: i) stakeholders’ engagement revealed an important potential to manage uncertainty and increase consensus; ii) flexibility and robustness with respect to medium–to–high uncertainty levels already recognized at the time of the plan (immediately before the COVID-19 crisis) were an appreciated feature of the plan; iii) separation between mature decisions and those needing further analysis was a success strategy, reducing political “decision costs” (from naysaying) by delaying decisions still recognizing underlying needs; iv) reduced decision time (a largely bi-partisan vote in less than one year).

Most of the existing Carsharing business models mainly rely on gasoline vehicles and diesel vehicles, but in recent years there has been a significant increase in hybrid electric vehicles (HEVs) and a resurgence in electric vehicles (EVs). Within this framework, this paper investigates and models the choice to switch from a private car trip to a carsharing service available in peripheral parks as well as the propensity to choose an electric vehicle for such a service. In particular, three issues are addressed: (i) investigating and modelling the propensity to choose carsharing as a transport alternative within a neighbourhood residential carsharing business model; (ii) estimating the effect of also having an EV option available; (iii) measuring the "pure preference", if any, in using electric vehicles over traditional ones, in a context excluding factors that may bias such users preference (e.g. purchase price, energy costs, recharging facilities etc). The analyses are based on a stated preferences survey undertaken on 600 car drivers entering the city centre of Salerno (Southern Italy), and on the estimation of a binomial Logit model with serial correlation. Results allow an interpretation of the main determinants of the short-term choice of carsharing services (i.e. without any car-ownership changes), give general behavioural insights, make it possible to quantify the "pure preference" for EV and the demand elasticity with regard to different pricing strategies of the carsharing services.

Methodologies for defining standard quality indicators are important to assess service quality in public transport. The article proposes a method to integrate EU service quality standards in the transport planning process. It also investigates the effects of a set of large-scale transport policies in Campania region, Italy, in terms of service quality and ridership variations. Results of 10 years-long transport policies (€3,115 million invested) shows an impressive increase in both service quality and ridership. The article also compares perceived and measured service quality indicators for a new high quality metro line. Results point to new perspectives in modelling users’ travel behavior and related quality measures.

Transportation systems are complex sociotechnical systems and this dual nature is reflected in the literature dealing with their planning, i.e. defining courses of action from both public and private points of view. On the one hand, the social sciences literature makes it clear that most decisions related to transportation are "wicked", i.e. they cannot be tackled with traditional engineering approaches since they are poorly defined. On the other, transportation systems have a strong technical component affecting most of such decisions, as they have to (or should) comply with compelling technical and economic requirements. The literature on transport engineering and economics deals with transportation planning mostly as a rational process based on the formulation and comparison of alternative options. In this paper, we propose an approach to planning and designing transportation systems, at least in the more complex cases, meant to bridge the gap between social and technical points of view. The proposed approach is cross-disciplinary, as it integrates notions from cognitive sciences, organization and management sciences with transportation systems analysis. Transportation planning, both under public and private market-oriented viewpoints, is seen as a complex decision-making process where different actors (decision-makers, stakeholders, professionals) interact in different contexts and according to different "models" or approaches. After a brief discussion of the main elements of most planning processes and of the different decision making models, the paper proposes a decision-making model based on three parallel and intertwined processes: a cognitive rational approach to organizing the decision-making process, a five-level stakeholder engagement process, and a revised role of quantitative analyses and methods drawing on tools from engineering and economics, amongst other disciplines. We also present a real application of the proposed decision-making model to the case study of the Regional Metro System (RMS) project in Campania (southern Italy), where a complex decisional context with different stakeholders and multiple (often contrasting) interests was managed in the integrated framework for over a decade, resulting in the largest and most effective of such projects in Italy. Finally, the paper proposes new roles and new challenges for quantitative analyses and mathematical tools to support participated decision-making processes, extending their well-established functions for designing and assessing transportation solutions. Language: en

The impact of the transport sector range between 20% and 40% in terms of consumption of fossil fuels and emissions. Many urban areas are trying to adopt planning strategies aimed at reducing the car usage and their emissions reaching a sustainable mobility. The introduction of a toll for the use of a road infrastructure and/or a service is one of the most common Transportation Demand Management policy. The development of the Intelligent Transportation System (ITS) technologies allow extending and applying these pricing schemes (both for passenger and for freight) in several different and most rational (sustainable) ways, connecting the price both to the individual characteristics of the trip and to the type of vehicle used. The aim of the study is to propose an original transport-related acceptance & equity measure useful for enlarging both the consensus and the equity in the design of a road pricing scheme. Results of the research underline the importance in introducing acceptance&equity measures as an additional design criteria in road pricing schemes, jointly with the traditional ones (e.g. road traffic congestion reduction and pollutant reduction).

