A key challenge for rapidly growing cities of today is to provide effective public transport services to satisfy the increasing demands for urban mobility. Toward this goal, the Internet of Things (IoT) has great potential to overcome existing deficiencies of public transport systems given its ability to embed smart technology into real-life urban contexts. In this paper, we show how this paradigm can be applied to the public transport domain and present the Urban Bus Navigator (UBN), an IoT enabled navigation system for urban bus riders. UBN provides two novel information services for bus users: 1) micro-navigation and 2) crowd-aware route recommendation. Micro-navigation refers to fine-grained contextual guidance of passengers along a bus journey by recognizing boarded bus vehicles and tracking the passenger's journey progress. Crowd-aware route recommendation collects and predicts crowd levels on bus journeys to suggest better and less crowded routes to bus riders. We present the technical system behind the UBN and report on results from an in-the-wild study in Madrid, Spain, that indicates removed barriers for public transport usage and a positive impact on how people feel about bus journeys.

@en

This paper presents an analysis of regularity in public transport usage based on a large-scale bus transportation data of Lisbon, Portugal. By exploring the combined information from the bus boarding history of riders and bus arrivals at each bus stop, an analysis of individual bus usage was performed. Daily and weekly patterns were extracted, from which it was observed that a rider takes, on average, 2 trips, visits 1.93 distinct stops, and uses 1.55 distinct bus lines daily. Inter-trip time analysis revealed a daily cycle, and a study of the interaction between riders and bus infrastructure explored how usage was concentrated on particular bus lines and stops.

@en

Electric vehicles are an increasingly attractive option for households to reduce carbon emissions, especially when they are powered by renewable energy. In this paper we report the results of an 18-month field trial investigating the desirability and feasibility of powering electric vehicles (EVs) with domestic solar electricity. Based on extensive collection of data from 7 households including over 75,000 miles of daily EV use, home electricity consumption and generation, and in-depth interviews with householders we develop a detailed understanding of what drives EV decisions in households, quantify to what extent our participating households currently power their EVs with solar electricity, and investigate how feasible the vision of "self-sustaining electric mobility" is. We use this understanding to draw implications for future research into supporting emerging practices of EV drivers.@en

This paper presents a study of the predictability of bus usage based on massive bus ride data collected from Lisbon, Portugal. An understanding of public bus usage behavior is important for future development of personalized transport information systems that are equipped with proactive capabilities such as predictive travel recommender systems. In this study, we show that there exists a regularity in the bus usage and that daily bus rides can be predicted with a high degree of accuracy. In addition, we show that there are spatial and temporal factors that influence bus usage predictability. These influential factors include bus usage frequency, number of different bus lines and stops used, and time of rides.@en

Direct and easy access to public transport information is an important factor for improving the satisfaction and experience of transport users. In the future, public transport information systems could be turned into personalized recommender systems which can help riders save time, make more effective decisions and avoid frustrating situations. In this paper, we present a predictive study of the mobility patterns of public transport users to lay the foundation for transport information systems with proactive capabilities. By making use of travel card data from a large population of bus riders, we describe algorithms that can anticipate bus stops accessed by individual riders to generate knowledge about future transport access patterns. To this end, we investigate and compare different prediction algorithms that can incorporate various influential factors on mobility in public transport networks, e.g., travel distance or travel hot spots. In our evaluation, we demonstrate that by combining personal and population-wide mobility patterns we can improve prediction accuracy, even with little knowledge of past behavior of transport users.

@en

Public bus services are widely deployed in cities around the world because they provide cost-effective and economic public transportation. However, from a passenger point of view urban bus systems can be complex and difficult to navigate, especially for disadvantaged users, i.e. tourists, novice users, older people, and people with impaired cognitive or physical abilities. We present Urban Bus Navigator (UBN), a reality-aware urban navigation system for bus passengers with the ability to recognize and track the physical public transport infrastructure such as buses. Unlike traditional location-aware mobile transport applications, UBN acts as a true navigation assistant for public transport users. Insights from a six-month long trial in Madrid indicate that UBN removes barriers for public transport usage and has a positive impact on how people feel about public transport journeys.

@en

In order to increase the attractiveness of public transport systems, information technology has great potential to add value to their usage. In particular, the availability of digital sources of behavioural transport data opens up new directions for the development of transport information services which are focused on the passengers' engagement in public transport. This will enable novel perceptions of transport services, encompassing aspects of personal transport behaviour - information related to the transport routines of individual travellers, social transport behaviour - information which creates an understanding of the collective transport usage of social groups - And dimensions of quality-of-transport information which include novel measures of travel experiences such as overcrowding. In this paper, we introduce and discuss a design space of how behavioural transport data can shape more user-centric transport information services in order to inform future research activities in this area.

@en

There is huge potential in increasing the value of public transportation by creating novel travel information systems which are centred on the individual transport user. Especially, in dense urban cities where it is hard to oversee complex transport networks that are subject to frequent changes, maintenance and construction works, travellers want to be proactively notified about disruptions and traffic incidents relevant to their future behaviour. In this paper, we show how to mine characteristic patterns of the transport routines of urban bus riders for the design of novel travel information system that have the ability to understand forthcoming travel needs of individual users. We leverage on travel histories collected from automated fare collection system (AFC) to extract features of personal transport usage and study their predictive power to forecast whether people access public transport services on a future day or not.

@en