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In this paper, a pilot study of a pre-test preference model in the context of mobility as a service (MaaS) is defined by following the steps required for transport system engineering: survey, specification, calibration, and validation. The availability of a MaaS preference model is crucial to support decision takers and decision makers before starting planning activities for new, sustainable transport services. In this paper, a pre-test model is proposed for evaluating user preferences. The pre-test model was specified with a Logit random utility model and the parameters were estimated using the maximum likelihood method. To define the preference model, a pilot survey was conducted in the Gioia Tauro area, an extra-urban area in southern Italy. For the pre-test model, a pilot sample of users was considered. In the area, a high percentage of users traveled by an individual transport system; this high percentage was also present in the survey, with 76% traveling by private car. Short- and long-distance scenarios were proposed to users. In the calibrated model, it emerged that bundles were more attractive for long-distance journeys and decreased with the cost of the package. The additional cost in the present scenario influenced the preference for bundle cost. Considering the parking cost in the present scenario (scenario 2), the MaaS preference probability started at higher probability values but increased less quickly. The pre-test model was defined starting from a pilot sample and represents the basis for a larger MaaS preference model built starting from a larger survey and a sample with a greater number of calibrated parameters.

This research concerns the topic of Land Use and Transport Interaction (LUTI) models. In particular, the patterns between residential households’ location and mobility choices are analyzed and simulated. The attributes that influence household residential location choices belong to four categories: socioeconomic and mobility attributes of households and/or of their components; land use; real-estate market; transport system. The paper presents the results of a pilot study on households’ location and mobility patterns in the metropolitan area of Reggio Calabria (Southern Italy). The pilot study is divided into two stages. In the first stage, a survey allowed to collect information and identify existing patterns about residential and mobility choices of a sample of households. In the second stage, a residential location model is proposed and some preliminary calibrations are presented in a prototypal way. The pilot study could be extended and improved in terms of spatial extension and sample dimension in order to allow a complete specification-calibration-validation process of the model. The model development can support the land use-transport planning process in the Metropolitan City of Reggio Calabria.

The paper presents a framework to estimate energy consumption of Electric Vehicles (EVs) by combining: (a) the use of models derived from traffic flow theory and from mechanics of locomotion and (b) the great amount of Floating Cara Data (FCD) from available Information and Communications Technology (ICT) devices. Existing energy consumption models may be classified into aggregate vs. disaggregate, according to the level of aggregation of variables related to driver, vehicle, and infrastructure. The proposed models have a hybrid nature: the aggregate component allows to estimating the values of vehicular speed and acceleration on a road link; the disaggregate one allows to estimating the discrete variability of EVs’ energy consumption inside a spatial-temporal domain. The energy consumption models are estimated using traffic data extracted from FCD. The proposed framework is structured into four steps: FCD processing, estimation of vehicular speeds and accelerations, estimation of resistance/energy consumption. The framework is applied in a pilot study area, composed by the backward (sub-)urban area of the port of “Porto delle Grazie” of Roccella Jonica (South of Italy). The preliminary results show that the methodology allows relative inexpensive and accurate calculation of EVs’ energy consumption and that it can be integrated into Intelligent Transportation System (ITS) applications.

This paper deals with the stochastic frequency-based assignment for transit systems, considering pre-trip/en-route path choice behaviour; this problem is relevant for (uncongested or congested) urban transit networks, where travelers may not completely know the status of service, say bus arrivals at stops, when they leave the origin; under mild conditions, travel strategy can be modelled by hyperpaths. Hyperpath choice behaviour can be described through random utility models thus properly modelling several unavoidable sources of uncertainty, which cannot be considered by the commonly used deterministic choice model. Effective methods suitable for large scale applications are proposed for solving stochastic assignment based on probit or gammit choice models, which properly model the effects of hyperpath overlapping, even though their application requires Montecarlo techniques; Montecarlo techniques based on Sobol numbers are compared with those based on the commonly used Mersenne Twister ones; several MSA-based algorithms for equilibrium assignment are discussed and compared with the commonly used basic implementation. Applications to a toy and a large scale network is also discussed.

