基于无记忆非线性变换的相关非高斯雷达杂波的仿真

大量的实测数据表明,雷达杂波的分布特性明显偏离高斯分布。相关非高斯杂波的仿真主要包括对数正态、韦伯尔、K分布杂波的仿真。在杂波信号模拟中,需同时考虑杂波幅度概率分布和其相关特性。文中利用广义维纳过程的无记忆非线性变换法（ZMNL）来建模仿真,产生相关非高斯杂波。该仿真系统不仅可以实时动态地模拟实际采集的雷达杂波,而且可按给定的功率谱模型来产生满足非高斯分布的随机数据序列。仿真结果证明了此方法的有效性。     

交通运输规划模型的基本原理及应用

交通运输规划模型的开发过程,就是运输需求与运输供给的平衡过程.基本原理是通 过四步模型中的“生成、分布、方式选择和路径分配”开发建立基本交通模型和交通规划模型,以此作为评价各项建议和政策的基础.同时给出交通规划模型的实际应用问题和一些处理方法.     

结合忻阜高速谈曲线梁桥设计

结合工程实例,详细分析了曲线梁桥的受力、建模、结构设计要点,总结出曲线梁桥区别直线梁桥的特点,指导实际设计过程,有针对性与可操作性,避免工程人员因认识不足而造成工程事故.     

一种新型四象限变流器：拓扑,建模与仿真

针对传统的升压式四象限变流器直流侧工作电压要求较高的缺点，提出了一种新型四象限变流器拓扑，并运用状态空间平均法进行建模。ＭＡＴＬＡＢ数值仿真与样机试验结果证实了该拓扑及模型的正确性。     

Exploring the propensity to perform social activities: a social network approach

Conceptual and empirical models of the propensity to perform social activity–travel behavior are described, which incorporate the influence of individuals’ social context, namely their social networks. More explicitly, the conceptual model develops the concepts of egocentric social networks, social activities, and social episodes, and defines the three sets of aspects that influence the propensity to perform social activities: individuals’ personal attributes, social network composition, and information and communication technology interaction with social network members. Using the structural equation modeling (SEM) technique and data recently collected in Toronto, the empirical model tests the effect of these three aspects on the propensity to perform social activities. Results suggest that the social networks framework provides useful insights into the role of physical space, social activity types, communication and information technology use, and the importance of “with whom” the activity was performed with. Overall, explicitly incorporating social networks into the activity–travel behavior modeling framework provides a promising framework to understand social activities and key aspects of the underlying behavioral process. Juan Antonio Carrasco a PhD candidate in Civil Engineering at the University of Toronto, holds a MSc degree in Transportation Engineering from the Pontificia Universidad Católica de Chile. His doctoral research explores the relationships between social networks, activity–travel behavior, and ICTs. His research interests also include microsimulation, land use-transportation, and econometric modeling. Eric J. Miller is Bahen-Tanenbaum Professor of Civil Engineering at the University of Toronto where he is also Director of the Joint Program in Transportation. His research interests include integrated land-use/transportation modeling, activity-based travel modeling, microsimulation and sustainable transportation planning.     

A range-restricted recharging station coverage model for drone delivery service planning

Unmanned Aerial Vehicles (UAVs) are attracting significant interest for delivery service of small packages in urban areas. The limited flight range of electric drones powered by batteries or fuel cells requires refueling or recharging stations for extending coverage to a wider area. To develop such service, optimization methods are needed for designing a network of station locations and delivery routes. Unlike ground-transportation modes, however, UAVs do not follow a fixed network but rather can fly directly through continuous space. But, paths must avoid barriers and other obstacles. In this paper, we propose a new location model to support spatially configuring a system of recharging stations for commercial drone delivery service, drawing on literature from planar-space routing, range-restricted flow-refueling location, and maximal coverage location. We present a mixed-integer programming formulation and an efficient heuristic algorithm, along with results for a large case study of Phoenix, AZ to demonstrate the effectiveness and efficiency of the model.     

Modeling the effect of electric vehicle adoption on pedestrian traffic safety: An agent-based approach

When operated at low speeds, electric and hybrid vehicles have created pedestrian safety concerns in congested areas of various city centers, because these vehicles have relatively silent engines compared to those of internal combustion engine vehicles, resulting in safety issues for pedestrians and cyclists due to the lack of engine noise to warn them of an oncoming electric or hybrid vehicle. However, the driver behavior characteristics have also been considered in many studies, and the high end-prices of electric vehicles indicate that electric vehicle drivers tend to have a higher prosperity index and are more likely to receive a better education, making them more alert while driving and more likely to obey traffic rules. In this paper, the positive and negative factors associated with electric vehicle adoption and the subsequent effects on pedestrian traffic safety are investigated using an agent-based modeling approach, in which a traffic micro-simulation of a real intersection is simulated in 3D using AnyLogic software. First, the interacting agents and dynamic parameters are defined in the agent-based model. Next, a 3D intersection environment is created to integrate the agent-based model into a visual simulation, where the simulation records the number of near-crashes occurring in certain pedestrian crossings throughout the virtual time duration of a year. A sensitivity analysis is also carried out with 9000 subsequent simulations performed in a supercomputer to account for the variation in dynamic parameters (ambient sound level, vehicle sound level, and ambient illumination). According to the analysis, electric vehicles have a 30% higher pedestrian traffic safety risk than internal combustion engine vehicles under high ambient sound levels. At low ambient sound levels, however, electric vehicles have only a 10% higher safety risk for pedestrians. Low levels of ambient illumination also increase the number of pedestrians involved in near-crashes for both electric vehicles and combustion engine vehicles.     