The paper proposes a “quasi-dynamic” framework for estimation of origin-destination (o-d) flow from traffic counts, under the assumption that o-d shares are constant across a reference period, whilst total flows leaving each origin vary for each sub-period within the reference period. The advantage of this approach over conventional within-day dynamic estimators is that of reducing drastically the number of unknowns given the same set of observed time-varying traffic counts. Obviously, the gain in accuracy depends on how realistic is the underlying assumption that total demand levels vary more rapidly over time than o-d shares. Firstly, the paper proposes a theoretical specification of the quasi-dynamic estimator. Subsequently, it proposes empirical and statistical tests to check the quasi-dynamic assumption and then compares the performances of the quasi-dynamic estimator of o-d flows with both classical off-line simultaneous dynamic estimators and on-line recursive Kalman filter-based estimators. Experiments are carried out on the real test site of A4-A23 motorways in North-Eastern Italy. Results confirm the acceptability of the assumption of quasi-dynamic o-d flows, even under the hypothesis of constant distribution shares for the whole day and show that the quasi-dynamic estimator outperforms significantly the simultaneous estimator. Data also suggest that using the quasi-dynamic estimates instead of the simultaneous estimates as historical o-d flows improves significantly the performances of the Kalman filter, which strongly depends of the quality of the seed o-d flows. In addition, it is shown that the aggregation of quasi-dynamic o-d estimates across subsequent time slices represents also the most effective way to obtain o-d estimates for larger time horizons (e.g. hourly estimates). Finally, a validation based on an hold-out sample of link flows (i.e. counts not used as inputs in the o-d estimation/updating process) revealed the quasi-dynamic estimator to be overall more robust and effective with respect to the other tested estimators.

Despite some substantial limitations in the simulation of low-frequency scheduled services, frequency-based (FB) assignment models are by far the most widely used in practice. They are less expensive to build and less demanding from the computational viewpoint with respect to schedule-based (SB) models, as they require neither explicit simulation of the timetable (on the supply side), nor segmentation of OD matrices by desired departure/arrival time (on the demand side). The objective of this paper is to assess to what extent the lack of modeling capabilities of FB models is acceptable, and, on the other hand, the cases in which such approximations are substantial and more detailed SB models are needed. This is a first attempt to shed light on the trade-off between (frequency-based) model inaccuracy and (scheduled-based) model development costs in the field of long-distance (e.g. High-speed Rail, HSR) service modeling. To this aim, we considered two modeling specifications estimated using mixed Revealed Preferences (RP) and Stated Preferences (SP) surveys and validated with respect to the same case study. Starting from an observed (baseline) scenario, we artificially altered the demand distributions (uniform vs. time-varying demand) and the supply configuration (i.e. train departure times), and analyzed the differences in modal split estimates and flows on individual trains, using the two different model specifications. It resulted that when the demand distribution is uniform within the period of analysis, such differences are significant only when departure times of trains are strongly unevenly spaced in time. In such cases, the difference in modal shares, using FB w.r.t. SB, is in the range of [0%, +5%] meaning that FB models tend to overestimate HSR modal shares. When the demand distribution is not uniform, the difference in modal shares, using FB w.r.t. SB, is in the range of [−10%, +10%] meaning that FB models can overestimate or underestimate HSR modal shares, depending on timetable settings with respect to travelers’ desired departure times. The differences in on-board train flow estimates are more substantial in both cases of uniform and not uniform demand distribution.