It is commonly accepted in literature that the path choice decision process of a road user (Path Choice Problem, PCP) is composed by two levels/models: definition of perceived alternative paths (choice set); and choice of path belonging to the path choice set. Consolidated modelling frameworks simulate the PCP process, based on the Mansky paradigm. The general objective of the research concerns the extension of the Mansky paradigm (two-levels models) to the route choice decision process (Vehicle Routing Problem, VRP). According to the above objective, the paper contribution concerns the integration of the PCP and VRP levels through methodological and experimentation activities: (1) a similarity analysis of criteria for generating route alternatives; and (2) building of a route choice model of freight vehicles. The similarity analysis provided indications about the most frequently adopted criteria for choosing paths. In the case examined, the set consisted of three criteria: minimum distance, minimum energy, minimum time. The results of the specification-calibration-validation of route choice models, using label variables associated to the criteria, showed the statistical significance of the label parameters, in terms of t-student, and a greater capacity of the model to capture the observed patterns, in terms of ρ2-statistics, respect to models’ specifications without labels.

The paper proposes a methodology for the evaluation of an Urban Distribution Center (UDC) location in order to pursue sustainability goals. The methodology integrates indications deriving from public policies (objectives and constraints) and efficiency needs of carriers and retailers. For this reason, two planning levels are integrated into the methodology: an outer level in which a set of feasible UDC locations is defined; an inner level in which, for each location, the carrier behaviour in delivering freight to a set of retailers, characterized by variable restocking demand, is simulated (by means of a vehicle routing problem). The aim of the paper is to support the planning of city logistics measures by means of quantitative evaluations; that could be carried out by public authority to verify sustainability of logistic operations. A laboratory test is implemented in the town of Reggio Calabria (Southern Italy) to assess the effects of a combination of two city logistics measures: a nodal physical measure, such as an UDC, and an equipment measure, specifically Fully Electric Vehicles (FEVs).

This research aims to verify the influence of traffic control strategies using signal plans at junctions on observed Network Macroscopic Fundamental Diagram (NMFD), as a variable to describe the aggregate traffic conditions of an urban road network. After establishing the theoretical basis of its existence, the research on NMFDs has been finalized for applications connected to traffic congestion control and management. The degree NMFD depends on traffic control strategies has not been fully demonstrated, nor has its relationship with different congestion levels. In the opinion of the authors, experimental NMFDs based on observed traffic data in the presence of different control strategies are key elements in supporting the understanding of this relationship. The paper has been structured into two parts. The first part presents the formulation of an optimization model for the design of signal plans at junctions which incorporates NMFD variables. The more extensive second part examines the effects of traffic control strategies on observed NMFDs, built with real traffic data. The investigation follows a procedure where observed traffic data are analysed to find variables, functions and diagrams at link and network levels (NMFD). The procedure is applied in two adjacent urban zones located in the central area of Santander (Spain). These zones were identified according to traffic characteristics and land uses. The junctions on the road sub-networks in the two zones have different control regulations, but they are used by the same road users (with the same behavioural strategy). Observed traffic data (flows, densities) from loop sensors have been collected and correlated with the signal control plans at different periods of a working day. What emerges from the analysis is that junction regulation affects traffic flow variables at a link and network level. The NMFDs are statistically different when comparing junction regulations at two homogenous adjacent zones. The results apply only to the studied area and should be confirmed in other real transport systems.

In assignment models a key role is played by the path choice model, which computes the set of decisions made by the user before (and during) the routing on the network according to perceived paths and related costs. The path choice is a component of the assignment models. This paper considers the effects of an interference term in addition to the effect of the traditional covariance term in the choice process. It shows how the quantum path choice models can be developed and applied by integrating an interference term into the probability computation. The interference term can be considered for perception of alternatives, during their generation, where the interference is significant; covariance can be taken into account when choosing alternatives, where the overlap represents a relevant aspect. The path choice model, embedded inside the assignment procedure, is specified, calibrated, and validated using traffic counts in two real systems. The numerical results obtained in the analysed systems, in terms of reproduction of the counted flows, demonstrate that the new term can explain aspects that are not represented by consolidated random utility models. Results confirm the theoretical hypotheses stated in the paper.

