Coffee plays a key role in the generation of rural employment in Colombia.More than 785,000 workers are directly employed in this activity,which represents the 26%of all jobs in the agricultural sector.Colombian coffe...Coffee plays a key role in the generation of rural employment in Colombia.More than 785,000 workers are directly employed in this activity,which represents the 26%of all jobs in the agricultural sector.Colombian coffee growers estimate the production of cherry coffee with the main aim of planning the required activities,and resources(number of workers,required infrastructures),anticipating negotiations,estimating,price,and foreseeing losses of coffee production in a specific territory.These important processes can be affected by several factors that are not easy to predict(e.g.,weather variability,diseases,or plagues.).In this paper,we propose a non-destructive time series model,based on weather and crop management information,that estimate coffee production allowing coffee growers to improve their management of agricultural activities such as flowering calendars,harvesting seasons,definition of irrigation methods,nutrition calendars,and programming the times of concentration of production to define the amount of personnel needed for harvesting.The combination of time series and machine learning algorithms based on regression trees(XGBOOST,TR and RF)provides very positive results for the test dataset collected in real conditions for more than a year.The best results were obtained by the XGBOOST model(MAE=0.03;RMSE=0.01),and a difference of approximately 0.57%absolute to the main harvest of 2018.展开更多
The most salient problems of transit vehicle service in Latin American intermediate cities include:the high number of passengers involved in traffic accidents;traffic congestion caused by transit vehicles,and pollutio...The most salient problems of transit vehicle service in Latin American intermediate cities include:the high number of passengers involved in traffic accidents;traffic congestion caused by transit vehicles,and pollution generated by these vehicles,which increases in high congestion scenarios.To improve upon this situation,a research was conducted on the transit vehicle tracking service,which is a basic service for implementing mobility solutions for the aforementioned problems,the most relevant characteristics of this service for the context of Latin American intermediate cities were identified,and an implementation was proposed.This paper presents the four stages of the study:(a)a review of the state-of-the-art of services or systems related to vehicle tracking,including wireless communications technologies,implemented sustainability approaches,usage of special algorithms for efficiency improvement,and the intelligent transportation system(ITS)architecture used as a basis;(b)the process of identifying relevant characteristics of the service for a given context;(c)proposal of an ITS architecture for this service in an intermediate city,its requirements and the suggested technologies;and(d)development of experiments for validating usage of the key suggested technologies.The review allowed to identify the main service characteristics,with regard to vehicle positioning technologies,the recommended wireless communication technology(long range,LoRa),energy consumption considerations,and use of artificial intelligence(AI)to calculate waiting time of users at bus stops.Finally,an ITS architecture for the city of Popayan(Colombian city)considering the aforementioned characteristics is proposed,and the experiments related to the use of these technologies are described in detail.展开更多
Traffic accidents are one of the most serious problems worldwide,being one of the leading causes of death and economic loss in the world.Low-and middle-income countries,mainly their medium-sized cities,are among the m...Traffic accidents are one of the most serious problems worldwide,being one of the leading causes of death and economic loss in the world.Low-and middle-income countries,mainly their medium-sized cities,are among the most affected by this problem.93%of traffic accidents occur in low and middle-income countries,even though these countries have approximately 60%of the world’s vehicles.This occurs mainly because in these types of countries,especially in medium-sized cities(target context),there are no ideal conditions for driving,such as adequate road infrastructure,good condition of vehicles,and rigorous safety policies.Advanced data analysis techniques including machine learning(ML)have increasingly been used to solve this problem.Naturalistic driving(ND)can be applied as a data collection method that provides information on traffic accidents.ND commonly uses a vehicle’s kinematic data to detect high-risk driving behaviors that could cause an accident.The objectives of this document are to present a review of different alternatives that help in data collection and creation of intelligent solutions related to detection of possible traffic accidents,principally using ND;and to propose an intelligent collision risk detection system(ICRDS)for identification of areas with a high probability of TA in the target context.Through the review,it was possible to analyze and evaluate the devices,variables and algorithms that help characterize a risk event in driving,considering the target context.The development of a prototype of an ICRDS for a medium-sized city in a developing country is considered viable,considering the identified components,with the aim of identifying risk events in driving,and areas of high probability of accidents in the city.展开更多
基金We thank to the Telematics Engineering Group(GIT)of the University of Cauca and Tecnicaféfor the technical support.In addition,we are grateful to COLCIENCIAS for PhD scholarship granted to PhD.David Camilo Corrales.This work has been also supported by Innovacción-Cauca(SGR-Colombia)under project“Alternativas Innovadoras de Agricultura Inteligente para sistemas productivos agrícolas del departamento del Cauca soportado en entornos de IoT ID 4633-Convocatoria 04C-2018 Banco de Proyectos Conjuntos UEES-Sostenibilidad”.
