Recently, several prominent logistic companies in Europe and the USA are seriously considering the idea of using drones launched from trucks and working in parallel to deliver packages. In the relevant literature, a novel problem formulation called travelling salesman problem with drone has been introduced, and some modelling and solution approaches have been presented. Existing approaches are based on the main assumption that the truck can dispatch and pick up a drone only at a node, i.e. the depot or a customer location. Here, the authors present a novel approach aimed to maximise the drone usage in parcel delivering. The authors consider that a truck can deliver and pick a drone up not only at a node but also along a route arc (en route). In this way, the operations of a drone are not strictly related to the customers’ position, but it can serve a wider area along the route. The authors tested the proposed heuristic on benchmark instances and analysed the benefits introduced with the en route approach.

City Logistics has attracted considerable interest from the operations research and logistics communities during past decades. It resulted in a broad variety of promising approaches from different fields of combinatorial optimisation. However, research on urban freight transportation is currently slowing down due to two different lacks, limiting the exploratory capacity and compromise the technology transfer to the industry. First, the majority of the instances in the literature are based on the generalisation of classical instances, often not created for urban applications, or on artificial data, i.e. data not coming from any historical or empirical datasets. Thus, the validation of models and methods becomes more difficult, being the results not directly compared with real or realistic settings. Second, even when some data sources become available, there is no standard way to mixing data gathered from different sources and, from them, generate new instances for urban applications. This study aims to overcome these issues, proposing a simulation–optimisation framework for building instances and assess operational settings. To illustrate the usefulness of the framework, the authors conduct a case study, in order to evaluate the impact of multimodal delivery options to face the demand from e-commerce, in an urban context as Turin (Italy).

This study introduces a general methodology to process sparse floating car data, reconstruct the routes followed by the drivers, and cluster them to achieve suitable choice sets of significantly different routes for calibrating behavioural models. This methodology is applied to a large set of floating car data collected in Rome in 2010. Results underlined that routes assigned to different clusters are actually very different to each other. Nevertheless, as expected according to Wardrop's principle, the clusters belonging to the same origin–destination have rather similar average route travel times, even if there is a large range between their minimum and maximum values. A focus on drivers’ behaviour highlighted their propensity to follow the same route to their usual destination, though the 12% of the drivers switched to an alternative route. However, the analysis conducted over the 1 month of observations did not reveal the existence of any systematic correlation between neither the change of route nor the change of departure time and the travel time experienced the day before.

GPS traces from cyclists are used to retrieve their path by matching the traces to a detailed, attribute-rich urban road network. The main objective of this research is to explore the influence of road network characteristics on the cyclist's path choice behaviour. The dataset used in this study consists of ∼27,500 GPS traces, which cyclists have recorded in Bologna, Italy, over a period of 4 weeks using a special smartphone application. Work trips are extracted from all traces by selecting only straight trips during the mornings of work days. After matching the traces to a specially prepared road map, the distributions of trip length, trip time and trip speed are determined. The shortest possible path between origin and destination of each trip is determined and compared with the chosen path. Results show that most cyclists tend to use the shortest path and accept only small detours. However, comparing the shortest path with the chosen path for each trip, it is possible to identify the network characteristics causing the cyclists to deviate from the shortest path. The main results of this study indicate that the chosen paths contain more cycleways and less intersections compared with the respective shortest paths.

The European Bus System of the Future (EBSF_2) is a research project funded by the European Union with the aim of developing a new generation of buses across Europe. The goals are to increase the attractiveness and efficiency of buses by testing advanced operational and technological solutions. Among these are more comfortable internal layouts, the implementation of a standard IT architecture, intelligent garage procedures, energy-efficient auxiliaries and green driving assistance systems, all of which are being tested in several demonstrators. This study focuses on the innovative features of the methodology and test process adopted in Ravenna, Italy, where a demonstrator is being used to improve predictive maintenance management. The demonstrator involves maintenance software used to analyse data from CANbus and sensors to assess oil quality, detect potential breakdowns and, on this basis, replace spare parts in advance. The system also detects which substances contribute to poor oil quality. The study describes performance before and during the implementation of the demonstrator in relation to various impact areas: maintenance, operations, fuel consumption, costs, staff training and the efficiency of the intelligent transport system in processing data. The achieved results are reported with the aim of providing advanced knowledge for applications beyond EBSF_2.

The safety of a building can be explicated in two different ways: the ability to withstand a critical event that affects the structure and the ability to safely allow people to leave the building. This article present a new methodology that can allow engineers to detect people trajectories in evacuation drills and provide a measure of effectiveness for buildings evacuation plans. In the proposed system Bluetooth beacon passive emitters are positioned as reference points and the sensing device is made up by the smartphones of people evacuating a building. The proposed methodology is thus based on using beacons that are capable of emitting a radio signal. The radio signal can be picked up by the sensors present in smartphones. The signal is then processed by a dedicated application on the device. Information is transferred to a computer and elaborated obtaining a measure of people positions. In the system people movements in emergency evacuation drills can practically be traced with very simple and affordable equipment. Once all data from mobile phones are gathered the system is able to reconstruct the trajectories of smartphones, in indoor (or outdoor) environments, and therefore to assess the overall performance of the building evacuation plan.

This work presents a decision support system for providing information and suggestions to airport users. The aim of the study is to design a system both to improve passengers' experience by reducing time-spent queuing and waiting and to raise airport revenues by increasing the time passengers spend in discretionary activities. Passengers' behaviour is modelled with an activity-choice model to be calibrated with their mobile phone traces. The model allows predicting activity sequences for passengers with given socio-demographic characteristics. To predict queue length at check-in desks and security control and congestion inside commercial areas, passengers' movements are simulated with a microscopic simulation tool. A system to generate suggestion has been designed: passengers are advised to perform mandatory activities when the predicted queue length is reasonable and specific discretionary activities according to time available, user profiles, location distance, location congestion and airport management preferences. A proof-of-concept case study has been developed: passengers' behaviour in both cases of receiving and not receiving suggestion has been simulated. In the first case, passengers experienced less queuing and waiting time; the time saved was spent in discretionary activities, improving passengers' airport experience and increasing airport revenues.

The inland terminals play an important role in the intermodal freight transport network to transfer loading units and achieve seamless cross-modal processes. Their efficiency contributes to competiveness of intermodal transport which shifts medium distance freight journeys from road to other modes as required by European Policies. Based on the scientific literature, a set of selected performance indicators for inland terminals is identified and classified, considering the terminal subprocess and the actors involved. Also, the main relations between the key performance indicators identified and the possible solutions for automatic identification to detect vehicles and units during the gate operations are analysed. These applications are useful for two main reasons: they contribute to terminal performance improvement, affecting the indicator value, and also may enable the computation of the indicators itself. The aim of the study is to provide a method to measure a particular indicator even in different procedures for check-in. To compare solutions in homogeneous conditions, with a consistent calculation of the indicator, the scenarios are analysed and modelled with a standard system architectures representation. The approach allows stakeholders a standard and simple analysis to effective compare different possible scenarios, in which alternative detection solutions may be implemented.