Using a Machine Learning Approach to Implement and Evaluate Product Line Features

Bike-sharing systems are a means of smart transportation in urban environments with the benefit of a positive impact on urban mobility. In this paper we are interested in studying and modeling the behavior of features that permit the end user to access, with her/his web browser, the status of the Bike-Sharing system. In particular, we address features able to make a prediction on the system state. We propose to use a machine learning approach to analyze usage patterns and learn computational models of such features from logs of system usage. On the one hand, machine learning methodologies provide a powerful and general means to implement a wide choice of predictive features. On the other hand, trained machine learning models are provided with a measure of predictive performance that can be used as a metric to assess the cost-performance trade-off of the feature. This provides a principled way to assess the runtime behavior of different components before putting them into operation.Comment: In Proceedings WWV 2015, arXiv:1508.0338

D3P : Data-driven demand prediction for fast expanding electric vehicle sharing systems

The future of urban mobility is expected to be shared and electric. It is not only a more sustainable paradigm that can reduce emissions, but can also bring societal benefits by offering a more affordable on-demand mobility option to the general public. Many car sharing service providers as well as automobile manufacturers are entering the competition by expanding both their EV fleets and renting/returning station networks, aiming to seize a share of the market and to bring car sharing to the zero emissions level. During their fast expansion, one determinant for success is the ability of predicting the demand of stations as the entire system is growing continuously. There are several challenges in this demand prediction problem: First, unlike most of the existing work which predicts demand only for static systems or at few stages of expansion, in the real world we often need to predict the demand as or even before stations are being deployed or closed, to provide information and decision support. Second, for the new stations to be deployed, there is no historical data available to help the prediction of their demand. Finally, the impact of deploying/closing stations on the other stations in the system can be complex. To address these challenges, we formulate the demand prediction problem in the context of fast expanding electric vehicle sharing systems, and propose a data-driven demand prediction approach which aims to model the expansion dynamics directly from the data. We use a local temporal encoding process to handle the historical data for each existing station, and a dynamic spatial encoding process to take correlations between stations into account with Graph Convolutional Neural Networks (GCN). The encoded features are fed to a multi-scale predictor, which forecasts both the long-term expected demand of the stations and their instant demand in the near future. We evaluate the proposed approach with real-world data collected from a major EV sharing platform for one year. Experimental results demonstrate that our approach significantly outperforms the state of the art, showing up to three-fold performance gain in predicting demand for the expanding EV sharing systems

Quantification and description of braking during mountain biking using a novel brake power meter : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Sport & Exercise at Massey University, Palmerston North, New Zealand

Olympic format cross country mountain biking is both physically and technically demanding. The demands of this cycling genre are in contrast to road cycling because of the demanding off-road terrain. With its many obstacles and different surfaces, riders must make their way up and over steep hills a number of times throughout a lap. It’s very easy to be able to measure the performance of the riders on ascending sections of the track thanks to on-the-bike personal power meter that measure the propulsive work rates in the pedals. However, there is currently no commercially available method to assess the way the rider handles the bike on descending sections. This thesis first highlighted the differences in physiological demand of descending on off-road versus on-road (Chapter 4). An interesting finding in Chapter 4 also showed that riders might be able to save energy by adopting a coasting strategy down hills. This caused the researchers to question the bicycle handling attributes that might allow this, which led to the development and validation of a device designed to measure how the rider uses the brakes while riding/racing (Chapter 5). From there, we completed an investigation akin to the early mountain biking descriptive studies (Chapter 6), but instead of focusing on data related to respiratory and metabolic load, the brake power meter was employed. The finding that braking patterns were related to mountain biking performance was not surprising, but being the first team to quantify this was very exciting. Since most of the braking was occurring on the descents in that study, we examined the differences in braking between training groups on an isolated turn (Chapter 7). The finding that inexperienced riders use their brakes differently—and that this results in reduced performance—left no doubt to the importance of braking. From there, we revisited the method used to calculate rear brake power, since current methods led to inaccurate measurement during skidding (Chapter 8). This thesis culminated with the exploration of an algorithm that could quickly and easily describe mountain bike descending performance with one single metric (Chapter 9); the hope is that the normalized brake work algorithm should increase the utility of the brake power meter for training purposes and post-competition performance analysis. Overall, this thesis highlights the need, importance and utility of a bicycle brake power meter to assess mountain bike performance

User producer interaction in context: a classification

Science, Technology and Innovation Studies show that intensified user producer interaction (UPI) increases chances for successful innovations, especially in the case of emerging technology. It is not always clear, however, what type of interaction is necessary in a particular context. This paper proposes a conceptualization of contexts in terms of three dimensions – the phase of technology development, the flexibility of the technology, and the heterogeneity of user populations – resulting in a classification scheme with eight different contextual situations. The paper identifies and classifies types of interaction, like demand articulation, interactive learning, learning by using and domestication. It appears that each contextual situation demands a different set of UPI types. To illustrate the potential value of the classification scheme, four examples of innovations with varying technological and user characteristics are explored: the refrigerator, clinical anaesthesia, video cassette recording, and the bicycle. For each example the relevant UPI types are discussed and it is shown how these types highlight certain activities and interactions during key events of innovation processes. Finally, some directions for further research are suggested alongside a number of comments on the utility of the classification

CASP-DM: Context Aware Standard Process for Data Mining

We propose an extension of the Cross Industry Standard Process for Data Mining (CRISPDM) which addresses specific challenges of machine learning and data mining for context and model reuse handling. This new general context-aware process model is mapped with CRISP-DM reference model proposing some new or enhanced outputs

Let’s shuffle: Facility Optimal Location for Stations within Bicycle Sharing Systems in the City of Buenos Aires after the pandemic

People’s habits have changed after the pandemic and cycling around the city of Buenos Aires is no exception. This thesis leverages literature on Capacitated Facility Location Problems (CFLP) to build an optimal bike-sharing network to minimize the total system’s cost. The objective is to decide which stations should be left open to meet projected demand in the worst-possible cases, ensuring that users do not have to walk more than a predefined distance to the facility that is closest to them. Results suggest that there is an excess of stations in the downtown area and idle capacity that could be relocated in peripheral areas, reflected by a positive load factor increase of 2x after the optimization is done. The solution shows that up to 70% of total costs could be saved after using our optimization model, by closing down facilities while meeting demand. While total cost is estimated as the budget that needs to be invested to ramp up the system from scratch, it is a useful metric that shows us how the network could be optimized taking away stations from overcrowded areas without losing any of the current demand. All of these bike-sharing facilities could be relocated to areas that have a low-density of bikes, improving access to the cycling system in the city of Buenos Aires