Nicklas Sindlev Andersen

Nicklas Sindlev Andersen, Marco Chiarandini, Kristian Debrabant

Given the counters of vehicles that traverse the roads of a traffic network, we reconstruct the travel demand that generated them expressed in terms of the number of origin-destination trips made by users. We model the problem as a bi-level optimization problem. At the inner-level, given a tentative demand, we solve a Dynamic Traffic Assignment (DTA) problem to decide the routing of the users between their origins and destinations. Finally, we adjust the number of trips and their origins and destinations at the outer-level to minimize the discrepancy between the counters generated at the inner-level and the given vehicle counts measured by sensors in the traffic network. We solve the DTA problem by employing a mesoscopic model implemented by the traffic simulator SUMO. Thus, the outer problem becomes an optimization problem that minimizes a black-box Objective Function (OF) determined by the results of the simulation, which is a costly computation. We study different approaches to the outer-level problem categorized as gradient-based and derivative-free approaches. Among the gradient-based approaches, we look at an assignment matrix-based approach and an assignment matrix-free approach that uses the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm. Among the derivative-free approaches, we investigate Machine Learning (ML) algorithms to learn a model of the simulator that can then be used as a surrogate OF in the optimization problem. We compare these approaches computationally on an artificial network. The gradient-based approaches perform the best in terms of solution quality and computational requirements. In contrast, the results obtained by the ML approach are currently less satisfactory but provide an interesting avenue for future research.

Nicklas Sindlev Andersen, Marco Chiarandini, Stefan Jänicke et al.

Wandering is a problematic behavior in people with dementia that can lead to dangerous situations. To alleviate this problem we design an approach for the real-time automatic detection of wandering leading to getting lost. The approach relies on GPS data to determine frequent locations between which movement occurs and a step that transforms GPS data into geohash sequences. Those can be used to find frequent and normal movement patterns in historical data to then be able to determine whether a new on-going sequence is anomalous. We conduct experiments on synthetic data to test the ability of the approach to find frequent locations and to compare it against an alternative, state-of-the-art approach. Our approach is able to identify frequent locations and to obtain good performance (up to AUC = 0.99 for certain parameter settings) outperforming the state-of-the-art approach.

Nicklas Sindlev Andersen, Marco Chiarandini, Jacopo Mauro

We describe the architecture of Sammen Om Demens (SOD), an application for portable devices aiming at helping persons with dementia when wandering and getting lost through the involvement of caregivers, family members, and ordinary citizens who volunteer. To enable the real-time detection of a person with dementia that has lost orientation, we transfer location data at high frequency from a frontend on the smartphone of a person with dementia to a backend system. The backend system must be able to cope with the high throughput data and carry out possibly heavy computations for the detection of anomalous behavior via artificial intelligence techniques. This sets certain performance and architectural requirements on the design of the backend. In the paper, we discuss our design and implementation choices for the backend of SOD that involve microservices and serverless services to achieve efficiency and scalability. We give evidence of the achieved goals by deploying the SOD backend on a public cloud and measuring the performance on simulated load tests.