新冠肺炎交通防控政策对长沙市人口流动的影响

为探究新冠肺炎疫情下交通防控政策对长沙市人口流动的影响,本文根据长沙市在新冠 肺炎疫情期间颁布的交通防控政策和疫情实时防控情况划分防控阶段,基于百度迁徙大数据,利 用双重差分模型,识别长沙市不同阶段的交通防控政策以及量化防控效果,分析交通防控政策对 长沙市人口流动的影响。结果显示,长沙市在交通管制阶段,平均人口迁出强度、平均人口迁入 强度及城市内部出行强度分别下降了83.68%、69.24%及59.74%,有效地控制了人口流动,降低了 疫情扩散危险。在交通恢复阶段,长沙市人口流动强度逐渐反弹,城市内部出行强度基本恢复到 2019年同期水平。本文研究结果显示了交通管制对疫情扩散限制的有效性,为常态化疫情防控 下精准防控政策和复工复产政策制定提供参考。     

地铁网络服务韧性评估与最优恢复策略

针对已有基于拓扑效率的地铁网络韧性指标无法反映地铁运营实际的不足,构建考虑线 路流量影响的路网服务效率指标和基于服务效率的路网服务韧性指标,以及基于路网服务效率 的节点重要度指标;提出以路网服务韧性最大化为目标的优化模型,并基于遗传算法求解模型获 得最优恢复策略。算例结果表明：分别以服务效率和拓扑效率作为路网性能指标,获得的失效节 点恢复次序明显不同;蓄意攻击下,最优恢复策略获得的路网服务韧性分别比基于重要度的优先 恢复策略、基于节点度的优先恢复策略和随机恢复策略高16.76%、72.11%和86.21%。上述结果 表明,必须根据地铁运营实际合理选择路网性能指标和恢复策略,否则可能得到次优甚至明显偏 离实际的方案,无法实现预期目标。     

天气因素对福州地铁客流的影响分析

地铁作为一种绿色出行方式,是缓解城市交通拥堵的重要手段。地铁客流受到多种因素影响,其中天气因素变化较快,会造成地铁客流的快速变化。了解天气因素对地铁客流的影响,有助于建立相应的运输组织响应措施。本文旨在量化分析天气因素对福州地铁客流量的影响,并考虑原始天气指标的局限性,引入体现舒适度的指标。建立地铁客流与天气因素(包括气压、相对湿度、风力、降水、风寒指数等级、综合舒适度指数等级等)之间的多元线性回归模型,量化影响方向和影响程度。此外,工作日与非工作日的客流模式差异较大,将两者分别建模分析。研究发现：工作日,降水、风寒指数等级和综合舒适度指数等级对地铁客流有显著影响;非工作日,降水、气压、相对湿度、风寒指数等级和综合舒适度指数等级对地铁客流有显著影响。总体而言,非工作日地铁客流对天气因素更加敏感。     

基于网络交通分配方法族谱的交通分配 一体化技术与工程应用

为满足交通规划、建设与管理等应用场景对交通分配多样化的需求,结合目前交通分配方 法族谱中的众多模型与方法,本文构建能够满足族谱中所有交通分配特征的一体化交通分配技 术框架,提出交通网络交通分配一体化技术体系,并将该体系嵌入交通分析平台软件“交运之星- TranStar”中。该技术体系包括：“模型关键参数”“交通阻抗函数”“交通网络交通分配基础模型与 快速算法”3部分模块组合的分析模型一体化;面向步行、自行车、机动车及公共交通等多模式交 通网络的分析对象一体化;针对城市土地开发,交通网络建设,交通管理控制,公共交通系统,以 及交通政策制定等应用场景一体化。选取南京市道路网络和公交网络进行实证分析。结果表 明,本文提出的交通分配一体化技术具有处理超万节点多模式交通网络的能力,对各类交通模 式、典型业务场景的分析结果可为城市交通系统规划、建设与管理提供决策支持。     

Controller design for gating traffic control in presence of time-delay in urban road networks

Recent studies demonstrated the efficiency of feedback-based gating control in mitigating congestion in urban networks by exploiting the notion of macroscopic or network fundamental diagram (MFD or NFD). The employed feedback regulator of proportional-integral (PI)-type targets an operating NFD point of maximum throughput to enhance the mobility in the urban road network during the peak period, under saturated traffic conditions. In previous studies, gating was applied directly at the border of the protected network (PN), i.e. the network part to be protected from over-saturation. In this work, the recently developed feedback-based gating concept is applied at junctions located further upstream of the PN. This induces a time-delay, which corresponds to the travel time needed for gated vehicles to approach the PN. The resulting extended feedback control problem can be also tackled by use of a PI-type regulator, albeit with different gain values compared to the case without time-delay. Detailed procedures regarding the appropriate design of related feedback regulators are provided. In addition, the developed feedback concept is shown to work properly with very long time-steps as well. A large part of the Chania, Greece, urban network, modelled in a microscopic simulation environment under realistic traffic conditions, is used as test-bed in this study. The reported results demonstrate a stable and efficient behaviour and improved mobility of the overall network in terms of mean speed and travel time.     

