液压挖掘机司机室乘座舒适性测试与分析

对四川省沪州市长江挖掘机现有的液压挖掘机司机室乘座舒适性进行了实测，分析和评价，在人机工程学范畴内进行论述并提出主要参数和改进措施。     

基于知识的汽车驾驶员座椅布置系统

在对多种驾驶员座椅布置方法进行分析的基础上，提出了在汽车布置设计过程中更为实用的布置方法，并且在CATIA软件平台上研制了一个基于知识的汽车驾驶员座椅布置系统。     

Bus arrival time calculation model based on smart card data

Bus arrival time is usually estimated using the boarding time of the first passenger at each station. However, boarding time data are not recorded in certain double-ticket smart card systems. As many passengers usually swipe the card much before their alighting, the first or the average alighting time cannot represent the actual bus arrival time, either. This lack of data creates difficulties in correcting bus arrival times. This paper focused on developing a model to calculate bus arrival time that combined the alighting swiping time from smart card data with the actual bus arrival time by the manual survey data. The model was built on the basis of the frequency distribution and the regression analysis. The swiping time distribution, the occupancy and the seating capacity were considered as the key factors in creating a method to calculate bus arrival times. With 1011 groups of smart card data and 360 corresponding records from a manual survey of bus arrival times, the research data were divided into two parts stochastically, a training set and a test set. The training set was used for the parameter determination, and the test set was used to verify the model’s precision. Furthermore, the regularity of the time differences between the bus arrival times and the card swiping times was analyzed using the “trend line” of the last swiping time distribution. Results from the test set achieved mean and standard error rate deviations of 0.6% and 3.8%, respectively. The proposed model established in this study can improve bus arrival time calculations and potentially support state prediction and service level evaluations for bus operations.     

Passengers’ valuations of train seating layout,position and occupancy

The layout of seating within train carriages, of which there are numerous possibilities, and also the occupancy of that seating can be expected to impact on passengers’ experiences of a train journey. However, there is very little evidence on how rail passengers value different seating experiences. On the back of exploratory research, and including attitudinal evidence, this paper provides significant and original insights into rail passengers’ preferences in this area. The primary evidence base is a Stated Preference experiment, complemented by a novel Revealed Preference exercise that uses CCTV footage to observe where rail passengers prefer to sit.The valuations, expressed as travel time multipliers, obtained from the Stated Preference exercise are generally plausible and exhibit a wide range according to the precise seating configuration, the occupancy level and the seating position within a layout. The innovative Revealed Preference analysis provides an encouraging degree of support to the Stated Preference results which, reassuringly, can themselves be reconciled with related valuations widely used in the railway industry in Britain and also the findings of the exploratory and attitudinal research. Whilst we are interested here solely in obtaining valuations, we demonstrate how they can be used in demand forecasting.