搬运作业的工效学研究

研究调查了某地区４个火车站搬运作业工人的作业特点，劳动休息制度，劳动负荷及健康状况等，铁路搬运作业采用“三班两运转”轮班制度，工人劳动过程中负荷量较大，且主要是弯腰和转身这类易造成腰部损伤的动作，调查结果表明搬运工人的腰痛人数明显高于对照组，应加强劳动组织和劳动保护，以保障工人健康和提高工作效率。     

汽车车身轻量化及其相关成形技术综述

综述了应用于汽车车身轻量化的成形方法(如拼焊板成形、液压成形等)以及特点,介绍了实现汽车车身轻量化的新成形工艺及其发展动态,并且对目前存在的问题进行了探讨。     

铅对鼠红细胞免疫功能的影响

选大鼠及小鼠用醋酸铅腹腔染毒法，初步探讨了铅对鼠红细胞免疫功能的影响。结果表明，连续染毒２周后，大鼠及小鼠的ＲＢＣ－Ｃ＿３ｂ受体花环率未见明显改变，而ＲＢＣ－ＩＣ受体花环率随染毒剂量的增加呈递增趋势，高剂量组与对照组比较均有显著性差异（Ｐ＜０．０５）。     

苏州市中心区防汛工作若干问题的探讨

苏州城市中心区是苏州政治、经济、商贸中心,由于地处太湖之滨,水网稠密,地势低洼,历史上洪涝灾害频繁,现状防洪排涝标准偏低,与社会经济发展要求不相适应。为适应新时期社会、经济、环境协调发展,保障城市防洪安全,实施苏州市城市中心区防洪大包围工程调整原防洪分片是十分必要和迫切的。     

Large-scale application of MILATRAS: case study of the Toronto transit network

This paper documents the efforts to operationalize the conceptual framework of MIcrosimulation Learning-based Approach to TRansit Assignment (MILATRAS) and its component models of departure time and path choices. It presents a large-scale real-world application, namely the multi-modal transit network of Toronto which is operated by the Toronto Transit Commission (TTC). This large-scale network is represented by over 500 branches with more than 10,000 stops. About 332,000 passenger-agents are modelled to represent the demand for the TTC in the AM peak period. A learning-based departure time and path choice model was adopted using the concept of mental models for the modelling of the transit assignment problem. The choice model parameters were calibrated such that the entropy of the simulated route loads was optimized with reference to the observed route loads, and validated with individual choices. A Parallel Genetic Algorithm engine was used for the parameter calibration process. The modelled route loads, based on the calibrated parameters, greatly approximate the distribution underlying the observed loads. 75% of the exact sequence of transfer point choices were correctly predicted by the off-stop/on-stop choice mechanism. The model predictability of the exact sequence of route transfers was about 60%. In this application, transit passengers were assumed to plan their transit trip based on their experience with the transportation network; with no prior (or perfect) knowledge of service performance.     

Analysis of the environmental benefits of a motorcycle idling stop policy at urban intersections

In an attempt to reduce CO₂ emissions from motorized transport, the Taiwanese government introduced an idling stop policy for vehicles in early 2007. This paper seeks to quantify the environmental benefits of the policy based on a stated preference analysis. Motorcyclists were surveyed at urban intersections in Taiwan, to identify the amount of time they would be willing to turn off their engines while waiting at traffic lights (the WTO). A contingent valuation framework based on stated preference questions was designed to determine the WTO. Results obtained from the Spike model showed that the average motorcyclist’s WTO is 82 s. In another analysis, in which other variables were taken into consideration, such as the possibility that the policy will be enacted as legislation, the expected WTO increased to 101 s. In both cases, an idling stop policy would have positive environmental effects, reducing gasoline usage by 1021 L per hour and reducing CO₂ emissions by 0.56 metric tons per hour at the intersection studied during peak periods.     

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.     

Exploring the impact of walk–bike infrastructure,safety perception,and built-environment on active transportation mode choice: a random parameter model using New York City commuter data

This study estimates a random parameter (mixed) logit model for active transportation (walk and bicycle) choices for work trips in the New York City (using 2010–2011 Regional Household Travel Survey Data). We explored the effects of traffic safety, walk–bike network facilities, and land use attributes on walk and bicycle mode choice decision in the New York City for home-to-work commute. Applying the flexible econometric structure of random parameter models, we capture the heterogeneity in the decision making process and simulate scenarios considering improvement in walk–bike infrastructure such as sidewalk width and length of bike lane. Our results indicate that increasing sidewalk width, total length of bike lane, and proportion of protected bike lane will increase the likelihood of more people taking active transportation mode This suggests that the local authorities and planning agencies to invest more on building and maintaining the infrastructure for pedestrians. Further, improvement in traffic safety by reducing traffic crashes involving pedestrians and bicyclists, will increase the likelihood of taking active transportation modes. Our results also show positive correlation between number of non-motorized trips by the other family members and the likelihood to choose active transportation mode. The model would be an essential tool to estimate the impact of improving traffic safety and walk–bike infrastructure which will assist in investment decision making.     

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.