移动式格栅除污机的应用与设计

针对大型泵站进水口设置多台并列布置的固定格栅,其工程投资大、设备利用率不高的弊端,建议使用移动式格栅除污机,并对其结构形式、主要构件设计计算及关键技术进行了阐述。移动式格栅除污机的成功应用为我国的水处理事业增添了一项新的专用设备。     

某全驱沙漠车悬架侧倾刚度设计研究

以某4x4全驱沙漠车为研究对象,提出了一种简化的整车数学模型,建立了整车侧倾角与板簧、横向稳定杆刚度的函数关系,得到悬架侧倾角刚度对整车侧倾性能的影响。并介绍了横向稳定杆角刚度计算方法,前后悬架侧倾角刚度匹配原则。     

激光熔覆方法增强自卸车钢板耐磨性能研究

磨损是影响自卸车车厢使用寿命的主要原因之一,研究利用激光熔覆方法,制备出含有TiC、VC增强颗粒的熔覆层。利用X射线衍射仪、电子探针、透射电镜对增强颗粒进行微结构分析;利用滑动磨损试验机研究了熔覆层的耐磨损性能。结果表明,激光熔覆层的耐磨性明显好于Q235板材。     

SS4改进型电力机车转向架构架仿真分析及局部结构优化

对改进设计后的SS4改进型电力机车构架整体结构进行了静强度、疲劳强度计算和模态分析,计算结果均满足相关的标准规定和运营要求;对在段运营中出现裂纹的砂箱安装支座和摩擦减振器座进行了局部结构优化及强度计算,优化后,新结构的计算应力值大幅降低,满足了长期在线运营的要求。     

Timing sequence changes of landscape plants in Shanghai Area,China

Based on published reports and direct observation, the common resources of landscape plant species in the Shanghai area were identified and studied in terms of the landscape characteristics of timing sequence changes over a one year time period. The results showed that there were 199 common landscape plant species in Shanghai, distributed in 67 families and 129 genera. These plants were categorized by three ornamental characteristics: flowers, foliage and fruits. The species were thus further analyzed according to the time periods which these characteristics were present. The results showed that flower ornamental distributed throughout the year, particularly from April to June, and plant species with colorful foliage could be seen every month, and particularly in November and December. On the other hand, few plant species with fruit ornamentals were observed in Shanghai area. The diversity of landscape timing sequence changes of the 199 plant species under examination was then analyzed by using the Shannon-Weiner and Simpson index formulas. The results showed that the higher diversity was observed in January, February and from August to October, while the lowest diversity was recorded from March to July and during November and December. The diversity of landscape characteristics of these plants was ranked according to foliage, flowers and fruit. Using this method, one park and one plant community were selected to evaluate the advantages and disadvantages of their time-changing plant landscape, with the goal of assessing the validity of the chosen ranking method. These results in the present study would be helpful to evaluate the plant landscape in a specified space and further enrich the diversity of plant and landscape by a target-oriented way.     

Estimating the value of risk reductions for car drivers when pedestrians are involved: a case study in Spain

We estimated the benefits associated with reducing fatal and severe injuries from traffic accidents using a stated choice experiment where choice situations were generated through a statistically efficient design. Specifically, the risk variables were defined as the expected annual number of vehicle car-users that suffered their death or were severely injured in a traffic accident. In addition, and differing from previous research, the number of pedestrians that died or were severely injured in traffic accidents per year was also included as a risk attribute in the choice experiment, to attempt at measuring drivers’ willingness to pay to reduce the risk of hitting pedestrians in a crash. The empirical setting was a choice of route for a particular trip that a sample of car drivers periodically undertakes in Tenerife, Spain. Models were estimated accounting for random taste heterogeneity and pseudo-panel data correlation. The median of the distribution of simulated parameters was used to obtain a representative measure for the monetary valuation of risk reductions. We found that the ratio between the values of reducing the risk of suffering a serious injury and that of reducing a fatality was approximately 18 %. Further, and quite novel, we also found that the value of reducing a pedestrian fatality was 39 % of the value of reducing a car occupant fatality.     

