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Pedestrian flow efficiency and safety are primary requirements in the effective configuration and management of urban gathering spaces, such as railway stations, stadiums, or shopping malls. Moreover, the quality and comfort level of available walking environments play a key role in the challenge of sustainable mobility. As a consequence, there has been growing interest in developing methodologies for analysing the walking transport mode. In this context, the authors deem crucial to build a pedestrian dynamics simulation framework that they can completely control, from the modelling and calibration under different flow conditions to the implementation of a user-friendly tool that allows testing the model in a variety of case studies. In this study, they focus on the implementation and calibration of an agent-based model that microscopically simulates the interactions between individuals and with the environment. They calibrate the model parameters with a behavioural-based approach that relies on observed motion behaviours. Additionally, they present preliminary findings from pedestrian flow experiments performed and monitored with dedicated video recording systems. The collected data are meant to improve the calibration and validate the simulator, but they also provide insights into the emergence of collective behaviours, which can have significant upshots on the theoretical framework.

This study deals with downstream logistics of Liquefied Petroleum Gas (LPG) by tank trucks both at regional and urban level. The distribution problem is formulated as a capacitated Vehicle Routing Problem (VRP) with time windows. Its solution is based on a two steps procedure with the aim of maximizing the revenue of the logistic operator: 1) in the first step, the problem is solved at suburban level, where each delivery point represents a municipality in the study area. The employed objective function of the VRP has been conceived as to integrate the usual operating costs with aspects related to infrastructure characteristics of the used paths; 2) in the second step, door to door deliveries at urban level are attempted, taking into account the operating costs and the last delivery time for each potential client. The method has been applied in a real case study based in the city of Perugia, Italy. Sensitivity analysis considering different values for the parameters regulating the relative weights of the objective function components has been performed. Results show that the potential revenues for the operator can be doubled through the proposed optimization procedure. Moreover, explicitly taking into account that not all roads are suitable for vehicles transporting hazardous materials can increase the security of the distribution process, resulting in a service cost increase of just +1.4% with respect to extant conditions.

Electric micromobility systems such as e-bikes and e-scooters represent sustainable mobility options especially for specific classes of travelled distances. Moreover, the coverage and accessibility of transit services can be expanded through the implementation and promotion of these systems. Therefore, transport engineering is dealing with the development of new tools to support the forecast of the potential demand both for door-to-door trips and its integration with transit. With respect to these incoming research challenges, the paper proposes a methodology to investigate private mobility through floating car data (FCD) to identify the potential demand that can be shifted from cars to electric micromobility (e-micromobility) systems while also exploring the opportunity to increase transit usage. The benefits of such methodology have been evaluated on a real large test case, i.e. Rome (Italy), through an FCD dataset of about 240,000 monitored vehicles. The developed methodology is parametric and, thus, it can be easily transferred to other city contexts taking into consideration the compatibility of the local network infrastructures and the micromobility solutions. In Rome, it was estimated that the potential demand for e-micromobility can reach a maximum value of about 20% of weekday-trips, while about 10% of the morning peak trips could potentially be interested in a multimodal trip (i.e. mass transit services and e-micromobility for the access/egress). Results can be adopted by local authorities, transport companies and electric mobility providers to optimize infrastructural measures or the location of shared e-scooters and e-bikes to increase potential e-micromobility demand, as well as to increase the number of multimodal mobility options.

Investigating the correlation between urban mobility patterns and the built environment is crucial to support an integrated approach to transportation and land-use planning in modern cities. In this study, we aim to conduct a data-driven analysis of these two interrelated parts of the urban environment through the estimation of a set of metrics to assist city planners in making well-informed strategic decisions. Metrics are computed by aggregating and correlating different types of data sources. Floating Car Data (FCD) are used to compute metrics on mobility demand and traffic patterns. The built environment metrics are mainly derived from population and housing census data, as well as by investigating the topology and the functional classification adopted in the OpenStreetMap Repository to describe the importance and the role of each street in the overall network. Thanks to this set of metrics, accessibility indexes are then estimated to capture and explain the interaction between traffic patterns and the built environment in three Italian cities: Brescia, Catania, and Salerno. The results confirm that the proposed data-driven approach can extract valuable information to support decisions leading to more sustainable urban mobility volumes and patterns. More specifically, the application results show how the physical shape of each city and the related street network characteristics affect the accessibility profiles of different city zones and, consequently, the associated traffic patterns and travel delays. In particular, the combined analysis of city layouts, street network distributions, and floating car profiles suggests that cities such as Brescia, which is characterized by a homogeneously distributed radial street system, exhibit a more balanced spread of activities and efficient mobility behaviors.