The present study analyses a transport system in a discontinuous space. Classical specifications of transport models cannot be applied directly when evaluating the influence of territorial discontinuity and related barriers on user behaviour. Adjustments are required for this specific case because studies relative to discontinuous spaces are limited. The influence concerns the different travel components (access, barrier, on board, and egress) and could impact travel choices (departure time, destination, mode, and path). In this paper, the models refer to access and egress components with a focus on the mode of travel choice level. The paper focusses on the influence of discontinuity, introducing some adjustments to the classical demand models used to simulate discontinuity crossing. The main variables influencing the user’s choice, and their relative weight in discontinuous space, are investigated. These elements are fundamental for any planning and design procedures to improve the quality of mobility. In the paper, the case study of the Strait of Messina in southern Italy is analysed. In this case, the barrier is constituted by the sea that physically separates the two shores. In this work, only strait crossings via hydrofoil, from Reggio Calabria to Messina, are considered in random sample interviews.

An ongoing two-year research is performing with the general objective to assess the feasibility of a system integrating the production of green-energy and its consumption inside and close to port areas for mobility services. The system is composed by two elements: (a) a “sea-to-grid” technological component harvesting and producing electrical energy from sea waves; and (b) a “green” logistic service based on the use of Fully Electric Vehicles (FEVs). A pilot study will be conducted near an Italian port area supporting passengers and freight mobility between a port and a backward (sub)-urban area. The proposed system is within the environmental goals set by the EU (Europe 2020 Strategy) and the Italian Government (National Energy Masterplan). Indeed, the energy-producing technology reduces dependence from traditional energy sources (coal, gas, oil) and consequently reduces their negative effects (greenhouse gases, air pollution, etc.). Considering that the energy is produced by sea waves, the system transfers the entire amount of produced (green) energy to the electric vehicles. The system will be experimented in a medium size urbanized area and the energy will be produced in a small size port.

This paper attempts to integrate data from models, traditional surveys and big data in a situation of limited information. The goal is to increase the capacity of transport planners to analyze, forecast, and plan passenger mobility. (Big) data are a precious source of information and substantial effort is necessary to filter, integrate, and convert big data into travel demand estimates. Moreover, data analytics approaches without demand models are limited because they allow: (a) the analysis of historical and/or real-time transport system configurations, and (b) the forecasting of transport system configurations in ordinary conditions. Without the support of travel demand models, the mere use of (big) data does not allow the forecasting of mobility patterns. The paper attempts to support traditional methods of transport systems engineering with new data sources from ICTs. By combining traditional data and floating car data (FCD), the proposed framework allows the estimation of travel demand models (e.g., trip generation and destination). The proposed method can be applied in a specific case of an area where FCD are available, and other sources of information are not available. The results of an application of the proposed framework in a sub-regional area (Calabria, southern Italy) are presented.

According to recent literature, the aggregate traffic conditions of an urban road network may be measured by an asymmetric inverse-U shaped diagram, called Network or Macroscopic Fundamental Diagram (NFD or MFD). The research on NFD was finalizes for applications connected to congestion control by means of gating, pricing schemes, multi-modal network analysis, freight vehicle routing. The control of urban road networks by means of NFD is a promising research area, where new methods and models are proposed to reduce traffic congestion and delay. The general objective of the research is to investigate if and in which measure the NFD profile (estimated by means of observed traffic data) changes according to the control strategy adopted for junction signals in an urban area. The first empirical evidences presented in this paper are related to a portion of Santander urban area, where a specific zone has been identified according to traffic characteristics and land uses. Data from traffic loops are collected and correlated with the signal control plans during a working day at link (flow-density diagrams) and network levels (NFD). Some preliminary considerations are derived from the empirical results. The cycle length with a fixed regulation plan does not influence the main traffic variables (flow, density) at link and network level, but these results cannot be generalized.

This paper presents a procedure for the solution of the Vehicle Routing Problem (VRP) based on reliable link travel times. They are obtained as a combination of spatially disaggregated and ...