High speed rail (HSR) represents the future of medium-haul intercity transport. In fact, a number of HSR projects are being developed all over the world despite the financial and economic crisis. Such large investments require reliable demand forecasting models to develop solid business plans aiming at optimizing the fares structures and the timetables (operational level) and, on the other hand, at exploring opportunities for new businesses in the long period (strategic level). In this paper, we present a models system developed to forecast the national passenger demand for different macroeconomic, transport supply, and HSR market scenarios. The core of the model is based on the simulation of the competition between transportation modes (i.e. air, auto, rail), railways services (intercity vs. high speed rail) and HSR operators, using an explicit representation of the timetables of all competing modes/services/runs (schedule-based assignment). This requires, in turn, a diachronic network representation of the transport supply for scheduled services and a nested logit model of mode, service, operator, and run choice. To authors’ knowledge this represents the first case of elastic demand, schedule-based assignment model at national scale to forecast HSR demand. The overall modeling framework has been calibrated based on extensive traffic counts and mixed RP–SP interviews gathered between 2009 and 2011, on the Italian multimodal transportation system. The results of the models estimation are presented, and, some applications to test HSR service options (i.e. fares and timetable) of a new operator entering the HSR market and competing with the national incumbent are discussed.

The decrease in travel times, the better quality of the supplied services and the improved accessibility are the main factors of success of High Speed Rail links. In this paper, evidence of the impacts of the High Speed Rail line between Rome and Naples in Italy will be provided on the basis of a survey which was carried out in March 2008. A Revealed Preference (RP) survey was undertaken by Trenitalia (the Commercial Division of Italian railways) and three different questionnaires were prepared to be submitted to car users, Intercity train users and High Speed train users. They all have in common questions concerning the socioeconomic characteristics of the users, questions related to the existence of possible time constraints for the trip undertaken and questions concerning previous travel choices. Data from Trenitalia highlighted that the use of car and of Intercity trains had almost remained unchanged during the few years of operation of the High Speed service. However, a generated demand was derived from the use of this High Speed Rail link and this means that the introduction of the High Speed service between Rome and Naples probably had impacts, as excepted, on mobility choices (i.e. increase in trip frequency, new trips never done before, etc.). Data gathered were used first of all to understand this phenomenon and then to estimate a mode choice model to reproduce/forecast modal share. Specifically the choice between car and rail was modelled through a schedule based approach and with a Nested Logit model with the “train” utility function including late and early penalties.

In many discrete choice contexts the actual choice set, including the alternatives effectively perceived and considered by the decision maker, may substantially differ from the universal choice set, including all available alternatives: one of the most relevant examples within transport demand simulation is probably the choice of destination, wherein the universal choice set normally includes hundreds of traffic zones. In these cases, proper simulation of the choice set is crucial for correct simulation of the choice context. In this regard, our paper has two main objectives. The first is to give a general contribution to choice set modelling by extending and applying the concept of dominance among alternatives to the framework of random utility theory. The main result is the definition of a methodology for the generation of new dominance attributes, which can be used in choice set modelling. The second aim is to make a specific contribution to destination choice modelling: dominance attributes are defined from the above methodology and introduced into this choice context, and new spatial variables reproducing better knowledge of zones with a privileged spatial position are also proposed. Methodology and attributes are tested both on synthetic and on real data.

Modeling a destination choice is a difficult task and often represents the weakest step in travel demand modeling. This weakness is mainly due to the high number of potential alternatives and to the limited number of available attributes. Indeed, alternative destinations in a distribution model are generally all the traffic zones identified in the zoning phase. Moreover, the most widely adopted specification of random utility (RU) destination choice models introduces just 2 categories of attributes: attractiveness attributes of the destination zone and impedance attributes reproducing the origin-destination generalized cost. Through this kind of attribute alone it is quite difficult reproducing the real choice context faced by the decisionmaker, who generally knows only a limited part of the study area with sufficient detail to evaluate its attractiveness and the generalized transport costs of reaching it. In this regard, this paper proposes 2 sets of new dummy-like attributes to be used within the destination choice models to identify, within the whole choice set, a smaller subset of zones (those with nonzero value of dummy-like attributes), more or less likely to be perceived. The former are generated by extending and applying the concept of dominance among alternatives to the framework of RU theory and can be used to identify a set of alternatives less likely to be perceived (exclusion variables) whose systematic utility will be penalized as a function of these variables. The latter are spatial variables reproducing better knowledge of zones with a privileged spatial position and can be used to identify a set of alternatives more likely to be perceived (selection variables) whose systematic utility will be improved as a function of these variables. These new attributes are tested on empirical data related to nonsystematic trips in Rome. It is also important to underline that the proposed dominance variables can be conveniently used in any other choice context.