This study focuses on a modeling framework to support mobility planners and energy providers in the sustainable development of electric mobility in urban areas. Specifically, models are provided to simulate measures for the optimal management of energy demand and thoughtful planning of charging infrastructures in order to avoid congestion on the power grid. The measures, and consequently the models, are classified according to short-term initiatives based on multimodality between electric vehicles and public transport (Park and Ride), as well as medium to long-term initiatives based on the development of an energy-oriented land use of the city. All the models are data-driven, and different sets of floating car data available for the city of Rome (Italy) have been exploited for this aim. The models are currently being implemented in an agent-based simulator for electric urban mobility adopted by the National Agency for Energy and Environment in Italy (ENEA).

Electric micromobility is increasing in popularity as an urban transportation option. It might help reducing traffic externalities as well as providing last mile solutions and complementary modes to access public transport. This work focuses on electric scooter (e-scooter) sharing service with the aim of investigating strengths and weaknesses according to different viewpoints: the operator viewpoint, the policy maker viewpoint, and the user viewpoint. Then, the study moves to an in depth focus of the e-scooter sharing service in Italy with a focus on the city of Rome, where data of the operator Dott are available. Results underline a pre-competitive stage of the e-scooter sharing market in Italy with respect to US and Europe, with several differences between cities.

Congestion pricing is classified as a transportation demand management measure adopted to reduce impacts modern cities are suffering in terms of traffic congestion, road accidents and air and noise pollution. Such a measure allows linking road transport externalities directly to travelers producing them. The definition of proper tolling schemes enables this measure to act on demand short-term choices, forcing travellers to shift towards low impact road itineries (route diversion) and sustainable transport systems (modal diversion). This paper presents key findings in the design of pricing policies to a specific real size and complex case (city of Rome, Italy) addressing overall transport performances (on the multimodal network) and impacts (affecting the entire community), in a demand elastic context. Main contribution derives from equity matters dealt with in the impact assessment of pricing policies, so filling a gap not extensively studied in this field. Specifically, different tolling schemes have been defined from a quantitative (toll level) and spatial (city zones where pricing is implemented) viewpoint in order to guarantee equity aspects in the application of the measure: affected road users are limited to those travelling in city zones where the mass public transport network (metro and rail) is available; besides, the toll amount is related to the level of accessibility to public transport (whether only in origin/destination of the trip or in both). Results, evaluated adopting a simulation-based approach, are consistent with those obtained in other real world cases, and highlight that the adoption of a proper pricing policy in the city of Rome guarantees a demand diversion towards sustainable transport modes up to 25% for those zones directly involved by the pricing implementation; promising benefits have been observed even in a wider area, entire Province of Rome, not directly affected by the measure, in terms of reduction both of road users (up to 6%) and congestion costs (about 2%).

Urban delivering is facing some significant changes that are heading towards unsustainable scenarios. At the same time, local administrations as well as city planners are involved in promoting new solutions that can help to improve city sustainability and livability. In this context, electric micromobility could offer a valuable contribution. In fact, electric micromobility systems such as e-bikes and e-scooters, both at an individual level or as a shared service, could represent sustainable mobility options for city logistics, especially for specific classes of parcel delivery, users’ characteristics and travelled distances. Considering both the growth of e-commerce and the spreading of new options for delivering parcels (e.g., crowdshipping), electric micromobility (e-bikes and e-scooters) could support the penetration and acceptability of such new options, limiting the impacts of delivery operations. After analysis of the current e-commerce background and a review of the current delivery options to satisfy delivery demand, crowdshipping stands out. Thus, the potential shift from private transport to e-micromobility for crowdshipping is investigated, assuming that potential crowdshippers may, mainly, be commuters. The methodology is based on using probabilistic-behavioral models developed within random utility theory, which allow the potential shift towards e-micromobility for commuting to be forecasted. The models were calibrated in Rome, where more than 200 interviews with commuters were available.

