扰动下的生物质能供应链收益共享契约研究

以协调扰动下的生物质能供应链为目的,在"公司+基地+农户"组织模式的基础上,分别研究供应链在无扰动、供给扰动和需求扰动下的最优订购量、收益分配系数以及单位收购价的变化,从而协调扰动的生物质能供应链,实现系统利润最大化。     

Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach

This study proposes a novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations between stations to predict station-level hourly demand in a large-scale bike-sharing network. Two architectures of the GCNN-DDGF model are explored; GCNN_reg-DDGF is a regular GCNN-DDGF model which contains the convolution and feedforward blocks, and GCNN_rec-DDGF additionally contains a recurrent block from the Long Short-term Memory neural network architecture to capture temporal dependencies in the bike-sharing demand series. Furthermore, four types of GCNN models are proposed whose adjacency matrices are based on various bike-sharing system data, including Spatial Distance matrix (SD), Demand matrix (DE), Average Trip Duration matrix (ATD), and Demand Correlation matrix (DC). These six types of GCNN models and seven other benchmark models are built and compared on a Citi Bike dataset from New York City which includes 272 stations and over 28 million transactions from 2013 to 2016. Results show that the GCNN_rec-DDGF performs the best in terms of the Root Mean Square Error, the Mean Absolute Error and the coefficient of determination (R²), followed by the GCNN_reg-DDGF. They outperform the other models. Through a more detailed graph network analysis based on the learned DDGF, insights are obtained on the “black box” of the GCNN-DDGF model. It is found to capture some information similar to details embedded in the SD, DE and DC matrices. More importantly, it also uncovers hidden heterogeneous pairwise correlations between stations that are not revealed by any of those matrices.     

A dynamic station strategy to improve the performance of flex-route transit services

As an innovative combination of conventional fixed-route transit and demand responsive service, flex-route transit is currently the most popular type of flexible transit services. This paper proposes a dynamic station strategy to improve the performance of flex-route transit in operating environments with uncertain travel demand. In this strategy, accepted curb-to-curb stops are labeled as temporary stations, which can be utilized by rejected requests for their pick-up and drop-off. The user cost function is defined as the performance measure of transit systems. Analytical models and simulations are constructed to test the feasibility of implementing the dynamic station strategy in flex-route transit services. The study over a real-life flex-route service indicates that the proposed dynamic station strategy could reduce the user cost by up to 30% without any additional operating cost, when an unexpectedly high travel demand surpasses the designed service capacity of deviation services.     

Taxi vacancy duration: a regression analysis

Taxi vacancy duration is a major efficiency measure for taxi services. A clear understanding of the various factors and their effect on vacancy duration is necessary for the optimal operational management of taxis. Previous research has only dealt with vacancy duration by assuming probability distributions and has not investigated heterogeneity in the data caused by various factors. We develop a parametric duration model using not only new operational characteristics but also variables associated with taxi demand, such as weather, land use, demographics, socioeconomic variables, and accessibility of public transportation. The model is applied to a large-scale New York City (NYC) taxi trip dataset that covers operations for 2013. The results show that all the attributes have significant associations with vacancy duration that follows a log-normal distribution. Our study is expected to help improve the efficiency of taxi operations by decreasing the time spent in vacant states.     

The asymmetric effects of income and fuel price on air transport demand

Forecasts of passenger demand are an important parameter for aviation planners. Air transport demand models typically assume a perfectly reversible impact of the demand drivers. However, there are reasons to believe that the impacts of some of the demand drivers such as fuel price or income on air transport demand may not be perfectly reversible. Two types of imperfect reversibility, namely asymmetry and hysteresis, are possible. Asymmetry refers to the differences in the demand impacts of a rising price or income from that of a falling price or income. Hysteresis refers to the dependence of the impacts of changing price or income on previous history, especially on previous maximum price or income. We use US time series data and decompose each of fuel price and income into three component series to develop an econometric model for air transport demand that is capable of capturing the potential imperfectly reversible relationships and test for the presence or absence of reversibility. We find statistical evidence of asymmetry and hysteresis – for both, prices and income – in air transport demand. Implications for policy and practice are then discussed.     

