中小城市中心区停车问题研究

目前我国中小城市中心区的停车问题,已经成为制约城市可持续发展、影响城市交通顺畅、安全及人们生活质量的焦点问题.因此研究中小城市中心区的停车问题,提出近期建设管理策略和远期规划方案,对于缓解停车难问题具有必要性和紧迫性.     

城市道路平面交叉口通行能力、控制效率的研究

针对城市交通拥挤和堵塞现象日趋严重的问题,从交通信号理论的角度,详细分析平面交叉口入口处车辆的排队情况,给出不拥挤交叉口的交通流量方程,确定信号周期和绿信比的取值范围,从而提出交通需求强度和相对需求强度的概念,并将其作为衡量交叉口交通管理效果的质量指标;同时对交通信号的损失时间加以探讨,可为如何提高绿灯信号效率提供理论基础.     

中小企业财务管理中存在的问题及对策

中小企业由于规模小、资本和技术构成较低、受传统体制和外部宏观经济影响大等因素,使得中小企业在财务管理方面存在着与自身发展和市场经济不适应的情况。通过对中小企业财务管理中企业内部和外部两个方面存在的主要问题进行的分析,提出了解决中小企业财务管理中存在问题的对策及相应的解决办法。     

上海市出行者信息需求分析

在出行者信息需求调查的基础上,分析了出行者获取信息方式、出行者信息需求的内容、不同出行目的下出行者信息需求的差异和出行信息获取方式的意愿,并对不同人群的信息需求特征进行了比较分析。出行者信息需求分析对于准确把握出行者信息需求特征,进而提供高水平的信息服务具有重要意义。     

高等学校财务预算管理

预算管理是高校财务管理的重要组成部分,通过分析目前高校预算管理的发展及现状,从财务预算的原则性、编制方法和执行力等方面就高校财务预算管理中存在的问题提出解决对策。     

关于城市主干路设置辅路条件的研究

结合实际项目经验,通过分析道路功能定位、交通需求、交通组织方式及城市主干路辅路的设置条件等,根据所建立的计算模型,在设计规范的基础上对城市主干路设置辅路的可能性条件和必要性条件进行了深入研究,以期为类似设计研究提供参考。     

Machine learning applications in activity-travel behaviour research: a review

ABSTRACT

This paper reviews the activity-travel behaviour literature that employs Machine Learning (ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which attempt to build intelligence utilizing the availability of large amounts of data, have emerged as powerful tools in the fields of pattern recognition and big data analysis. These techniques have been applied in activity-travel behaviour studies since the early ’90s when Artificial Neural Networks (ANN) were employed to model mode choice decisions. AMOS, an activity-based modelling system developed in the mid-’90s, has ANN at its core to model and predict individual responses to travel demand management measures. In the dawn of 2000, ALBATROSS, a comprehensive activity-based travel demand modelling system, was proposed by Arentze and Timmermans using Decision Trees. Since then researchers have been exploring ML techniques like Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Bayes Classifiers, and more recently, Ensemble Learners to model and predict activity-travel behaviour. A large number of publications over the years and an upward trend in the number of published articles over time indicate that Machine Learning is a promising tool for activity-travel behaviour analysis and prediction. This article, first of its kind in the literature, reviews these studies and explores the trends in activity-travel behaviour research that apply ML techniques. The review finds that mode choice decisions have received wide attention in the literature on ML applications. It was observed that most of the studies identify the lack of interpretability as a serious shortcoming in ML techniques. However, very few studies have attempted to improve the interpretability of the models. Further, some studies report the importance of feature engineering in ML-based studies, but very few studies adopt feature engineering before model development. Spatiotemporal transferability of models is another issue that has received minimal attention in the literature. In the end, the paper discusses possible directions for future research in the area of activity-travel behaviour modelling using ML techniques.     

基于预约需求的共享停车平台泊位分配方法

为解决在预约需求下，考虑预约时刻、时长及延时需求的共享停车分配问题，提出一种共享停车泊位分配模型. 以平台收益和停车步行距离为优化目标，将需求分为基本及延时两种情况，确定停车预约请求的分配策略. 根据模型结构，设计随机解集生成方法，利用蒙特卡洛法确定模型的最优解. 以医院停车场及周边停车场为案例，测试模型. 结果表明，模型能较好地服务于共享停车泊位的分配，实现平台收益与满足需求之间的平衡.     

金融期货对推进利率市场化的作用研究

分析了我国金融期货市场及利率市场化改革发展的现状,研究了金融期货发展对利率市场化改革的作用。