In dependence upon a given geometric configuration, an actual or forecasted number of vehicles arriving at an intersection can turn left or right, otherwise proceed straight through it. This article can be placed in between a research working paper, and a kind of informational brief report. Indeed, it deals with forecasting techniques for estimation of intersection turning movements. Such issue is crucial, both in network planning and in traffic engineering, while its applications span from traffic safety and environmental impacts, to signal timing, roundabout design and setting of traffic control strategies. The number of vehicles making each movement on an existing intersection can be manually collected, especially when operational analyses are undertaken. Nevertheless, when the intersection is at the planning or design stage, an estimation process is required. In its first part, this paper provides a brief literature review of some of theoretical and practical methods focused to forecast the intersection turning movements. Such a review is limited on methods used to distribute the incoming and exiting traffic volumes within the different intersection branches, then generating an estimation of the so-called intersection O/D matrix of turning flows. The second part of the paper is experimental. Two different, but similar, heuristic procedures have described. Then, they have successively applied to some selected intersection real traffic data sets, and the respective computational performances were compared. Namely, the first one is known as proportion methods, while the second one is called as the difference, or deviation, method. Each method of these two starts from an initial matrix, and through iterative steps it reaches the best estimate of the matrix of turning flows, with respect to a given distribution model. Test intersections with their related set of real traffic data have been used as input, and each of the two procedures, as described in advance, was applied to the same numeric instances. The obtained values were compared in respect to few selected performance indicators. Finally, the computational results were displayed and discussed. On this basis, some insights are drawn and useful remarks for application and future research have been addressed.

The utilization and the quality of public transport are important for the customers, maintainers and service providers. Passive measurement techniques, when humans are not involved are the cheapest way for collecting large amounts of long-term data from multiple public transport lines. Useful data can be collected from various sources, such as from cameras, infrared sensors and Wi-Fi routers. We addressed the problems of estimating passenger counts in two different ways, and also to get travel statistics like the number of passengers getting on or off a vehicle at a bus stop; and even to compute an origin–destination matrix from Wi-Fi monitoring data. In this study, we focus on Wi-Fi data, which can be still useful for extracting relevant data after many years. here we describe Wi-fi data collection methods, and then prove the usefulness of applying simple artificial intelligence-based methods to extract information from the huge amount of Wi-Fi data. We will also show that ‘lower-level re-estimation’ can be useful for further optimization, which means that globally modelled data may have to be re-modelled on partially selected groups to get better results. Namely, after building linear models and estimating absolute and relative errors, we found that the relative error of the Wi-Fi-based estimation can be markedly reduced if data are processed and analysed in more detail. When a daily Wi-Fi analysis is split into between-stops parts, an additive linear correction can be computed and applied to these parts, and as a result, the relative error of estimates can be reduced.

Given the growth in container transport volumes and the use of mega-ships calling at a limited number of hub ports, it is necessary to increase the capacity of port-hinterland connections. Rail transport should play a key role in providing a sustainable response to this need and innovative solutions must be developed which can address the current problems and limitations through a more collaborative approach involving all the relevant stakeholders. The present study contributes to the development of an innovative Just-in-Time Rail Shuttle Service for a major port-hinterland corridor of the port of Valencia (Valencia-Zaragoza) and its service-dominant business logic, which emphasises the interaction between different transport agents as they co-create value through collaborative processes. The service-dominant business model radar (SDBM/R) methodology has been applied in close collaboration with industry experts and the resulting model evaluated through an extensive series of hands-on workshops with industry professionals from the port and logistics sector. This paper focuses on the application and evaluation of this methodology in the inland container transport domain, aimed at harnessing digital innovation in the design of new business models for a Just-in-Time Rail Shuttle Service. In summary, it contributes a novel and collaborative business design approach that has an academic grounding and practical relevance.

Large speeds and axle-loads are required for modern freight trains, which can cause a big rise in in-train forces on wagon coupling elements for both tensile and compressive states, thus possibly leading to breaking of the coupling systems and to train derailments, respectively. Therefore, longitudinal train dynamics (LTD) simulations are a key tool for the prediction of the in-train forces and for the design of coupling and braking systems as well as for the optimization of the train composition. LTD simulations are typically carried out in time domain, to account for all the system non-linearities, mainly the hysteretic behaviour of the coupling system mechanical impedance characteristic. Although time domain simulations are a powerful tool to predict in-train forces considering all the system non-linearities, also frequency domain analyses can be useful to quickly compute the system dynamic behaviour. More in detail, modal analysis can provide important information on the system natural frequencies, so that the frequency content of the input forces can be checked to avoid the excitation of the system natural vibration modes.