One of the main components of the transport system is users’ choice behaviour. Choices result from users’ behaviour and are simulated by means of demand models. These models simulate how users’ behaviour is influenced by system activities and supply performance. The most common demand approaches are based on the Random Utility Model (RUM). According to the RUM, a user knows of and considers mutually exclusive alternatives and associates each alternative with a perceived utility. The choice probability for each alternative is estimated using the RUM. An analyst evaluates the same value of pre-trip choice probability in the case of a unique sequence of decisions for his final choice of an alternative as in the case of a not-unique sequence of decisions for his final choice of an alternative. A new class of models simulates the case in which the user has an unclear sequence of decision for his final choice of an alternative. This model, the Quantum Utility Model (QUM), derives from quantum mechanics models. In QUM, it is possible to simulate the sequence of decisions in the cases of unique or not-unique pre-trip decision in the intermediate levels. In this paper, a comparison between the RUM and the QUM for the transport demand simulation is reported. A specification of the model is reported for the route choice level. The models are specified and compared in terms of numerical results in two test networks.

According to the literature, the path choice decision process of a user of a (road) transport network, named path choice problem (PCP), is composed of two levels/models: the definition of perceived alternative paths (choice set) and the choice of one path in the path choice set. The path choice probability can be estimated with two models: a choice model of the path choice set and a choice model of a path (Mansky paradigm). In this research, the paper&rsquo, s contribution concerns two elements: extension of the PCP paradigm (two-level models) consolidated in the literature to the route choice decision process (vehicle routing problem (VRP)) and identification of common elements in the PCP and VRP concerning the criteria in the two decision levels and the procedure for route and path selection and choice. The experiment concerns the comparison of observed routes with simulated and optimized routes of commercial vehicles to analyse the level of similarity and coverage. The observed routes are extracted from floating car data (FCD) from commercial vehicles travelling inside a study area inside the Calabria Region (Southern Italy). The comparison is executed in terms of similarity of the sequences of nodes visited between observed routes and simulated/optimized routes.

This paper analyses certain aspects related to the route choice model in transport systems. The effects of an interference term have been taken into consideration in addition to the effect of a traditional covariance term. Both the specification and calibration of an interference term in a quantum route choice model are shown in the context of an assignment model. An application to a real system is reported where the calibration of QUMs (Quantum Utility Models) was performed using traffic counts. Results are compared with traditional and consolidated models belonging to the Logit family. Based on the theoretical and numeric results, it is highlighted how the interference term and quantum model can consider other aspects (such as information) with respect to traditional RUMs (Random Utility Models).

Traffic signal setting is an important element in traffic planning of urban road networks in ordinary conditions and/or in emergency conditions. As traffic signal setting affects user behaviour mainly at the path choice dimension, this dependence has to be studied by means of assignment models. We thus present a system of models and procedures for signal setting optimization with assignment of travel demand to a congested road transport network. Two interacting procedures are developed to solve the system of models: (i) an optimization procedure to obtain an optimal configuration of signal setting parameters and (ii) an assignment procedure, incorporating a path choice model with explicit path enumeration and a flow propagation model, to capture the effects of signal setting configuration on user path choice behaviour. The system of models and procedures is applied and validated on a real test site, where the transport system is in dynamic conditions due to the time-varying road network and travel demand. The test site is a portion of a small-size town where the population needs to be evacuated due to an imminent calamitous event. The results show considerable reductions in total delays on the network and evacuation times in the case of optimized signal setting parameters.

Calibration and validation of dynamic assignment models to simulate urban road transport systems in ordinary conditions are still an open issue. This problem is further emphasized in the case of transport systems analysis in emergency conditions, as there are no standardised methods and there is a lack of experience of real-world applications. The paper presents a DA model and a procedure able to simulate transport supply and transport supply – travel demand interaction of an urban road transport system in emergency conditions. The transport supply models are calibrated and validated through traffic data observed during a real-world evacuation experiment conducted in the town of Melito di Porto Salvo (Italy). The DA model has been applied in order to reproduce the observed real-world evacuation experiment and a set of indicators for testing the performance of a road network in emergency conditions is estimated. We think that the findings reported in the paper represent a contribution in the field of transportation systems analysis in emergency conditions at urban scale. The specified and calibrated DA model and the applied set of indicators can be a useful tool to support the planning and management of road networks and mobility in emergency conditions.

