Modelling vehicular interactions for heterogeneous traffic flow using cellular automata with position preference

This paper proposes and validates a modified cellular automata model for determining interaction rate （i.e. number of car-following/overtaking instances） using traffic flow data measured in the field. The proposed model considers lateral position preference by each vehicle type and introduces a position preference parameter fl in the model which facilitates gradual drifting towards preferred position on road, even if the gap in front is sufficient. Additionally, the model also improves upon the conven- tional model by calculating safe front and back gap dynamically based on speed and deceleration properties of leader and follower vehicles. Sensitivity analysis was carried out to determine the effect of β on vehicular interac- tions and the model was calibrated and validated using interaction rates observed in the field. Paired tests were conducted to determine the determining interaction rates validity of the model in Results of the simulations show that there is a parabolic relationship between area occupancy and interaction rate of different vehicle types. The model performed satisfactorily as the simulated interaction rate between different vehicle types were found to be statistically similar to those observed in field. Also, as expected, the interaction rate between light motor vehicles （LMVs） and heavy motor vehicles （HMVs） were found to be higher than that between LMVs and three wheelers because LMVs and HMVs share the same lane. This could not be done using conventional CA models as lateral movement rules were dictated by only speeds and gaps. So, in conventional models, the vehicles would end up in positions which are not realistic. The position preference parameter introduced in this model motivates vehicles to stay in their preferred positions. This study demonstrates the use of interaction rate as a measure to validate micro- scopic traffic flow models.

Design and Realization of an NFC-Driven Smart Home System to Support Intruder Detection and Social Network Integration

The Internet of thing（Io T） emerges as a possible solution to realize a smart life in the modern age. In this article, we design and realize a novel near field communication（NFC）-driven smart home system for Io T, which integrates the wireless sensor network（WSN）, social networks, and the cloud computing. NFC technology provides a way for users to exchange information between them and the system by simply contacting. So, we propose to use NFC as the system drive in the architecture, such that users can intuitively interact with the system and deliver their intentions. Then, the corresponding service over the system will control or adjust the “things” at home to fit users＇ needs. Furthermore, the proposed system provides a platform for developers to easily and rapidly implement their smart home related services. In the system, WSN sensing and control, NFC communications and identification, user profile management and preference analysis, and social network integration are all provided as platform services. We will show how the system works for home automation, intruder detection, and social network sharing.

Preferable Orientations of Interacting C_(60) Molecules inside Single Wall Boron Nitride Nanotubes

This work focuses on the preferable orientation analysis of the hybrid system where the C60 molecules are encap- sulated inside the boron nitride nanotubes by using the two-molecule model. The low-energy state can be acquired in the contour map, which provides the visual information of the systematical van der Waals interaction potential for the C60 molecules adopting different orientations. Our results show that the C60 molecules exhibit the pre- ferred pentagon and hexagon orientations with the tube＇s diameter smaller and larger than 13.55A, respectively. The preferred two-bond orientation obtained in the single-molecule model is absent in this study, indicating that the intermolecular interaction of adjacent C60 molecules plays an important role in the orientational behaviors of this peapod structure.

Synergetic Optimization of Missile Shapes for Aerodynamic and Radar Cross-Section Performance Based on Multi-objective Evolutionary Algorithm

A multiple-objective evolutionary algorithm (MOEA) with a new Decision Making (DM) scheme for MOD of conceptual missile shapes was presented, which is contrived to determine suitable tradeoffs from Pareto optimal set using interactive preference articulation. There are two objective functions, to maximize ratio of lift to drag and to minimize radar cross-section (RCS) value. 3D computational electromagnetic solver was used to evaluate RCS, electromagnetic performance. 3D Navier-Stokes flow solver was adopted to evaluate aerodynamic performance. A flight mechanics solver was used to analyze the stability of the missile. Based on the MOEA, a synergetic optimization of missile shapes for aerodynamic and radar cross-section performance is completed. The results show that the proposed approach can be used in more complex optimization case of flight vehicles.

DIP-MOEA:a double-grid interactive preference based multi-ob jective evolutionary algorithm for formalizing preferences of decision makers

The final solution set given by almost all existing preference-based multi-objective evolutionary algorithms(MOEAs)lies a certain distance away from the decision makers’preference information region.Therefore,we propose a multi-ob jective optimization algorithm,referred to as the double-grid interactive preference based MOEA(DIPMOEA),which explicitly takes the preferences of decision makers(DMs)into account.First,according to the optimization ob jective of the practical multi-ob jective optimization problems and the preferences of DMs,the membership functions are mapped to generate a decision preference grid and a preference error grid.Then,we put forward two dominant modes of population,preference degree dominance and preference error dominance,and use this advantageous scheme to update the population in these two grids.Finally,the populations in these two grids are combined with the DMs’preference interaction information,and the preference multi-ob jective optimization interaction is performed.To verify the performance of DIP-MOEA,we test it on two kinds of problems,i.e.,the basic DTLZ series functions and the multi-ob jective knapsack problems,and compare it with several different popular preference-based MOEAs.Experimental results show that DIP-MOEA expresses the preference information of DMs well and provides a solution set that meets the preferences of DMs,quickly provides the test results,and has better performance in the distribution of the Pareto front solution set.

GRIP： A Group Recommender Based on Interactive Preference Model

Numerous applications of recommender systems can provide us a tool to understand users. A group recommender reflects the analysis of multiple users＇ behavior, and aims to provide each user of the group with the things they involve according to users＇ preferences. Currently, most of the existing group recommenders ignore the interaction among the users. However, in the course of group activities, the interactive preferences will dramatically affect the success of recommenders. The problem becomes even more challenging when some unknown preferences of users are partly influenced by other users in the group. An interaction-based method named GRIP （Group Recommender Based on Interactive Preference） is presented which can use group activity history information and recommender post-rating feedback mechanism to generate interactive preference parameters. To evaluate the performance of the proposed method, it is compared with traditional collaborative filtering on the MovieLens dataset. The results indicate the superiority of the GRIP recommender for multi-users regarding both validity and accuracy.