文摘Coffee plays a key role in the generation of rural employment in Colombia.More than 785,000 workers are directly employed in this activity,which represents the 26%of all jobs in the agricultural sector.Colombian coffee growers estimate the production of cherry coffee with the main aim of planning the required activities,and resources(number of workers,required infrastructures),anticipating negotiations,estimating,price,and foreseeing losses of coffee production in a specific territory.These important processes can be affected by several factors that are not easy to predict(e.g.,weather variability,diseases,or plagues.).In this paper,we propose a non-destructive time series model,based on weather and crop management information,that estimate coffee production allowing coffee growers to improve their management of agricultural activities such as flowering calendars,harvesting seasons,definition of irrigation methods,nutrition calendars,and programming the times of concentration of production to define the amount of personnel needed for harvesting.The combination of time series and machine learning algorithms based on regression trees(XGBOOST,TR and RF)provides very positive results for the test dataset collected in real conditions for more than a year.The best results were obtained by the XGBOOST model(MAE=0.03;RMSE=0.01),and a difference of approximately 0.57%absolute to the main harvest of 2018.
基金Authors wish to thank Universidad del Cauca(Telematics Department)and Universidad Icesi(ICT Department)for supporting this research.
文摘The most salient problems of transit vehicle service in Latin American intermediate cities include:the high number of passengers involved in traffic accidents;traffic congestion caused by transit vehicles,and pollution generated by these vehicles,which increases in high congestion scenarios.To improve upon this situation,a research was conducted on the transit vehicle tracking service,which is a basic service for implementing mobility solutions for the aforementioned problems,the most relevant characteristics of this service for the context of Latin American intermediate cities were identified,and an implementation was proposed.This paper presents the four stages of the study:(a)a review of the state-of-the-art of services or systems related to vehicle tracking,including wireless communications technologies,implemented sustainability approaches,usage of special algorithms for efficiency improvement,and the intelligent transportation system(ITS)architecture used as a basis;(b)the process of identifying relevant characteristics of the service for a given context;(c)proposal of an ITS architecture for this service in an intermediate city,its requirements and the suggested technologies;and(d)development of experiments for validating usage of the key suggested technologies.The review allowed to identify the main service characteristics,with regard to vehicle positioning technologies,the recommended wireless communication technology(long range,LoRa),energy consumption considerations,and use of artificial intelligence(AI)to calculate waiting time of users at bus stops.Finally,an ITS architecture for the city of Popayan(Colombian city)considering the aforementioned characteristics is proposed,and the experiments related to the use of these technologies are described in detail.
基金Universidad del Cauca(Colombia)Universidad Icesi(Colombia)for supporting this research。
文摘Traffic accidents are one of the most serious problems worldwide,being one of the leading causes of death and economic loss in the world.Low-and middle-income countries,mainly their medium-sized cities,are among the most affected by this problem.93%of traffic accidents occur in low and middle-income countries,even though these countries have approximately 60%of the world’s vehicles.This occurs mainly because in these types of countries,especially in medium-sized cities(target context),there are no ideal conditions for driving,such as adequate road infrastructure,good condition of vehicles,and rigorous safety policies.Advanced data analysis techniques including machine learning(ML)have increasingly been used to solve this problem.Naturalistic driving(ND)can be applied as a data collection method that provides information on traffic accidents.ND commonly uses a vehicle’s kinematic data to detect high-risk driving behaviors that could cause an accident.The objectives of this document are to present a review of different alternatives that help in data collection and creation of intelligent solutions related to detection of possible traffic accidents,principally using ND;and to propose an intelligent collision risk detection system(ICRDS)for identification of areas with a high probability of TA in the target context.Through the review,it was possible to analyze and evaluate the devices,variables and algorithms that help characterize a risk event in driving,considering the target context.The development of a prototype of an ICRDS for a medium-sized city in a developing country is considered viable,considering the identified components,with the aim of identifying risk events in driving,and areas of high probability of accidents in the city.