Dynamic Traffic Assignment (DTA) models represent fundamental tools to forecast traffic flows on road networks, assessing the effects of traffic management and transport policies. As biased models lead to incorrect predictions, which can cause inaccurate evaluations and huge social costs, the calibration of DTA models is an established and active research field. When it comes to estimating Origin-Destination (OD) demand flows, perhaps the most important input for DTA models, one algorithm suggested to outperform all the others for real-time applications: the Kalman Filter (KF). This paper introduces a non-linear Kalman Filter framework for online dynamic OD estimation that reduces the number of variables and can easily incorporate heterogeneous data sources to better explain the non-linear relationship between traffic data and time-dependent OD-flows. Specifically, we propose a model that takes advantage of Principal Component Analysis (PCA) to capture spatial correlations between variables and better exploit the local nature of a specific KF recently proposed in literature, the Local Ensemble Transformed Kalman filter (LETKF). The main advantage of the LETKF is that the Kalman gain is not explicitly formulated which means that, differently from other approaches proposed in the literature, there is no need to compute the assignment matrix or its approximation. The paper shows that the LETKF can easily incorporate different data sources, such as traffic counts and link speeds. Additionally, thanks to the PCA, the model can identify local patterns within the data and better explain the correlation between variables and data. The effectiveness of the proposed methodology is demonstrated first through synthetic experiments where non-linear functions are used to benchmark the model in different conditions and then on the real-world network of Vitoria, Spain (2,884 nodes, 5,799 links) using the mesoscopic simulator Aimsun. Results show that the proposed method leads to better state estimation performances with respect to other Ensemble-based Kalman filters, providing improvements as high as 64% in terms of traffic data reproduction with a 17-fold problem dimensionality reduction.

Starting from the promotion of the Motorways of the Sea (MoS) in the White Paper 2001 on European Transport Policy, short-sea shipping became one of the key aspects in the development of the Trans-European Transport Network (TEN-T). Indeed, short-sea shipping is considered as one of the possible strategies to find competitive and sustainable transport alternatives with respect to the road-only transport, if a length of over 300 km characterizes the connections. This study proposes and investigates several policies in order to increase the use of the short-sea shipping, moving freight from the road-only alternative to intermodal logistics chains based on Ro-Ro services. The objective is to quantify the potential demand of the combined road-sea transport as a function of different actions in terms of new Ro-Ro services/routes and subsidies to the road hauliers or to the shipping companies. The case study refers to domestic services involving the Italian seaports.

The study evaluates the added value generated by estimating dynamic demand matrices by information gathered from Floating Car Data (FCD).Firstly, adopting a large dataset of FCD collected in Rome, Italy, during May 2010, all the monitored trips on a specific district of the city (Eur district) have been collected and analysed in terms of (i) spatial and temporal distribution; (ii) actual route choices and travel times. The data analysis showed that demand data from FCD are usually not suitable to retrieve directly demand matrices, due to a strong dependence of this information from the penetration rate of the monitoring device. Instead, origin–destination travel times and route choice probabilities from FCD are a much more reliable and powerful information with respect to FCD origin–destination flows, since they represent the traffic conditions and behaviors that vehicles experiment along the path.Thus, several synthetic experiments have been conducted adopting both travel times and route choice p...

