Comparison of Estimated Cycle Split Failures from High-Resolution Controller Event and Connected Vehicle Trajectory Data

Current traffic signal split failure (SF) estimations derived from high-resolution controller event data rely on detector occupancy ratios and preset thresholds. The reliability of these techniques depends on the selected thresholds, detector lengths, and vehicle arrival patterns. Connected vehicle (CV) trajectory data can more definitively show when a vehicle split fails by evaluating the number of stops it experiences as it approaches an intersection, but it has limited market penetration. This paper compares cycle-by-cycle SF estimations from both high-resolution controller event data and CV trajectory data, and evaluates the effect of data aggregation on SF agreement between the two techniques. Results indicate that, in general, split failure events identified from CV data are likely to also be captured from high-resolution data, but split failure events identified from high-resolution data are less likely to be captured from CV data. This is due to the CV market penetration rate (MPR) of ~5% being too low to capture representative data for every controller cycle. However, data aggregation can increase the ratio in which CV data captures split failure events. For example, day-of-week data aggregation increased the percentage of split failures identified with high-resolution data that were also captured with CV data from 35% to 56%. It is recommended that aggregated CV data be used to estimate SF as it provides conservative and actionable results without the limitations of intersection and detector configuration. As the CV MPR increases, the accuracy of CV-based SF estimation will also improve.

Evaluation of Arterial Signal Coordination with Commercial Connected Vehicle Data: Empirical Traffic Flow Visualization and Performance Measurement

Emerging connected vehicle (CV) data sets have recently become commercially available, enabling analysts to develop a variety of powerful performance measures without deploying any field infrastructure. This paper presents several tools using CV data to evaluate traffic progression quality along a signalized corridor. These include both performance measures for high-level analysis as well as visualizations to examine details of the coordinated operation. With the use of CV data, it is possible to assess not only the movement of traffic on the corridor but also to consider its origin-destination (O-D) path through the corridor. Results for the real-world operation of an eight-intersection signalized arterial are presented. A series of high-level performance measures are used to evaluate overall performance by time of day, with differing results by metric. Next, the details of the operation are examined with the use of two visualization tools: a cyclic time-space diagram (TSD) and an empirical platoon progression diagram (PPD). Comparing flow visualizations developed with different included O-D paths reveals several features, such as the presence of secondary and tertiary platoons on certain sections that cannot be seen when only end-to-end journeys are included. In addition, speed heat maps are generated, providing both speed performance along the corridor and locations and the extent of the queue. The proposed visualization tools portray the corridor’s performance holistically instead of combining individual signal performance metrics. The techniques exhibited in this study are compelling for identifying locations where engineering solutions such as access management or timing plan change are required. The recent progress in infrastructure-free sensing technology has significantly increased the scope of CV data-based traffic management systems, enhancing the significance of this study. The study demonstrates the utility of CV trajectory data for obtaining high-level details of the corridor performance as well as drilling down into the minute specifics.

Autonomous UAV 3D trajectory optimization and transmission scheduling for sensor data collection on uneven terrains

This paper considers a time-constrained data collection problem from a network of ground sensors located on uneven terrain by an Unmanned Aerial Vehicle(UAV),a typical Unmanned Aerial System(UAS).The ground sensors harvest renewable energy and are equipped with batteries and data buffers.The ground sensor model takes into account sensor data buffer and battery limitations.An asymptotically globally optimal method of joint UAV 3D trajectory optimization and data transmission schedule is developed.The developed method maximizes the amount of data transmitted to the UAV without losses and too long delays and minimizes the propulsion energy of the UAV.The developed algorithm of optimal trajectory optimization and transmission scheduling is based on dynamic programming.Computer simulations demonstrate the effectiveness of the proposed algorithm.

Using Anonymous Connected Vehicle Data to Evaluate Impact of Speed Feedback Displays, Speed Limit Signs and Roadway Features on Interstate Work Zones Speeds

Annually, there are over 120,000 crashes in work zones in the United States. High speeds in construction zones are a well-documented risk factor that increases the frequency and severity of crashes. This study used connected vehicle data to evaluate the spatial and temporal impact that regulatory signs, speed feedback displays, and construction site geometry had on vehicle speed. Over 27,000 unique trips over 2 weeks on a 15-mile interstate construction work zone near Lebanon, IN were analyzed. Spatial analysis over a 0.2-mi segment before and after the posted speed limit signs showed that the regulatory signs had no statistical impact on reducing speeds. A before/after analysis was also conducted to study the impact of radar-based speed feedback that displays the motorists’ speed on a sign below a regulatory speed limit sign. Results showed a maximum drop in median speeds of approximately 5 mph. Speeds greater than 15 mph above the speed limit dropped by 10% - 15%. The reduction in speeds began approximately 1000 feet ahead of the sign and results were found to be statistically significant. The analysis also revealed that larger speed drops inside the work zone were due to geometric constraints that required additional driver workloads, especially during shoulder width changes and lane shifts. The results from this study will be helpful for agencies to understand driver behavior in the work zones and to identify proper speed limit compliance techniques that significantly reduce driver speeds in and around work zones.

