Online Predictive Optimization Framework for Stochastic Demand-Responsive Transit Services

This study develops an online predictive optimization framework for dynamically operating a transit service in an area of crowd movements. The proposed framework integrates demand prediction and supply optimization to periodically redesign the service routes based on recently observed demand. To predict demand for the service, we use Quantile Regression to estimate the marginal distribution of movement counts between each pair of serviced locations. The framework then combines these marginals into a joint demand distribution by constructing a Gaussian copula, which captures the structure of correlation between the marginals. For supply optimization, we devise a linear programming model, which simultaneously determines the route structure and the service frequency according to the predicted demand. Importantly, our framework both preserves the uncertainty structure of future demand and leverages this for robust route optimization, while keeping both components decoupled. We evaluate our framework using a real-world case study of autonomous mobility in a university campus in Denmark. The results show that our framework often obtains the ground truth optimal solution, and can outperform conventional methods for route optimization, which do not leverage full predictive distributions.Comment: 34 pages, 12 figures, 5 table

Probabilistic modelling and inference of human behaviour from mobile phone time series

With an estimated 4.1 billion subscribers around the world, the mobile phone offers a unique opportunity to sense and understand human behaviour from location, co-presence and communication data. While the benefit of modelling this unprecedented amount of data is widely recognised, a number of challenges impede the development of accurate behaviour models. In this thesis, we identify and address two modelling problems and show that their consideration improves the accuracy of behaviour inference. We first examine the modelling of long-range dependencies in human behaviour. Human behaviour models only take into account short-range dependencies in mobile phone time series. Using information theory, we quantify long-range dependencies in mobile phone time series for the first time, demonstrate that they exhibit periodic oscillations and introduce novel tools to analyse them. We further show that considering what the user did 24 hours earlier improves accuracy when predicting user behaviour five hours or longer in advance. The second problem that we address is the modelling of temporal variations in human behaviour. The time spent by a user on an activity varies from one day to the next. In order to recognise behaviour patterns despite temporal variations, we establish a methodological connection between human behaviour modelling and biological sequence alignment. This connection allows us to compare, cluster and model behaviour sequences and introduce novel features for behaviour recognition which improve its accuracy. The experiments presented in this thesis have been conducted on the largest publicly available mobile phone dataset labelled in an unsupervised fashion and are entirely repeatable. Furthermore, our techniques only require cellular data which can easily be recorded by today's mobile phones and could benefit a wide range of applications including life logging, health monitoring, customer profiling and large-scale surveillance

Associative Processes in Statistical Learning: Paradoxical Predictions of the Past

The ability to process sequences of input and extract regularity across the distribution of input is fundamental for making predictions from the observed past to the future. Prediction is rooted in the extraction of both frequency- and conditional statistics from the distribution of inputs. For example, an animal hunting for food may consistently return to a particular area to hunt if relative to all other areas visited, that area has the highest frequency of prey. In contrast, humans asked to predict the next word in a sentence must make a prediction based upon higher-order regularities rather than simple frequency statistics (the most frequent word in the English language is the). The Serial Reaction Time (SRT) task, a model for studying sequential behavior, is used to quantify sensitivity to sequential constraints present in structured environments (Nissen & Bullemer, 1987). The SRT task requires Ss to make a unique response to each individually presented element from a sequence of elements. The statistics of SRT sequences, such as the relative frequency of elements and simple pairwise associations between elements, can be controlled to create dependencies that can only be predicted by learning higher-order associations. Sensitivity to the sequential constraints present in the structured input is demonstrated through differences in reaction time to elements that are, and are not, predictable based upon the statistics of the input environment. Sensitivity to statistical regularity in the environment is also a critical dimension of various episodic learning methodologies. Graded associations have been demonstrated among elements extending in both forward and backward directions in episodic memory tasks, and are suggested to reflect a gradient of the underlying structural relationships among the study elements. Graded associations are beneficial to the extent that they increase the probability of recalling sequence elements.However, unlike free and serial recall tasks, backward associations, and remote associations in general, are anti-predictive in the SRT task. The formation of associations beyond the immediately predictive element in prediction tasks could be suggestive of a ubiquitous underlying associative mechanism, which universally gives rise to graded contiguity effects, regardless of the specifc application (Howard, Jing, Rao, Provyn, & Datey, 2009). The following experiment employed a probabilistic SRT task to quantify sensitivity to immediately backward, backward-remote, and forward-remote associations. Ss were presented sequences of elements probabilistically sampled from an underlying ring-structure, with the dependent measure Ss\u27 reaction time to elements that either followed, or deviated from, the structure. Results from the SRT task indicated that Ss demonstrated a robust backward association, as well as evidence for forward-graded associations. Moreover, in an explicit test of sequence knowledge, while Ss did not generate the probabilistic statistics from the structured learning environment, Ss did generate a statistically signifcant amount of backward-transitions, relative to other remote-backward transitions. The graded associations that were formed beyond the immediately predictive element in the prediction task provide evidence that a similar mechanism that mediates episodic learning may also mediate statistical learning. Backward and graded associations may be explained by a ubiquitous underlying associative mechanism, which universally gives rise to graded contiguity effects, regardless of the specific application

A metamodel to integrate business processes time perspective in BPMN 2.0

Context: Business Process Management (BPM) is becoming a strategic advantage for organizations tostreamline their operations. Most business experts are betting for OMG Business Process Model and No- tation (BPMN) as de-facto standard (ISO/IEC 19510:2013) and selected technology to model processes. Thetemporal dimension underlies in any kind of process however, technicians need to shape this perspectivethat must also coexist with task control flow aspects, as well as resource and case perspectives. BPMNpoorly gathers temporary rules. This is why there are contributions that extend the standard to coversuch dimension. BPMN is mainly an imperative language. There are research contributions showing timeconstraints in BPMN, such as (i) BPMN patterns to express each rule with a combination of artifacts, thusthese approaches increase the use of imperative BPMN style, and (ii) new decorators to capture timerules semantics giving clearer and simpler comprehensible specifications. Nevertheless, these extensionscannot yet be found in the present standard.Objective: To define a time rule taxonomy easily found in most business processes and look for an ap- proach that applies each rule with current BPMN 2.0 standard in a declarative way.Method: A model-driven approach is used to propose a BPMN metamodel extension to address time- perspective.Results: We look at a declarative approach where new time specifications may overlie the main controlflow of a BPMN process. This proposal is totally supported with current BPMN standard, giving a BPMNmetamodel extension with OCL constraints. We also use AQUA-WS as a software project case study whichis planned and managed with MS Project. We illustrate business process extraction from project plans.Conclusion: This paper suggests to handle business temporal rules with current BPMN standard, alongwith other business perspectives like resources and cases. This approach can be applied to reverse engi- neering processes from legacy databases.Ministerio de Economía y Competitividad TIN2013-46928-C3-3-RMinisterio de Economía y Competitividad TIN2015- 71938-RED