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A data-driven method for flight time estimation based on air traffic pattern identification and prediction 基于空中交通模式识别和预测的数据驱动飞行时间估算方法
IF 3.6 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2024-05-03 DOI: 10.1080/15472450.2022.2130693
Chunwei Yang , Junfeng Zhang , Xuhao Gui , Zihan Peng , Bin Wang

Flight time estimation is expected to play a crucial role in predicting the Estimated Time of Arrival, which could help detect conflicts and manage arrivals. This paper proposes a novel data-driven method for flight time estimation based on arrival pattern identification and prediction. Firstly, a trajectory clustering algorithm is employed to group the arrival trajectories into different arrival patterns. A new trajectory representation technique is presented during the clustering process for better-describing arrival patterns. Secondly, we extract features from radar tracks for data-driven flight time estimation. These features consist of current states related, historical information related, traffic situation related, and environmental conditions related features. Furthermore, the permutation feature importance and recursive feature elimination method are adopted to reduce feature dimensions. Then, we develop three widely used tree-based models to estimate the flight time for each arrival pattern. We also propose an image-based flight patterns prediction method to classify each new arrival aircraft into the corresponding arrival pattern for actual operation. Finally, we take the Guangzhou arrival operation as a case to validate our proposed method. The results indicate that our proposed method could improve flight time estimating accuracy. Besides, through the data-driven strategy, we could also find several significant factors affecting the flight time within the Terminal Maneuvering Area.

航班时刻估计在预测预计到达时间方面将发挥关键作用,有助于发现冲突和管理到达航班。本文提出了一种基于到达模式识别和预测的新型数据驱动飞行时间估计方法。首先,采用轨迹聚类算法将到达轨迹分为不同的到达模式。在聚类过程中,提出了一种新的轨迹表示技术,以更好地描述到达模式。其次,我们从雷达轨迹中提取特征,用于数据驱动的飞行时间估计。这些特征包括当前状态相关特征、历史信息相关特征、交通状况相关特征和环境条件相关特征。此外,我们还采用了排列特征重要性和递归特征消除方法来减少特征维度。然后,我们开发了三种广泛使用的基于树的模型来估计每种到达模式的飞行时间。我们还提出了一种基于图像的航班模式预测方法,将每架新抵达的飞机划分为相应的抵达模式,以便实际操作。最后,我们以广州到达运行为例,验证了我们提出的方法。结果表明,我们提出的方法可以提高航班时刻估算的准确性。此外,通过数据驱动策略,我们还找到了影响航站操纵区内飞行时间的几个重要因素。
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引用次数: 0
Including network level safety measures in eco-routing 在生态路由中纳入网络层面的安全措施
IF 3.6 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2024-05-03 DOI: 10.1080/15472450.2022.2129022
Sepehr G. Dehkordi , Grégoire S. Larue , Michael E. Cholette , Andry Rakotonirainy , Sébastien Glaser

Following the most energy-efficient route can have a significant impact on reducing energy consumption. While most eco-routing research has focused on reducing energy consumption and travel time, the safety aspect of route choice is currently neglected. In this paper, a multi-objective optimization methodology is formulated to concurrently minimize fuel consumption, travel time and safety risk, which is quantified using a novel methodology based on network-level safety measures. The proposed optimization framework provides a transparent way to intuitively include driver preferences via “budgets” for time, fuel consumption and safety – which represent the driver’s willingness to sacrifice these factors for fuel consumption improvements. The performance of the proposed method was tested on urban road networks in Brisbane-Australia, with a rear-end collision risk model as the safety measure. The results demonstrate that eco-routing with safety considerations has the potential to improve fuel efficiency while simultaneously reducing safety risks.

