返回
类型 基础研究 预答辩日期 2018-03-15
开始(开题)日期 2016-08-19 论文结束日期 2017-12-25
地点 九龙湖校区交通楼323室 论文选题来源 973、863项目     论文字数 6.62 (万字)
题目 子网络交通需求估计方法与应用
主题词 交通需求估计,子网络,交通拥堵,扩样系数,检测器布设
摘要 对城市道路网络交通起讫点(OD)需求的建模和预测,是实现区域交通规划、交通管控措施设计等工作的基础和先决条件,城市交通路径诱导、限行限速、拥堵控制等措施都离不开准确的OD需求。OD需求不仅为城市路网供需平衡状况和政府建设投资提供决策依据,也可以为城市机动车的发展规模提供数据支持,只有准确估计OD需求,才能把握现状路网的交通特性,才能在交通规划以及城市总体规划过程中更加有针对性和目的性地低缓解交通拥堵。本文从实际工程应用角度出发,主要对全网络和子网络交通需求估计理论展开深入研究,具体而言,本文的研究工作主要包括以下几个方面: (1)基于交通流数据的OD估计:拥堵网络下OD需求估计 根据观测的路段和路径旅行时间,构建了两个双层规划模型来评估拥堵网络状况下交通OD需求,其中一个模型观测路径旅行时间轨迹已知而另一个模型中部分观测路径旅行时间轨迹未知。提出的模型同时利用路段和路径信息来决定网络OD需求,其中上层模型以最小化历史/观测交通信息(即OD需求、路段旅行时间和路径旅行时间)和估计交通信息之间的距离为目标,下层模型为随机用户均衡(SUE)模型。与此同时,观测路段旅行时间可以捕捉到拥堵网络中流量和费用(出行时间)的关系。运用K-means(硬分类)和高斯混合模型(GMM,软分类)这两种聚类方法来识别观测路径的轨迹。设计了迭代算法来求解建立的OD估计模型,该算法包含最速下降法、相继平均算法和最大期望(EM)算法来分别求解上层模型、下层模型和GMM。数值实验表明在拥堵网络状态下,基于旅行时间预测的OD需求优于基于交通流量预测的OD需求;比仅使用路段信息,同时使用路段和路径信息可以预测出更加准确的OD值;基于GMM聚类方法估计的OD需求显优于基于K-means聚类方法的估计值,尤其当观测一些错误数据时。 (2)基于样本数据的OD估计:扩样系数推断 基于样本数据的OD需求估计方法即将调查数据或者手机定位数据集计到交通网络中,不可避免地需要将样本估计的需求扩样到整个出行者数量上,因而分别研究了随机性和确定性扩样系数推断模型。提出了两阶段优化模型来决定检测器布局和推断随机性扩样系数,其中第一阶段通过在一定的预算下最小化扩样系数的可变性(即检测器布局模型)来识别最优的检测器布局策略,第二阶段利用扩样系数的先验信息和第一阶段布设检测器路段观测的流量来推断随机性扩样系数(即贝叶斯扩样系数推断模型)。同样根据观测的路段流量,用双层规划方法构建了确定性扩样系数推断模型,该模型的上层目标为最小化观测的和估计的路段流量之间的距离,下层为SUE模型。设计了逐次识别检测器布局位置的策略来求解检测布局模型,该方法可以避免求解矩阵的逆。采用迭代算法来获得贝叶斯扩样系数推断模型和确定性扩样系数推断模型的最优解。数值实验表明在一定的预算下,检测器布局策略可以提供最可靠的检测器位置,从而为随机性扩样系数推断提供观测数据;测试结果也表明同时利用内生信息(即扩样系数的先验信息)和外生因素(即观测路段流量)可以更好地推断扩样系数。 (3)基于全网络流量集计(增量均衡分配方法)的子网络OD估计 提出了基于用户均衡(UE)和SUE的增量均衡分配方法来对OD需求进行分配,从而分别获取网络的UE解和SUE解。基于UE的增量均衡分配方法将OD矩阵均分成一定的份数,每次将每份OD矩阵加载在当前的最短路上,当所有份数的OD矩阵加载完后,提取出每个OD对非最短路径上的流量集计成新的OD矩阵,从而再次执行OD矩阵的均分和加载,反复迭代直到满足精度。与基于UE的增量均衡分配方法不同的是,基于SUE的增量均衡分配方法在流量加载时,需要运用Logit公式计算路径选择概率,同时计算出当前路径流量占该OD对总需求量的比例,进而将每份OD矩阵加载在每个OD对选择概率与比例差值最大的路径上;同样提取路径集计新OD矩阵时,提取出每条路径多出路径选择概率这部分流量。将建立的基于UE和SUE的增量均衡分配方法获得路径流量解集计到子网络上,从而获得子网络OD需求。数值实验表明基于UE和SUE的增量均衡分配方法保留了所有迭代过程中的路径集;且这两种方法均能稳定收敛到很高精度。 (4)基于子网络拓扑分析的子网络OD估计 同时考虑到子网络与外部网络联系和交通OD需求一致性,构建了基于网络拓扑的子网络OD需求估计模型,该模型使用OD需求的最大熵原则作为目标函数,使用子网络每个OD点的总交通发生量、吸引量和部分已知的OD需求作为约束条件。通过子网络边界点转换和拓扑结构分析获得这些的总交通发生量、吸引量和部分OD需求。设计了凸组合算法来求解建立的模型,该算法将非线性的原问题转换为经典的线性运输问题,从而使用表上作业法求解该运输问题。运用Sioux Falls和昆山市路网对算法和模型进行了测试,结果表明任意两个不同起点(终点)但是同样终点(起点)交通需求的比值是相等的;设计的算法可以快速收敛到很高精度;且全网络OD和估计的子网络OD分配出来的路段量之间误差比较小,即在可应用范围内。
英文题目 SUBNETWORK TRAFFIC DEMAND ESTIMATION: THEORY AND APPLICATION
英文主题词 traffic demand estimation, subnetwork, traffic congestion, scaling rate, sensor location deployment
英文摘要 Origin-destination (OD) demand modeling and estimation in urban road network is a foundamental component when a transportation planner or operator makes long term transportation planning and short term traffic management. Urban traffic route guidance, road space rationing, speed limit, or congestion control cannot be implemented without the accurate OD demands. OD demands can not only provide the decision of government investment construction projects and balance of supply-demand in urban road network, but also provide the data support of development scale of urban vehicles. Accurate OD demand estimation is helpful to catch on the traffic characteristics of road network, and hence alleviate traffic congestion when do some transportation planning and management. From the perspective of practical engineering application, the theories of full network and subnetwork OD demands estimation is studied. Specifically, the following main contributions are made in this thesis. (1) Traffic flow based OD estimation: OD demand estimation under congested network Two bi-level models to estimate OD demand under congested network are explored in terms of the observed link and route travel times, where one model has the known trajectories of observed route travel times and the other model has both known and unknown trajectories of observed route travel times. The proposed models leverage both the link and route traffic information to determine the network OD demand that minimizes the distances between