CRAWDAD queensu/crowd_temperature

Outdoor temperature data collected by taxis in Rome, Italy.This dataset is to be used in conjunction with the roma/taxi dataset and provides the outdoor temperature of the areas in Rome where the taxis were located (289 taxicabs over 4 days).date/time of measurement start: 2012-08-15date/time of measurement end: 2014-02-04collection environment: We simulate taxicabs as if they were equipped with temperature sensors attached to their vehicles. The city of Rome is divided into 9 areas and temperate readings were gathered from a weather service, allowing us to simulate each taxicab sending its sensed temperature to a central server every 6 hours for each area.parent data name: roma/taxinote: The original dataset is the CRAWDAD roma/taxi dataset that comprises the position of each taxicab using GPS. This dataset adds the outdoor temperature of the areas that taxicabs visit during their services.Traceset temperatureSimulated outdoor temperature data collected by taxis in Rome, Italy.files: crowd_temperature.csvmethodology: We generate a temperature value for every active taxicab by applying Gaussian distribution. To fill out the parameters of Gaussian function, we need to assign the mean mu; and standard deviation sigma; for every run. Therefore, we assign a ground truth temperature mu; for every period in every grid on every day. We use data from The Weather Network (http://www.theweathernetwork.com/) to assign the right ground truth to the right period and grid. For every taxicab, we assign a fixed error range sigma; that remains the same in all of its contributions. To do so, we randomly classify participant taxicabs into three classes. First class, called "honest", consists of taxicabs that usually sense accurate temperature within a 10% error range from the ground truth. The population of honest class is 145 taxicabs (50% of all participant taxicabs). Second class, called "dishonest", consists of taxicabs that usually sense inaccurate temperature within a 30% error range from the ground truth. The population of the dishonest class is 72 taxicabs (25%). Third class, called "misleading", consists of the rest of the participant taxicabs that is 72 (25%) that usually sense either accurate or inaccurate temperature. The data generator function makes a random decision of generating accurate or inaccurate temperature for each taxicab among the misleading class. The latter class plays a major role in the results of applying the data on a system, such as participants reputation system, since the accuracy of their contributions is not even. As a result, each taxicab has a sensed temperature contribution based on its fixed error range and the ground truth of the day, period and grid of its location.

本数据集汇集了意大利罗马市出租车采集的户外气温数据。该数据集旨在与roma/taxi数据集相结合使用，提供出租车所在地区的户外气温信息（共289辆出租车在4天内采集）。测量起始日期/时间：2012-08-15，测量结束日期/时间：2014-02-04。收集环境：我们模拟出租车，仿佛其车辆上装备了温度传感器。罗马市被划分为9个区域，气温读数由气象服务提供，从而模拟每辆出租车每6小时向中央服务器发送其感知的温度。父数据名称：roma/taxi。备注：原始数据集为CRAWDAD roma/taxi数据集，该数据集包含使用GPS定位的每辆出租车的位置。本数据集补充了出租车在其服务期间访问的区域的户外气温。温度迹集：模拟收集自罗马意大利出租车的户外气温数据。文件：crowd_temperature.csv。方法论：我们通过应用高斯分布为每辆活跃出租车生成一个温度值。为了填充高斯函数的参数，我们需要为每轮运行分配均值μ和标准差σ。因此，我们为每天每个网格的每个时间段分配一个地面真实温度μ。我们使用The Weather Network（http://www.theweathernetwork.com/）的数据来为正确的时段和网格分配正确的真实温度。对于每辆出租车，我们分配一个固定的误差范围σ，该范围在其所有贡献中保持不变。为此，我们将参与者出租车随机分类为三类。第一类，称为“诚实”，包括那些通常在地面真实温度的10%误差范围内感知准确温度的出租车。诚实类出租车数量为145辆（所有参与者出租车的50%）。第二类，称为“不诚实”，包括那些通常在地面真实温度的30%误差范围内感知不准确温度的出租车。不诚实类出租车数量为72辆（25%）。第三类，称为“误导”，包括剩余的参与者出租车，共计72辆（25%），这些出租车通常感知到的温度既准确又可能不准确。数据生成函数对误导类中的每辆出租车是否生成准确或不准确温度做出随机决策。后者在将数据应用于系统，如参与者信誉系统时，扮演着至关重要的角色，因为这些贡献的准确性并不均匀。因此，每辆出租车都有基于其固定误差范围和当天、时段及位置网格的地面真实温度的感知温度贡献。