选择最节能的路线对减少能源消耗有重大影响。虽然大多数生态路线研究都侧重于减少能耗和旅行时间,但目前却忽视了路线选择的安全方面。本文提出了一种多目标优化方法,以同时最大限度地减少燃料消耗、旅行时间和安全风险,并使用一种基于网络级安全措施的新方法对安全风险进行量化。提出的优化框架提供了一种透明的方法,通过时间、油耗和安全的 "预算 "直观地将驾驶员的偏好纳入其中--这代表了驾驶员为改善油耗而牺牲这些因素的意愿。在澳大利亚布里斯班的城市公路网中测试了所提方法的性能,并以追尾碰撞风险模型作为安全衡量标准。结果表明,考虑到安全因素的生态路线有可能在提高燃油效率的同时降低安全风险。
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引用次数: 0
A multi-state merging based analytical model for an operation design domain of autonomous vehicles in work zones on two-lane highways 基于多状态合并的分析模型,适用于双车道高速公路工作区自动驾驶车辆的运行设计域
IF 3.6 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2024-05-03 DOI: 10.1080/15472450.2022.2130697
Qing Tang , Xianbiao Hu

As a special application of connected and automated vehicles (CAVs), the Autonomous Truck Mounted Attenuator (ATMA) vehicle system is promoted to reduce fatalities in work zone locations. In this manuscript, we focus on the Operational Design Domain (ODD) problem of two-lane highways, i.e., under what traffic conditions should an ATMA be deployed. Due to the dramatic speed difference between ATMA vehicles and general vehicles, a queue will be formed, leading to a percent-time-spent-following (PTSF) increase during maintenance. General vehicles in the queue will assess a gap on the opposite lane to perform a passing maneuver, which is broken down into multi-stage merging behavior. As such, an analytical model is first made, based on queuing theory in which the arrival rate and service rate are analyzed to estimate the PTSF. In this way, the linkage between annual average daily traffic (AADT) and level of service (LOS) is analytically established. Then, the proposed model is validated by comparing the estimated PTSF with that of the Highway Capacity Manual (HCM) values. The comparison results show that the mean error is 9.58%, and the mean absolute error is 12.36%, which demonstrate that the developed model is able to generate satisfactory results when compared with the HCM model. Numeric analysis also shows that roadway performance is sensitive to the K factor and D factor, as well as the operating speed of an ATMA. If LOS = C is a desirable design objective, a good AADT threshold to use would be around 11,000 vehicles per day.

作为互联与自动驾驶汽车(CAVs)的一种特殊应用,自动驾驶车载衰减器(ATMA)车辆系统得到了推广,以减少工作区的死亡事故。在本手稿中,我们重点关注双车道高速公路的运行设计域(ODD)问题,即在何种交通条件下应部署 ATMA。由于 ATMA 车辆与普通车辆的速度相差悬殊,因此会形成队列,导致维护期间的跟车时间百分比(PTSF)增加。队列中的一般车辆会评估对面车道的空隙,以执行超车动作,这被细分为多阶段并线行为。因此,首先要根据排队理论建立一个分析模型,通过分析到达率和服务率来估算 PTSF。这样,年平均日交通量(AADT)和服务水平(LOS)之间的联系就通过分析建立起来了。然后,通过将估算的 PTSF 与《公路通行能力手册》(HCM)的值进行比较,验证了所提出的模型。比较结果表明,平均误差为 9.58%,平均绝对误差为 12.36%,这表明所开发的模型与《公路通行能力手册》模型相比能够产生令人满意的结果。数值分析还表明,道路性能对 K 系数和 D 系数以及自动交通管理系统的运行速度非常敏感。如果 LOS = C 是理想的设计目标,那么良好的 AADT 临界值应为每天约 11,000 辆车。
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引用次数: 0
A stochastic microscopic based freeway traffic state and spatial-temporal pattern prediction in a connected vehicle environment 互联车辆环境中基于随机微观的高速公路交通状态和时空模式预测
IF 3.6 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2024-05-03 DOI: 10.1080/15472450.2022.2130291
Seiran Heshami , Lina Kattan