Transportation systems are "internally" complex systems, made up of many elements influencing each other both directly and indirectly, often nonlinearly, with many feedback cycles. Apart from the internal complexity, transportation systems are closely interrelated with other systems, which can be defined as external. Transport projects may have implications for the economy, the location and intensity of the activities in a given area, the environment, the quality of life and social cohesion. Actually, today, in order to have a better view of the urban problems and mainly for better governance of mobility, the urban system per se is no longer studied, but rather the whole metropolitan area surrounding it. Transport and land use are clearly integrated. Each technological innovation in transport has encouraged both people and industry to change their location to take advantage of the improved mobility; similarly, each change in land development (e.g. houses, factories, offices and shops) has influenced the demand for travel and ultimately the provision of new transport infrastructures and services. An example of integrated land use and transport planning is the Regional Metro System (RMS) project, carried out in Campania, which is finally reported.

Various alternative approaches are available in the literature for modelling trip distribution. The objective of this paper is to provide a review of this literature with the view of summarising the general nature of the current state of practise and what is now available for practical modelling work. Both aggregate and disaggregate destination choice models are considered, a classification of them will be proposed and suggestions for further research will be given as well. One outcome of this review is that significant contributions are emerging from mixed models (e.g., gravity-opportunity, random utility, intervening opportunities models, etc). In particular, the review will highlight the benefit of integrating different approaches like the introduction of intervening opportunities factors within random utility models.

The paper deals with the proposition of a Kalman filter specification for quasi-dynamic estimation/updating of o-d flows from traffic counts, i.e. under the assumption that o-d shares are constant across a reference period (i.e. a quasi-dynamic interval), whilst total flows leaving each origin vary for each sub-period within the reference period. Drawing upon the effectiveness and the reliability of the assumption of quasi-dynamic o-d flow pattern and of the performances of the quasi-dynamic estimator in offline contexts, the paper illustrates a first formulation of a non-linear quasi-dynamic Kalman filter, which can embed diverse specifications of the state variables and of the corresponding transition and measurement equations. Results of preliminary tests on a synthetic network are presented, and the overall research pattern is also outlined, together with concerned research and practical perspectives.

The new 1,000 km of high-speed rail (HSR) line between Turin and Salerno, Italy, were completed in 2009, and new service by the Italian railways state-owned company Trenitalia started in December of that year. Furthermore, in anticipation of the European open-access regulatory framework, starting from April 2012, the new HSR private operator Nuovo Trasporto Viaggiatori entered the market and is competing with the incumbent Trenitalia. This is the first case of large-scale competition between nonsubsidized HSR operators on the same line (i.e., infrastructure managed by the state-owned company, Rete Ferroviaria Italiana). This study provides an overview of the short-term dual effects of the opening of the new HSR line operating with a single service provider (between 2010 and 2012) and of the competition between the two operators starting from 2012. Before-and-after effects on services, prices, and attracted and generated demand based on several data sources, including ad hoc surveys and a system of mathematical models, are presented. Results suggest that the dual effect of new HSR lines and the opening of competition in the market has been producing a significant increase in HSR supply (+94% of trains per day) and demand (+98% of passenger-kilometers per year). The market share in the HSR core area increased about 20% at the expense of air and automobile modes. About 13.8 million extra trips per year were estimated in 2013 with respect to rail demand in 2009 (8.3 million diverted from other modes and 5.5 million induced trips) despite the severe economic crisis faced by the country in the same period.

Transit systems offer substantially different services in urban and inter-urban areas; this usually leads to different assumptions about user behaviour in path choice modelling. These differences have not yet been recognised in mode choice modelling. Consistent with the above, in this paper we propose a joint mode-transit service choice model where the elementary alternatives are specific services of the available transit modes together with the private car. Among the attributes of transit services we propose inclusion of the difference between the desired arrival time of the traveller and the arrival time of the specific service (early/late arrival penalty), instead of service/line frequency. Different utility specifications were tested and their results compared for different correlation structures (both nested and cross-nested). In general a high significance was observed in the case of the early/late arrival penalty parameters, indicating the importance of such factors in mode choice.

