Stable Hotspot Analysis for Intra-Urban Heat Islands

The Urban Heat Island (UHI) effect describes the difference in temperature between cities and their surrounding areas. However, temperature differences within city limits, so-called Intra-Urban Heat Islands (IUHI), affect human health as well as the energy demands in local areas. In order to anticipate and mitigate the resulting impacts of heat through urban planning, a method to reliably detect these local areas is needed. Existing methods from the geo-statistical field can identify these areas. But these statistics, depending on their parametrization, can be unstable in their detection of hotspots, in particular temperature hotspots. In this paper, we propose a modification of the well-known Getis-Ord (G$^{*}$) statistic, called the Focal G$^{*}$ statistic. This modification replaces the computation of the global mean and standard deviation with their focal counterparts. We define the stability of our approach by introducing a stability metric called Stability of Hotspot (SoH), which requires that hotspots have to be in similar areas regardless of the chosen weight matrix. The results are evaluated on real-world temperature data for the city of Karlsruhe

Road Condition Estimation Based on Heterogeneous Extended Floating Car Data

Road condition estimation based on Extended Floating Car Data (XFCD) from smart devices allows for determining given quality indicators like the international roughness index (IRI). Such approaches currently face the challenge to utilize measurements from heterogeneous sources. This paper investigates how a statistical learning based self-calibration overcomes individual sensor characteristics. We investigate how well the approach handles variations in the sensing frequency. Since the self-calibration approach requires the training of individual models for each participant, it is examined how a reduction of the amount of data sent to the backend system for training purposes affects the model performance. We show that reducing the amount of data by approximately 50 % does not reduce the models’ performance. Likewise, we observe that the approach can handle sensing frequencies up to 25 Hz without a performance reduction compared to the baseline scenario with 50 Hz

Weighted aggregation in the domain of crowd-based road condition monitoring

This paper focuses on crowd-based road condition monitoring using smart devices, such as smartphones and evaluates different strategies for aggregating multiple measurements (arithmetic mean and weighted means using R2 and RMSE) for predicting the longitudinal road roughness. The results confirm that aggregating predictions from single drives leads to a higher model performance. This has been expected and confirms the intuition. The overall R2 could be increased from 0.69 to 0.75 on average and the NRMSE could be decreased from 9% to 8% on average. However, contrary to the intuition, the results show that weighted aggregations of single predictions should be avoided, which is consistent with previous findings in other domains, such as financial forecasting

Road Condition Measurement and Assessment: A Crowd Based Sensing Approach

The widespread adoption of smart devices and vehicle sensors has the potential for an unprecedented real time assessment of road conditions. The international roughness index (IRI) is an important road profile quality indicator well suited for a crowd based sensing approach. One of the challenges, however, is the heterogeneous nature of sensor measurements from multiple cars that need to be integrated. In this paper, we propose a self-calibration approach that utilizes multiple statistical models trained individually for each car, which in turn get integrated into an overall view of the road segment’s IRI. We evaluate our approach on a dataset collected from seven drives with a total distance of 32 km, with a smartphone equipped car. The dataset contains GPS, accelerometer and gyroscope measurements. Our results show that this approach can reach a mean R² of 0.68 for single car predictions and a R² of 0.75 for combined predictions

A Prediction-Driven Adaptation Approach for Self-Adaptive Sensor Networks

International audienceEngineering self-adaptive software in unpredictable environments such as pervasive systems, where network's ability, remaining battery power and environmental conditions may vary over the lifetime of the system is a very challenging task. Many current software engineering approaches leverage run-time architectural models to ease the design of the autonomic control loop of these self-adaptive systems. While these approaches perform well in reacting to various evolutions of the runtime environment, implementations based on reactive paradigms have a limited ability to anticipate problems, leading to transient unavailability of the system, useless costly adaptations, or resources waste. In this paper, we follow a proactive self-adaptation approach that aims at overcoming the limitation of reactive approaches. Based on predictive analysis of internal and external context information, our approach regulates new architecture recon figurations and deploys them using models at runtime. We have evaluated our approach on a case study where we combined hourly temperature readings provided by National Climatic Data Center (NCDC) with re reports from Moderate Resolution Imaging Spectroradiometer (MODIS) and simulated the behavior of multiple systems. The results confirm that our proactive approach outperforms a typical reactive system in scenarios with seasonal behavior