Abstract
This paper describes an application of genetic algorithms to the bus driver scheduling problem. The application of genetic algorithms extends the traditional approach of Set Covering/Set Partitioning formulations, allowing the simultaneous consideration of several complex criteria. The genetic algorithm is integrated in a DSS but can be used as a very interactive tool or a stand-alone application. It incorporates the user's knowledge in a quite natural way and produces solutions that are almost directly implemented by the transport companies in their operational planning processes. Computational results with airline and bus crew scheduling problems from real world companies are presented and discussed.

Abstract
The public transport system is responsible for the displacement of a large part of the population, particularly in developing countries. This fact makes it relevant to evaluate the performance of public transport to provide an efficient and effective service. The purpose of this study is to conduct a performance evaluation of the public transport operation in the Metropolitan Region of Fortaleza (MRF), in the State of Ceara, Brazil. The analysis is based on DEA and the Malmquist index, based on three inputs (total operating time, fleet age, and the mileage traveled) and two outputs (fare revenue and number of passengers). Data were obtained through automated fare collection systems (AFCs) that were implemented in the MRF. Although there were no major fluctuations in performance during the analyzed period, the results indicate that the system's performance declined in certain years. In addition, the analysis enables a better understanding of route performance, considering the operating company or the area of operation, which helps to diagnose and comprehend the operation more effectively. By analyzing the operational performance over time, the proposed approach provides an additional contribution by offering a comprehensive overview to the involved stakeholders, fostering decision-making processes based on evidence.

Abstract
Electronic fare payment systems have gained much popularity around the world. These systems adopt a convenient and almost instantaneous payment process for public transport while also gathering data regarding onboard transactions in public transport. Much information about public transport passengers can be extracted, such as travel patterns, activities performed, and travel behavior. Despite the continuous growth of studies regarding these systems, there is still a lack of research to understand occasional passengers’ movement, such as tourists. This work presents the state of the art in these areas and presents a proposal to explore AFC data to understand the mobility profiles of tourists. This manuscript represents an advance in the literature and opens doors to the definition of policies to promote less visited places and mobility services adapted to tourists’ needs, resulting in a positive impact on the city’s economy and the overall enjoyment of the city for tourists. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
This article presents a framework to assist in the design of technology-based interactive services for active mobility, which allows the data collected from the sensors to be made available to citizens. The proposed framework was developed based on data collected in focus group sessions held with potential stakeholders and on related models and frameworks. It consists of 8 steps, namely: strategy, scope, structure, skeleton, aesthetics and execution. It will enable the presentation of relevant information that will help users of active modes of transport in decision making in choosing a safe and comfortable route, assist professionals involved in the elaboration of interactive projects and promote more collaborative urban planning. © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)

Abstract
Sustainable mobility has become one of the most pressing issues in modern society. The need to raise awareness of climate change, combined with the overcrowding of metropolitan and urban areas, has produced a situation that requires an urgent solution. Some earlier approaches dealt primarily with transport-related issues, while some conceptual models attempted to increase the appeal of public transport by linking the services provided by public transport operators to a variety of city services. A practical and empirical answer, on the other hand, has not yet been given. This research addrebes these issues by taking a holistic approach and presenting a personalized recommendation system based on users' everyday activities as well as their mobility profiles. The crossing of both sources of information allows for a more user-centric experience, ensuring that the offers presented are adapted to the tastes of customers. The potential of such a system is proven using data from Porto, Portugal. Two types of data sources were used to obtain more accurate results: data from the automated fare collection system of the Porto Metropolitan Area, Portugal, and data from city services taken from Google Places. The fundamental idea behind tackling this problem is to encourage people to use public transport by providing them with incentives such as discounts, promotions and service offers to encourage them to use cleaner and more efficient modes of transport. © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)

Abstract
This study estimates alighting stops and transfers from entry-only Automatic Fare Collection (AFC) data. The methodology adopted includes two main steps: an implementation of the Trip Chaining Method (TCM) to estimate the alighting stops from AFC records and the subsequent application of criteria for the identification of transfers. For each pair of consecutive AFC records on the same smart card, a transfer is identified considering a threshold for the walking distance, a threshold for the time required to perform an activity, and the validation of different boarding routes. This methodology was applied to the case study of Porto, Portugal, considering all trips performed by a set of 19999 smart cards over one year. The results of this methodology allied with visualization techniques allowed to study Origin-Destination (OD) patterns by type of day, seasonally, and by user frequency, each analyzed at the stop level and at the geographic area level.