Pemodelan Dan Simulasi VANETs Menggunakan Federated Mobility Model; Sebuah Artikel Tinjauan

The emergence of Vehicular Ad-hoc Networks (VANETs) as part of the Intelligent Transportation Systems (ITS) technology development was expected to become an advanced method to solve the transportation system problem. The implementation of VANETs is expected to provide a new solution for traffic management strategy. Its main targets is to continue prioritizing traffic safety and to prevent the accidents on the roads. One of the VANETs problems before being implemented in the real world is the degree of freedom of the vehicle's mobility that limited by the road topologies. Various modellings and simulations have been performed to produce the most realistic mobility model. However, those models had become new paradigms due to various factors that limited them. The presence of the federated mobility model as an approach for traffic mobility modeling is considered to be able to provide more realistic and accurate VANETs simulation. Therefore, this article presents some brief reviews and contrast a number of the simulation and mobility models that have been used widely as compared to the federated mobility models that have developed until the present. The article's objective is to facilitate a better understanding of the traffic mobility modeling for the VANETs simulation that started from the interaction process until the integration between simulators. The understanding of the traffic mobility models will complement the knowledge that enable to perform the simulation of the VANETs implementation approaching the real conditions

Eras of electric vehicles: electric mobility on the Verge. Focus Attention Scale

Daily or casual passenger vehicles in cities have negative burden on our finite world. Transport sector has been one of the main contributors to air pollution and energy depletion. Providing alternative means of transport is a promising strategy perceived by motor manufacturers and researchers. The paper presents the battery electric vehicles-BEVs bibliography that starts with the early eras of invention up till 2015 outlook. It gives a broad overview of BEV market and its technology in a chronological classification while sheds light on the stakeholders’ focus attentions in each stage, the so called, Focus-Attention-Scale-FAS. The attention given in each era is projected and parsed in a scale graph, which varies between micro, meso, and macro-scale. BEV-system is on the verge of experiencing massive growth; however, the system entails a variety of substantial challenges. Observations show the main issues of BEVsystem that require more attention followed by the authors’ recommendations towards an emerging market

Probabilistic Human Mobility Model in Indoor Environment

Understanding human mobility is important for the development of intelligent mobile service robots as it can provide prior knowledge and predictions of human distribution for robot-assisted activities. In this paper, we propose a probabilistic method to model human motion behaviors which is determined by both internal and external factors in an indoor environment. While the internal factors are represented by the individual preferences, aims and interests, the external factors are indicated by the stimulation of the environment. We model the randomness of human macro-level movement, e.g., the probability of visiting a specific place and staying time, under the Bayesian framework, considering the influence of both internal and external variables. We use two case studies in a shopping mall and in a college student dorm building to show the effectiveness of our proposed probabilistic human mobility model. Real surveillance camera data are used to validate the proposed model together with survey data in the case study of student dorm.Comment: 8 pages, 9 figures, International Joint Conference on Neural Networks (IJCNN) 201

An integrated method for short-term prediction of road traffic conditions for intelligent transportation systems applications

The paper deals with the short-term prediction of road traffic conditions within Intelligent Transportation Systems applications. First, the problem of traffic modeling and the potential of different traffic monitoring technologies are discussed. Then, an integrated method for short-term traffic prediction is presented, which integrates an Artificial Neural Network predictor that forecasts future states in standard conditions, an anomaly detection module that exploits floating car data to individuate possible occurrences of anomalous traffic conditions, and a macroscopic traffic model that predicts speeds and queue progressions in case of anomalies. Results of offline applications on a primary Italian motorway are presented