Predicting the adoption of connected autonomous vehicles: A new approach based on the theory of diffusion of innovations

On the grounds that individuals heavily rely on the information that they receive from their peers when evaluating adoption of a radical innovation, this paper proposes a new approach to forecast long-term adoption of connected autonomous vehicles (CAVs). The concept of resistance is employed to explain why individuals typically tend to defer the adoption of an innovation. We assume that there exists a social network among individuals through which they communicate based on certain frequencies. In addition, individuals can be subject to media advertisement based on certain frequencies. An individual’s perceptions are dynamic and change over time as the individual is exposed to advertisement and communicates with satisfied and dissatisfied adopters. We also explicitly allow willingness-to-pay (WTP) to change as a result of peer-to-peer communication. An individual decides to adopt when (i) there is a need for a new vehicles; (ii) his/her WTP is greater than CAV price; and (iii) his/her overall impression about CAVs reaches a cutoff value. Applicability of the proposed approach is shown using a survey of employees of the University of Memphis. Our results show that the automobile fleet will be near homogenous in about 2050 only if CAV prices decrease at an annual rate of 15% or 20%. We find that a 6-month pre-introduction marketing campaign may have no significant impact on adoption trend. Marketing is shown to ignite CAV diffusion but its effect is capped. CAV market share will be close to 100% only if all adopters are satisfied with their purchases; therefore, the probability that an individual becomes a satisfied adopter plays an important role in the trend of adoption. The effect of the latter probability is more pronounced as time goes by and is also more prominent when CAV price reduces at greater rates. Some caveats may be inserted when considering the study results as the findings are subject to sample bias and data limitations.     

Capturing correlation with a mixed recursive logit model for activity-travel scheduling

Representing activity-travel scheduling decisions as path choices in a time–space network is an emerging approach in the literature. In this paper, we model choices of activity, location, timing and transport mode using such an approach and seek to estimate utility parameters of recursive logit models. Relaxing the independence from irrelevant alternatives (IIA) property of the logit model in this setting raises a number of challenges. First, overlap in the network may not fully characterize perceptual correlation between paths, due to their interpretation as activity schedules. Second, the large number of states that are needed to represent all possible locations, times and activity combinations imposes major computational challenges to estimate the model. We combine recent methodological developments to build on previous work by Blom Västberg et al. (2016) and allow to model complex and realistic correlation patterns in this type of network. We use sampled choices sets in order to estimate a mixed recursive logit model in reasonable time for large-scale, dense time-space networks. Importantly, the model retains the advantage of fast predictions without sampling choice sets. In addition to estimation results, we present an extensive empirical analysis which highlights the different substitution patterns when the IIA property is relaxed, and a cross-validation study which confirms improved out-of-sample fit.     

Airport capacity increase via the use of braking profiles

Many airports are encountering the problem of insufficient capacity, which is particularly severe in periods of increased traffic. A large number of elements influence airport capacity, but one of the most important is runway occupancy time. This time depends on many factors, including how the landing roll procedure is performed. The procedure usually does not include the objective to minimize the runway occupancy time. This paper presents an analysis which shows that the way of braking during landing roll has an essential impact on runway throughput and thus on airport capacity. For this purpose, the landing roll simulator (named ACPENSIM) was created. It uses Petri nets and is a convenient tool for dynamic analysis of aircraft movement on the runway with given input parameters and a predetermined runway exit. Simulation experiments allowed to create a set of nominal braking profiles that have different objective functions: minimizing the runway occupancy time, minimizing noise, minimizing tire wear, maximizing passenger comfort and maximizing airport capacity as a whole. The experiments show that there is great potential to increase airport capacity by optimizing the braking procedure. It has been shown that by using the proposed braking profiles it is possible to reduce the runway occupancy time even by 50%.     