The mobility of people and goods represents an essential activity that influences sustainability owing to energy consumption. Generally, electrical mobility problems concern the selection of technology (e.g. electrical vehicles (EVs)), monitoring the ex-post effects of their implementation. However, an ex-ante quantitative assessment of transport services in relation to travel demand is necessary in decision-making processes. The objective of this study is to propose a framework that supports decision-making regarding the optimisation of transport services for people in an effort to minimise renewable energy resources. Transport system models (TSMs) are the core of the framework, adopted to design transport services operated with EVs. TSMs are examined in a practical scenario, represented by the touristic port “Porto delle Grazie”, located in the Citta Metropolitana of Reggio Calabria, south of Italy. Clean energy is produced from maritime waves. Transport services are designed considering travel demand by means of TSMs. The novelties of this study are a framework to support decision-making processes aimed at optimising renewable energy consumption, while ensuring the satisfaction of mobility requirements, and an application in a practical scenario where energy is produced by the sea waves to facilitate green transport services for people mobility. The application represents a prototypical experiment aimed at validating the proposed framework. A discussion section presents the main potentialities and limitations of this work, which can be extended to wider geographical scales and over different time periods.

The paper deals with the integration of data provided from traditional transport surveys (small data) with big data, provided from Information and Communication Technology (ICT), in building Transport System Models (TSMs). Big data are used to observe historical mobility patterns and transport facilities and services, but they are not able to assess ex-ante effects of planned interventions and policies. To overcome these limitations, TSMs can be specified, calibrated and validated with small data, but they are expensive to obtain. The paper proposes a procedure to increase the benefits of TSMs&rsquo, building in forecasting capabilities, on one side, and limiting the costs connected to traditional surveys thanks to the availability of big data, on the other side. Small data (e.g., census data) are enriched with Floating Car Data (FCD). At the current stage, the procedure focuses on two specific elements of TSMs: zoning and graph building. These processes are both executed considering the estimated values of an intensity function of FCDs, consistently with traditional methods based on small data. The data-fusion of small and big data, operated with a Geographic Information System (GIS) tool, in a real extra-urban context is presented in order to validate the proposed procedure.

In this paper, a multi-step model related to freight movements in urban areas is formalised to solve a Vehicle Routing Problem with Time Windows (VRPTW). In modelling terms, the VRPTW is formulated to consider the optimum paths between all the customers combined to determine the best vehicle routes. The optimum path search with a selective mono-criterion approach is tackled. A model for forecasting the link cost is calibrated. Some procedures (traffic assignment, real-time system measurement, reverse assignment) for estimating system performance in terms of travel cost are also proposed. In terms of procedures, the VRPTW is solved with a genetic algorithm and two different crossover operators are reported and used simultaneously. A real case application is made, comparing the results of the proposed procedure with the routes of a truck (

The performance of a transportation network is the consequence of users’ choices and the interaction of these choices. Several aspects influence users’ choices. For the analyst of the transportation system, being able to forecast users’ behaviour is necessary in order to obtain a simulation close to the reality, and in order to predict the impact of hypothetical policy measures. In private car transport networks, the route choice of drivers is one of the key choice dimensions to be considered. We suppose that three main elements influence route choice: i) the subjective perception of travel times, ii) experience directly obtained on the network by the user, and iii) information acquired through information systems like Advanced Traveler Information Systems (ATIS). In particular, in the present paper we delve into the influence of information on choices, by exploring the influence of the process by which the perception of travel times is updated day-by-day due to experience obtained on the network. In our mathematical model, the users’ awareness/knowledge of actual conditions on the network changes through an evolution process. Although this kind of problem has been previously studied, a novel feature of our approach is that as a consequence of actual experience or information, users may become aware of new alternatives: hence the ‘choice set’ perceived to be available may change day-by-day, not only the perception of travel times within a given choice set. In this paper, therefore, the time-evolution of both the choice (the alternative actually chosen) and the choice set (the alternative considered) is investigated. An illustrative specification of the model is proposed, and a series of simulation experiments conducted.

Traffic signal setting is an important element in traffic control and in the management of urban road networks both in ordinary conditions (for example, recurrent traffic congestion) and/or in emergency conditions (for example, non-recurrent traffic congestion caused by unexpected events). This paper presents models and procedures for signal setting optimisation of signalised junctions in a congested road network. Two interacting procedures are developed to solve the system of models: an optimisation procedure, based on a genetic algorithm, to obtain an optimal configuration of signal setting parameters and a simulation procedure, incorporating a path choice model with explicit path enumeration and a flow propagation model, to capture effects of signal setting configuration on user path choice behaviour. The main research contributions of this work are related to: (1) the modelling of path choice behaviour in a dynamic context with vehicular flow propagation of on link and spill-back; (2) the optimisation procedure of signal setting parameters. The system of models and procedures has been applied and validated on a real experimental test site, where the transport system is in dynamic conditions due to time-varying road network and travel demand.

