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Multispectral pedestrian detection based on feature complementation and enhancement 基于特征互补和增强的多光谱行人检测
IF 2.3 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-17 DOI: 10.1049/itr2.12562
Linzhen Nie, Meihe Lu, Zhiwei He, Jiachen Hu, Zhishuai Yin

Multispectral pedestrian detection with visible light and infrared images is robust to changes in lighting conditions and therefore is of great importance to numerous applications that require all-day environmental perception. This paper proposes a novel method named FCE-RCNN, which integrates saliency detection as a sub-task and utilizes global information for enhanced feature representation. The approach enhances thermal inputs by incorporating gradients at the raw-data level before feature extraction. Utilizing a dual-stream backbone, a global semantic information extraction module is introduced that combines pooling with horizontal–vertical attention mechanisms, capturing high-quality global semantic information for lower-level feature enrichment and guidance. Additionally, the pedestrian locality enhancement module is designed to enhance spatial locality information of pedestrians through saliency detection. Furthermore, to alleviate the challenges posed by positional shifts between cross-spectral features, deformable convolution is innovatively employed. Experimental results on the KAIST dataset demonstrate that FCE-RCNN significantly improves nighttime detection, achieving a log-average miss rate of 6.92%, outperforming the new method ICAFusion by 0.93%. These results underscore the effectiveness of FCE-RCNN, and the method also maintains competitive inference speed, making it suitable for real-time applications.

利用可见光和红外图像进行多光谱行人检测对光照条件的变化具有很强的鲁棒性,因此对需要全天候环境感知的众多应用具有重要意义。本文提出了一种名为 FCE-RCNN 的新方法,它将显著性检测整合为一个子任务,并利用全局信息来增强特征表示。该方法通过在特征提取前将梯度纳入原始数据级别来增强热输入。利用双流骨干网,引入了全局语义信息提取模块,该模块结合了池化与水平-垂直注意机制,可捕获高质量的全局语义信息,用于低层次特征的丰富和引导。此外,还设计了行人位置增强模块,通过显著性检测增强行人的空间位置信息。此外,为了缓解交叉光谱特征之间的位置偏移所带来的挑战,还创新性地采用了可变形卷积技术。在 KAIST 数据集上的实验结果表明,FCE-RCNN 显著提高了夜间检测能力,其对数平均漏检率为 6.92%,比新方法 ICAFusion 高出 0.93%。这些结果凸显了 FCE-RCNN 的有效性,而且该方法的推理速度也很有竞争力,适合实时应用。
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引用次数: 0
Exploring changes in residents' daily activity patterns through sequence visualization analysis 通过序列可视化分析探索居民日常活动模式的变化
IF 2.3 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-17 DOI: 10.1049/itr2.12511
Xiaoran Peng, Ruimin Hu, Xiaochen Wang, Nana Huang

The analysis of people's daily activities has played a crucial role in various applications, such as urban geography, activity prediction, and homogeneous population detection. However, limited studies have explored changes in the residents’ activity patterns in a particular region across various periods. To explore the changes, a methodological framework of sequence visualization analysis based on machine learning that extracts the activity patterns across various periods using sequence analysis, visualizes the activity patterns by calculating the frequency of different activities at time points and categorizes them through graphical similarity, and then compares the activity patterns in terms of activity and demographic characteristics is proposed. Empirical testing on the New York Metropolitan data of the National Household Travel Survey (NHTS) is conducted for 2001, 2009, and 2017. The findings reveal significant intra-similarities, inter-differences, and distinct changes in activity patterns across three periods for different social populations in the New York Metropolitan. From the perspective of information analysis, this work is anticipated to enhance the understanding of travel needs for diverse social populations in a particular region, thereby facilitating targeted policy adjustments for the departments concerned.