A new logit‐artificial neural network ensemble for mode choice modeling: a case study for border transport

Logit model is one of the statistical techniques commonly used for mode choice modeling, while artificial neural network (ANN) is a very popular type of artificial intelligence technique used for mode choice modeling. Ensemble learning has evolved to be very effective approach to enhance the performance for many applications through integration of different models. In spite of this advantage, the use of ANN‐based ensembles in mode choice modeling is under explored. The focus of this study is to investigate the use of aforementioned techniques for different number of transportation modes and predictor variables. This study proposes a logit‐ANN ensemble for mode choice modeling and investigates its efficiency in different situations. Travel between Khobar‐Dammam metropolitan area of Saudi Arabia and Kingdom of Bahrain is selected for mode choice modeling. The travel on this route can be performed mainly by air travel or private vehicle through King Fahd causeway. The results show that the proposed ensemble gives consistently better accuracies than single models for multinomial choice problems irrespective of number of input variables. Copyright © 2015 John Wiley & Sons, Ltd.     

基于AutoCAD_VBA的无缝线路一体化设计系统开发

该系统利用Auto CAD VBA API成熟的内部封装机制,将大量查表计算工作和参数运算整合到程序模块内,将多个分步计算公式在程序内部进行推导合并,并利用可视化编程工具形成用户界面。使用户输入最少的参数完成整部计算,加快计算速度,减少错误。该系统还包含绘图模块,运行VBA宏指挥CAD自动绘制无缝线路单元轨节布置图。经过多个无缝线路设计项目验证,结果证明该系统准确可靠。     

大力推进信息化建设 提升企业综合竞争力

结合企业计算机应用的实际,以及造船生产劳动强度大、劳动环境恶劣、船用配件和材料种类繁多等特点,介绍在造船工业中,将设计、制造、管理等集为一体纳入计算机综合管理的集成系统,目的在于寻求一种采用计算机技术挖掘企业内部潜力,提高工作效率、推进企业信息化、加快企业生产和管理现代化、提升企业综合竞争力的新途径。     

动力定位系统舵桨组合推力分配研究

针对船舶动力定位系统推力分配中舵桨组合的推力建模及优化分配问题,将舵桨组合的非凸推力区域转化成4个凸区域,采用切换控制理论把非线性最优化问题转换为线性最优化问题。将舵、桨组合起来进行推力建模,以最小推力、舵角变化和推力误差为优化目标,对推进器的推力变化率、舵角变化率、推力误差范围和推力大小作了约束,采用多边形的方法把推力范围约束转化为线性不等式约束,基于总功率与总推力误差在不同推力区域设计了切换逻辑,实现了在不同的推力分配器中的切换。实船试验结果表明舵桨组合推力模型及推力分配策略是切实可行的,满足了推力分配的要求,在配备舵的动力定位船上具有良好的应用前景。     

基于 Petri 网的装备临抢修业务流程建模研究*

为了提高装备临抢修业务工作的科学性和准确性,论文利用Petri网建模的优势,将排队系统运用到装备临抢修业务流程建模研究中,利用库所优先级实现对故障装备按重要程度进行优先修理。论文讨论了将排队系统转化为HTCPN模型的建模规则,这种方法既可以给出简单直观的模型顶层结构,又可以根据实际情况对模型进行细化,并有利于对模型进行分析和仿真。最后利用CPN Tools工具对所建立的模型进行了性能分析,验证了模型的实用性和有效性。     

激光焊接夹层板结构设计程序开发

基于U-I型、U-IV型、V-I型、V-IV型4种夹层板结构形式,利用PCL(Patran Command Language)语言对MSC.Patran进行二次开发,设计开发出折叠式夹层板结构设计程序(FEA-LASCOR)。该程序仅需设计者在用户界面简单操作即能够实现夹层板板格快速建模、屈服强度分析、结果处理等功能。验算分析表明FEALASCOR程序具有与传统数值仿真分析一样的精度,并缩短了繁琐的建模工作,显著提高设计分析效率,为夹层板船体结构设计高效、快捷的分析工具。     

行人头部与车辆碰撞中旋转速度对颅脑组织响应影响

为了探究旋转速度在行人头部与车辆碰撞中对颅脑组织响应影响,对46例真实交通事故进行了运动学重建获得行人头部与车辆碰撞线性、旋转速度分布特征,并依此建立了头部与风挡、发动机罩碰撞有限元仿真模型,分析了头部旋转速度在不同线性速度和碰撞位置下对颅脑应力应变响应影响特征.结果表明:在行人头部与风挡碰撞中,当线性速度小于40 k...