Are we ready to embrace connected and self-driving vehicles? A case study of Texans

While connected, highly automated, and autonomous vehicles (CAVs) will eventually hit the roads, their success and market penetration rates depend largely on public opinions regarding benefits, concerns, and adoption of these technologies. Additionally, the introduction of these technologies is accompanied by uncertainties in their effects on the carsharing market and land use patterns, and raises the need for tolling policies to appease the travel demand induced due to the increased convenience. To these ends, this study surveyed 1088 respondents across Texas to understand their opinions about smart vehicle technologies and related decisions. The key summary statistics indicate that Texans are willing to pay (WTP) $2910, $4607, $7589, and $127 for Level 2, Level 3, and Level 4 automation and connectivity, respectively, on average. Moreover, affordability and equipment failure are Texans’ top two concerns regarding AVs. This study also estimates interval regression and ordered probit models to understand the multivariate correlation between explanatory variables, such as demographics, built-environment attributes, travel patterns, and crash histories, and response variables, including willingness to pay for CAV technologies, adoption rates of shared AVs at different pricing points, home location shift decisions, adoption timing of automation technologies, and opinions about various tolling policies. The practically significant relationships indicate that more experienced licensed drivers and older people associate lower WTP values with all new vehicle technologies. Such parameter estimates help not only in forecasting long-term adoption of CAV technologies, but also help transportation planners in understanding the characteristics of regions with high or low future-year CAV adoption levels, and subsequently, develop smart strategies in respective regions.     

How household transportation expenditures have evolved in Canada: a long term perspective

In recent years, increasing recognition of the challenges associated with global climate change and inequity in developed countries have revived researcher’s interest towards analyzing transportation related expenditure of households. The current research contributes to travel behaviour literature by developing an econometric model of household budgetary allocations with a particular focus on transportation expenditure. Towards this end, we employ the public-use micro-data extracted from the Survey of Household Spending (SHS) for the years 1997–2009. The proposed econometric modeling approach is built on the multiple discrete continuous extreme value model (MDCEV) framework. Specifically, in our analysis, the scaled version of the MDCEV model outperformed its other counterparts. Broadly, the model results indicated that a host of household socio-economic and demographic attributes along with the residential location characteristics affect the apportioning of income to various expenditure categories and savings. We also observed a relatively stable transportation spending behaviour over time. Additionally, a policy analysis exercise is conducted where we observed that with increase in health expenses and reduction in savings results in adjustments in all expenditure categories.     

Robust network pricing and system optimization under combined long-term stochasticity and elasticity of travel demand

Network pricing serves as an instrument for congestion management, however, agencies and planners often encounter problems of estimating appropriate toll prices. Tolls are commonly estimated for a single-point deterministic travel demand, which may lead to imperfect policy decisions due to inherent uncertainties in future travel demand. Previous research has addressed the issue of demand uncertainty in the pricing context, but the elastic nature of demand along with its uncertainty has not been explicitly considered. Similarly, interactions between elasticity and uncertainty of demand have not been characterized. This study addresses these gaps and proposes a framework to estimate nearest optimal first-best tolls under long-term stochasticity in elastic demand. We show first that the optimal tolls under the deterministic-elastic and stochastic-elastic demand cases coincide when cost and demand functions are linear, and the set of equilibrium paths is constant. These assumptions are restrictive, so three larger networks are considered numerically, and the subsequent pricing decisions are assessed. The results of the numerical experiments suggest that in many cases, optimal pricing decisions under the combined stochastic-elastic demand scenario resemble those when demand is known exactly. The applications in this study thus suggest that inclusion of demand elasticity offsets the need of considering future demand uncertainties for first-best congestion pricing frameworks.     

A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choice

The modeling of travel decision making has been a popular topic in transportation planning. Previous studies focused on random-utility discrete choice models and machine learning methods. This paper proposes a new modeling approach that utilizes a mixed Bayesian network (BN) for travel decision inference. The authors use a predetermined BN structure and calculate priori and posterior probability distributions of the decision alternatives based on the observed explanatory variables. As a “utility-free” decision inference method, the BN model releases the linear structure in the utility function but assumes the traffic level of service variables follow multivariate Gaussian distribution conditional on the choice variable. A real-world case study is conducted by using the regional travel survey data for a two-dimensional decision modeling of both departure time choice and travel mode choice. The results indicate that a two-dimensional mixed BN provides better accuracy than decision tree models and nested logit models. In addition, one can derive continuous elasticity with respect to each continuous explanatory variable for sensitivity analysis. This new approach addresses a research gap in probabilistic travel decision making modeling as well as two-dimensional travel decision modeling.