To simulate important aspect of some transportation systems (e.g. demand peaks, temporary capacity variations, temporary over-saturation of supply elements, and formation and dispersion of queues) a new class of models, referred to in the literature as Dynamic Traffic Assignment (DTA) models, have been recently developed. Although Dynamic Traffic Assignment to networks is a relatively new research subject, a great number of models have been proposed in the last two decades. These can be divided in two main classes according to the typology of service they aim at simulating. These are continuous services, considering transportation services available at any time and accessible from several points, such as the services offered by individual road modes (car, bicycle etc.), and scheduled services simulating services available only at certain times and that can be accessed only at certain locations (terminals, stations, airports etc.). In this paper the focus is on continuous services. Models proposed in the literature are reviewed and classified according to basic assumptions on the flow structure, i.e. whether a continuous or a discrete approach is followed, and on the representation of time (discrete vs. continuous). A general modeling framework consisting of supply, demand, and demand-supply interaction models, and including most of the existing specifications is presented both for the discrete time-discrete flow and continuous time continuous flow cases.

Transit services quality has long been recognized as an important factor in influencing travelers behaviour and terminals quality is certainly part of it. As a matter of fact a number of transit promotion policies explicitly based on qualitative factors and specifically high architectural standards are being adopted in designing new railways stations and several examples of remarkable architecture applied to railways stations can be found all round the world. In spite of this, the literature in transportation modelling has not yet analyzed the impact of the hedonic quality on travelers’ behaviour quantifying whether and by how much it increases their propensity to use rail services. A binomial logit model, simulating the choice between a traditional rail line and a new line open in 2009 in the northern area of Naples – Italy (high architectural and aesthetic standards railways) was specified and estimated for trips having both routing options between the same origin–destination pair. The model was specified with serial correlation in residuals and estimated using RP–SP data for different users’ segments. The main difference between the two alternatives in the real scenario was the stations architectural quality as all other attributes, including travel time, frequency, access and egress times, trains and riding comfort, security, were basically the same. In Stated Preference experiments several scenarios were presented to users with four levels of level of service attributes and factorial fractional design. The results show a significant impact of stations architectural quality on users’ choices and allow to estimate reciprocal substitution coefficients with respect to other level of service attributes. The average monetary “value of stations quality” was quantified in 35 Euro cents/trip for students and in 50 Euro cents/trip for commuters (+43%). Alternative-specific waiting time coefficients showed a context effect for both students and commuters (respectively 31% and 35% lower values for traditional stations) but they did not explain entirely the preference for high architectural railways line. It also resulted that female travelers showed a significant preference for stations quality (+33% with respect to male). Other results related to access and egress time suggest that, if everything else being equal, the high architectural line have a larger “catchment area” with respect to a traditional rail of approximately 400 meters by walking. The results of this research should be compared with those from other contexts as they have a potential impact for railways planning showing that architectural quality of stations should be considered as an explicit design variable and could be compared with other, possibly more expensive, improvements (e.g. frequency increases, accessibility improvements) and poses new challenges for modelling user behaviour and quality-related measures.

Recent High Speed Rail (HSR) investments in Italy, together with the entrance in the HSR market of a new private operator competing with the national incumbent, create the conditions for a unique case study, to investigate the behaviour of long- distance passengers. In this paper a modelling framework developed to forecast the national passenger demand for different macroeconomic, transport supply, and HSR marketing scenarios is presented, focusing on the sub-models forecasting induced demand.

The principles of Station Renaissance are initially addressed exclusively to the redevelopment of stations no longer in use, but soon also extend to the design of new stations in addition to urban planning. In fact, for new projects the use of simple architectural forms, the harinony of colors and materials used is preferred, but always in perfect harmony with the historical identity of the city and of the areas in which they are realized. Aesthetics become a crucial parameter also for this movement in the design of new transport infrastructure, in order to increase the quality of services offered in addition to the liveability of the surrounding areas. In CSDR the concept of aesthetics is not limited to simple visual quality based on shapes, dimensions and colors but also includes more subjective aspects for the traveler such as the same utility of commuting.

This paper gives an overview of the dual effects of the opening, in Italy, of the new HSR line with a single operator (between 2005-2012) and the effects of a new operator entering the HSR market (2012-onward), on supply, demand and prices, thus inferring the effects of competition in this typically monopolistic market. The analyses are based on source data (laws and regulations, business plans, timetables, prices) as well as ad hoc extensive surveys, such as on-board counts on the HSR and intercity trains, retrospect surveys, and RP/SP interviews. In addition, an integrated modeling system has been developed to forecast the effects of competing timetables/services/prices in terms of different HSR operators, competing modes (air/auto/railways), services (1st class/2nd class, etc.) and newly generated trips.