the historical/observed and estimated traffic information (OD demand, link and route travel time) in the upper-level, and optimizes the stochastic user equilibrium (SUE) in the lower-level. Meanwhile, the observed information of travel time can capture the relationship between flow and cost (trip time) in congested network. The K-means (hard assignment) and Gaussian mixture model (GMM, soft assignment) clustering methods are presented to identify the trajectories of observed route travel times. An iterative solution algorithm, which contains the method of gradient descent, the method of successive average and Expectation-Maximization (EM) algorithm for solving the upper-level model, lower level model and GMM, respectively, is proposed to solve the built OD estimation models. Results from numerical experiments demonstrate the superiority of the travel time based model over the traditional flow based method in congested traffic network; using both the route and link information outperforms only using link information in the estimation of OD demand; and also suggest that the GMM is superior to the method of K-means especially in the case of observing some wrong data. (2) Sample based OD estimation: scaling rate inference The sample-based origin-destination (OD) demand estimation is to aggregate the survey data or mobile phone location data into the traffic network. Inevitably, the estimated OD demands should be scaled up to the population level counts. Hence, both the stochastic and deterministic scaling rate inferences are studied. A two-stage optimization model to determine the sensor location and stochastic scaling rate in an integrated manner, where the first stage which is sensor deployment model identifies the optimal sensor location strategy through minimizing the variability of the scaling rate inference under a budget constraint, and the second stage which is Bayesian-based scaling rate inference model leverages the date observed from the identified sensor locations and the prior information to determine the stochastic scaling rate. Deterministic scaling rate inference model which is a bi-level program is explored in terms of the observed link flows, which minimizes the distances between the observed and estimated link flows in the upper-level, and optimizes the SUE in the lower-level. A sequential identifying sensor location algorithm that avoids matrix inversions is proposed to solve the sensor deployment model. And the iterative solution algorithm is developed to solve the built Bayesian-based and deterministic scaling rate inference models. Results from numerical experiments demonstrate that the sensor deployment model can provide the most reliable scheme of sensor locations under certain budgets, further contribute to make a reliable estimation of stochastic scaling rate. The results also illustrate that both the endogenous information (i.e., prior information of scaling rate) and exogenous factors (i.e., observed link flows) can help make a better scaling rate inference. (3) Subnetwork OD estimation using the method of full network route flow (obtained from incremental assignment method) aggregating The user equilibrium (UE) based and SUE based incremental equibrium assignment methods are proposed to assign the OD demands and hence obtaion the UE and SUE solutions. In UE based incremental equibrium assignment method, the OD demand matrix is firstly divided into several matrixes, whose demands are divided from the whole OD demands in average. Each OD matrix is loaded on the shortest routes with the current link costs. When all the OD matrixes are loaded, the traffic flows on the route whose travel costs are not the shortest are picked up to aggregate the new OD matrix. Then divided the OD matrix and load each matix until the accuracy is satisfied. Unlike the UE based incremental equibrium assignment method, when loading the traffic flows in the SUE