对人们日常活动的分析在城市地理、活动预测和同质人口检测等各种应用中发挥着至关重要的作用。然而,对特定地区居民活动模式在不同时期的变化进行探讨的研究却很有限。为了探索这些变化,本文提出了一种基于机器学习的序列可视化分析方法框架,该框架利用序列分析提取不同时期的活动模式,通过计算不同活动在时间点上的频率将活动模式可视化,并通过图形相似性对其进行分类,然后从活动和人口特征方面对活动模式进行比较。对 2001 年、2009 年和 2017 年全国家庭旅行调查(NHTS)的纽约大都市数据进行了实证检验。研究结果表明,纽约大都会不同社会人群在三个时期的活动模式存在明显的内相似性、间差异性和明显的变化。从信息分析的角度来看,这项工作有望加强对特定地区不同社会人群出行需求的了解,从而促进相关部门进行有针对性的政策调整。
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引用次数: 0
Self-supervised vessel trajectory segmentation via learning spatio-temporal semantics 通过学习时空语义进行自监督血管轨迹分割
IF 2.3 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-14 DOI: 10.1049/itr2.12570
Rui Zhang, Haitao Ren, Zhipei Yu, Zhu Xiao, Kezhong Liu, Hongbo Jiang

The study of vessel trajectories (VTs) holds significant benefits for marine route management and resource development. VT segmentation serves as a foundation for extracting vessel motion primitives and enables analysis of vessel manoeuvring habits and behavioural intentions. However, existing methods relying on predefined behaviour patterns face high labelling costs, which hinder accurate pattern recognition. This paper proposes a self-supervised vessel trajectory segmentation method (SS-VTS), which segments VTs based on their inherent spatio-temporal semantics. SS-VTS adaptively divides VTs into cells of optimal size. Then, it extracts split points on different semantic levels from the multi-dimensional feature sequence of the VTs using self-supervised learning. Finally, spatio-temporal distance fusion module is performed on split points to determine change points and obtain VT segments with multiple semantics. Experiments on a real automatic identification system datasets show that SS-VTS achieves state-of-the-art segmentation results compared to seven baseline methods.

对船舶轨迹(VT)的研究对海洋航线管理和资源开发具有重大意义。船舶轨迹分割是提取船舶运动基元的基础,可用于分析船舶操纵习惯和行为意图。然而,依赖于预定义行为模式的现有方法面临着高昂的标记成本,这阻碍了准确的模式识别。本文提出了一种自监督船只轨迹分割方法(SS-VTS),该方法根据船只固有的时空语义对船只轨迹进行分割。SS-VTS 自适应地将血管分成最佳大小的单元。然后,它利用自我监督学习从 VT 的多维特征序列中提取不同语义层次的分割点。最后,在分割点上执行时空距离融合模块,以确定变化点并获得具有多种语义的 VT 片段。在真实的自动识别系统数据集上进行的实验表明,与七种基准方法相比,SS-VTS 实现了最先进的分割结果。
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引用次数: 0
Optimizing customized bus services for multi-trip urban passengers: A bi-objective approach 为多趟城市乘客优化定制公交服务:双目标方法
IF 2.3 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-04 DOI: 10.1049/itr2.12569
Yunlin Guan, Yun Wang, Haonan Guo, Xiaobing Liu, Xuedong Yan

Customized bus services typically focus on single-trip requests, which often struggle to accommodate the growing needs for varied multiple trips in urban daily travel. This paper addresses the customized bus routing problem for passengers with multiple trips. A bi-objective mathematical model is established for maximizing the operational profit and minimizing the travel costs by considering the characteristics of the multi-trip requests and time-dependent travel time. Besides, a novel profit objective function is proposed considering the service's completion status and the starting price. Since the proposed mixed integer linear programming model is an NP-hard problem, a non-dominated sorting genetic algorithm II-based method is proposed to handle different sizes of instances. Finally, the instances with multi-trip requests are carried out to test the accuracy of the model and the effectiveness of our method compared with Gurobi and the local search-based multi-objective algorithm approach.