Over recent years, increasing attention has been devoted to the problem of estimating Origin/Destination (O/D) matrices by using traffic counts, named in the following O/D Count Based Estimation (ODCBE) problem. These methods combine traffic flow measures with other available information to “correct” and improve an initial estimate of the O/D trip matrix. Virtually all models and procedures proposed in the literature are formulated as mathematical programming problems. Most ODCBE models use a relationship relating traffic counts to the unknown O/D matrix; this relationship is often in the form of an explicit estimate of the assignment matrix, i.e., of the fractions of O/D flows using each link of the network for which traffic counts are available. The computation of the assignment matrix is not trivial for congested networks, where travel costs and path choice fractions depend on flows. This problem has been dealt with in relatively few papers in the literature, usually formulated as a bilevel optimisation model under the assumption of a Deterministic User Equilibrium (DUE) assignment model. In this paper, the general within-day static ODCBE problem for congested networks is formulated as a fixed-point problem of an implicit function which results from the solution of a mathematical programming problem. In other words, the solution of the ODCBE problem is an O/D matrix that, once assigned to the network, reproduces flows and costs consistent with the values used to compute the assignment matrix. Fixed-point theorems and algorithms have been extended to the problem at hand. Different fixed-point algorithms, namely, Functional Iteration, Method of Successive Averages, and Method of Successive Averages with Decreasing Reinitialisation are proposed, and their performances are compared on a small test network. It has been verified that all algorithms converge to the same solution, though with different speeds. Furthermore, the fixed-point solution outperforms the initial solution, both in terms of bias and capability to reproduce both counted and noncounted flows.

In this paper a preliminary analysis of alternative models for “feasible” path generation and choice is presented. In particular a k-shortest path multi-criteria model for path enumeration is explored and different choice models (Logit, recently proposed C-Logit and Probit) are tested by comparing SUE assignment link flows with counts on an urban road network. Flows are also compared for more traditional DUE and SUE Probit implicit path enumeration models. The results obtained show that a limited number (4-7) of paths generated with rather “simple” criteria give satisfactory results, SUE with explicit path enumeration is largely comparable with, and in some cases superior to, traditional implicit SUE and DUE models. Explicit path enumeration allow also the specification of more sophisticated non additive attributes in the utility function of route choice models. From the computational point of view the explicit path C-Logit and Probit SUE algorithms are from three to twenty times superior to the implicit Probit SUE assignment.

In this paper, a system of models simulating intercity passenger travel demand in Italy is presented. The models are part of a wider project for a Decision Support System at the national level (SIMPT) covering both passenger and freight transport systems.The core of the SIMPT is composed by integrated demand, supply and assignment models. Behavioural demand models, calibrated on a large RP survey, tour frequency, destination, mode and path choices for different purposes, days and periods of the year. Supply models consist in infrastructural (roads, railways) and a service (railway services, extraprovincial buses, airlines) networks. The road assignment model is a pseudo-within-day-dynamic SUE with explicit path enumeration and takes into account the calibrated path choice models for different time slices, while assignment to transit services is a SNL line-based modal.

Traffic counts on network links constitute an information source on travel demand which is easy to collect, cheap and repeatable. Many models proposed in recent years deal with the use of traffic counts to estimate Origin/Destination (O/D) trip matrices under different assumptions on the type of "a-priori" information available on the demand (surveys, outdated estimates, models, etc.) and the type of network and assignment mapping (see Cascetta & Nguyen 1988). Less attention has been paid to the possibility of using traffic counts to estimate the parameters of demand models. In this case most of the proposed methods are relative to particular demand model structures (e.g. gravity-type) and the statistical analysis of estimator performance is not thoroughly carried out. In this paper a general statistical framework defining Maximum Likelihood, Non Linear Generalized Least Squares (NGLS) and Bayes estimators of aggregated demand model parameters combining counts-based information with other sources (sample or a priori estimates) is proposed first, thus extending and generalizing previous work by the authors (Cascetta & Russo 1992). Subsequently a solution algorithm of the projected-gradient type is proposed for the NGLS estimator given its convenient theoretical and computational properties. The algorithm is based on a combination of analytical/numerical derivates in order to make the estimator applicable to general demand models. Statistical performances of the proposed estimators are evaluated on a small test network through a Monte Carlo method by repeatedly sampling "starting estimates" of the (known) parameters of a generation/distribution/modal split/assignment system of models. Tests were carried out assuming different levels of "quality" of starting estimates and numbers of available counts. Finally NGLS estimator was applied to the calibration of the described model system on the network of a real medium-size Italian town using real counts with very satisfactory results in terms of both parameter values and counted flows reproduction.

