Abstract:Population concentration in cities brings new risks as an increase in pollution, which causes urban health problems. In order to address this problem, traffic reduction measures are being implemented, as pedestrianization areas and the definition of Low Emissions Zones (LEZ). When the effectiveness of these types of measures is in doubt, smart city tools provide data that can be used to scientifically asses their impact. This article analyzes the situation of Madrid Central (Spain), a LEZ subject to controvers… Show more
“…Figure 6Spatio-temporal monitoring of air quality with sensor networks: locations of 22 out of the 24 sensors (i.e. excluding sensors #27 (Barajas Pueblo) and #59 (Juan Carlos I)) used to monitor air quality in the Madrid metropolitan area (original figure from [50]).…”
We present a new type of acquisition function for online decision-making in multi-armed and contextual bandit problems with extreme payoffs. Specifically, we model the payoff function as a Gaussian process and formulate a novel type of upper confidence bound acquisition function that guides exploration towards the bandits that are deemed most relevant according to the variability of the observed rewards. This is achieved by computing a tractable likelihood ratio that quantifies the importance of the output relative to the inputs and essentially acts as an
attention mechanism
that promotes exploration of extreme rewards. Our formulation is supported by asymptotic zero-regret guarantees, and its performance is demonstrated across several synthetic benchmarks, as well as two realistic examples involving noisy sensor network data. Finally, we provide a JAX library for efficient bandit optimization using Gaussian processes.
“…Figure 6Spatio-temporal monitoring of air quality with sensor networks: locations of 22 out of the 24 sensors (i.e. excluding sensors #27 (Barajas Pueblo) and #59 (Juan Carlos I)) used to monitor air quality in the Madrid metropolitan area (original figure from [50]).…”
We present a new type of acquisition function for online decision-making in multi-armed and contextual bandit problems with extreme payoffs. Specifically, we model the payoff function as a Gaussian process and formulate a novel type of upper confidence bound acquisition function that guides exploration towards the bandits that are deemed most relevant according to the variability of the observed rewards. This is achieved by computing a tractable likelihood ratio that quantifies the importance of the output relative to the inputs and essentially acts as an
attention mechanism
that promotes exploration of extreme rewards. Our formulation is supported by asymptotic zero-regret guarantees, and its performance is demonstrated across several synthetic benchmarks, as well as two realistic examples involving noisy sensor network data. Finally, we provide a JAX library for efficient bandit optimization using Gaussian processes.
“…Following previous research about the pollution in Madrid, the daily NO 2 concentration is classified into eight classes according to the season (winter, spring, summer, and autumn) and the type of day (i.e., working days, from Mondays to Fridays, and weekends, Saturdays and Sundays) [9,10]. This classification follows the idea that warmer seasons have lower NO 2 concentration due to meteorological reasons and weekends have better air quality because the road traffic is lower than in working days.…”
Section: Training Dataset Descriptionmentioning
confidence: 99%
“…However, it is not easy to understand the different phenomena that may have implications for the production or dissipation of pollutants. For this reason, there have been different approaches to evaluate the real impact of mobility policies in the air quality [8,10,18,25].…”
This is a relevant problem because the design of most cities prioritizes the use of motorized vehicles, which has degraded air quality in recent years, having a negative effect on urban health. Modeling, predicting, and forecasting ambient air pollution is an important way to deal with this issue because it would be helpful for decision-makers and urban city planners to understand the phenomena and to take solutions. In general, data-driven methods for modeling, predicting, and forecasting outdoor pollution requires an important amount of data, which may limit their accuracy. In order to deal with such a lack of data, we propose to train models able to generate synthetic nitrogen dioxide daily time series according to a given classification that will allow an unlimited generation of realistic data. The main experimental results indicate that the proposed approach is able to generate accurate and diverse pollution daily time series, while requiring reduced computational time.
“…Road transportation is one of the main sources of air pollutants in our cities [12]. Reducing the road vehicles' emissions would have an important impact on tackling global warming [17] and improving inhabitants' health [11].…”
Section: Introductionmentioning
confidence: 99%
“…Smart cities provide a series of tools for advanced knowledge and decisionmaking support [2,3,8,12]. In this line, this work focuses on providing a solution to efficiently allocate EV charging stations to ease EV uptake by the citizens.…”
This article presents the problem of locating electric vehicle (EV) charging stations in a city by defining the Electric Vehicle Charging Stations Locations (EV-CSL) problem. The idea is to minimize the distance the citizens have to travel to charge their vehicles. EV-CSL takes into account the maximum number of charging stations to install and the electric power requirements. Two metaheuristics are applied to address the relying optimization problem: a genetic algorithm (GA) and a variable neighborhood search (VNS). The experimental analysis over a realistic scenario of Malaga city, Spain, shows that the metaheuristics are able to find competitive solutions which dramatically improve the actual installation of the stations in Malaga. GA provided statistically the best results.
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