Estimation of Connected Vehicle Penetration on US Roads in Indiana, Ohio, and Pennsylvania

Connected vehicle data is an important assessment tool for agencies to evaluate the performance of freeways and arterials, provided there is sufficient penetration to provide statistically robust performance measures. A common concern by agencies interested in using crowd sourced probe data is the penetration rate across different types of roads, different hours of the day, and different regions. This paper describes and demonstrates a methodology that uses data from state highway performance monitoring systems in Indiana, Ohio and Pennsylvania. The study analyzes 54 locations over the 3 states for select Wednesdays and Saturdays in 2020 and 2021. Overall, across all locations and dates, the median penetration was approximately 4.5%. The median penetration for August 2020 for Indiana, Ohio, and Pennsylvania was 4.6%, 4.3%, and 4.0%, respectively. The median penetration for those same states in August 2020 on interstates and non-interstates was 3.9% and 4.6%, respectively. Additionally, the study conducted a longitudinal evaluation of Indiana penetration for selected months between January 2020 and June 2021. Indiana penetration increased modestly between December 2020 and June 2021, perhaps due to the post-COVID rebound of passenger vehicle traffic. This paper concludes by recommending that the techniques described in this paper be scaled to other states so that traffic engineers can make informed decisions on the use and limitations of connected vehicle data for various use cases.

UAV trajectory planning algorithmfor data collection in wireless sensor networks

In order to maximize the value of information(VoI)of collected data in unmanned aerial vehicle(UAV)-aided wireless sensor networks(WSNs),a UAV trajectory planning algorithm named maximum VoI first and successive convex approximation(MVF-SCA)is proposed.First,the Rician channel model is adopted in the system and sensor nodes(SNs)are divided into key nodes and common nodes.Secondly,the data collection problem is formulated as a mixed integer non-linear program(MINLP)problem.The problem is divided into two sub-problems according to the different types of SNs to seek a sub-optimal solution with a low complexity.Finally,the MVF-SCA algorithm for UAV trajectory planning is proposed,which can not only be used for daily data collection in the target area,but also collect time-sensitive abnormal data in time when the exception occurs.Simulation results show that,compared with the existing classic traveling salesman problem(TSP)algorithm and greedy path planning algorithm,the VoI collected by the proposed algorithm can be improved by about 15%to 30%.

Trajectory-Based Data Forwarding Schemes for Vehicular Networks

This paper explains trajectory-based data forwarding schemes for multihop data delivery in vehicular networks where the trajectory is the GPS navigation path for driving in a road network. Nowadays, GPS-based navigation is popular with drivers either for efficient driv- ing in unfamiliar road networks or for a better route, even in familiar road networks with heavy traffic. In this paper, we describe how to take advantage of vehicle trajectories in order to design data-forwarding schemes for information exchange in vehicular networks. The design of data-forwarding schemes takes into account not only the macro-scoped mobility of vehicular traffic statistics in road net- works, but also the micro-scoped mobility of individual vehicle trajectories. This paper addresses the importance of vehicle trajectory in the design of multihop vehicle-to-infrastructure, infrastructure-to-vehicle, and vehicle-to-vehicle data forwarding schemes. First, we explain the modeling of packet delivery delay and vehicle travel delay in both a road segment and an end-to-end path in a road net- work. Second, we describe a state-of-the-art data forwarding scheme using vehicular traffic statistics for the estimation of the end-to- end delivery delay as a forwarding metric. Last, we describe two data forwarding schemes based on both vehicle trajectory and vehicu- lar traffic statistics in a privacy-preserving manner.