based incremental equibrium assignment method, the Logit formula is needed to compute the route choice probability. And also the ratio of current route flow to total demands needs to be derived. Hence, each OD matrix is loaded on the route with maximum difference between the route choice probability and the ratio. The redundant route flows that exceed the route choice probability are aggregated to form the new OD matrix. The subnetwork OD demands are obtained through aggregating all the route flows assigned from the UE based and SUE based incremental equibrium assignment methods. Results from numerical experiments demonstrate that the UE based and SUE based incremental equibrium assignment methods reserve all the route set in the iterative processes; and all these two methods can steadily converge to a high accuracy. (4) Topology based subnetwork OD matrix estimation A topology based subnetwork OD matrix estimation model under traffic demand constraints that explicitly considers both internal-external subnetwork connection and OD demand consistency between the subnetwork and full network is proposed. This new model uses maximum entropy of OD demands as the objective function, and uses total traffic generations (attractions) along with some fixed OD demands of subnetwork OD nodes as the constraints. The total traffic generations and attractions along with the fixed OD demands of subnetwork OD nodes are obtained through OD nodes transformation and subnetwork topology analysis. For solving the proposed model, a convex combination method is used to convert nonlinear topology based subnetwork OD matrix estimation model to classical linear transportation problem, and tabular method is used to solve the transportation problem. Results from experiments of the Sioux Falls network and Kunshan network demonstrate that the ratios of any two demands from different origin (destination) but the same destination (origin) are equivalent; the designed algorithm can rapidly make convergence to a high accuracy; and also the relative errors of traffic flows assigned from subnetwork and full network in most links are very small, hence the proposed model satisfies the application requirement.
学术讨论
主办单位时间地点报告人报告主题
东南大学交通学院 2017年11月21日 交通学院三楼会议室 孙超 OD估计方法研究
东南大学交通学院 2014年9月3日 交通学院三楼会议室 孙超 基于鲁棒优化的随机网络交通流量分配研究
东南大学交通学院 2015年11月11日 交通学院三楼会议室 杨超 基于手机数据的个体活动特性研究
东南大学交通学院 2016年5月15日 交通学院三楼会议室 孙超 最大熵双目标均衡模型
东南大学交通学院 2016年6月4日 交通学院三楼会议室 王帅安 Reducing shipping emissions: models and algorithms
东南大学交通学院 2016年7月12日 交通学院三楼会议室 Anthony Chen Measuring redundancy of transport networks
东南大学交通学院 2016年9月3日 交通学院三楼会议室 孙超 Origin-destination estimation using observed travel time under congested network
东南大学交通学院 2017年10月23日 交通学院三楼会议室 龙建成 城市动态交通分配问题研究
     
学术会议
会议名称时间地点本人报告本人报告题目
Transportation Research Board 2015年1月 美国华盛顿 Multiclass stochastic user equilibrium model with elastic demand: considering systematic and accidental delays
Transportation Research Board 2017年1月 美国华盛顿 Travel time reliability with boundedly rational travelers
Transportation Research Board 2017年1月 美国华盛顿 Subnetwork origin-destination matrix estimation under traffic demand constraints
     
代表作
论文名称
快速收敛的牛顿路径算法在交通分配中的应用
Range of User-equilibrium Route Flow with Applications
Multiclass Stochastic User Equilibrium Model with Elastic Demand: Considering Systematic and Acciden
Stochastic Traffic Distribution Model Based on Robust Optimization
Boundedly Rational User Equilibrium with Restricted Unused Routes
Multi-criteria user equilibrium model considering travel time, travel time reliability and distance
Travel time reliability with boundedly rational travelers
Stochastic network equilibrium model with reliable travel time confidence level
 
答辩委员会组成信息
姓名职称导师类别工作单位是否主席备注
邓卫 正高 教授 博导 东南大学交通学院
常玉林 正高 教授 博导 江苏大学汽车与交通工程学院
陈学武 正高 教授 博导 东南大学交通学院
郑长江 正高 教授 博导 河海大学土木与交通学院
杨敏 正高 教授 博导 东南大学交通学院
何杰 正高 教授 博导 东南大学交通学院
      
答辩秘书信息
姓名职称工作单位备注
李大韦 副高 副教授 东南大学交通学院