Davide Giglio

Mobility-as-a-Service (MaaS) is one of the sustainable solutions proposed and implemented by transportation planners and policy-makers to cater for the rising issue of congestion and associated external social and environmental costs. However, it is seldom evaluated how a planned provision of the MaaS system can impact the transport network performance. This research is carried out as a part of the Green MaaS for Adaptive Urban Planning (GeTUP) Project. It aims to critically evaluate the road network structure of the city of Genova and skim out the network’s most congested components (nodes and links), thus highlighting the much-needed places for an efficient MaaS provision based on adaptive planning that relies on various reliability measures quantified here. The network structure is analysed by using demand-based dynamic network centrality measures for its components (nodes and links). Whereas, the critical points of the network in each component cluster are skimmed out using a mixed-machine learning approach. Later, various demand elasticity scenarios are analysed for each cluster given adaptive green MaaS system presence and demand shift to that system. It is revealed from the analysis that in some parts of the road network the demand is as much as 78% higher than the provided capacity. Thus generating large travel times. However, when MaaS system is provided in terms of different demand elasticity scenarios the travel times are reduced improving the traffic network performance.

Modern drivers equipped with GPS-enabled devices digest traffic information and work as traffic information providers. Driving experiences and fuel consumption are also shared in novel systems to help users’ route choices. Navigators in special terrain cities like Genova, Italy achieve sub-optimal results when they reroute the traffic greedily towards light-congested roads inducing new traffic jams. Furthermore, the absence of user preferences and advanced profiling curtail the competency of navigators to provide need-based responses to their users. This article aims to present a deliverable for Green MaaS for an adaptive urban planning project Liguria region in Italy to fill the gaps present in current navigators suggesting more optimal routing solutions considering the user requirements. The GeTUP application provides a specific pertinent and germane service equipped with an interface that is competent to acclimate to the characteristics of its users. The logical navigation algorithm is based on automated self-learning internet-of-things-based architecture that works on user choices to generate high-level route information. On receiving the request, the server executes the search algorithm based on the provided origin, destination and percentage of desired high-level information required based on the user profiling. The results are based on both the real-time traffic rhythm and near-future traffic distribution estimates based on the rerouting decisions. GeTUP application is implemented as a pilot MaaS solution for the city of Genova. The real-time data obtained upon evaluation revealed that the application contributes actively to coordinating the traffic based on user decisions and preferences between different alternatives and alleviates congestion situations.

The weather route presents significant potential in identifying the most efficient route for vessels, enabling the optimisation of distance, time, or fuel consumption. Although there have been notable advancements in most technologies in recent years, weather routing has failed to keep pace with these developments. The purpose of this document is to determine whether a specific IMO’s concept called e-navigation may have an impact on vessel’s route optimisation. Today, mariners are required to integrate heterogeneous data types (hydrographic, weather, oceanographic, etc.) that are transmitted on board via various devices and in diverse formats in order to execute weather routing. When e-navigation becomes operational, relevant marine-related data will be accessible in a standardised format on a unified device. The objective of this work is to examine the potential of S-100 in the context of weather routing and route optimisation for the benefit of navigation. There are two methodologies discussed: manual and automated. A range of S-100-based products will be evaluated in relation to a variety of optimisation algorithms in order to predict whether e-navigation could be advantageous in addressing the weather routing problem. Constraints regarding the implementation of electronic navigation and the mariners’ duties are outlined in the final part of the paper.

The present work is focused on analyzing how e-navigation will affect the daily work of seafarers involved in bridge-related operations. Within e-navigation, the International Hydrographic Organization (IHO) is currently working in the development of the new standard (S-100) whose role is to guarantee a homogeneous management of the maritime domain data. S-100 is called to act as the Common Maritime Data Structure (CMDS), it represents the technical framework required to guarantee a wider and better use of maritime data. The mission of the standard is to create a common foundation that can be used for multiple purposes; meteorologists, physicists, and whoever is interested in developing maritime related products will refer to the same standard. Not having a homogeneous type of data processed with standardized procedures will allow a better combination and processing of maritime data. Considering the perspective of Hydrographic Offices, the objective of the present document is to analyze the impact which e-navigation will have over seafarers. The study is focused on the evaluation of the risks connected to S-100-based products and on the analysis of specific bridge operations. Considering that e-navigation products are still at their design phase, being aware of the consequences for the final users is essential to make S-100-based products more customer oriented and to allow seafarers who are involved in bridge operations to get familiar with this new technology.

A model of a smart road network consisting of unsignalised intersections and smart roads connecting them is considered in this work with the aim of presenting a traffic management system for self‐driving cars (or, more generally, autonomous vehicles) which travel the network. The proposed system repeatedly solves a set of mathematical programming problems (each of them relative to a single intersection or to a single road stretch of the network) within a decentralised control scheme in which each local intersection controller and each local road controller communicates with the fully autonomous vehicles in order to receive travel data from vehicles and to provide speed profiles to them once determined the optimal solution of the problem. In order to reduce the computational effort required to provide the optimal solution, a discrete‐time approach is adopted so that, in each time interval, a limited number of vehicles are taken into consideration; in this way, solutions can be determined in a very short time thus making the proposed model compatible with a practical application to real traffic systems. The proposed model is general enough, and can be adapted to different scenarios of smart road networks reserved for self‐driving cars.

Multi-sided assembly line balancing problems usually occur in plants producing big-sized products such as buses, trucks, and helicopters. In this type of assembly line, in each workstation, it is possible to install several workplaces, in which a single operator performs his/her own set of tasks at an individual mounting position. In this way, the operators can work simultaneously on the same product without hindering each other. This paper considers for the first time the multi-sided assembly line balancing problem with the objective of minimising the cycle time, proposing a new mathematical formulation to solve small-sized instances of this problem. Besides, a metaheuristic algorithm based on variable neighbourhood search hybridised with simulated annealing is developed to solve large-sized instances. The algorithm is called adaptive because of the adopted neighbourhood selection mechanism. A novel three-string representation is introduced to encode the problem solutions and six different neighbourhood generation structures are presented. The developed approach is compared to other meta-heuristics, considering some well-known in literature test instance and a real world assembly line balancing problem arising in a car body assembly line. The experimental results validate the effectiveness of the proposed algorithm.