Analysis of Electric and Hybrid Vehicle Usage in Proximity to Charging Infrastructure in Indiana

This paper explores the movement of connected vehicles in Indiana for vehicles classified by the NHTSA Product Information Catalog Vehicle listing as being either electric (EV) or hybrid electric (HV). Analysis of trajectories from July 12-18, 2021 for the state of Indiana observed nearly 33,300 trips and 267,000 vehicle miles travelled (VMT) for the combination of EV and HV. Approximately 53% of the VMT occurred in just 10 counties. For just EVs, there were 9814 unique trips and 64,700 Electric Vehicle Miles Traveled (EVMTs) in total. A further categorization of this revealed that 18% of these EVMTs were on Interstate roadways and 82% on non-interstate roads. Proximity analysis of existing DC Fast charging stations in relation to interstate roadways revealed multiple charging deserts that would be most benefited by additional charging capacity. Eleven roadway sections among the 9 interstates were found to have a gap in available DC fast chargers of 50 miles or more. Although the connected vehicle data set analyzed did not include all EV’s the methodology presented in this paper provides a technique that can be scaled as additional EV connected vehicle data becomes available to agencies. Furthermore, it emphasizes the need for transportation agencies and automotive vendors to strengthen their data sharing partnerships to help accelerate the adoption of EV and reduce consumer range anxiety with EV. Graphics are included that illustrate examples of counties that are both overserved and underserved by charging infrastructure.

支持无线采能及簇间负载均衡的无人机辅助数据调度及轨迹优化算法

该文研究了无人机(UAV)辅助无线传感器网络的数据收集问题。首先提出基于均值漂移算法的传感器节点(SN)初始分簇策略,进而以簇间负载均衡为目标,设计SN切换算法。基于所得成簇策略,将UAV数据收集及轨迹规划问题建模为系统能耗最小化问题。由于该问题是一个非凸问题,难以直接求解,将其分为两个子问题,即数据调度子问题及UAV轨迹规划子问题。针对数据调度子问题,提出一种基于多时隙库恩-蒙克雷斯算法的时频资源调度策略。针对UAV轨迹规划子问题,将其建模为马尔可夫决策过程,并提出一种基于深度Q网络的UAV轨迹规划算法。仿真结果验证了所提算法的有效性。

基于COMGRU的AUV航路轨迹预测方法

针对采用神经网络预测自主水下机器人航迹存在滞后性的问题,本文提出一种基于信息压缩的改进门控循环神经网络,用于水下自主机器人航路多步轨迹预测。该算法将水下自主机器人航行轨迹附近的障碍物位置信息、海流信息以及时空轨迹信息共同构成的地理位置信息进行数据压缩处理,作为本文预测网络的输入,以提高网络训练效率。实验验证该算法减少了水下自主机器人航迹多步预测的滞后性且具有较高的准确率。

基于轨迹数据的非机动车左转信号灯安全效应评估

【目的】对城市交叉口采用的左转非机动车信号灯设施进行交通安全性量化评估。【方法】提出一种基于拓展碰撞时间(extended time to collision,ETTC)指标的左转非机动车信号灯安全效应评估方法。针对现有的碰撞时间(time to collision,TTC)指标不适于评估交叉口左转非机动车冲突的问题,考虑非机动车车辆尺寸与加速度对交通冲突的影响,采用拓展碰撞时间指标,评估交叉口非机动车交通冲突。收集长沙市4个信号交叉口的视频大数据,利用视频软件Tracker提取车辆微观轨迹后,开展案例分析。【结果】左转非机动车信号灯在时间上明确了非机动车的通行权,其设置能显著降低非机动车冲突率,在平峰、高峰时段非机动车冲突率分别降低了40.11%、25.27%。在直行相位末期、左转相位即将启亮时,设置组的左转非机动车在待行区等待,冲突率降为0;而对比组近50%的非机动车违规左转,冲突严重。设置左转非机动车信号灯的改善效果随非机动车流量的增大呈先增加后降低趋势,而随机动车流量的增大呈逐步波动下降趋势。【结论】本研究揭示了非机动车左转信号灯的设置对减少交叉口交通冲突的影响,可为城市交叉口非机动车交通安全管控提供有益参考。

基于Informer算法的网联车辆运动轨迹预测模型

自动驾驶汽车可以根据轨迹预测算法计算周边车辆的运动轨迹,并作出反应以降低行车风险,而传统的轨迹预测模型在长时间序列预测的情况下会产生较大的误差。为解决这一问题,提出了一种以Informer算法为基础的轨迹预测模型,并根据公开数据集NGSIM进行实验分析。首先通过对称指数移动平均法(sEMA)对原始数据进行滤波处理,并在原有的Informer编码器中加入了联合归一化层对不同车辆进行特征提取处理,减少了不同车辆之间的运动误差,通过考虑车辆的本身速度信息与周围环境的车辆运动信息,提高了预测精度,最后经过解码器得到未来时刻的车辆轨迹分布。结果表明,模型对车辆的轨迹预测误差在0.5 m以内;通过对轨迹预测的MAE与MSE结果分析可知,预测时间超过0.3 s以后,Informer模型的轨迹预测效果明显优于其他算法,验证了模型和算法的有效性。

