基于D-optimal试验设计的公共交通出行logit模型

以成都到龙泉片区的交通通道为研究对象,假设通道开通地铁的情况下,采用D-optimal试验设计意愿调查问卷,选择费用、时间等变量以及个人属性作为效用变量,建立公共交通出行的多项logit模型,结合意愿调查数据对模型进行求解,分析通道中公共交通出行选择行为,并预测成都到龙泉片区主要交通方式的客流分担率。分析表明,地铁将承担通道中大部分的客流。     

停车换乘设施使用者调查

为了探讨停车换乘设施吸引的出行需求行为特性,在北京市进行了停车换乘设施使用者问卷调查.采用行为调查和意向调查相结合的方式,深入分析停车换乘设施使用者的出行行为特性和换乘行为意愿.结果显示,停车换乘者主要是全职通勤和中等偏高收入出行者,其驾车到达停车换乘设施的时间集中在6～30 min,乘坐公共交通的时间为30～60 min.最后,利用非集计价格敏感度测定法,得到换乘者可接受的最大换乘步行距离和可行候车时间分别为300 m和10 min.     

车内噪声声品质偏好性评价与分析实验研究

运用分组成对比较法对车内噪声样本进行偏好性主观评价实验,并在精确计算评价结果误判率的基础上,对评价结果进行相关分析和多元回归分析,得到回归方程.最后采用频域滤波技术,识别出指定频率段对车内噪声偏好性的影响.     

基于偏好函数与深度信念网络的自动化码头AGV实时调度算法

为提高自动化集装箱码头岸桥作业效率、降低自动导引车(Automated Guided Vehicle,AGV)空载行驶距离,以偏好函数和深度信念网络(Deep Belief Network,DBN)为核心设计AGV实时调度算法,完成AGV任务分配指派,优化AGV作业任务序列。利用偏好函数为处于待分配状态的AGV筛选最优集装箱,产生训练样本并更新训练集,通过DBN实时更新集装箱的偏好函数,重复该过程直至集装箱作业完毕。算例分析表明:同两种现行调度规则对比,AGV空载距离和岸桥作业时间显著下降;与人工神经网络(Artificial Neural Network,ANN)相比,DBN可有效提高算法精度与求解效率;算法针对环境动态变化实时分配集装箱,为自动化码头提高效率和降低能耗提供依据。     

Route Choice Behaviour of Freeway Travellers Under Real-time Traffic Information Provision – Application of the Best Route and the Habitual Route Choice Mechanisms

Abstract

This paper investigates route choice behaviour on freeways between Taipei and Taichung in Taiwan under the provision of real-time traffic information. Two types of route choice selection rules (the best-route and habitual-route) are analysed using ordered probit models to identify the major influences on freeway travellers’ route choice behaviour. The level of service associated with each route is defined as a generalised cost saving (GCS) and specified non-linearly with a threshold inherent to travellers. The marginal (dis)utility thresholds in the ‘best’ and ‘habitual’ behaviour models are identified through a goodness-of-fit grid. The results confirm that the thresholds for changing the inertia behaviour of drivers should be larger than the ones for choosing the best routes. In addition, the drivers are more likely to choose either the best or the habitual routes once the GCS are greater than the identified threshold values.     

Drivers’ parking location choice under uncertain parking availability and search times: A stated preference experiment

To assess parking pricing policies and parking information and reservation systems, it is essential to understand how drivers choose their parking location. A key aspect is how drivers’ behave towards uncertainties towards associated search times and finding a vacant parking spot. This study presents the results from a stated preference experiment on the choice behaviour of drivers, in light of these uncertainties. The attribute set was selected based on a literature review, and appended with the probabilities of finding a vacant parking spot upon arrival and after 8 min (and initially also after 4 min, but later dropped to reduce the survey complexity). Efficient Designs were used to create the survey design, where two rounds of pilot studies were conducted to estimate prior coefficients. Data was successfully collected from 397 respondents. Various random utility maximisation (RUM) choice models were estimated, including multinomial logit, nested logit, and mixed logit, as well as models accounting for panel effects. These model analyses show how drivers appear to accept spending time on searching for a vacant parking spot, where parking availability after 8 min ranks second most important factor in determining drivers’ parking decisions, whilst parking availability upon arrival ranks fourth. Furthermore, the inclusion of heterogeneity in preferences and inter-driver differences is found to increase the predictive power of the parking location choice model. The study concludes with an outlook of how these insights into drivers’ parking behaviour can be incorporated into traffic assignment models and used to support parking systems.     

基于多属性群决策层次分析的预防性养护措施决策研究

基于高速公路路面预防性养护决策问题的内涵,将多属性多目标的预防性养护决策整体结构划分为目标层、准则层、指标层和措施层4层。综合考虑预防性养护措施的费用、行车特征、技术因素、施工因素等作为目标层的评价值,并用群决策模型决策来减少主观性因素,得出最佳预防性养护决策措施。最后,结合案例来说明如何制定合理的养护措施。