Toward V2I communication technology-based solution for reducing road traffic congestion in smart cities

Due to the limited capacity of road networks and sporadic on-route events, road traffic congestions are posing serious problems in most big cities worldwide and resulting in considerable number of casualties and financial losses. In order to deal efficiently with these problems and alleviate their impact on individuals, environment, and economic activities, advanced traffic monitoring and control tools (e.g., SCATS and SCOOT) are being widely used in hundreds of major cities in the world. However, due to increasing road traffic and dynamic spatio-temporal events, additional proactive mechanisms remain needed to prevent traffic congestions. Within this context, we argue that the emergent V2X communication technologies, and especially V2I (Vehicle to Infrastructure), would be of great help. To this end, we investigate in this paper the opportunities that could be offered by V2I technology in improving commuters' journey duration and mitigating the above irritating and frequent problems. We then propose an approach where road-side facilities (e.g. traffic light controllers at road intersections) communicate traffic light cycle information to approaching vehicles. Based on this information, the vehicles collaboratively determine their optimal speeds and other appropriate actions to undertake in order to cross road intersections with minimum delays while ultimately avoiding stoppings. The obtained evaluation results show that our approach achieves a significant gain in terms of the commuters' average travel time reduction

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Big Data y áreas de oportunidad para la proyección del Sistema Inteligente de Transporte en Bogotá, Colombia

Today, the large cities of Colombia – especially Bogotá, due to the growth of its population (9.3 million with the arrival of immigrants) – demand the projection of intelligent public and private transport systems, as an achievement of the mobility policy of the Bogota Humana administration. Hence, this question arises: What are the challenges and areas of opportunity of adapting Big Data to project an Intelligent Transportation System for all citizens in Bogotá? Based on this question, our aim is to determine the contributions that Big Data offers as a collection center for the projection of an intelligent system for the city. Our research was proposed with a qualitative approach and a descriptive study. The review of some studies developed using Big Data techniques and content data analysis of their organized structure by the District Mobility Secretariat in Bogotá was included. The results allow guiding the contributions of Big Data after analyzing the structure of indicators offered by the data set. From these, we found gaps and voids that are concerning for the Intelligent Transportation System that is expected in the future for Bogotá.Hoy en día, en las grandes ciudades de Colombia, en especial en Bogotá, y debido al crecimiento de su población (9,3 millones con la llegada de inmigrantes), se exige una demanda de aporte a la proyección de sistemas inteligentes de transporte públicos y privados como un logro de la política de movilidad de la administración de la Bogotá Humana. De ahí surge el interrogante: ¿cuál es el desafío y las áreas de oportunidad de adaptar un Big Data en la proyección de un Sistema Inteligente de Transporte para todos los ciudadanos en Bogotá? A partir de esta pregunta, se propone determinar los aportes que el Big Data ofrece como centro de acopio en la proyección de un sistema inteligente para la ciudad. La indagación se plantea desde un enfoque cualitativo y un estudio descriptivo. Se incluye la revisión de algunos estudios realizados mediante las técnicas del Big Data y del análisis de datos de contenido de la estructura organizada de estos por la Secretaría Distrital de Movilidad en Bogotá. Los resultados permiten orientar los aportes del Big Data después de analizar la estructura de indicadores que ofrecen estos el conjunto de datos. A partir de estos, se encuentran brechas y vacíos preocupantes para el Sistema Inteligente de Transporte que se espera en el futuro para Bogotá

Toward V2I communication technology-based solution for reducing road traffic congestion in smart cities

Due to the limited capacity of road networks and sporadic on-route events, road traffic congestions are posing serious problems in most big cities worldwide and resulting in considerable number of casualties and financial losses. In order to deal efficiently with these problems and alleviate their impact on individuals, environment, and economic activities, advanced traffic monitoring and control tools (e.g., SCATS and SCOOT) are being widely used in hundreds of major cities in the world. However, due to increasing road traffic and dynamic spatio-temporal events, additional proactive mechanisms remain needed to prevent traffic congestions. Within this context, we argue that the emergent V2X communication technologies, and especially V2I (Vehicle to Infrastructure), would be of great help. To this end, we investigate in this paper the opportunities that could be offered by V2I technology in improving commuters' journey duration and mitigating the above irritating and frequent problems. We then propose an approach where road-side facilities (e.g. traffic light controllers at road intersections) communicate traffic light cycle information to approaching vehicles. Based on this information, the vehicles collaboratively determine their optimal speeds and other appropriate actions to undertake in order to cross road intersections with minimum delays while ultimately avoiding stoppings. The obtained evaluation results show that our approach achieves a significant gain in terms of the commuters' average travel time reduction

An Overview of Vehicle-to-Infrastructure Communication Technology

As a part of solutions to reduce problems associated with transportation in cities, technologies can have noticeable impacts. Due to efficiency and low costs, innovative transportation technologies can reshape and improve human’s transportation. This research aims to explore Vehicle-to-Infrastructure communication technology (V2I) and its benefits to safety, mobility, and environment. In addition, it explores the planning aspect of deploying V2I technology and its opportunities, challenges and concerns, and implication to communities. The research will also look at several case studies including pilot projects that have been taking place in the United States and studies that have been done to have a better understanding of the current situation of V2I technology and its future needs. Advisor: Rodrigo Cantarer

Examining Preference For Autonomous Vehicle (Av) Among Qatari Residents

Because of growing body of researches that predict the autonomous vehicles to be the future mode of transport. It is important to investigate the preference of Autonomous vehicles among Qatari citizens for fast developing country such as Qatar. Stated Preference survey is distributed to 315 individuals living across Qatar. Based on the participants characteristics, the drivers are exposed to different scenarios and asked to choose one of the presented four modes of transport (Normal car, Private own autonomous vehicles, Shared autonomous vehicles, Public transport).The characteristics of each respondent have an impact on the preferences and attitude toward autonomous vehicles AVs and this was quantified through multinomial logit model. Currently, the key observations were as following: ? There is substantial hesitation toward adoption of AVs in Qatar, with 52% of choice decision that supports normal cars. ? Comfortable scale is an important factor in Qatar because good comfortable scale will increase the utility to use such mode of transport. ? Public transport is considered the least preferred mode of transport in Qatar especially if the individual owns a private car. In other word, people in Qatar give less utility value for SAV and public transport. Educating the young generation about the benefits of using AVs and public transport will enhance their background regarding the advanced modes of transport and encourage them to use conventional car alternatives in the future