苜蓿叶立交交织区冲突特征及严重程度影响因素分析

为明确城市快速路苜蓿叶立交交织区的冲突特征及冲突严重程度的关键影响因素,以重庆市北环立交为研究对象,使用无人机拍摄交织区车辆自然驾驶视频。搭建基于YOLOX和Deep-SORT的车辆目标检测和追踪框架,从航拍视频中获取10483条车辆轨迹数据,根据交互车辆的相对位置提取纵向和侧向冲突样本,并基于二维拓展碰撞时间指标将冲突事件划分为轻微、一般和严重;考虑包括宏观交通流参数和车辆微观运动参数等17个解释变量,计算多元Logistic回归模型、随机森林模型和CatBoost模型的评估指标,并分别选择对纵向冲突和侧向冲突识别性能更优的模型,进一步分析影响交织区内冲突严重程度的关键影响因素。结果表明,侧向冲突是苜蓿叶立交交织区的主要冲突类型,冲突持续时间较纵向冲突更长,碰撞风险更高;一般冲突在交织区内分布范围最广,严重冲突其次,轻微冲突分布最集中,主要分布于合流三角区附近;不同解释变量对冲突严重程度的影响程度存在一定差异,纵向严重冲突与相对速度差和目标车长度等6种指标显著相关,对于侧向冲突,重要性前5的变量分别是相对速度差、平均车头间距、目标车速度、目标车横向位置及目标车纵向位置,且当相对速度超过20 km·h^(-1),车速超过60 km·h^(-1)及平均车头间距低于12 m时,侧向碰撞风险将大幅增加。研究成果可对苜蓿叶立交交织区危险驾驶行为预警系统的设计和主动交通安全管理提供依据。

信息收集中无人机节能轨迹设计与资源优化

为优化无线传感器网络(wireless sensor networks,WSN)中无人机(unmanned aerial vehicle,UAV)辅助信息收集系统的能量消耗,在综合考虑无人机飞行能耗和传感器节点上行数据传输能耗的基础上,提出了一种考虑链路间干扰的无人机轨迹和传感器功率分配联合优化算法。首先,基于实际约束构建了系统能耗最小化问题模型;然后,针对多约束优化问题的非凸性特征,采用块坐标下降法(block coordinate descent,BCD)将系统能耗最小化问题分解为固定无人机轨迹下的功率分配和固定功率分配下的无人机轨迹优化两个子问题,根据子问题的数学特征,采用凸近似法将非凸问题转化成可以求解的近似凸优化问题,通过对两个子问题的交替迭代优化,得到原非凸问题的近似次优解;最后,通过仿真实验验证了所提算法的可行性和有效性。结果表明:所提算法最大可使系统能量消耗降低22%,明显优于对比算法;且随着传感器节点数量的增加,所提算法的优势更加明显。研究结果为WSN中的信息收集提供了一种系统能耗优化思路,即在有限提高无人机飞行能耗的基础上,有效降低系统的能量消耗。

路侧感知车辆轨迹数据的质量评估方法

路侧传感器已大量部署在高速公路上,用来实时采集路段全样本车辆轨迹数据,为交通流全时空管控、微观驾驶行为分析等提供数据支持,但数据质量的快速评估一直是困扰行业管理部门的难题。现有的车辆轨迹数据评估方法大多存在操作复杂、维度单一等问题,难以满足对动态交通流中实时产生的车辆轨迹数据的评价需求。为快速判别路侧毫米波雷达车辆轨迹数据的质量,文中通过挖掘数据自身信息提出了一种数据质量评价方法。首先,在分析实测轨迹数据典型问题的基础上,从轨迹完整性、一致性、准确性及有效性4个维度建立了9个二级评价指标;然后,基于CRITIC赋权法计算综合指标;最后,针对4种不同场景的3549条毫米波雷达实测轨迹进行了实证分析。结果表明,毫米波雷达的安装方式、型号等会显著影响车辆轨迹数据的质量,所提出的数据质量评价方法能够量化不同车辆轨迹数据的质量差异。文中研究结果可为路侧传感器采集数据性能衰变的短时监测及数据采集设备的选型提供支持,也可为车辆轨迹数据质量的提升提供方法参考。