定制公交服务通常以单次出行需求为主,往往难以满足城市日常出行中日益增长的多次出行需求。本文探讨了乘客多次出行的定制公交路线问题。考虑到多趟出行请求的特点和随时间变化的出行时间,建立了一个双目标数学模型,以实现运营利润最大化和出行成本最小化。此外,考虑到服务的完成状态和起始价格,还提出了一个新的利润目标函数。由于所提出的混合整数线性规划模型是一个 NP 难问题,因此提出了一种基于非支配排序遗传算法 II 的方法来处理不同规模的实例。最后,通过多行程请求实例来检验模型的准确性,以及我们的方法与 Gurobi 和基于局部搜索的多目标算法方法相比的有效性。
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引用次数: 0
Assessing the performance of a hybrid max-weight traffic signal control algorithm in the presence of noisy queue information: An evaluation of the environmental impacts 评估混合式最大权重交通信号控制算法在队列信息噪声情况下的性能:环境影响评估
IF 2.3 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-02 DOI: 10.1049/itr2.12571
Muwahida Liaquat, Shaghayegh Vosough, Claudio Roncoli, Themistoklis Charalambous

Max-weight (or max-pressure) is a popular traffic signal control algorithm that has been demonstrated to be capable of optimising network-level throughput. It is based on queue size measurements in the roads approaching an intersection. However, the inability of typical sensors to accurately measure the queue size results in noisy queue measurements, which may affect the controller's performance. A possible solution is to utilise the noisy max-weight algorithm to achieve a throughput optimal solution; however, its application may lead to decreased controller performance. This article investigates two variants of max-weight controllers, namely, acyclic and cyclic max-weight (with and without noisy queue information) in simulated scenarios, by examining their impact on the throughput and environment. A detailed study of multiple pollutants, fuel consumption, and traffic conditions, which are proxied by a total social cost function, is presented to show the pros and cons of each controller. Simulation experiments, conducted for the Kamppi area in central Helsinki, Finland, show that the acyclic max-weight controller outperforms a fixed time controller, particularly in avoiding congestion and reducing emissions in the network, while the cyclic max-weight controller gives the best performance to accommodate maximum vehicles flowing in the network. The complementary positive characteristics motivated the authors to propose a new controller, herein called the hybrid max-weight, which integrates the characteristics of both acyclic and cyclic max-weight algorithms for providing better traffic load and performance through switching.

最大重量(或最大压力)是一种流行的交通信号控制算法,已被证明能够优化网络级吞吐量。该算法基于对接近交叉路口的道路上队列大小的测量。然而,典型的传感器无法准确测量队列大小,导致队列测量值产生噪声,从而可能影响控制器的性能。一种可能的解决方案是利用噪声最大权重算法来实现吞吐量最优解,但其应用可能会导致控制器性能下降。本文在模拟场景中研究了最大权重控制器的两种变体,即非周期性最大权重和周期性最大权重(有噪声队列信息和无噪声队列信息),考察了它们对吞吐量和环境的影响。通过对多种污染物、燃料消耗和交通状况的详细研究(以总社会成本函数为代表),展示了每种控制器的优缺点。在芬兰赫尔辛基市中心的 Kamppi 地区进行的模拟实验表明,非循环最大权重控制器优于固定时间控制器,尤其是在避免拥堵和减少网络排放方面,而循环最大权重控制器在适应网络中最大车辆流量方面表现最佳。这些互补的积极特性促使作者提出了一种新的控制器,即混合最大权重控制器,它综合了非循环最大权重算法和循环最大权重算法的特性,通过切换提供更好的交通负荷和性能。
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引用次数: 0
Map-matching for cycling travel data in urban area 城市地区自行车出行数据的地图匹配
IF 2.3 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-09-19 DOI: 10.1049/itr2.12567
Ting Gao, Winnie Daamen, Panchamy Krishnakumari, Serge Hoogendoorn

