David Jonietz

A Heuristic for Multi-modal Route Planning

Current popular multi-modal routing systems often do not move beyond combining regularly scheduled public transportation with walking, cycling or car driving. Seldom included are other travel options such as carpooling, carsharing, or bikesharing, as well as the possibility to compute personalized results tailored to the specific needs and preferences of the individual user. Partially, this is due to the fact that the inclusion of various modes of transportation and user requirements quickly leads to complex, semantically enriched graph structures, which to a certain degree impede downstream procedures such as dynamic graph updates or route queries. In this paper, we aim to reduce the computational effort and specification complexity of personalized multi-modal routing by use of a preceding heuristic, which, based on information stored in a user profile, derives a set of feasible candidate travel options, which can then be evaluated by a traditional routing algorithm. We demonstrate the applicability of the proposed system with two practical examples.

Continuous Trajectory Pattern Mining for Mobility Behaviour Change Detection

With the emergence of ubiquitous movement tracking technologies, developing systems which continuously monitor or even influence the mobility behaviour of individuals in order to increase its sustainability is now possible. Currently, however, most approaches do not move beyond merely describing the status quo of the observed mobility behaviour, and require an expert to assess possible behaviour changes of individual persons. Especially today, automated methods for this assessment are needed, which is why we propose a framework for detecting behavioural anomalies of individual users by continuously mining their movement trajectory data streams. For this, a workflow is presented which integrates data preprocessing, completeness assessment, feature extraction and pattern mining, and anomaly detection. In order to demonstrate its functionality and practical value, we apply our system to a real-world, large-scale trajectory dataset collected from 139 users over 3 months.

Demo Abstract: Extracting eco-feedback information from automatic activity tracking to promote energy-efficient individual mobility behavior

Nowadays, most people own a smartphone which is well suited to constantly record the movement of its user. One use of the gathered mobility data is to provide users with feedback and suggestions for personal behavior change. Such eco-feedback on mobility patterns may stimulate users to adopt more energy-efficient mobility choices. In this paper, we present a methodology to extract mobility patterns from users’ trajectories, compute alternative transport options, and aggregate and present them in an intuitive way. The resulting eco-feedback helps people understand their mobility choices and explore sustainable alternatives.

Identifying and Interpreting Clusters of Persons with Similar Mobility Behaviour Change Processes

With the emergence of new mobility options and various initiatives to increase the sustainability of our travel behaviour, it is desirable to gain a deeper understanding of our behavioural reactions to such stimuli. Although it is now possible to use GPS-tracking to record people’s movement behaviour over a longer period, there is still a lack of computational methods which allow to detect and evaluate such behaviour change processes in the resulting datasets. In this study, we propose a data mining method for describing individual persons’ mobility behaviour change processes based on their movement trajectories and clustering participants based on the similarity of these behavioural adaptations. We further propose to use a decision tree classifier to semantically explain the derived clusters in a human-interpretable form. We apply our method to a real, longitudinal movement dataset.

Assessing the Influence of Spatio-Temporal Context for Next Place Prediction using Different Machine Learning Approaches

For next place prediction, machine learning methods which incorporate contextual data are frequently used. However, previous studies often do not allow deriving generalizable methodological recommendations, since they use different datasets, methods for discretizing space, scales of prediction, prediction algorithms, and context data, and therefore lack comparability. Additionally, the cold start problem for new users is an issue. In this study, we predict next places based on one trajectory dataset but with systematically varying prediction algorithms, methods for space discretization, scales of prediction (based on a novel hierarchical approach), and incorporated context data. This allows to evaluate the relative influence of these factors on the overall prediction accuracy. Moreover, in order to tackle the cold start problem prevalent in recommender and prediction systems, we test the effect of training the predictor on all users instead of each individual one. We find that the prediction accuracy shows a varying dependency on the method of space discretization and the incorporated contextual factors at different spatial scales. Moreover, our user-independent approach reaches a prediction accuracy of around 75%, and is therefore an alternative to existing user-specific models. This research provides valuable insights into the individual and combinatory effects of model parameters and algorithms on the next place prediction accuracy. The results presented in this paper can be used to determine the influence of various contextual factors and to help researchers building more accurate prediction models. It is also a starting point for future work creating a comprehensive framework to guide the building of prediction models.