Abstract
Mobile devices are increasingly pervasive and are transforming information distribution paradigms. A rapidly growing segment of urban public transport passengers carry mobile computing devices, permanently and on the move. In a context of thinning financial resources, getting customers involved in the actual delivery of a public transport service as real-time information consumers and providers, may be a powerful method to enhance travel experience while reducing operational costs for the service operator. Each and every customer travelling on a public transport network has unique knowledge about the service operation as it unfolds. This paper proposes a framework that aims to unify public transport passengers' collective intelligence through crowdsourcing, using their mobile computing devices and dedicated web services. It strives to intensify win-win relationships between public transport passengers and operators. The structured exchange of information is sustained by a validation mechanism for data reliability, and an incentive mechanism to encourage passenger participation. Passengers benefit from rich real-time data tailored to their profiles, to ease their journeys and improve travel experience, in exchange for their own participation providing and validating information. Operators gain access to rich customer generated data, which in an aggregated format may provide a real-time assessment of customer experience and of local performance across the entire network operation. Operators may be required to reward travellers who become prolific co-creators of the public transport service, but higher customer experience levels, lesser needs for investment in controlling mechanisms, and continuous free monitoring of customer opinions can jointly lead to financial returns. (C) 2013 The Authors. Published by Elsevier Ltd.

Abstract

Abstract
The profit resulting from customer relationship is essential to ensure companies viability, so an improvement in customer retention is crucial for competitiveness. As such, companies have recognized the importance of customer centered strategies and consequently customer relationship management (CRM) is often at the core of their strategic plans. In this context, a priori knowledge about the risk of a given customer to mitigate or even end the relationship with the provider is valuable information that allows companies to take preventive measures to avoid defection. This paper proposes a model to predict partial defection, using two classification techniques: Logistic regression and Multivariate Adaptive Regression Splines (MARS). The main objective is to compare the performance of MARS with Logistic regression in modeling customer attrition. This paper considers the general form of Logistic regression and Logistic regression combined with a wrapper feature selection approach, such as stepwise approach. The empirical results showed that MARS performs better than Logistic regression when variable selection procedures are not used. However, MARS loses its superiority when Logistic regression is conducted with stepwise feature selection.

Abstract
A methodology for estimating the destination of passenger journeys from automated fare collection (AFC) system data is described. It proposes new spatial validation features to increase the accuracy of destination inference results and to verify key assumptions present in previous origin-destination estimation literature. The methodology applies to entry-only system configurations combined with distance-based fare structures, and it aims to enhance raw AFC system data with the destination of individual journeys. This paper describes an algorithm developed to implement the methodology and the results from its application to bus service data from Porto. The data relate to an AFC system integrated with an automatic vehicle location system that records a transaction for each passenger boarding a bus, containing attributes regarding the route, the vehicle, and the travel card used, along with the time and the location where the journey began. Some of these are recorded for the purpose of allowing onboard ticket inspection but additionally enable innovative spatial validation features introduced by the methodology. The results led to the conclusion that the methodology is effective for estimating journey destinations at the disaggregate level and identifies false positives reliably.

Abstract
This paper summarizes existing systems and research on information transport services, and proposes a hypothetic scenario for future travellers using public transport. Increased distributed intelligence in pervasive mobile smart devices and in sensor networks in public transport vehicles is enabling a new approach for enhancing the experience of public transport customers. Such environment could be modelled through a distributed multi-agent service system. This paper presents advanced information services already available on such environments, in particular the MOVE-ME smartphone application, and indicates a possible service environment where people's feedback may benefit all transport service stakeholders. Mobile computing and crowdsourcing are key enablers for enhancing user experience in the transport services, and also for enhancing overall public transport services. Better experience leads to increased usage of shared mobility modes, and therefore to more sustainable cities in the future. Concerns about data security, and anonymity of travellers will need to be adequately addressed in the future scenarios presented.

Abstract
The public transport system integrates a complex ecosystem, composed not only by transport operators and travellers but also by other services such as schools, firms, restaurants, museums, banks, and public establishments. Therefore, by adopting a holistic point of view, we propose a new service approach linking city services and public transport. This approach consists in partnerships that may include discounts, combined packages, reduced prices, deals and marketing campaigns, targeted to each specific audience. In order to develop these partnerships it is important to analyse the services located around the stations and the public transport usage. We use the city of Porto, Portugal, as an illustrative example and we rely on two data sources: Automated Fare Collection system data and business data points. The analysis of both datasets allowed us to determine the level of concentration of city services located around public transport stations and to identify the types of services that tend to agglomerate near the stations. We were also able to analyse the correlation between the number of travel card validations and the number of services located around the stations. Finally we present a case of a service exposure to different demographic segments.