To promote urban sustainability, many cities are adopting bicycle-friendly policies, leveraging GPS trajectories as a vital data source. However, the inherent errors in GPS data necessitate a critical preprocessing step known as map-matching. Due to GPS device malfunction, road network ambiguity for cyclists, and inaccuracies in publicly accessible streetmaps, existing map-matching methods face challenges in accurately selecting the best-mapped route. In urban settings, these challenges are exacerbated by high buildings, which tend to attenuate GPS accuracy, and by the increased complexity of the road network. To resolve this issue, this work introduces a map-matching method tailored for cycling travel data in urban areas. The approach introduces two main innovations: a reliable classification of road availability for cyclists, with a particular focus on the main road network, and an extended multi-objective map-matching scoring system. This system integrates penalty, geometric, topology, and temporal scores to optimize the selection of mapped road segments, collectively forming a complete route. Rotterdam, the second-largest city in the Netherlands, is selected as the case study city, and real-world data is used for method implementation and evaluation. Hundred trajectories were manually labelled to assess the model performance and its sensitivity to parameter settings, GPS sampling interval, and travel time. The method is able to unveil variations in cyclist travel behavior, providing municipalities with insights to optimize cycling infrastructure and improve traffic management, such as by identifying high-traffic areas for targeted infrastructure upgrades and optimizing traffic light settings based on cyclist waiting times.

为了促进城市的可持续发展,许多城市正在采用自行车友好政策,并将 GPS 轨迹作为重要的数据源加以利用。然而,由于 GPS 数据存在固有误差,因此需要进行一个关键的预处理步骤,即地图匹配。由于 GPS 设备故障、道路网络对骑车人的模糊性以及可公开获取的街道地图的不准确性,现有的地图匹配方法在准确选择最佳地图路线方面面临挑战。在城市环境中,高楼大厦往往会削弱 GPS 的准确性,而道路网络的复杂性也会增加,这些都加剧了上述挑战。为了解决这个问题,这项工作引入了一种针对城市地区自行车旅行数据的地图匹配方法。该方法有两大创新:一是对骑自行车者的道路可用性进行可靠分类,重点关注主要道路网络;二是扩展的多目标地图匹配评分系统。该系统集成了惩罚、几何、拓扑和时间评分,以优化地图路段的选择,共同构成一条完整的路线。荷兰第二大城市鹿特丹被选为案例研究城市,真实世界的数据被用于方法的实施和评估。对数百条轨迹进行了人工标注,以评估模型的性能及其对参数设置、GPS 采样间隔和旅行时间的敏感性。该方法能够揭示骑车人出行行为的变化,为市政当局优化自行车基础设施和改善交通管理提供洞察力,例如确定高流量区域进行有针对性的基础设施升级,以及根据骑车人等待时间优化交通灯设置。
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引用次数: 0
A multi-objective optimization model for RSU deployment in intelligent expressways based on traffic adaptability 基于交通适应性的智能高速公路 RSU 部署多目标优化模型
IF 2.3 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-09-16 DOI: 10.1049/itr2.12568
Xiaorong Deng, Yanping Liang, Dongyu Luo, Jiangfeng Wang, Xuedong Yan, Jinxiao Duan

The intelligent expressway exemplifies a prominent application of intelligent transportation systems. Roadside units (RSUs), strategically deployed alongside roadways, serve as pivotal infrastructure in facilitating interactions within intelligent expressways. A well-planned RSU deployment strategy is crucial for enhancing service quality, it necessitates balancing performance improvements with significant financial costs due to the limited transmission range and high deployment expenses of RSUs. To tackle these challenges, an adaptive approach for RSU deployment is proposed, which takes into account economic feasibility, service requirements, and dynamic traffic demands. A traffic adaptability-based RSU deployment (TARD) model, which integrates factors such as deployment cost, the effectiveness of information coverage, road network topology, and traffic flow characteristics have been devised. The TARD aims to minimize deployment expenses while maximizing the benefits of information coverage and alignment with road traffic demands. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to solve this optimization model. To validate its efficacy, simulations are conducted on the G2 expressway in Shandong Province, China, demonstrating the superior performance of the TARD compared to three other deployment strategies. Ablation experiments further underscore the critical role of tunnel deployments and comprehensive coverage along long sections in bolstering network connectivity and elevating service quality.

