非自由交通流的non-FIFO车辆轨迹估计算法

将三角基本图的交通状态细分为自由流、中断和拥堵;基于非自由流特性,重新划分了U型时空域,以此找到适合的波速范围;重新确定了上游边界累积流量,使得边界函数刻画不过于宽泛;建立了非自由流Newell模型,并提出了使用该模型的判断条件;引入了车辆秩参数,达到在多车道上描述车辆超车现象的目的,并建立了更精确的车辆秩估计模型,从而建立了非自由流Newell扩展模型;提出了针对非自由流下2种情形的车辆轨迹估计算法,根据是否存在超车现象分为先进先出(FIFO)情形与非先进先出(non-FIFO)情形;结合数值模拟和实际交通案例,验证了算法的有效性。研究结果表明：2种情形下的轨迹估计算法都是有效的,当超车现象存在时,non-FIFO情形的估计效果较准确和稳健;在数值模拟研究中,non-FIFO情形的估计误差相对FIFO情形下降13.45%,non-FIFO情形更优;实际交通案例中,2个小汽车数据集在non-FIFO情形的估计误差相对FIFO情形均有所下降,下降幅度分别为2.38%、2.04%,且估计误差均服从高斯混合模型;公交车数据集因不存在超车现象,non-FIFO与FIFO情形的估计误差相等,均为4.90%,且估计误差服从伽马分布。可见,所建立的非自由流Newell模型对于中断多或拥堵状态占比多的交通数据均是有效可行的,且所提出的non-FIFO和FIFO情形的轨迹估计算法效果表现良好。     

单向三车道高速公路合流区智能网联车辆协同汇入控制

为提高多车道高速公路入口匝道智能网联车辆合流安全性与通行效率,提出了一种基于规则的换道策略和一个时间离散的车辆轨迹优化模型,以进行协同汇入控制;以普通单向三车道高速公路匝道合流区为研究对象,将合流区及其上下游路段划分为4个区域,并对关键的提前换道区和协同合流区分别进行交通控制;在上游提前换道区,基于最小安全跟车间距和速度效益的换道规则,将部分主线外侧车道、中间车道的车辆在合流区上游提前换道至内侧相邻车道,以此减轻合流区外侧车道的交通压力,提高合流效率;在下游协同合流区,选取合适的周期时长,以周期内合流车辆行驶速度最大为目标,不固定合流点,规划合流车辆的纵向行车轨迹,引导匝道车辆在周期结束后汇入主线,实现协同合流;利用SUMO和Python仿真验证提出的协同汇入控制方法,并进行临界跟驰车头间距的敏感性分析。仿真结果表明：与无控制自然合流相比,提出的协同汇入控制方法在不同的交通需求水平下能使车辆平均速度提高4.9%~21.1%,平均延误降低29.9%~56.5%,且不会出现停车现象;与先进先出合流控制相比,在高匝道交通需求水平下能使车辆平均速度提高3.4%~9.6%,平均延误降低22.9%~39.4%;较低的临界跟驰车头间距可以更好地提高合流区通行效率,且在主线交通需求水平较高时更明显。     

货车轨迹数据在公路货运系统中应用研究综述

随着轨迹数据可获取性及精度的持续提高,货车轨迹数据被广泛应用于公路货运系统的 规划与管理中,同时,人工智能和大数据分析技术的快速发展也为公路货运系统研究带来新的机 遇与挑战。本文全面梳理并总结了公路货运轨迹数据应用领域的相关研究,从基于轨迹数据的 货运出行信息辨识、货运系统关键特征预测、货运轨迹数据进一步应用3个方面回顾现有文献的 研究目标、主要内容和研究方法。通过文献分析发现：货运出行信息辨识研究聚焦于货运停留 点、车辆和货物、活动出行模式等热点主题,但现有辨识方法多移植于旅客出行研究,需要更多地 考虑货运出行的独特特征。在货运系统关键特征预测方面,研究者主要针对货运行程时间、空间 位置、出行需求等主题展开研究,并证明了基于轨迹数据预测货运特征的可行性,但预测时空范 围较为局限,需要根据具体的货运任务、货车司机特征和货运政策进行深入研究。此外,轨迹数 据也被应用于货运出行路径选择行为、货运停车休息行为、行驶安全、货运排放和能耗分析、货运 政策评估等研究。最后,在总结现有研究不足的基础上,本文认为未来研究应重点将货运轨迹数 据与其他多源数据相结合,从3个关键技术进行突破：一是针对货运实践个体,重点探索高效货车 驾驶员的出行特征和出行模式,并在货运系统中进行推广应用;二是针对交通运输新技术和新形 势,重点开发和优化自动驾驶技术和重大应急事件影响下的货运组织模式与策略;三是针对货运 供需关系及匹配机制,重点研究货运全流程供需状态辨识与预测,并结合深度学习等方法训练和 开发智能供需匹配模型,从而优化货运系统调度,助力社会散乱运力资源整合,提高货运系统的 综合效率。     

