2 research outputs found
Reducing stress on habitual journeys
2015 IEEE 5th International Conference on Consumer ElectronicsâBerlin (ICCE-Berlin), September 6-9, 2015Stress is the cause of a large number of traffic accidents. The driver increases driving mistakes when he or she is in this mental state. Furthermore, the fuel consumption gets worse. In this paper, we propose an algorithm to estimate the optimum speed from the point of view of the stress level for each road section. When the driver completes a road section, the solution provides him or her with feedback. This feedback consists of recommendations such as: "You have driven too fast". The aim is that the driver adjusts speed when he or she repeats the trip. Optimization of the speed reduces stress and improves the driving from the point of view of energy saving. The optimal average speed is estimated using Particle Swarm Optimization (PSO) and MultiLayer Perceptron (MLP). The solution was deployed on Android mobile devices. The results show that the drivers drive smoother and reduce stress when they use the proposal.The research leading to these results has received funding from the âHERMES-SMART DRIVERâ project TIN2013-46801-C4-2-R within the Spanish "Plan Nacional de I+D+I" under the Spanish Ministerio de EconomĂa y Competitividad
and from the Spanish Ministerio de EconomĂa y Competitividad funded projects (co-financed by the Fondo Europeo de Desarrollo Regional (FEDER)) IRENE (PT-2012-1036-370000), COMINN (IPT-2012-0883-430000) and REMEDISS
(IPT-2012-0882-430000) within the INNPACTO progra
Benchmarking real-time vehicle data streaming models for a smart city
The information systems of smart cities offer project developers, institutions, industry and experts the possibility to handle massive incoming data from diverse information sources in order to produce new information services for citizens. Much of this information has to be processed as it arrives because a real-time response is often needed. Stream processing architectures solve this kind of problems, but sometimes it is not easy to benchmark the load capacity or the efficiency of a proposed architecture. This work presents a real case project in which an infrastructure was needed for gathering information from drivers in a big city, analyzing that information and sending real-time recommendations to improve driving efficiency and safety on roads. The challenge was to support the real-time recommendation service in a city with thousands of simultaneous drivers at the lowest possible cost. In addition, in order to estimate the ability of an infrastructure to handle load, a simulator that emulates the data produced by a given amount of simultaneous drivers was also developed. Experiments with the simulator show how recent stream processing platforms like Apache Kafka could replace custom-made streaming servers in a smart city to achieve a higher scalability and faster responses, together with cost reduction.This research is partially supported by the Spanish Ministry of Economy and Competitiveness and European Regional Development Fund (ERDF) through the âHERMES â SmartDriverâ project (TIN2013-46801-C4-2-R), the âHERMES â Smart Citizenâ project (TIN2013-46801-C4-1-R), and the âHERMES âSpace&Timeâ project (TIN2013-46801-C4-3-R)