Traffic state prediction forms the basis for effective and efficient traffic control and management strategies. A model-based traffic state prediction approach based on a stochastic microscopic three-phase model is developed to predict traffic flow, speed, and travel time in short prediction horizons consisting of multiple time steps ahead. The proposed model utilizes connected vehicles’ trajectory data including location and speed information and fuses this information with detector measurements using an Adaptive Kalman filter. Stochastic driver behaviors in merging, lane-changing, and over-acceleration are considered in the three-phase microscopic model, which allows for a precise prediction of macroscopic parameters for a relatively long stretch of freeway. Traffic flow and speed predictions are conducted for each lane individually and, for a whole segment. Per-lane predictions provide valuable information regarding different speed fluctuations in each lane for identifying congestion and applying proactive freeway controls. Predicted traffic parameters are used for tracking and predicting the spatial-temporal traffic patterns in real-time. The accuracy of the proposed model is examined and validated for various penetration rates of connected vehicles and prediction horizons and outperforms the baseline prediction methods.

交通状态预测是有效和高效的交通控制和管理策略的基础。基于随机微观三阶段模型开发的基于模型的交通状态预测方法可在短预测范围内预测交通流量、速度和行驶时间,预测范围包括多个时间步长。该模型利用联网车辆的轨迹数据(包括位置和速度信息),并使用自适应卡尔曼滤波器将这些信息与检测器测量结果融合在一起。三阶段微观模型考虑了驾驶员在并线、变道和超速时的随机行为,从而可以精确预测相对较长高速公路路段的宏观参数。对每条车道和整个路段的交通流量和速度都进行了预测。每条车道的预测可提供有关每条车道不同速度波动的宝贵信息,以便识别拥堵情况并采取积极的高速公路控制措施。预测的交通参数用于实时跟踪和预测时空交通模式。针对不同的联网车辆渗透率和预测范围,对所提出模型的准确性进行了检查和验证,结果表明该模型优于基线预测方法。
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引用次数: 0
Comparative analysis of drowsiness and performance in conditionally automated driving and manual driving considering the effect of circadian rhythm 考虑到昼夜节律的影响,对有条件自动驾驶和手动驾驶中的嗜睡和表现进行比较分析
IF 3.6 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2024-05-03 DOI: 10.1080/15472450.2022.2130292
Qi Zhang , Chaozhong Wu , Hui Zhang , Sara Ferreira

Drowsiness in manual driving (MD) is influenced by circadian rhythms. Conditionally automated driving (CAD) affects drivers’ drowsiness. We conducted a simulator study with 30 participants (every ten subjects in morning group, afternoon group, and evening group) to investigate the effect of circadian rhythm on the changes in drivers’ drowsiness and performance in different driving modes. Each subject was required to complete CAD experiment first and MD experiment later, and experienced 8 risk scenarios in each experiment. The self-reported Karolinska Sleepiness Scale (KSS) was recorded by an investigator every time when the subject drove past the scenario as the drowsiness measurement. The speed, acceleration, time-related metrics, and vehicle lane position were collected as the performance measurements. KSS data were statistically analyzed, and the Spearman’s Rho test was used to confirm the correlation among performance measurements, KSS, and scenarios. The result of the KSS statistical analysis showed that the effect of circadian rhythm on fatigue in MD groups is consistent with the previous studies, but the existence of CAD changes the effect of the circadian rhythm. Compared with the MD, CAD slowed down the drowsiness growth rate in the morning group and promoted the drowsiness growth rate in the evening group. The brake input rate, mean longitude acceleration, max Standard Deviation of Lane Position (SDLP), and the time to pass (TTP) were significantly related to the driver´s drowsiness in both driving modes.

