Switching service types for multi-region bus systems

Conventional fixed-route bus services are generally preferred to flexible-route services at high demand densities, and vice versa. This paper formulates the problem of integrating conventional and flexible services that connect a main terminal to multiple local regions over multiple time periods. The system’s vehicle size, route spacing (for conventional services), service area (for flexible services), headways and fleet sizes are jointly optimized to minimize the sum of supplier costs and user costs. The route spacing for conventional bus services and service area for flexible bus services are also optimized for each region. The proposed solution method, which uses a genetic algorithm and analytic optimization, finds good solutions quickly. Numerical examples and sensitivity analyses confirm that the single fleet variable-type bus service may outperform either the single fleet conventional bus service or the single fleet flexible bus service when demand densities vary substantially among regions and time periods.     

Investigating travel utility elements in association with travel time and mode choice: the case of Seoul,South Korea

In this paper, travel utility is conceptualized into the elements of disutility, or derived utility, and positive utility, which includes synergistic and intrinsic utility, and then analyzed in terms of the effects of these elements on weekly travel time according to three travel modes – the automobile, public transit, and nonmotorized modes – and on the choice of the annually most used mode. Linear regressions on mode-specific travel time and a multinomial logistic regression on mode choice show that, compared to life situation and land-use characteristics, utility elements are among the strongest travel determinants. Specifically, while some utility elements contribute exclusively to shifting the mode of travel and others to increasing nonmotorized travel, modal shift is most strongly affected by a disutility element, trip timeliness, and the increase in nonmotorized travel by a positive utility element, amenities.     

船舶中央冷却水多段比值控制系统设计

针对目前国内船舶中央冷却水系统普遍存在能耗高的问题,提出一种依据三通阀开度变化的实时改变海水变频泵组转速的控制策略。将三通阀开度分为4个区段,每个区段对应1个比值函数,根据比值函数和三通阀开度计算出变频泵组对应的频率。此外,增加smith滞环比较器作为开关函数,避免变频器在安全温度界限附近频繁切换频率对系统产生冲击。试验证明,提出的控制策略不仅实现了海水泵变频节能控制,保证系统稳定、可靠运行,而且显著降低了系统能耗。     

Use characteristics and demographics of rural transit riders: a case study in Tennessee

Intercity bus (ICB), deviated fixed route transit (DFRT) and demand responsive transit (DRT) are three major modes of rural public transportation. This paper focuses on the characteristics and motivations of DFRT and DRT riders, compared to non-riders, in Tennessee. A rural DFRT rider survey, a rural DRT rider survey and a rural (non-rider) resident survey were performed. It is found that DFRT and DRT riders have similar demographics to ICB riders. The most common trip purpose for DFRT and DRT passengers is medical care, which is different from ICB trips. Ninety percent of the riders have difficulty finding alternative transportation modes, suggesting they are captive riders, not choice riders. Regression results indicate that people choosing transit modes tend to have lower personal and household income, own fewer cars, to not be homeowners, and be of non-white race. Rural residents who receive more education are more likely to be open-minded to use rural transit.     

基于改进BP算法神经网络的路况评价模型研究

通过利用在BP神经网络中增加反馈信号及偏差单元的网络模型,生成内部回归网络,对BP神经网络进行改进,据此得出改进的BP神经网络的具体步骤.在此基础上,结合陕西省高速公路沥青路面的实际情况建立了路况评价模型,并对该模型的具体应用做了研究.研究结果表明:网络的收敛速度很好,训练结果与实际路况结果相差很小,利用改进BP算法神经网络建立的路况评价模型不仅方便易用,而且精度很高.     

Examining determinants of rail ridership: a case study of the Orlando SunRail system

The current study contributes to the literature on transit ridership by considering daily boarding and alighting data from a recently launched commuter rail system in Orlando, Florida – SunRail. The analysis is conducted based on daily boarding and alighting data for 10 months for the year 2015. With the availability of repeated observations for every station, the potential impact of common unobserved factors affecting ridership variables are considered. The current study develops an estimation framework, for boarding and alighting separately, that accounts for these unobserved effects at multiple levels – station, station-week and station-day. In addition, the study examines the impact of various observed exogenous factors such as station level, transportation infrastructure, transit infrastructure, land use, built environment, sociodemographic and weather variables on ridership. The model system developed will allow us to predict ridership for existing stations in the future as well as potential ridership for future expansion sites.     

区间数多属性决策中属性值的灵敏度分析及在潜艇作战上的应用

针对区间数多属性决策中属性值灵敏度分析,文中基于加权模型和区间数的“期望—方差”排序法,给出了当两个方案之间排序位置颠倒时某一属性的属性值最小变化量的计算方法,最后给出了一个潜艇布雷作战方案的算例。     

带有多重能力等级选择的内陆港选址问题研究

为促进资源的合理利用,减少内陆港的重复建设,以系统总成本,包括建设成本和运输成本最小为目标函数,并考虑内陆港的通过能力限制,构建了两阶段的带有多重能力等级选择的内陆港选址模型,同时设计遗传算法求解.东北地区的实例分析结果表明,从系统最优角度出发,还应增设通过能力分别为5万TEU和15万TEU的齐齐哈尔内陆港、佳木斯内陆港,并形成由近、中、远距离内陆港群构成的区域内陆运输节点布局.结果同时表明,在选址决策中加入对通过能力的选择,可以使内陆港的布局与内陆地区的运输需求分布更加趋于均衡,减少不必要投资,提高资源使用效率.     

基于CNN-SVR混合深度学习模型的短时交通流预测

精准且快速的短时交通流预测是智能交通发展的重要组成部分.本文针对当前交通流预测模型不能充分提取交通流数据的时空特征、预测性能容易受到外界干扰因素影响的问题,提出一种基于深度学习的短时交通流预测模型,该模型结合卷积神经网络(Convolutional Neural Network,CNN)与支持向量回归分类器(Support Vector Regression,SVR)的特点:在网络底层应用CNN进行交通流特征提取,并将提取结果输入到SVR回归模型中进行流量预测.为验证模型的有效性,取G103国道的实际交通流量数据进行试验.结果表明,提出的预测模型与传统的预测模型相比具有更高的预测精度,预测性能提高了11%,是一种有效的交通流预测模型.     

基于Kriging模型的深潜器多球交接耐压壳结构优化

基于Kriging模型对深潜多球交接耐压壳进行了结构优化研究.通过试验设计选取样本点,采用非线性有限元方法计算样本的极限强度,建立了Kriging模型,针对不同工作深度的耐压壳进行结构优化.研究结果表明,Kriging模型具有较高的精度,并且克服了优化算法与非线性有限元模型直接结合所带来的计算量巨大且难以得到正确的梯度信息的困难,是求解深潜多球交接耐压壳结构优化问题的有效途径.