智能高速公路是智能交通系统的一个突出应用实例。路旁装置(RSU)战略性地部署在公路旁,是促进智能高速公路内互动的关键基础设施。精心策划的 RSU 部署策略对提高服务质量至关重要,但由于 RSU 的传输距离有限且部署费用高昂,因此必须在性能改进与高昂的财务成本之间取得平衡。为应对这些挑战,我们提出了一种 RSU 部署的自适应方法,该方法考虑了经济可行性、服务要求和动态流量需求。基于交通适应性的 RSU 部署(TARD)模型综合了部署成本、信息覆盖的有效性、路网拓扑和交通流特征等因素。该模型旨在最大限度地降低部署成本,同时最大限度地提高信息覆盖率和与道路交通需求的一致性。非优势排序遗传算法 II(NSGA-II)被用于解决该优化模型。为了验证其有效性,在中国山东省的 G2 高速公路上进行了仿真,结果表明与其他三种部署策略相比,TARD 的性能更加优越。消融实验进一步强调了隧道部署和长路段全面覆盖在加强网络连接和提高服务质量方面的关键作用。
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引用次数: 0
ADWNet: An improved detector based on YOLOv8 for application in adverse weather for autonomous driving ADWNet:基于 YOLOv8 的改进型检测器,用于恶劣天气下的自动驾驶应用
IF 2.3 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-09-15 DOI: 10.1049/itr2.12566
Xinyun Feng, Tao Peng, Ningguo Qiao, Haitao Li, Qiang Chen, Rui Zhang, Tingting Duan, JinFeng Gong

Drawing inspiration from the state-of-the-art object detection framework YOLOv8, a new model termed adverse weather net (ADWNet) is proposed. To enhance the model's feature extraction capabilities, the efficient multi-scale attention (EMA) module has been integrated into the backbone. To address the problem of information loss in fused features, Neck has been replaced with RepGDNeck. Simultaneously, to expedite the model's convergence, the bounding box's loss function has been optimized to SIoU loss. To elucidate the advantages of ADWNet in the context of adverse weather conditions, ablation studies and comparative experiments were conducted. The results indicate that although the model's parameter count increased by 18.4%, the accuracy for detecting rain, snow, and fog in adverse weather conditions improved by 22%, while the FLOPs (floating point operations) decreased by 5%. The results of the comparison experiments conducted on the WEDGE dataset show that ADWNet outperforms other object detection models in adverse weather in terms of accuracy, model parameters and FLOPs. To validate ADWNet's real-world efficacy, data was extracted from a car recorder under adverse conditions on highways, visual inference was conducted, and its accuracy was demonstrated in interpreting real-world scenarios. The config files are available at https://github.com/Xinyun-Feng/ADWNet.

从最先进的物体检测框架 YOLOv8 中汲取灵感,我们提出了一个新模型,称为恶劣天气网(ADWNet)。为了增强模型的特征提取能力,在骨干网中集成了高效的多尺度关注(EMA)模块。为了解决融合特征的信息损失问题,用 RepGDNeck 代替了 Neck。同时,为了加快模型的收敛速度,边界框的损失函数被优化为 SIoU 损失。为了阐明 ADWNet 在恶劣天气条件下的优势,进行了消融研究和对比实验。结果表明,虽然模型的参数数增加了 18.4%,但在恶劣天气条件下检测雨、雪和雾的准确率提高了 22%,而 FLOPs(浮点运算)减少了 5%。在 WEDGE 数据集上进行的对比实验结果表明,ADWNet 在恶劣天气下的准确率、模型参数和 FLOPs 方面都优于其他物体检测模型。为了验证 ADWNet 在现实世界中的功效,从高速公路恶劣条件下的行车记录仪中提取了数据,进行了视觉推理,并证明了其在解释现实世界场景时的准确性。配置文件可在 https://github.com/Xinyun-Feng/ADWNet 上查阅。
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引用次数: 0
Creep slope estimation for assessing adhesion in the wheel/rail contact 用于评估车轮/轨道接触面附着力的蠕变斜率估算
IF 2.3 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-09-14 DOI: 10.1049/itr2.12561
Peter Hubbard, Tim Harrison, Christopher Ward, Bilal Abduraxman