System optimum and pricing for the day-long commute with distributed demand,autos and transit

The day-long system optimum (SO) commute for an urban area served by auto and transit is modeled as an auto bottleneck with a capacitated transit bypass. A public agency manages the system’s capacities optimally. Commuters are identical except for the times at which they wish to complete their morning trips and start their evening trips, which are given by an arbitrary joint distribution. They value unpunctuality – their lateness or earliness relative to their wish times – with a common penalty function. They must use the same mode for both trips. Commuters are assigned personalized mode and travel start times that collectively minimize society’s generalized cost for the whole day. This includes unpunctuality penalties, queuing delays, travel times and out-of-pocket costs for users, as well as travel supply costs and externalities for society.It is shown that in a SO solution there can be no queuing and that the set of SO solutions forms a convex set. Furthermore, if the schedule penalty that users suffer due to unpunctuality is separable into morning and evening components, then the set of commuters traveling by the same mode arrive at work and depart from work in the order of their wishes. These orders are in general different in the morning and the evening. It is also shown that there always is a SO solution in which users are at all times, and on both modes, either punctual or flowing at capacity. These problem properties are used to identify search methods, both, for SO solutions and for time-dependent tolls and transit fares that preserve the solutions as Nash equilibriums. In every case studied, these prices exist. They must peak concurrently for the two modes in both periods.In special cases involving only one mode, only one period or concentrated demand the solution to the complete problem decomposes by period conditional on the number of transit users, and this facilitates the solution. In these cases the day-long SO cost is the sum of the SO costs for the two peaks considered separately. However, this is not true in general – the solution obtained by combining the two single-period solutions can be infeasible. When this happens, the optimum day-long cost will exceed the sum of the single-period costs. The discrepancy is about 40% of the total schedule penalty for an example representing a large city. Thus, to develop realistic policies the day-long problem must be addressed head on. An approximate method that yields closed form formulas for the case with uniformly distributed wishes is presented.     

Comparing decision tree algorithms to estimate intercity trip distribution

Traditional trip distribution models usually ignore the fact that destination choices are made individually in addition to aggregated factors, such as employment and average travel costs. This paper proposes a disaggregated analysis of destination choices for intercity trips, taking into account aggregated characteristics of the origin city, an impedance measurement and disaggregated variables related to the individual, by applying nonparametric Decision Tree (DT) algorithms. Furthermore, each algorithm’s performance is compared with traditional gravity models estimated from a stepwise procedure (1) and a doubly constrained procedure (2). The analysis was based on a dataset from the 2012 Origin-Destination Survey carried out in Bahia, Brazil. The final selected variables to describe the destination choices were population of the origin city, GDP of the origin city and travel distances at an aggregated level, as well as the variables: age, occupation, level of education, income (monthly), number of cars per household and gender at a disaggregated one. The comparison of the DT models with gravity models demonstrated that the former models provided better accuracy when predicting the destination choices (trip length distribution, goodness-of-fit measures and qualitative perspective). The main conclusion is that Decision Tree algorithms can be applied to distribution modeling to improve traditional trip distribution approaches by assimilating the effect of disaggregated variables.     

Deep subterranean railway system: Acceptability assessment of the public discourse in the Seoul Metropolitan Area of South Korea

The objective of this study is to analyze the public acceptability of deep subterranean railway systems, which will be constructed in the space 40 m below ground level and will be operated at twice the speed of the existing subway system. Although such railway systems have been feasible in terms of construction technologies and economics, public acceptability must be considered for the successful introduction of such a new public infrastructure. Therefore, to perform the analysis of public acceptability, a telephone-based survey was conducted for residents in the vicinity of the planned the deep subterranean railway systems. As a result, about 70% of the respondents answered that they took a neutral or opposing attitude to introducing the deep subterranean railway systems. Awareness of the deep subterranean railway systems has a positive impact on its acceptability. In addition, the acceptability is found to show a negative relationship with environment and inconvenience factors. Based on the analysis results, an affective approach through soft measures such as awareness campaigns and advertisements is recommended to effectively address and mitigate the concerns and issues raised by the public.