Clustering Of Vehicles Research Articles

ALCA: agent learning–based clustering algorithm in vehicular ad hoc networks

Vehicular ad hoc network (VANET) is an emerging technology which can be used in various applications such as intelligent transport technology, safety applications, etc. But one of the major issues in VANETs is how to cluster the vehicles on the road for efficient operations such as routing, mobility management and generating safety alarms. Clustering of vehicles has been widely used for routing and data dissemination in VANETs. But due to the high mobility of the vehicles/nodes on the road, it is quite difficult to find the exact route in VANETs. Keeping in view of the above issue, in this paper, we propose a new agent learning---based clustering and routing in VANETs. Agents learn from the environment in which they are deployed, and accordingly, their action performed is rewarded or penalized with certain values. Each agent performs its task in collaboration with the other agents, i.e. agents communicate with each other in collaborative manner for information sharing. The deployed agents estimate the mobility of the vehicles, and based upon their learning, clustering of vehicles is performed. An Agent Learning---based Algorithm for Clustering is proposed. The performance of the proposed scheme is evaluated using extensive simulation with respect to the various metrics such as message transmission ratio, percentage of connectivity, node participation, cluster head duration, and connectivity preservation ratio. The results obtained show that the proposed scheme is effective in performing fast clustering and converges quickly to the final solution.

Multiagent driven dynamic clustering of vehicles in VANETs

Cluster formation in vehicular ad hoc networks (VANETs) is a challenging problem due to rapidly changing network topology and frequent network disconnections of vehicles. Dynamic clustering is a technique to form grouping of vehicles on the fly. We propose a multiagent driven dynamic clustering scheme for VANETS on a lane between two intersections by considering vehicle speed, direction, connectivity degree to other vehicles and mobility pattern. The scheme comprises of heavy-weight static and light-weight mobile agents. Initially, cluster members are identified based on vehicle's relative speed and direction for dynamic clustering. Cluster head is selected among the cluster members based on stability metric derived from connectivity degree, average speed and time to leave the road intersection. Cluster head predicts future association of cluster members based on mobility patterns. The announcement of cluster mobility pattern to all cluster members is made by cluster head. The cluster members with similar mobility pattern can reconnect with cluster head after passing an intersection of the lane. We have evaluated the performance and effectiveness of proposed scheme by comparing with an existing clustering scheme. It is observed that proposed scheme performs better than existing stable clustering scheme in terms of cluster formation time, cluster member selection time, cluster head selection time and control overheads.

Travel Demand and Charging Capacity for Electric Vehicles in Rural States

As the number of electric vehicles (EVs) increases, planners must consider not only how this fuel switch may affect the electrical power infrastructure but also mobility. The suitability and charging requirements of these vehicles may differ in rural areas, where the electrical grid may be less robust and the number of miles driven higher. Although other studies have examined issues of regional power requirements of EVs, none has done so in conjunction with the spatial considerations of travel demand. For the forecast of both the future spatial distribution of EVs and the ability of these vehicles to meet current daily travel demand, this work used three data sets: the National Household Travel Survey, geocoded Vermont vehicle fleet data, and a geocoded data set of every building in the state. The authors considered spatial patterns in daily travel and home-based tours to identify optimal EV-charging locations and any area types that are unsuited for widespread electric vehicle adoption. Hybrid vehicles were found to be more likely to be adjacent to other hybrids than were conventional vehicles. This apparent clustering of current hybrid vehicles, in both urban and rural areas, suggests that the distribution of future EVs may also cluster. The analysis estimated that between 69% and 84% of the state's vehicles could be replaced by EVs with a 40-mi range, but that estimate was dependent on the availability of workplace charging. Problematic areas for EV adoption may be suburban areas, where both residential density (and potential clustering of hybrids) and miles driven are high. The results suggest that EVs are viable for rural mobility demand but require special consideration for power supply and vehicle-charging infrastructure.

Green Market Geography: The Spatial Clustering of Hybrid Vehicles and LEED Registered Buildings

Abstract This paper uses zip code level data to investigate the spatial distribution of two major "green" products. Using data from California, we document where hybrid vehicles cluster within the state. Using data for the entire nation, we study where LEED registered buildings cluster. By creating a novel measure of community environmentalism based on revealed preference political data, we document that green products cluster in environmentalist communities.

Clustering and maximal flow in vehicular traffic through a sequence of traffic lights

We study the maximal current (maximum traffic capacity) of vehicular traffic through a sequence of traffic lights on a highway, where all signals turn on and off synchronously. The dynamical model of vehicular traffic controlled by signals is expressed in terms of a nonlinear map, where the excluded-volume effect is taken into account. The dynamical behaviors of vehicles are clarified by analyzing traffic patterns. The clustering of vehicles varies with the cycle time of signals. The maximum current is closely connected to vehicular clustering. Clustering of vehicles is controlled by varying both split and cycle time of signals. The dependence of the maximal current on both split and cycle time is derived.

Theoretical prediction of highway noise fluctuations.

A theoretical model is developed to predict fluctuations in traffic noise due to statistical variation in properties of highway traffic, including differences in vehicle type, variations in individual vehicle emissions, clustering of vehicles by vehicle type, and variations in vehicle spacings within and between platoons. Traffic flow is described by a Markov renewal process (MRP), and the noise heard by an off-highway observer as a filtered MRP. Simple formulas for the arithmetic mean, variance, and covariance function of noise intensity are derived for the "shot noise" case (exponentially distributed spacings). A more complicated model with car and truck clustering is investigated numerically, based on traffic data given by Jewell for the southbound curb lane of U.S. 40 in Berkeley, California. Even with only a modest degree of truck clustering and variability in vehicle spacings and noise emission, the variation in noise level is much greater than usually predicted. The coefficient of variation of noise intensity is greater than unity at distances from the roadway less than 700 ft. Peak noise levels may thus be greatly underestimated (2 to 3 dB per highway lane) if these sources of variability are ignored.