A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters

With trajectory data, a complete microscopic and macroscopic picture of traffic flow operations can be obtained. However, trajectory data are difficult to observe over large spatiotemporal regions—particularly in urban contexts—due to practical, technical and financial constraints. The next best thing is to estimate plausible trajectories from whatever data are available. This paper presents a generic data assimilation framework to reconstruct such plausible trajectories on signalized urban arterials using microscopic traffic flow models and data from loops (individual vehicle passages and thus vehicle counts); traffic control data; and (sparse) travel time measurements from whatever source available. The key problem we address is that loops suffer from miss- and over-counts, which result in unbounded errors in vehicle accumulations, rendering trajectory reconstruction highly problematic. Our framework solves this problem in two ways. First, we correct the systematic error in vehicle accumulation by fusing the counts with sparsely available travel times. Second, the proposed framework uses particle filtering and an innovative hierarchical resampling scheme, which effectively integrates over the remaining error distribution, resulting in plausible trajectories. The proposed data assimilation framework is tested and validated using simulated data. Experiments and an extensive sensitivity analysis show that the proposed method is robust to errors both in the model and in the measurements, and provides good estimations for vehicle accumulation and vehicle trajectories with moderate sensor quality. The framework does not impose restrictions on the type of microscopic models used and can be naturally extended to include and estimate additional trajectory attributes such as destination and path, given data are available for assimilation.     

Experimental study and modeling of car-following behavior under high speed situation

To investigate the car-following behavior under high speed driving conditions, we performed a set of 11-car-platoon experiments on Hefei airport highway. The formation and growth of oscillations have been analyzed and compared with that in low speed situations. It was found that there is considerable heterogeneity for the same driver over different runs of the experiment. This intra-driver heterogeneity was quantitatively depicted by a new index and incorporated in an enhanced two-dimensional intelligent driver model. Using both the new high-speed and the previous low-speed experimental data, the new and three existing models were calibrated. Simulation results show that the enhanced model outperforms the three existing car-following models that do not take into account this intra-driver heterogeneity in reproducing the essential features of the traffic in the experiments.     

Studying the benefits of carpooling in an urban area using automatic vehicle identification data

Carpooling has been considered a solution for alleviating traffic congestion and reducing air pollution in cities. However, the quantification of the benefits of large-scale carpooling in urban areas remains a challenge due to insufficient travel trajectory data. In this study, a trajectory reconstruction method is proposed to capture vehicle trajectories based on citywide license plate recognition (LPR) data. Then, the prospects of large-scale carpooling in an urban area under two scenarios, namely, all vehicle travel demands under real-time carpooling condition and commuter vehicle travel demands under long-term carpooling condition, are evaluated by solving an integer programming model based on an updated longest common subsequence (LCS) algorithm. A maximum weight non-bipartite matching algorithm is introduced to find the optimal solution for the proposed model. Finally, road network trip volume reduction and travel speed improvement are estimated to measure the traffic benefits attributed to carpooling. This study is applied to a dataset that contains millions of LPR data recorded in Langfang, China for 1 week. Results demonstrate that under the real-time carpooling condition, the total trip volumes for different carpooling comfort levels decrease by 32–49%, and the peak-hour travel speeds on most road segments increase by 5–40%. The long-term carpooling relationship among commuter vehicles can reduce commuter trips by an average of 30% and 24% in the morning and evening peak hours, respectively, during workdays. This study shows the application potential and promotes the development of this vehicle travel mode.     