The paper shows the development of a new efficient time domain simulation LTD code implemented in MATLAB, provided with a modal analysis post-processing routine. The code was validated on the four time domain simulation scenarios suggested by the international benchmark of LTD simulators, and a simplified modal analysis was also carried out on the same train configurations. The validation process highlighted that the new code provides stable numerical outputs with a good computational efficiency, while the modal analysis routine showed that the train eigenfrequencies can vary significantly according to the deflection, relative speed and loading state on each coupler.

Mobility as a Service (MaaS) presents a shift from existing ownership-based transports and towards access-based ones and it has been recently gaining ground in urban mobility. MaaS is still surrounded by uncertainties and, its development and applicability are mainly centered in developed countries. However, MaaS is modular, adaptable and applicable to several realities. in this sense, this study aims to examine the perception of different transport models among students and to find the profile that can predict respondents’ willingness to use MaaS in a developing country. This survey was applied to over 300 university students in a Brazilian city (Lavras). Using the cart algorithm, it was obtained classification trees to predict favourable responses related to MaaS use, based on several predictor variables (socio-economic characteristics, means of transport used, distance and other). It was observed that, car users are a little less sensitive to cost than non-car users. For car users, commute alternatives that take longer, with less flexibility and availability – even when offered at lower costs – are not appealing, while non-car users accept and spend more time whether lower costs are available. Also, in general, the tree-based classification model predicted a positive adherence possibility for a MaaS scheme for both car users and non-car users (69%). As conclusions, this study suggests a willingness to MaaS model for creating value for commuters in a developing country. It was found that many MaaS’ characteristics (e.g. app payment, transport integration, monthly plan, customization, etc.) presented a positive predicted possibility of substitution, especially for millennials. Also, it was found that bicycle may be a modal that can be explored for MaaS schemes worldwide, and casual carpooling could be used as strategy to apply MaaS in places where the public transport lacks efficiency.

The rapid growth in the use of recommendation systems in the tourism sector is mainly related to the possibility to access updated data deriving from social networks, thus providing more appropriate and personalized suggestions. The paper presents a tourist trip recommendation system that suggests personalized itineraries defined as sequence of point of interest (PoI) to visit. The system core integrates two software modules: a neural network and an optimization engine. For every pair user-PoI typology, the neural network provides, on the basis of the analysis of the social media data, a score between 0 and 1. These latter values are then used as input parameters for a routing optimization problem that suggests the itinerary by considering additional restriction, as, for example, time windows, budget and time limitations, specified by the end user. Being a computational demanding problem, the model solution is carried out by applying a heuristic approach that is proven to provide high-quality solution in a limited amount of time.

Over the past 10 years, the fleet of electric vehicles has grown dramatically, due to the introduction of incentive programs for their operation, as well as the development of accessible charging infrastructure. However, the design and organization of this network come with a number of obstacles in countries where the number of electric vehicles is limited. A small number of operated electric vehicles leads to a decrease in the utilization rate of the charging infrastructure. A main obstacle to increasing the fleet of electric vehicles is an insufficiently developed charging network. In this instance, such a location of charging stations in the city that will help to reduce idle runs of electric vehicles and increase the utilization rates of charging stations is one of the priority practical tasks in the field of infrastructure organization. In the world, there are approaches to the location of charging stations in cities. They are based on the methods proposed for choosing the location of socially significant objects in the city, and therefore take into account the centres of attraction of the population, the features of the organization of parking space and car services. However, until now, no methodology has been developed aimed at calculating the number of charging stations in the city, taking into account the characteristics of the functioning of the existing infrastructure and their location. The developed approach to the location of charging stations will take into account the peculiarities of the city’s power distribution network, the complexity of the access roads to the charging station and the likelihood of a free parking space. This paper partially describes the results of a larger study and aims at developing an approach to the location of charging stations in the city and optimizing the existing network. The approach was developed in two stages. Initially, the authors identified the applicability of an existing model, and then at the second stage, they adapted it taking into account the proposed indicators for assessing the location of the charging station in the city of Tyumen. In the future, the developed approach will be applied in several cities of the Russian Federation.