Optimal design of electric vehicle public charging system in an urban network for Greenhouse Gas Emission and cost minimization

In this paper, we address the optimization problem of allocation of Electric Vehicle (EV) public fast charging stations over an urban grid network. The objective is to minimize Greenhouse Gas Emissions (GHG) under multiple constraints including a limited agency budget, accessibility of charging stations in every possible charging request and charging demands during peak hours. Additionally, we address bi-criteria problems to consider user costs as the second objective. A convex parsimonious model that depends on relatively few assumptions and input parameters is proposed and it is shown to be useful for obtaining conceptual insights for high-level planning. In a parametric study using a hypothetical urban network model generated based on realistic parameters, we show that GHG emissions decrease with agency budget, and that the reductions vary depending on multiple factors related to EV market and EV technologies. The optimal solutions found from the bi-criteria problems are shown to be close to the solution minimizing GHG emissions only, meaning that the emission minimizing policy can also minimize user costs.     

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比较了传统运输节点与物流中心的差异，指出物流中心选址重要性和独有特征．综合分析了当前一些文献对物流中心选址方法的研究，指出这些方法可能存在的不足．分析了AHP方法的扩展ANP应用于物流中心选址的优点，指出从物流中心选址的外部动态环境和内部决策条件出发可以更加全面分析物流中心选址涉及到的各种因素及其内部关系，提出采用ANP方法进行物流中心选址的影响因素结构及其分析方法、具体分析步骤和要点．     

A link-node complementarity model and solution algorithm for dynamic user equilibria with exact flow propagations

In this paper, we propose a link-node complementarity model for the basic deterministic dynamic user equilibrium (DUE) problem with single-user-class and fixed demands. The model complements link-path formulations that have been widely studied for dynamic user equilibria. Under various dynamic network constraints, especially the exact flow propagation constraints, we show that the continuous-time dynamic user equilibrium problem can be formulated as an infinite dimensional mixed complementarity model. The continuous-time model can be further discretized as a finite dimensional non-linear complementarity problem (NCP). The proposed discrete-time model captures the exact flow propagation constraints that were usually approximated in previous studies. By associating link inflow at the beginning of a time interval to travel times at the end of the interval, the resulting discrete-time model is predictive rather than reactive. The solution existence and compactness condition for the proposed model is established under mild assumptions. The model is solved by an iterative algorithm with a relaxed NCP solved at each iteration. Numerical examples are provided to illustrate the proposed model and solution approach. We particularly show why predictive DUE is preferable to reactive DUE from an algorithmic perspective.     

分布式实时控制系统的网络调度研究

基于实时网络分布式控制系统的性能不仅取决于控制算法，而且取决于网络信息调度的特点，以具有确定时间性与可靠性的TDMA（时分多路访问）网络作为控制网络，对网络中的控制信息进行调度，建立了分布控制系统的任务调度模型。并采用遗传算法求解网络调度问题，以提高控制系统性能和网络利用率，寻求最优的通信时序．仿真结果表明，经过优化调度后，控制回路的任务周期最短；与常规非优化状态下的系统性能相比，控制系统的总体性能指标减小，网络利用率提高．     

关于南京市街巷路整治的反思

本文阐述南京市街巷支路整治中存在的问题及其对策     

契合城市特点的轨道交通线网规划研究

以舟山、济南、厦门和宁波余慈4座城市为例说明城市轨道交通线网规划专题研究工作的重要性和关注要点,提出在研究对象选择上要围绕核心问题,在开展时机上要先期或同步开展,在工作深度上要保证研究的结论科学、明确并具有前瞻性,在工作广度上要保证研究的结论周到、全面并具有协调性,从而促进规划线网更加适合城市特点和要求。     

Forecasting current and next trip purpose with social media data and Google Places

Trip purpose is crucial to travel behavior modeling and travel demand estimation for transportation planning and investment decisions. However, the spatial-temporal complexity of human activities makes the prediction of trip purpose a challenging problem. This research, an extension of work by Ermagun et al. (2017) and Meng et al. (2017), addresses the problem of predicting both current and next trip purposes with both Google Places and social media data. First, this paper implements a new approach to match points of interest (POIs) from the Google Places API with historical Twitter data. Therefore, the popularity of each POI can be obtained. Additionally, a Bayesian neural network (BNN) is employed to model the trip dependence on each individual’s daily trip chain and infer the trip purpose. Compared with traditional models, it is found that Google Places and Twitter information can greatly improve the overall accuracy of prediction for certain activities, including “EatOut”, “Personal”, “Recreation” and “Shopping”, but not for “Education” and “Transportation”. In addition, trip duration is found to be an important factor in inferring activity/trip purposes. Further, to address the computational challenge in the BNN, an elastic net is implemented for feature selection before the classification task. Our research can lead to three types of possible applications: activity-based travel demand modeling, survey labeling assistance, and online recommendations.     

天津地铁客流发展规律及特征分析

天津地铁网络化运营初步形成后,线网客流出现了急剧性增长特征。详细介绍了天津地铁线网客流成长过程、发展规律和客流特征,包括客流构成、客流时间和空间特征等,以期为其他城市轨道交通网络化运营过程中的运营管理及客流预测、分析提供参考。     

改进BP神经网络的城市交通流预测研究

由于交通流预测具有高度的非线性特点,这与BP神经网络能够处理非线性问题的特征相符合。但BP神经网络算法易使解陷入局部极小,而遗传算法的全局优化能力则恰恰可以克服这一缺点。文中将遗传算法应用于对BP神经网络模型的改进来对交通流进行预测。通过对预测数据与实测数据的比较分析,证实了改进后的方法更为有效。     

基于无线数传的车轨耦合振动控制研究

提出直接在轨道上安装加速度传感器,再利用无线传感器网络将采集到的轨道振动信息无线传送至悬浮控制器,用于悬浮控制算法的研究思路。理论方面讨论了状态反馈变量的选取和状态反馈系数的确定并进行了仿真;实验方面讨论了面临的技术挑战,给出了研究的基本思路和可行的技术路线。