旅游型村镇住居院落与户型模式设计——以白石山景区村镇住居为例

近年市郊游逐渐成为人们业余生活的重要组成部分,而旅游型村镇住居逐步成为我国一种新型的城镇化居住模式,但在发展中仍存在一定的问题。笔者以白石山景区村镇为例,分析了该地区旅游型村镇住居存在的问题,就旅游型村镇住居单体元素,如平面布局、院落、院门、屋顶、墙身、窗、漏窗的设计提出改进建议,并针对住居院落与户型改建或重建进行多种设计模式探讨。

城市森林花园(第四代住宅)户型空间组织模式的探索与实践

“十四五”规划的统计数据显示,国内住宅的人均面积不再是突出问题,而高质量、高品质、以人为本的绿色发展才是时代发展趋势,结合国内自身发展的特点和需求,城市森林花园第四代住宅在这种环境下应运而生。本文将以四代住宅的实践案例从户型空间的组合模式进行探索,结合当下环境和政策条件,以期为人民提供新型住宅的舒适体验,践行人与自然和谐共处理念。

从户型到宅院组合——陕西关中地区农村住宅研究

以陕西关中地区农村住宅为研究对象,以农村经济社会转型、生活方式发生深刻变化为出发点,本着城乡统筹、生态、节地的原则,从用地、空间与功能等方面寻求户型设计及庭院组合的途径,并对地域文化与场所营造的多样化、人性化的规划设计方法进行探讨。

Time varying congestion pricing for multi-class and multi-mode transportation system with asymmetric cost functions

This paper considers the problem of time varying congestion pricing to determine optimal time-varying tolls at peak periods for a queuing network with the interactions between buses and private cars.Through the combined applications of the space-time expanded network（STEN） and the conventional network equilibrium modeling techniques,a multi-class,multi-mode and multi-criteria traffic network equilibrium model is developed.Travelers of different classes have distinctive value of times（VOTs）,and travelers from the same class perceive their travel disutility or generalized costs on a route according to different weights of travel time and travel costs.Moreover,the symmetric cost function model is extended to deal with the interactions between buses and private cars.It is found that there exists a uniform（anonymous） link toll pattern which can drive a multi-class,multi-mode and multi-criteria user equilibrium flow pattern to a system optimum when the system＇s objective function is measured in terms of money.It is also found that the marginal cost pricing models with a symmetric travel cost function do not reflect the interactions between traffic flows of different road sections,and the obtained congestion pricing toll is smaller than the real value.

Research on Services Modeling in LTE Networks

Service modeling offers fundamental guidance to the construction and operation of mobile telecommunication networks. As the domestic LTE networks have been deployed massively, the refined LTE services model needs to be established urgently. In this paper, we firstly extract characteristic parameters of services from statistical data in 3G networks, especially in time, space and user dimension. Secondly, the development trends of LTE services are analyzed. And the refined LTE service model is established. Finally, prediction results of LTE service development in China is given, which could provide effi cient support for networks' optimization and evolution.

A Hybrid Inexact Optimization Model for Land-use Allocation of China

Land scarcity has become the prominent obstacle on the way to sustainable development for China. Under the constraints of land shortage, how to allocate the finite land resources to the multiple land users in China considering various political, environmental, ecological and economic conditions have become research topics with great significance. In this study, an interval fuzzy national-scale land-use model(IFNLM) was developed for optimizing land systems of China. IFNLM is based on an integration of existing interval linear programming(ILP), and fuzzy flexible programming(FFP) techniques. IFNLM allows uncertainties expressed as discrete interval values and fuzzy sets to be incorporated within a general optimization framework. It can also facilitate national-scale land-use planning under various environmental, ecological, social conditions within a multi-period and multi-option context. Then, IFNLM was applied to a real case study of land-use planning in China. The satisfaction degree of environmental constraints is between 0.69 and 0.97, the system benefit will between 198.25 × 1012 USD and 229.67 × 1012 USD. The results indicated that the hybrid model can help generate desired policies for land-use allocation with a maximized economic benefit and minimized environmental violation risk. Optimized land-use allocation patterns can be generated from the proposed IFNLM.

Traffic assignment problem under tradable credit scheme in a bi-modal stochastic transportation network: A cumulative prospect theory approach

The traffic equilibrium assignment problem under tradable credit scheme(TCS) in a bi-modal stochastic transportation network is investigated in this paper. To describe traveler’s risk-taking behaviors under uncertainty, the cumulative prospect theory(CPT) is adopted. Travelers are assumed to choose the paths with the minimum perceived generalized path costs, consisting of time prospect value(PV) and monetary cost. At equilibrium with a given TCS, the endogenous reference points and credit price remain constant, and are consistent with the equilibrium flow pattern and the corresponding travel time distributions of road sub-network. To describe such an equilibrium state, the CPT-based stochastic user equilibrium(SUE) conditions can be formulated under TCS. An equivalent variational inequality(VI) model embedding a parameterized fixed point(FP) model is then established, with its properties analyzed theoretically. A heuristic solution algorithm is developed to solve the model, which contains two-layer iterations. The outer iteration is a bisection-based contraction method to find the equilibrium credit price, and the inner iteration is essentially the method of successive averages(MSA) to determine the corresponding CPT-based SUE network flow pattern. Numerical experiments are provided to validate the model and algorithm.

Preference transfer model in collaborative filtering for implicit data

Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative filtering, became an important task in collaborative filtering. Traditionally, the prediction uses the user item co-occurrence data based on users' buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative filtering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are factorized.Each user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then,two factor-user matrices can be used to construct a so-called ‘preference dictionary' that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group.