Abstract
This study pursues the operational improvement of urban transportation services. Non-foreseen events lead to the occurrence of delays, which are further propagated during the daily operations of bus services. This paper applies an optimization model to obtain robust schedules of bus lines. The model builds a new schedule which minimizes delays and anticipations from a set of observations. The decision variables are the slack time to be allocated at each segment of two subsequent stops. The solutions obtained are assessed with two robustness measures: price of robustness (i.e. the deviations from schedule) and the percentage of absorbed delays. The results obtained in a real-world case study (a bus line operating in Porto) are promising. © 2015, Springer International Publishing Switzerland.

Abstract
The synchronization of Public Transportation (PT) systems usually considers a simplified network to optimize the flows of passengers at the principal axes of the network. This work aims to identify the most relevant transfer-connections in a PT network. This goal is pursued with the development of a methodology to identify relevant transfer-connections from entry-only Automatic Fare Collection (AFC) data. The methodology has three main steps: the implementation of the Trip-Chaining-Method (TCM) to estimate the alighting stops of each AFC record, the identification of transfers, and finally, the selection of relevant transfer-connections. The adequacy of the methodology was demonstrated with its implementation to the case study of Porto. This methodology can also be applied to PT systems using entry-exit AFC data, and in that case, the TCM would not be required.

Abstract
This work proposes an architecture to treat georeferenced data from the OpenStreetMap to plan routes. The methodology considers the following steps: collecting data, incorporating data into a data manager, importing data into a data model, executing routing algorithms, and visualizing routes. Our proposal incorporates the following features characterizing each street segment: safety & security, comfort, accessibility, air quality, time, and distance. Routes can be calculated considering any specified weighting system of these features. The outcome of the application of this architecture allows to calculate and visualize routes from georeferenced data, which can support researchers in the study of multi-criteria routes. Furthermore, this framework enhances the OSM data model adding a multi-criteria dimension for route planning.

Abstract
Nowadays, online route planners for soft modes are provided by several platforms such as Google Maps, OpenStreetMap, Here, or Waze. Itineraries are usually built using Shortest Path Problem algorithms that minimize the travel time or distance. In this work, we aim to identify and quantify the main features that influence itineraries’ choice by soft modes users in urban areas, able to support multi-objective routing, using simulated scenarios. We propose a set of 21 indicators, grouped into five dimensions: Safety-Security, Comfort, Air Quality, Accessibility and Time-Distance. Another contribution of this work is the simulation of scenarios to study soft modes’ multi-objective routing within urban areas.

Abstract
The rising number of people living in city centers is connected to an increase of private vehicles, traffic, and associated harmful effects. Efforts have been made to promote a modal shift to the use of more sustainable transportation means, such as walking and cycling, but several factors hinged to safety, comfort, and accessibility, still hinder this goal. Current route planners often focus on two particular dimensions, time and distance, which might not be enough to support other personal perceptions. We need to consider new aspects and different dimensions, such as air quality, noise levels, or people density, fueled by the recent advances in the area of sensorization and the Internet of Things. We tested the idea of an innovative route planner with surveys and focus groups and concluded that there is an interest for more power to customize personal routes, which could be a key element boosting soft mode mobility.

Abstract
The continuous growth of the world population and its agglomeration in urban cities, demand an increasing need for mobility, which in turn contributes to the worsening of traffic congestion and pollution in cities. Therefore, it is necessary to promote active travel, such as walking and cycling. However, this is not an easy task, as pedestrians and cyclists are the most vulnerable link in the system, and low levels of safety, security and comfort can contribute to choosing private cars over active travel. Hence, it is essential to understand the determinants that affect the perceptions of pedestrians and cyclists, in order to support the definition of policies that promote the use of active modes of transport. Thus, this article fills an important gap in the literature by identifying and discussing the objective and subjective determinants that affect the perceptions of safety, security and comfort of pedestrians and cyclists, through a systematic review of the literature published in the last ten years. It followed the PRISMA statement guidelines and checklist, resulting in 68 relevant articles that were carefully analyzed. The results show that the perception of safety is negatively affected by fear of traffic-related injuries, fear of falling related to infra-structure and infrastructure maintenance, and negative behavior of drivers. Regarding security, crime was the major concern of pedestrians and cyclists, either with emphasis on the person or on personal property. With regard to comfort, high levels of air and noise pollution, lack of vege-tation, bad weather conditions, slopes and long commuting distances negatively affected the users' perception. The results also suggest that poor lighting affects all domains, providing a negative perception of safety, security and comfort. Similarly, the presence of people is seen as negatively influencing the perception of safety and comfort, while the absence of people nega-tively impacts the perception of security. Therefore, the findings achieved by this study are key to assist in the definition of transport policies and infrastructure creation in large smart cities. Additionally, new transport policies are proposed and discussed.