The paper analyses the willingness to act as a crowdshipper in the case of a last mile B2C e-commerce for pick up/delivery. Specifically, it focuses on crowdshipping services deployed using the public transport network and considering passengers as crowdshippers already moving for other reasons. In fact, this is the most environmental-friendly type of service one can develop given it avoids performing dedicated trips. The paper uses stated preference to identify the most important features associated with the choice of acting as a crowdshipper and discrete choice models to study the underlying behavior. The implementation case study refers to the city of Rome, Italy, and addresses its metro lines, thus understanding and quantifying the effects of this freight transport strategy for e-commerce in an urban context and providing local policy makers a good knowledge base for its future development.

The paper analyses a practical case of study related to the distribution of fuels for the Total Erg Oil Company to the service stations located in the Province of Rome (Italy). The problem is formulated as a capacitated vehicle routing problem with time windows, where several heuristic procedures have been tested, considering both static and dynamic travel times. With respect to the standard operational costs used typically, a multivariable objective function has been proposed which takes into account also a new risk index. The risk index proposed is function of the population density of the zones covered by each path and of the estimated number of road accidents on each road link. In such a way, we take into account the population’s exposure to the risk associated with an incidental event involving a fuel tank. The obtained output is the set of planned routes with minimum service cost and minimum risk. Results demonstrate how an accurate planning of the service saves up to 3 hours and 30 km on a daily basis compared to a benchmark. Moreover, the distribution company can parameterize the configuration of the service, by varying the weight adopted in order to include the risk index. Including the risk index may bring to a higher safety route planning, with an increase of the operating costs of only 2%.

Nowadays, new mobility information can be derived from advanced traffic surveillance systems that collect updated traffic measurements, both in fixed locations and over specific corridors or paths. Such recent technological developments point to challenging and promising opportunities that academics and practitioners have only partially explored so far. The paper looks at some of these opportunities within the Dynamic Demand Estimation problem (DDEP). At first, data heterogeneity, accounting for different sets of data providing a wide spatial coverage, has been investigated for the benefit of off-line demand estimation. In an attempt to mimic the current urban networks monitoring, examples of complex real case applications are being reported where route travel times and route choice probabilities from probe vehicles are exploited together with common link traffic measurements. Subsequently, on-line detection of non-recurrent conditions is being recorded, adopting a sequential approach based on an extension of the Kalman Filter theory called Local Ensemble Transformed Kalman Filter (LETKF). Both the off-line and the on-line investigations adopt a simulation approach capable of capturing the highly nonlinear dependence between the travel demand and the traffic measurements through the use of dynamic traffic assignment models. Consequently, the possibility of using collected traffic information is enhanced, thus overcoming most of the limitations of current DDEP approaches found in the literature.

Routes optimization in urban freight distribution is usually an off-line process based on the knowledge of historical conditions on the network. Real-time data provided by Intelligent Transportation Systems (ITSs) enable online re-optimization on the basis of actual traffic conditions. This paper evaluates the added value generated by re-optimizing the off-line solution with real-time information. The study is carried out for a practical application to the freight distribution of perishable goods in the city of Rome (Italy). The off-line problem is formulated as a vehicle routing problem with soft time windows while in the online problem it is also allowed to skip some customers or to re-sequence the deliveries. Both versions are solved with different algorithms and with different data sets. Results can be used to evaluate the potential return on investment on the acquisition of different kinds of traffic data. At the same time, results can be of interest for information providers, to fix the pric...

Cities crave innovative logistics solutions dealing with the requirements of the ‘on demand economy’. The paper estimates the willingness to act as a crowdshipper (supply) and to buy a crowdshipping service (demand) to get goods delivered/picked-up in the last mile B2C e-commerce situation. Specifically, it innovates by considering an environmental-friendly crowdshipping based on the use of the mass transit network of the city where parcels customers/crowdshippers pick-up/drop-off goods in automated parcel lockers located either inside the transit stations or in the surroundings. This issue is very important since “standard” crowdshipping is usually not able to reduce congestion and polluting emissions due to the dedicated trips performed using private motorized vehicles. The paper rests on an extensive stated preference survey. The hypothetical scenarios used to acquire both demand (customers’) and supply (crowdshippers’) preferences make use of the most relevant attributes emerging from a preliminary investigation performed in the study context. The investigation is performed in the city of Rome and the metro is the transit system considered. The results are useful in understanding and quantifying the potential of this freight transport strategy for e-commerce in an urban context and in providing local policy makers with a good knowledge base for its future development.