The UK rail network is subject to costly disruption due to the operational effects of adhesion variation between the wheel and rail. Causes of this are often environmental introduction of contaminants that require a wide-scale approach to risk mitigation such as defensive driving or rail-head maintenance. It remains an open problem to monitor the real-time status of the network to optimise resources and approaches in response to adhesion problems. This article presents an on-vehicle monitoring method designed to estimate the coefficient of friction by processing data from on-board sensors of typical rail passenger vehicles. This approach uses a multi-body physics analysis of a target vehicle to create estimators for both creep force and creep, allowing a curve fitting approach to estimate the coefficient for friction from the creep curves.

由于车轮与铁轨之间的附着力变化所造成的运行影响,英国铁路网受到了代价高昂的破坏。造成这种情况的原因通常是环境引入了污染物,需要采取大范围的风险缓解措施,如防御性驾驶或轨头维护。如何监控网络的实时状态,以优化资源和方法来应对附着问题,仍然是一个有待解决的问题。本文介绍了一种车载监控方法,旨在通过处理来自典型铁路客运车辆车载传感器的数据来估算摩擦系数。该方法使用目标车辆的多体物理分析来创建蠕变力和蠕变的估算器,从而采用曲线拟合方法从蠕变曲线中估算出摩擦系数。
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引用次数: 0
Evaluation of large-scale cycling environment by using the trajectory data of dockless shared bicycles: A data-driven approach 利用无桩共享单车的轨迹数据评估大规模骑行环境:数据驱动法
IF 2.3 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-09-10 DOI: 10.1049/itr2.12565
Ying Ni, Shihan Wang, Jiaqi Chen, Bufan Feng, Rongjie Yu, Yilin Cai

Cycling is increasingly promoted worldwide, but many urban areas lack satisfactory cycling environments. Assessing these environments is crucial, but existing methods face data challenges for large urban networks. This study proposes a data-driven framework using dockless shared bicycle data to efficiently evaluate large-scale cycling environments. First, critical cycling behaviour features that reflect cyclists’ perceptions are identified applying the fuzzy C-means and random forest model. Then, a distribution-oriented evaluation method is developed, ensuring the incorporation of cyclist heterogeneity and quantifying the quality differences among road segments by combining statistical analysis with a hierarchical clustering model. The evaluation framework is applied to Yangpu District, Shanghai, using Mobike data covering 114.9 km of cycling roads. Results show that indicators related to speed magnitude and fluctuation are critical, and an experimental study validates the effectiveness of the data-driven feature extraction method. A minimum trajectory sample size of 260 is required to account for cyclist heterogeneity for one road segment to be evaluated. Further analysis of lower-performing segments identifies vehicle-bicycle separation, on-street parking, and traffic volume as key influencing factors. The rationality of these findings further supports the reliability of the evaluation framework.

自行车运动在全球范围内日益得到推广,但许多城市地区缺乏令人满意的自行车运动环境。评估这些环境至关重要,但现有方法在大型城市网络中面临数据挑战。本研究提出了一个数据驱动框架,利用无桩共享单车数据有效评估大规模骑行环境。首先,利用模糊 C-means 和随机森林模型识别出反映骑车人感知的关键骑车行为特征。然后,开发了一种以分布为导向的评估方法,通过将统计分析与分层聚类模型相结合,确保纳入骑车人的异质性并量化不同路段的质量差异。评价框架应用于上海市杨浦区,使用摩拜单车数据,覆盖 114.9 公里的骑行道路。结果表明,与速度大小和波动相关的指标至关重要,实验研究验证了数据驱动特征提取方法的有效性。考虑到一个待评估路段的骑车人异质性,至少需要 260 个轨迹样本。对表现较差的路段进行进一步分析后发现,车辆与自行车分离、路边停车和交通流量是关键的影响因素。这些发现的合理性进一步证明了评估框架的可靠性。
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引用次数: 0
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