Recent openings of new high-speed rail (HSR) lines in Italy, together with the April 2012 entrance of a new private operator in the Italian HSR market in competition with the national incumbent, have created the conditions for an investigation of long-distance travel demand. In this paper, we present some empirical evidences on the evolution of national passenger demand before and after HSR major openings, a unique case study based on source data, a retrospective survey and extensive counts on board trains, gathered between 2008 and 2011. Results show a significant increase of traffic volume by rail due to the introduction of HSR and we estimate short-term direct HSR demand elasticities with respect to travel time in the range of –1.5 and –2.1, and in the range – 0.7 to – 1.1 in terms of HSR modal shares. The different elasticity values confirm the presence of a strong “induced demand” effect.

The paper discusses the importance of rational decisions in transport planning and investigate on the “false-friends” of sustainable transport comparing the effects of consolidated sustainable transport policies (e.g. renewal of vehicle fleet and new transit-points and light goods vehicles for urban distribution) simulated by a set of mathematical models extended to capture energy and pollutant emissions and implemented in a Decision Support System.

The chapter proposes the specification of a multi-regional input-output (MRIO) model with elastic trade coefficients and a multimodal freight supply model at an European geographic scale. After a brief review of the MRIO modelling approach, some important theoretical issues related to both demand and supply modelling were discussed. Specifically, from the demand side, an elastic trade coefficient MRIO model is presented and some relevant macroeconomic feedbacks incorporable into the model are discussed. From the supply side, a critical review of the complexity of the multimodal freight networks and on the corresponding modelling requirements is presented. From a practical standpoint, the implementation of a MRIO model at European level is reported, describing in particular both the database used to feed the model and the supply model applied for the calculation of transport impedances required by the trade coefficient model. Finally, simple applications of the implemented MRIO model with elastic trade coefficients are presented, and some conclusions and research developments are drawn

Traditionally, traffic assignment models, both for within-day static and dynamic demand, have been formulated following an equilibrium approach in which a state ensuring internal consistency between demand (flows) and costs is sought. However, equilibrium analysis is significant under some assumptions on its “representativeness” (coincidence or closeness with the actual attractor of the system) and analytical properties, such as existence, uniqueness, and stability. Moreover, transients due to modifications of demand and/or supply cannot be simulated through equilibrium models, nor can a statistical description of the state of the system, i.e. means, modes, moments and, more generally, frequency distributions of flows over time be obtained. In this paper, interperiodic (day-to-day) dynamic modeling of transportation networks is addressed following two different approaches, namely deterministic and stochastic processes. In both cases several theoretical results are shown by making use of a formal framework covering most models discussed in the literature as well as some possible extensions. Most of the results reported can be extended to cover within-day dynamic models but these models are not explicitly dealt with. Within the framework of deterministic processes the relevance of day-to-day dynamic models for demand/supply interaction in comparison with the traditional user equilibrium approach is discussed, and conditions for coincidence of fixed-point attractors and equilibrium states are stated. Conditions for existence and uniqueness of fixed-point attractors are proposed, generalizing and extending those presented in the literature for user equilibrium. Conditions for stability of both fixed-points and equilibrium states were formulated by making use of results from non-linear dynamic system theory. Moreover, it is possible to devise a new family of “dynamic” algorithms which simulate the system convergence to a fixed-point in order to obtain an equivalent equilibrium state, as opposed to conventional “optimisation” algorithms. In this case the fixed-point stability analysis can be viewed as a convergence analysis for the algorithms specified this way. Conditions for stochastic process regularity are proposed ensuring, among other things, existence and uniqueness of a stationary probability distribution of system states. These conditions generalize and extend results presented in the literature to a wider class of possible dynamic models. Relationships between a deterministic process, together with corresponding fixed-points or equilibrium states, and stochastic probability distribution are also briefly addressed. Finally, some numerical examples confirming theoretical results are reported for a small test network.