The paper focuses on the fuel transport optimization network for the Total Erg Oil Company to the distribution points sited in a zone near Rome, in Italy. The numerical method is based on a capacitated vehicle itinerary issue for time intervals, considering various heuristic procedures. The fixed and time-dependent travel times are considered. Compared to the standard operational costs, a multivariable objective function is developed that considers: (1) the risk associated with an incidental event involving a fuel tank; (2) that not all the roads are suitable for heavy vehicles transporting fuel products. These two additional terms permit to better quantify the costs for the operator, since it is assumed that roads with higher number of accidents or with not suitable infrastructure conditions have also higher probability of making the fuel tank experiences at least a delay during the day. Results show that a proper vehicle routes design on our multivariable objective function allows economizing about 40 km daily respect to a benchmark. The fuel company can also consider the itinerary design, modifying the weights used with the aim to comprise the additional variables with respect to the standard operational costs, thus assuring a higher safety route planning.

The paper focuses on the definition, calibration and testing of a simulation model that is able to represent multimodal choice behaviours for electric vehicles. Taking into account the interchange between public transport and electric private mobility, the model estimates the parking demand at the Park & Ride sites equipped with charging stations. The model is based on a data-driven approach, in which mainly Floating Car Data and open data of public transport have derived the explanatory variables. Specifically, a machine learning method (Random Forest) has been used to calibrate and test the model in the real case of the metropolitan area of Rome (Italy). We first perform a stability analysis, letting the parameters of the model vary. We then carry out a sensitivity analysis on the variables that can affect the user propensity to adopt the Park & Ride. Finally, we profile and test an incentive policy to boost the choice of Park & Ride. Results suggest that the model succeeds in simulating Park & Ride by electric vehicles and, therefore, it can be extremely valuable for planning financial support to the multimodal travel choice and forecasting vehicle-to-grid scenarios.

Fully Autonomous Vehicles (AVs) can self-drive in all situations, offering the highest comfort to drivers, who can pursue several activities during their trips. Under this assumption, AVs will probably affect the perception of time and its value. The research presented in this paper is a first attempt to understand if the introduction of fully AVs may generate residential relocation from the city center to the suburbs due to a different time perception. Using Stated Preferences (SP) survey technique, discrete choice modelling, and traffic simulation, the effect of urban sprawl of AV-cities on the road network in terms of congestion was evaluated. The relocated demand, estimated thorough ad hoc calibrated behavioral models, was assigned to the road network of the city of Rome (Italy). Results show an increase in travel times (about 12%) for suburban residents commuting to work in city center due to the extra-demand relocated. Instead, intra-urban road congestion would benefit from lower traffic volumes (about −10% in travel times). Moreover, this work shows the positive impact of the ride-sharing schemes with AVs by simulating different penetration rates. These results, however, should be taken with caution, as the sample is very small, travel time and travel monetary costs are ignored, as are housing costs, and mode choice and residential location are not modelled simultaneously.

In this paper we describe a simulation tool developed to study city wide scenarios of e-mobility. The tool is intended to support e-mobility stakeholders in finding effective solutions to facilitate a widespread and sustainable EV adoption in urban areas. The tool includes a suite of integrated models designed to reproduce e- mobility pattern, charging behaviour and resulting impact on electricity demand at a high spatiotemporal resolution. The suite also includes models to evaluate multimodal mobility approaches and smart charging strategies including the use of renewable energy. The simulation tool has been tested in the metropolitan area of Rome (IT). We have used large scale collections of vehicle trajectory data to calibrate and validate the suite of models.

