THOR: A Hybrid Recommender System for the Personalized Travel Experience

One of the travelers’ main challenges is that they have to spend a great effort to find and choose the most desired travel offer(s) among a vast list of non-categorized and non-personalized items. Recommendation systems provide an effective way to solve the problem of information overload. In this work, we design and implement “The Hybrid Offer Ranker” (THOR), a hybrid, personalized recommender system for the transportation domain. THOR assigns every traveler a unique contextual preference model built using solely their personal data, which makes the model sensitive to the user’s choices. This model is used to rank travel offers presented to each user according to their personal preferences. We reduce the recommendation problem to one of binary classification that predicts the probability with which the traveler will buy each available travel offer. Travel offers are ranked according to the computed probabilities, hence to the user’s personal preference model. Moreover, to tackle the cold start problem for new users, we apply clustering algorithms to identify groups of travelers with similar profiles and build a preference model for each group. To test the system’s performance, we generate a dataset according to some carefully designed rules. The results of the experiments show that the THOR tool is capable of learning the contextual preferences of each traveler and ranks offers starting from those that have the higher probability of being selected

A computation-enabled analytical construct for the assessment of alternative urban conditions towards sustainable transport system and to support sustainable travel activities: a space time constraint-based approach

The contribution of anthropological emissions to climate change has been a widely acknowledged topic of concern in recent decades towards environmental sustainability. Urban land-based transportation for the movement of people is one of the main contributors to emissions in the UK and other countries. Environmental sustainability is most directly achieved through the reduction of motorised transport activities, including passenger transport, but travel activities related to social and economic activities must be supported in cities. Chapter 1 problematises the goal to support social and economic activities whilst reducing environmental impact as a challenge in planning and city design. The challenge within city design processes is identifying the implications of alternative urban conditions towards urban sustainability before they are built. There is a lack of an adequate computational analytical framework that considers new urban developments and new transport services in the analysis of alternative urban conditions. Chapter 2 identifies known methodological approaches to analyse alternative urban conditions before the fact (ex-ante). Each supported by after the fact (ex-post) studies correspond to the theoretical perspective underlying each approach. A space-time approach has advantages over other probable outcome-based approaches in transportintegrated city design towards transport and travel-related sustainability. However, a space-time approach has been underdeveloped for before the fact (ex-ante) analysis. This study aims address this research gap by extend ingthe space-time approach as a computational analytical construct to facilitate computational scenario modelling and analysis for transport-integrated city design. Chapter 3 outlines the theoretical framework of a space-time approach for architecture in city design. A space-time approach includes the opportunities for activity participation related to the spatial and temporal organisation of building functions and programme, physical spatial transport infrastructure, travel modes, space and time-sensitive public transport services. Together, they facilitate and constrains the inhabitants' possibilities to conduct different combinations of everyday activities. Chapter 4 details the constructive research method employed in this study. This study results in the construction of a computational analytical construct in two parts. First, an operational model extends a space-time analytical approach by integrating new technologies and data sources, with the ability to manipulate the model reflecting alternative urban conditions. Second part consist of an analytical framework for travel mode comparison in alternative urban conditions to address questions in transport and travel-related sustainability within future built environments. Chapter 5 describes the construct implementation and the interpretation of results. The operational model is tested using two reconstructed cases from previous studies. First test with a similar construct set in Karlstad where the results are found to be similar. The second test with a study in Reading with a different construct found the difference 9 between the measures as expected. The operational model's utility is demonstrated through the analytical framework in two case study experiments set in Manchester. This study contextualises a space-time approach for architecture to analyse and think about the possibilities of travel activities facilitated and constrained by alternative urban conditions as part of a city design process. The analytical construct aligns with activitybased travel analysis in transport geography, social sequence analysis, and GPS activity data analysis in geography. Thus, the analytical construct enables a conceptual link between applied research and fundamental research in understanding cities' functioning and evolution. The analytical construct provides a foundation for further research related to system changes in the wider social context that substantially modifies everyday travel-related activity patterns and how city design of alternative urban conditions can respond to changing circumstances

Characterizing user behavior in journey planning

Journey planners support users in the organization of their trips, by presenting them results with multimodal solutions. While the benefits for the users are straightforward, other stakeholders (such as transport operators and planners) might benefit from understanding how users behave. In this paper, we analyze and characterize user behavior in journey planners, with the aim of getting insights from different perspectives (namely, trip search and both sorting and selection actions related to trip options). Our results show that, in order to characterize user behavior, multiple perspectives have to be taken into account, and that users speaking different languages behave differently