Dimitrios Rizopoulos

The growing adoption of electric buses in public transport has intensified the need for efficient scheduling algorithms. In the context of tactical planning, public transport operators must address two interdependent scheduling problems: the Single Depot Vehicle Scheduling Problem for Electric Buses (EB-SD-VSP) and the Crew Scheduling Problem for Electric Buses (EB-CSP). This study introduces a sequential approach, solving EB-SD-VSP via a Mixed-Integer Quadratic Programming (MIQP) model, and then using its solution to generate service blocks for the EB-CSP, which is then solved as a Mixed-Integer Linear Programming (MILP) model. The proposed sequential optimization approach ultimately solves the combined problem of Vehicle and Crew Scheduling for a fleet of Electric Buses (EB-SD-VCSP). Experiments on real-world bus line data from Athens, Greece demonstrate practical applicability of the approach. When compared to a baseline scenario where the services are executed with conventional buses, the proposed method can calculate efficient vehicle timetables and crew schedules for operations with electric buses. The results highlight the benefit of decomposing joint electric bus and crew planning into tractable subproblems while preserving solution quality. These findings offer a scalable tactical-level planning tool for transit agencies transitioning to electric fleets and suggest promising directions for future extensions to multi-depot and real-time scenarios.

Understanding how people spend time on daily activities is key to modeling travel behavior. However, accurately estimating the duration of these activities remains a significant challenge, especially when generating synthetic activity-travel data. This article introduces an activity-based approach that addresses this issue by applying statistical and machine learning models to improve the precision of activity duration estimates. The method utilizes real-world Origin-Destination (OD) datasets to generate additional synthetic data that can support transportation planning processes. Unlike conventional approaches that rely solely on OD matrices, this framework incorporates Cox and Cox-based hazard models to more precisely estimate activity durations, as well as arrival and departure times across trip segments. Statistical tests and comparative evaluations show that the proposed method produces more accurate synthetic data than existing open-source tools that do not employ hazard-based modeling. A case study using real-world data from Athens, Greece, demonstrates the effectiveness of the proposed approach.

5G systems are capable of facilitating remarkable technological advances and promoting sustainability in Internet-of-Things-centric fields, such as the Transport and Logistics (T&L) sector and applications in Connected-and-Automated Mobility (CAM). The current article provides an overview of the latest developments in the applications of the 5G mobile network standard in CAM and is specifically focused on analyzing existing and potential applications within European T&L corridors and subsequent markets. A focused marketplace analysis is conducteFd, highlighting the demands of commercialization clusters within European cross-border markets, as well as an analysis of how some of these demands can supplement the deployment of 5G products and services in the coming years. With the aim of providing a macro-level description of the landscape of 5G applications in CAM, the authors further discuss and classify the markets’ influencing factors according to the PESTEL framework, which is further enhanced to include the recent impacts of the COVID-19 pandemic and the war in Ukraine. The findings display that while 5G is a crucial underlying technology for the establishment of CAM, several factors, such as international security, political coordination at the EU level, and common technological standards across EU member states, will have a central role in the successful deployment of its applications.