This paper aims at understanding and evaluating the environmental and economic impacts of a crowdshipping platform in urban areas. The investigation refers to the city of Rome and considers an environmental-friendly crowdshipping based on the use of the mass transit network of the city, where customers/crowdshippers pick-up/drop-off goods in automated parcel lockers located either inside the transit stations or in their surroundings. Crowdshippers are passengers that would use the transit network anyhow for other activities (e.g., home-to-work), thus avoiding additional trips. The study requires firstly, estimating the willingness to buy a crowdshipping service like the one proposed here, in order to quantify the potential demand. The estimation is realized adopting an extensive stated preference survey and discrete choice modeling. Then, several scenarios with different features of the service are proposed and evaluated up to 2025 in terms of both externalities (local and global pollutant emissions, noise emissions and accidents reductions) and revenues. The results are useful to understand and quantify the potential of this strategy for last mile B2C deliveries. Moreover, it provides local policy-makers and freight companies with a good knowledge base for the future development of a platform for public transport-based crowdshipping and for estimating the likely impact the system could have both from an economic and environmental point of view. Keywords: crowdshipping; on demand; e-commerce; urban freight; discrete choice model; transport externalities; COPERT 1. Introduction

This paper examines the impact of applying dynamic traffic assignment (DTA) and quasi-dynamic traffic assignment (QDTA) models, which apply different route choice approaches (shortest paths based on current travel times, User Equilibrium: UE, and system optimum: SO), on the accuracy of the solution of the offline dynamic demand estimation problem. The evaluation scheme is based on the adoption of a bilevel approach, where the upper level consists of the adjustment of a starting demand using traffic measures and the lower level of the solution of the traffic network assignment problem. The SPSA AD-PI (Simultaneous Perturbation Stochastic Approximation Asymmetric Design Polynomial Interpolation) is adopted as a solution algorithm. A comparative analysis is conducted on a test network and the results highlight the importance of route choice model and information for the stability and the quality of the offline dynamic demand estimations.

The paper analyzes an existing Land Use Transport Integration (LUTI) model in terms of its capacity to generate sustainable mobility behaviours in an urban area promoting the use of the public transport. The cited LUTI assumes the residual capacity of the mass transit system as a key element to indicate location and intensity of new residential and activities developments. It appears as a tool for the adoption of integrated transport-land use policies, especially for the short term planning. A systematic and accurate analysis of this LUTI is reported considering a real case of study (the city of Rome, Italy): five scenarios with different characteristics both in terms of supply network and travel demand have been considered. Finally, suggestions are reported to overcome some weaknesses that can arise in practical applications of the LUTI.

This paper focuses on the introduction of an innovative electric vehicle car sharing system in Rome. Specifically, the system is designed for a university community (Roma Tre University), thus it is reserved for students and employees. The fleet is composed by 30 electric vehicles and 56 recharging points have been installed in several departments of the University. The service is designed as a free floating system, allowing commuting and leisure trips. The trips are not spatially limited, but the rental has to be opened and closed within a delimited area in the city. This paper analyses the starting stage of the project. The results of a RP (Revealed Preferences) and SP (Stated Preferences) survey are discussed. The survey has been filled out by a sample of 950 potential users. Important indications have been obtained on the behavioural attributes that mostly affect the electric vehicle car sharing subscriptors and service utilization. Afterwards, specific behavioural models based on random utility theory have been proposed for predicting modal switch, considering the introduction of the new service. Attributes such as the “Green Attitude”, the “Sharing Attitude” and the “Device attitude” have been analysed in terms of their impacts on the mode choice, underlining their weight and statistical significance compared to attributes as distances and travel times.

In this work we evaluate the changes in energy demand and resulting air pollutant emissions from the introduction of Battery Electric Buses into the public transport fleet operating in Rome. Our approach to evaluation is based on the adoption of geo-referenced open-data set, published in GTFS format by the Rome's public transport agency, that contains detailed information about transit stops, routes and service schedules of the bus lines. We use an electric bus simulation tool to obtain specific energy consumption models based on real driving cycles. The findings of this study indicate noticeable reductions in the primary energy consumption and the emissions of both GHG gases and toxic local air pollutants such as NOx and PM. © 2017 IEEE.