Synopsis of bicycle demand in the City of Toronto: Investigating the effects of perception,consciousness and comfortability on the purpose of biking and bike ownership

This paper provides an empirical basis for the evaluation of policies and programs that can increase the usage of bikes for different purposes as well as bike ownership. It uses an integrated econometric model of latent variable connecting multiple discrete choices. Empirical models are estimated by using a bicycle demand survey conducted in the City of Toronto in 2009. Empirical investigations reveal that latent perceptions of ‘bikeability’ and ‘safety consciousness’ directly influence the choice of biking. It is also found that the choice of the level of bike ownership (number of bikes) is directly influenced by latent ‘comfortability of biking’. The number of bikes owned moreover has a strong influence on the choices of biking for different purposes. It is clear that bike users in the City of Toronto are highly safety conscious. Increasing on-street and separate bike lanes proved to have the maximum effects on attracting more people to biking by increasing the perception of bikeability in the city, comfortability of biking in the city and increasing bike users’ sense of safety. In terms of individuals’ characteristics, older males are found to be the most conformable and younger females are the least comfortable group of cyclists in Toronto.     

公共自行车动态调度系统需求预测模型研究

基于交通出行理论,将公共自行车需求分为直接需求和间接需求,并研究需求等待、转移和消退的变化规律.根据各租赁点的历史出行OD和以往时段的借、还车信息,预测出调度车辆从当前位置到达任意租赁点时该位置的车辆数,从而确定调度车数并制定调度计划,建立租赁点短期多时段的需求预测模型.将需求预测模型与调度计划模型组合成具有迭代反馈特性的双层模型,并通过计算机仿真方法求解,得到最优动态调度方案下的需求预测结果.通过算例对模型进行了验证,结果表明:公共自行车动态调度系统需求预测模型与调度计划模型结合后能有效改善公共自行车实际运营中借车时无车可借、还车时车位已满的情况.     

旅游景区新建公路交通量预测方法研究

对旅游景区新建公路交通量的预测主要采用4阶段法,但该模型对隐性交通需求反应不敏感,同时也没有考虑到景区本身的环境容量限制,针对不足,文中通过对传统的交通生成预测模型进行改进,突显旅游特性,弥补对隐性交通需求的估计不充分,在交通分布预测阶段以景区环境容量为约束条件进行交通分布预测,并以位于苏州市吴中区的东、西山连接线工程为例给出对旅游景区新建公路交通量的预测过程,并验证模型的可操作性。     

唐山世园会交通需求预测及分配研究

世园会的举办,将给唐山发展带来巨大机遇,但也考验着唐山交通管理能力。如何提高交通出行是我们要解决的关键问题。通过分析唐山交通现状及世园会对交通的需求,运用恩格尔系数法进行交通需求预测,根据唐山世园会交通活动特点,采取非平衡溢流模型,对唐山世园会的交通流量进行交通分配。     

城市居住区停车配建标准研究

为了解决居住区的停车问题,更好地规范停车供给、促进居住区动静态交通的协调,通过调查与分析居住区的停车供需现状,分析了中国城市居住区停车配建标准的现存问题及其影响因素;结合潍坊市居住小区的停车特性,对建筑物进行了更加合理、细化的分类,并把商住楼单独列为一类,对现状停车调查数据进行多元回归分析,建立了居住区停车需求率预测模型;根据停车需求率预测值,应用城市可持续发展理论,引入区位系数、区位修正系数和建筑物类别修正系数对停车需求率预测值进行修正,提出了一种基于修正系数并结合城市自身特性制定居住区停车配建标准的方法.研究结果表明:该方法可为停车需求率预测提供依据,使停车配建指标的制定过程更趋于定量化.