In this paper, an integrated Network and Vehicle Routing Optimisation Problem (NVROP) for freight vehicles is proposed. The variables involved in the problem are discrete (link topology, routes for freight vehicles) and continuous (link capacity in term of signal settings). The discrete variables are related to the link direction, lane numbers, lane use and sequence of retailers reached by the vehicles. The continuous variables, assuming that the link capacity is a function of the characteristics of the final node in the link, are related to the regulations at the intersection. The NVROP is, hence, formulated as a mixed (discrete/continuous) problem in a congested network. A heuristic procedure is used both to find the best network configuration and the best vehicle route.

This paper addresses the problem of traffic signal setting with demand assignment. Approaches to this in the literature and in practice are based on equilibrium assignment. However, it is widely acknowledged that there are some relevant issues that may not be effectively addressed under the equilibrium approach - mainly uniqueness and stability, sensitivity to parameters and/or starting state. These issues should be better dealt with a day-to-day dynamic approach, through deterministic (or stochastic) process models. The issue seems relevant because optimization of signal timings under equilibrium assumptions may not guarantee that an effective solution is obtained, as it may well not be an attractor of the evolution over time. This paper presents a simple but effective deterministic process model based on exponential smoothing filters, which also include effects of signal setting. This model allows to state local stability of fixed-point states (consistent with equilibrium patterns) through the spectral analysis of the Jacobian matrix of the recursive equations modelling the evolution over time of the system. The paper also presents an expression of equilibrium stability conditions that can be included as constraints within global optimization models for signal setting. Such models guarantee that stability conditions are satisfied by the solution. Results from an application to a toy network, supporting major theoretical findings, are also reported. The very simple example allows for graphical representation to develop a general method useful to address implementation at real scale.

In this paper a method for goods distribution optimization in urban areas is reported considering the time-dependent networks. Goods distribution optimization refers to the best tour optimization for each fright vehicle. A first step in the route design is the definition of the best path from the actual client to all others client. The traffic conditions change during the day; hence the link cost is a variable that depends on the time. The path topology and the path cost are also time-dependent and the path search must be generalized considering the time variability. In the paper a model, a solution procedure and a test in a small system are reported.

The objective of this work is the specification, calibration and validation of a route choice model distinguishing the phases of set perception and route choice among the alternatives belonging to the chosen set. Route generation is performed with a selective multi-criteria approach; route choice is performed using random and fuzzy utility models. As regards the fuzzy utility models, a new specification of a fuzzy model is introduced. Random and fuzzy utility models are compared and applied on a test road network and were specified and calibrated for national road freight transport on a real Italy-wide network.

In the literature, various models and procedures have been proposed for calibrating parameters of demand models or updating the demand values from traffic counts. Different models in terms of theoretical approaches have been used for congested and uncongested networks. Also for supply, models have been proposed for calibrating parameters of link cost functions. Link cost functions are calibrated separately from the demand. Congruence between the two calibrations is not guaranteed. This article proposes a unified formulation to obtain at the same time the parameters of link performance functions and the demand values (and/or the values of demand model parameters) of a static traffic assignment model. Model input consists of the a priori demand values, travel times measured on different links on the network, the link cost functions for each link category and the simulation model for user behaviour in path choice. Model output consists of the optimal demand, the parameters calibrated on link cost functions a...

The paper deals with methodologies to support an ex-ante assessment of the impacts generated by the introduction of city logistics measures on last-mile freight distribution services. Some models and algorithms are applied and compared to solve the vehicle routing problem for freight deliveries in the town of Reggio Calabria with the introduction of an urban distribution centre and of green freight vehicles. Heuristic routing algorithms are compared in terms of sequence of clients (retailers) visited subject to some constraints given by the presence of UDC and the limited autonomy and capacity of freight vehicles.