Categorizing bicycling environments using GPS-based public bicycle speed data

A promising alternative transportation mode to address growing transportation and environmental issues is bicycle transportation, which is human-powered and emission-free. To increase the use of bicycles, it is fundamental to provide bicycle-friendly environments. The scientific assessment of a bicyclist’s perception of roadway environment, safety and comfort is of great interest. This study developed a methodology for categorizing bicycling environments defined by the bicyclist’s perceived level of safety and comfort. Second-by-second bicycle speed data were collected using global positioning systems (GPS) on public bicycles. A set of features representing the level of bicycling environments was extracted from the GPS-based bicycle speed and acceleration data. These data were used as inputs for the proposed categorization algorithm. A support vector machine (SVM), which is a well-known heuristic classifier, was adopted in this study. A promising rate of 81.6% for correct classification demonstrated the technical feasibility of the proposed algorithm. In addition, a framework for bicycle traffic monitoring based on data and outcomes derived from this study was discussed, which is a novel feature for traffic surveillance and monitoring.     

W-SPSA in practice: Approximation of weight matrices and calibration of traffic simulation models

The development and calibration of complex traffic models demands parsimonious techniques, because such models often involve hundreds of thousands of unknown parameters. The Weighted Simultaneous Perturbation Stochastic Approximation (W-SPSA) algorithm has been proven more efficient than its predecessor SPSA (Spall, 1998), particularly in situations where the correlation structure of the variables is not homogeneous. This is crucial in traffic simulation models where effectively some variables (e.g. readings from certain sensors) are strongly correlated, both in time and space, with some other variables (e.g. certain OD flows). In situations with reasonably sized traffic networks, the difference is relevant considering computational constraints. However, W-SPSA relies on determining a proper weight matrix (W) that represents those correlations, and such a process has been so far an open problem, and only heuristic approaches to obtain it have been considered.This paper presents W-SPSA in a formally comprehensive way, where effectively SPSA becomes an instance of W-SPSA, and explores alternative approaches for determining the matrix W. We demonstrate that, relying on a few simplifications that marginally affect the final solution, we can obtain W matrices that considerably outperform SPSA. We analyse the performance of our proposed algorithm in two applications in motorway networks in Singapore and Portugal, using a dynamic traffic assignment model and a microscopic traffic simulator, respectively.     

Analysis of air traffic control operational impact on aircraft vertical profiles supported by machine learning

The Air Traffic Management system is under a paradigm shift led by NextGen and SESAR. The new trajectory-based Concept of Operations is supported by performance-based trajectory predictors as major enablers. Currently, the performance of ground-based trajectory predictors is affected by diverse factors such as weather, lack of integration of operational information or aircraft performance uncertainty.Trajectory predictors could be enhanced by learning from historical data. Nowadays, data from the Air Traffic Management system may be exploited to understand to what extent Air Traffic Control actions impact on the vertical profile of flight trajectories.This paper analyses the impact of diverse operational factors on the vertical profile of flight trajectories. Firstly, Multilevel Linear Models are adopted to conduct a prior identification of these factors. Then, the information is exploited by trajectory predictors, where two types are used: point-mass trajectory predictors enhanced by learning the thrust law depending on those factors; and trajectory predictors based on Artificial Neural Networks.Air Traffic Control vertical operational procedures do not constitute a main factor impacting on the vertical profile of flight trajectories, once the top of descent is established. Additionally, airspace flows and the flight level at the trajectory top of descent are relevant features to be considered when learning from historical data, enhancing the overall performance of the trajectory predictors for the descent phase.     

Integrating mesoscopic dynamic traffic assignment with agent-based travel behavior models for cumulative land development impact analysis

A number of approaches have been developed to evaluate the impact of land development on transportation infrastructure. While traditional approaches are either limited to static modeling of traffic performance or lack a strong travel behavior foundation, today’s advanced computational technology makes it feasible to model an individual traveler’s response to land development. This study integrates dynamic traffic assignment (DTA) with a positive agent-based microsimulation travel behavior model for cumulative land development impact studies. The integrated model not only enhances the behavioral implementation of DTA, but also captures traffic dynamics. It provides an advanced yet practical approach to understanding the impact of a single or series of land development projects on an individual driver’s behavior, as well as the aggregated impacts on the demand pattern and time-dependent traffic conditions. A simulation-based optimization (SBO) approach is proposed for the calibration of the modeling system. The SBO calibration approach enhances the transferability of this integrated model to other study areas. Using a case study that focuses on the cumulative land development impact along a congested corridor in Maryland, various regional and local travel behavior changes are discussed to show the capability of this tool for behavior side estimations and the corresponding traffic impacts.     

Conflict-free arrival and departure trajectory planning for parallel runway with advanced point-merge system

In this paper, an efficient trajectory planning system is proposed to solve the integration of arrivals and departures on parallel runways with a novel route network system. Our first effort is made in designing an advanced Point Merge (PM) route network named Multi-Level Point Merge (ML-PM) to meet the requirements of parallel runway operations. Then, more efforts are paid on finding a complete and efficient framework capable of dynamically modelling the integration of arrival and departure trajectories on parallel runways, modelling the conflict detection and resolution in presence of curved trajectory and radius-to-fix merging process. After that, a suitable mathematical optimization formulation is built up. Receding Horizon Control (RHC) and Simulated Annealing (SA) algorithms are proposed to search the near-optimal solution for the large scale trajectories in routine dense operations. Taking Beijing Capital International Airport (BCIA) as a study case, the experimental results show that our system shows good performances on the management of arrivals and departures. It can automatically solve all the potential conflicts in presence of dense traffic flows. With its unique ML-PM route network, it can realize a shorter flying time and a near-Continuous Descent Approach (CDA) descent for arrival aircraft, an economical climbing for departure aircraft, an easier runway allocation together with trajectory control solutions. It shows a good and dynamic sequencing efficiency in Terminal Manoeuvring Area (TMA). In mixed ML-PM mode, under tested conditions, our proposed system can increase throughput at BCIA around 26%, compared with baseline. The methodology defined here could be easily applied to airports worldwide.