Estimating traffic volumes for signalized intersections using connected vehicle data

Recently connected vehicle (CV) technology has received significant attention thanks to active pilot deployments supported by the US Department of Transportation (USDOT). At signalized intersections, CVs may serve as mobile sensors, providing opportunities of reducing dependencies on conventional vehicle detectors for signal operation. However, most of the existing studies mainly focus on scenarios that penetration rates of CVs reach certain level, e.g., 25%, which may not be feasible in the near future. How to utilize data from a small number of CVs to improve traffic signal operation remains an open question. In this work, we develop an approach to estimate traffic volume, a key input to many signal optimization algorithms, using GPS trajectory data from CV or navigation devices under low market penetration rates. To estimate traffic volumes, we model vehicle arrivals at signalized intersections as a time-dependent Poisson process, which can account for signal coordination. The estimation problem is formulated as a maximum likelihood problem given multiple observed trajectories from CVs approaching to the intersection. An expectation maximization (EM) procedure is derived to solve the estimation problem. Two case studies were conducted to validate our estimation algorithm. One uses the CV data from the Safety Pilot Model Deployment (SPMD) project, in which around 2800 CVs were deployed in the City of Ann Arbor, MI. The other uses vehicle trajectory data from users of a commercial navigation service in China. Mean absolute percentage error (MAPE) of the estimation is found to be 9–12%, based on benchmark data manually collected and data from loop detectors. Considering the existing scale of CV deployments, the proposed approach could be of significant help to traffic management agencies for evaluating and operating traffic signals, paving the way of using CVs for detector-free signal operation in the future.     

Predicting market potential and environmental benefits of deploying electric taxis in Nanjing,China

This paper investigates the market potential and environmental benefits of replacing internal combustion engine (ICE) vehicles with battery electric vehicles (BEVs) in the taxi fleet in Nanjing, China. Vehicle trajectory data collected by onboard global positioning system (GPS) units are used to study the travel patterns of taxis. The impacts of charger power, charging infrastructure coverage, and taxi apps on the feasibility of electric taxis are quantified, considering taxi drivers’ recharging behavior and operating activities. It is found that (1) depending on the charger power and coverage, 19% (with AC Level 2 chargers and 20% charger network coverage) to 56% (with DC chargers and 100% charger network coverage) of the ICE vehicles can be replaced by electric taxis without driving pattern changes; (2) by using taxi apps to find nearby passengers and charging stations, drivers could utilize the empty cruising time to charge the battery, which may increase the acceptance of BEVs by up to 82.6% compared to the scenario without taxi apps; and (3) tailpipe emissions in urban areas could be significantly reduced with taxi electrification: a mixed taxi fleet with 46% compressed-natural-gas-powered (CNG) and 54% electricity-powered vehicles can reduce the tailpipe emissions by 48% in comparison with the fleet of 100% CNG taxis.     

柴油发电机组并联运行的鲁棒控制研究

船舶电力系统通常是多台柴油发电机组并联运行,多机组并联运行容易发生功率传递,从而产生功率振荡现象。本文给出了两台柴油发电机组并联运行的数学模型,为柴油发电机组设计了非线性鲁棒综合控制器,并给出了将非线性鲁棒综合控制器应用于柴油发电机组并联运行控制的仿真结果。表明,两台柴油发电机组的功角差为零,抑制了柴油发电机组并联运行的功率振荡现象,改善了电力系统的稳定性。     

基于秩最小化矩阵去噪的船舶轨迹重构方法

针对船舶自动识别系统(Automatic Identification System，AIS)在实际应用中存在错误数据频发、数据丢包等问题，本文提出一种基于秩最小化矩阵去噪的船舶轨迹重构方法，利用去噪实现轨迹重构，同时，实现对轨迹的去噪和缺失补全。该方法通过线性插值实现经度对齐，将轨迹数据转化为轨迹矩阵，从而补全轨迹中的缺失值。由于补全结果存在非常大的误差，因此，引入 PLR(Patch-Based Low-Rank Minimization)算法去噪，消除误差。同时，为进一步提升补全效果，通 过2D-VMD(Two-Dimensional Variational Mode Decomposition)算法将矩阵分解为不同频率的IMF (Intrinsic Mode Function)，并分别进行PLR去噪，合并去噪结果，得到最终重构后轨迹。本文以长江武汉段水域船舶AIS轨迹为研究对象，通过实验证明该方法在不同缺失比例以及随机缺失和连续缺失两种情境下具有鲁棒性和较强的稳定性；并与 HALRTC(High-Accuracy Low-Rank Tensor Completion)、TRMF(Temporal Regularized Matrix Factorization)等方法进行比较，结果表明， 该方法相较于HALRTC等方法具有更高的精度，并在高损失率下表现出较好的重构效果。