手动驾驶(MD)中的嗜睡受昼夜节律的影响。有条件自动驾驶(CAD)会影响驾驶员的嗜睡程度。我们对 30 名受试者(上午组、下午组和晚上组每 10 名受试者)进行了模拟研究,探讨昼夜节律对不同驾驶模式下驾驶者嗜睡程度和表现变化的影响。每位受试者需先完成 CAD 实验,后完成 MD 实验,并在每个实验中体验 8 种风险情景。研究人员在受试者每次驾车经过该场景时记录其自我报告的卡罗林斯卡嗜睡量表(KSS),作为嗜睡度测量值。车速、加速度、时间相关指标和车道位置作为性能测量指标。对 KSS 数据进行统计分析,并使用 Spearman's Rho 检验来确认性能测量、KSS 和场景之间的相关性。KSS 统计分析结果表明,昼夜节律对 MD 组疲劳的影响与之前的研究一致,但 CAD 的存在改变了昼夜节律的影响。与 MD 相比,CAD 减慢了晨间组的嗜睡增长速度,促进了晚间组的嗜睡增长速度。在两种驾驶模式下,制动输入率、平均经度加速度、最大车道位置标准偏差(SDLP)和通过时间(TTP)都与驾驶员的嗜睡程度显著相关。
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引用次数: 0
Uncertainty analysis of autonomous delivery robot operations for last-mile logistics in European cities 欧洲城市最后一英里物流配送机器人自主操作的不确定性分析
IF 3.6 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2024-04-18 DOI: 10.1080/15472450.2024.2324388
Clément Lemardelé, Miquel Estrada, Laia Pagès
Although autonomous delivery robots (ADRs) are widely anticipated to significantly enhance the efficiency of last-mile logistics operations in dense urban environments in the coming years, their im...
尽管人们普遍认为自动送货机器人(ADRs)将在未来几年内显著提高密集城市环境中最后一英里物流作业的效率,但它们的影响还远远不够。
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引用次数: 0
Capturing the true bounding boxes: vehicle kinematic data extraction using unmanned aerial vehicles 捕捉真正的边界框:利用无人飞行器提取车辆运动学数据
IF 3.6 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2024-04-18 DOI: 10.1080/15472450.2024.2341395
Tian Mi, Dénes Takács, Henry Liu, Gábor Orosz
This paper presents a methodology by which kinematic variables of road vehicles can be extracted from unmanned aerial vehicle (UAV) footage. The oriented bounding boxes of the vehicles are identifi...
本文介绍了一种从无人驾驶飞行器(UAV)镜头中提取道路车辆运动学变量的方法。车辆的定向边界框是通过识别车辆的运动变量来确定的。
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引用次数: 0
Multi-head attention-based intelligent vehicle lane change decision and trajectory prediction model in highways 基于多头注意力的高速公路智能车辆变道决策和轨迹预测模型
IF 3.6 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2024-04-18 DOI: 10.1080/15472450.2024.2341392
Junyu Cai, Haobin Jiang, Junyan Wang, Aoxue Li
With the aim to improve the interaction between intelligent vehicles and human drivers, this article proposes the MCLG (multi-head attention + convolutional social pooling + long short-term memory ...
为了改善智能车辆与人类驾驶员之间的交互,本文提出了 MCLG(多头注意力+卷积社会池+长短期记忆)方法。
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引用次数: 0
A framework of transportation mode detection for people with mobility disability 行动不便者交通模式检测框架
IF 3.6 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2024-03-19 DOI: 10.1080/15472450.2024.2329901
Jiwoong Heo, Sungjin Hwang, Jucheol Moon, Jaehwan You, Hansung Kim, Jaehyuk Cha, Kwanguk (Kenny) Kim
Transportation mode detection (TMD) is an important computational technique that aids human life at the social and individual levels. However, previous studies on TMD were focused on people without...
交通模式检测(TMD)是一项重要的计算技术,可在社会和个人层面帮助人类生活。然而,以往关于 TMD 的研究主要集中在没有...
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引用次数: 0
Robust learning control for autonomous vehicle with network delays and disturbances 具有网络延迟和干扰的自主车辆鲁棒学习控制
IF 3.6 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2024-03-19 DOI: 10.1080/15472450.2024.2329912
Jing Wang, Engang Tian, Huaicheng Yan
This paper deals with a robust learning nonlinear model predictive control (RL-NMPC) scheme under time-varying delays and disturbances. It is well known that the in-vehicle network has considerable...
本文探讨了时变延迟和干扰条件下的鲁棒学习非线性模型预测控制(RL-NMPC)方案。众所周知,车载网络具有相当大的...
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引用次数: 0
期刊
Journal of Intelligent Transportation Systems
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