Because speed has been recognized as the most important contributory factor in fatal road crashes, speed management has been widely implemented in several countries to improve road safety. The automated section speed enforcement system is increasingly being used as an effective way to tackle speeding, especially on motorways and in tunnels. However, the effects on traffic flow patterns of such systems remain controversial. An empirical analysis of traffic flow patterns before and after the introduction of such a system along a freeway section with recurrent congestion is presented. Gathered data consisted of point measurements at detectors and average travel speeds of each vehicle crossing the stretch. The main observed features were (a) a strong homogenization of individual speeds and of mean speeds among the lanes, (b) a reduction in the strength of the bottleneck, (c) the emergence of significant oscillations in time of traffic characteristics, and (d) a sensible reduction of travel times during the congestion pattern caused by the bottleneck moving downstream of the section. Empirical evidence suggests that driver compliance with speed limits is the key factor in analysis of such speed management systems and that their concurrent application with dynamic speed limit strategies should be thoroughly evaluated with a particular focus on this measure.

This paper proposes different “dynamic” estimators using time-varying traffic counts to obtain (discrete) time-varying OD flows or average OD flows. All the estimators can combine counts with other available information, e.g., out-dated matrices and surveys, on a general network and can be formulated as optimization problems. In the case of time-varying OD flows, two types of estimators are proposed. Simultaneous estimators produce joint estimates of the whole set of OD matrices, one for each time slice, whereas sequential estimators produce a sequence of OD estimates for successive time slices. A particular type of simultaneous and sequential estimator (namely Generalized Least Squares) was tested on the Italian Brescia–Padua motorway for which “true” OD flows over a whole day were available. Results were generally satisfactory, showing that also in the “no a priori information” case significant estimates could be obtained.

Analysis and design of transportation systems require, respectively, the estimation of present demand and the forecasting of (hypothetical) future demand. These can be obtained by using different sources of information and statistical procedures.

The decision-making process can address the evaluation of an individual project or the comparison of multiple alternative projects. The first case examines the economic, financial and other impacts of carrying out a particular project. In the second case, the decision-making process is intended to help choose the best among different possible solutions whose economic or financial impacts have been previously determined. This chapter focuses on the activities relating to project evaluation and comparison. Economic and financial analysis techniques used in project evaluation such as Cost-Benefit analysis and Cost-Revenue analysis as well as Multi-Criteria analysis methods for the comparison of alternative projects are described in detail.

Assignment to a transportation network has already been introduced in Chap. 5. Here we continue the analysis of assignment in the absence of within-day dynamics. In Sect. 6.2 we describe assignment with preventive-adaptive path choice, Sect. 6.3 covers the extension to the case of variable demand and/or multimodal assignment, and Sect. 6.4 deals with multiclass assignment models. In Sect. 6.5 we introduce assignment with day-to-day dynamics (dynamic process). As each of these extensions may be combined in banal fashion with each of the others, combined cases are not treated explicitly (e.g., multiclass assignment with variable demand); in Sect. 6.6 we present an overall scheme that allows straightforward comparison of these extensions. Also in this chapter, as in Chap. 5, the algorithms described are only those used more commonly or that are simpler to implement.

As stated in Chap. 1, travel demand derives from the need to carry out activities in multiple locations. Thus, the level and characteristics of travel demand are influenced by the activity system and the transportation opportunities in the area.

The mathematical models described in the previous chapters are based on the assumptions of intra-period stationariety. This is equivalent to assuming, as stated in Chapter 1, that all significant variables are constant, at least on average, over successive sub-intervals of a reference period long enough to allow the system to reach stationariety condition. This assumption, although acceptable for many applications, does not allow for the satisfactory simulation of some transportation systems such as heavily congested urban road networks or low frequency scheduled services. In the first case, some important phenomena cannot be reproduced by traditional intra-period static models, including demand peaks, temporary capacity variations, temporary over-saturation of supply elements, and formation and dispersion of queues. In the second case, low-frequency services (e.g. two flights per day) may call into question the assumption of intra-period uniform supply and mixed preventive-adaptive users’ choice behavior introduced in the previous chapters. To simulate these aspects, different intra-periodal or within-day dynamic models have recently been developed; these models are usually referred to in the literature as (within-day) Dynamic Traffic Assignment (DTA) models, implying that dynamic assignment models require within-day dynamic demand and supply models.