物联网数据收集中基于负载均衡的无人机-车联合轨迹规划

为了提升大规模物联网数据收集的效率,提出了一种基于负载均衡区域划分的多无人机-车联合轨迹规划算法,其中,无人机作为空中基站收集物联网设备的数据,地面无人车作为移动电池更换站以弥补无人机能量的不足。为了缩短整体任务完成时间,优化目标为最小化所有无人机-车中最长的任务完成时间,将该问题建模为多站点车辆路由问题的一个变种,并从负载均衡的角度对其进行求解。具体来说,首先通过负载均衡区域划分算法将物联网设备分配到无人机-车的服务区,在此基础上,多站点无人机-车的轨迹规划问题退化为多个独立的单站点单组无人机-车的轨迹规划问题,进而设计联合轨迹规划策略优化各个服务区中的路径。数值结果验证了所提算法在任务完成时间和负载均衡度方面优于对比算法。

基于浮动车轨迹数据的干线信号协调配时优化

为了提高城市干线的通行效率,缩短车辆在干线行驶时的平均延误,基于浮动车轨迹数据提出一种城市干线交通信号协调控制优化方法。该方法根据浮动车采集的时空轨迹数据,使用最大期望算法对混合车流(浮动车和其他车辆)到达率进行预测。结合信号状态,通过计算干线道路上、下行双向饱和度,以总延误最短为优化目标建立了干线协调配时优化算法,并采用遗传算法进行求解。以合肥市黄山路路段为例,比较无协调控制、传统定时协调控制和基于轨迹数据的信号协调控制。结果表明,所提供的方案可有效缩短干线交叉口车辆总延误,减少燃油消耗。

基于低频采集数据的城市道路车辆轨迹重构

在进行城市道路交通流量调查及部分重要路网节点、交叉口交通数据采集时,采用低频数据收集方式会使车辆轨迹匹配精度低、交通流量数据误差大。通过研究隐式马尔可夫理论及最小费用最大流模型,提出一种车辆轨迹重构方法。该方法利用多源数据融合技术以及地理信息定位匹配技术,对无检测器路段缺失的各项交通基础数据进行合理的演进推算,为车辆轨迹重构研究提供重要的数据支撑。利用成都市某区域出租车的高频轨迹点位数据集进行验证。结果表明,利用车辆低频轨迹点位进行轨迹重构的完全覆盖率达到了89.4%,验证了所提出的车辆轨迹重构方法的有效性及可行性。

智能网联车和人驾车辆混合交通流排队长度估计模型

为了解决智能网联车(ICVs)和人驾车辆(HDVs)混行交叉口的排队估计问题,提出基于概率统计和贝叶斯定理的排队长度估计模型.综合考虑队列中智能网联车位置、速度和渗透率等因素,分别构建可观测队列排队长度估计模型、不可观测队列排队长度估计模型和渗透率估计模型,通过迭代实现排队长度和渗透率的实时估计.利用随机种子模拟不同渗透率条件下智能网联车在队列中的分布特征,分析不同交通条件下模型的估计精度.与已有模型的对比表明,在智能网联车低渗透率(10%)条件下,在非高峰时段,本研究模型、已有模型的平均绝对百分比误差(MAPE)分别为29.35%、59.68%;在高峰时段,本研究模型、已有模型的MAPE分别为26.50%、34.66%.在智能网联车高渗透率条件下(90%),在非高峰时段,本研究模型、已有模型的MAPE分别为6.90%、17.85%;在高峰时段,本研究模型、已有模型的MAPE分别为1.45%、1.05%,误差接近.本研究所提出的排队估计模型在低渗透率和高渗透率条件下均具有更好的估计精度.

基于传感器融合技术的自动驾驶车辆轨迹跟踪与控制方案研究

文章旨在探讨基于传感器融合技术的自动驾驶车辆轨迹跟踪与控制方案。针对自动驾驶汽车在实际道路环境中需要准确跟踪车辆轨迹以实现安全、高效的驾驶需求,提出了一种综合利用多传感器数据的轨迹跟踪与控制方法。文章首先分析了传感器融合技术在自动驾驶领域的重要性和应用前景,并介绍了基于雷达、摄像头、激光雷达等传感器的数据融合方法。接着,提出了一种基于模型预测控制(MPC)的轨迹跟踪算法,利用多传感器数据实时更新车辆动态模型,并通过优化控制器参数以实现精确轨迹跟踪。最后,通过仿真实验和实际道路测试验证了所提方案的有效性和鲁棒性,结果表明该方法在各种道路场景下都能够实现稳定、准确的轨迹跟踪与控制。