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学者姓名：刘小明
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Abstract ：
Nowadays, a large percentage of people use smartphones frequently. The mobile phone signaling data contains various attributes that can be used to infer when and where the user is. Compared with other big data sources (e.g., social media and GPS data) for the human movement, mobile phone signaling data demonstrate the advantages of a high coverage of population, strong temporal continuity, and low cost of collection. Taking advantage of such mobile phone signaling data, this work aims to identify tourists and locals from a large volume of mobile phone signaling data in a tourism city and analyze their spatiotemporal patterns to better promote tourism service and alleviate possible disturbance to local residents. In this paper, we present a framework to differentiate these two types of people by the following procedure: first, the hidden behavior characteristics of users are extracted from mobile phone signaling data; and then, the K-means clustering method is adopted to identify tourists and locals. With the identification of both tourists and local residents, we analyze the distribution and interaction characteristics of tourists and locals in an urban area. An experimental study is conducted in a famous tourism city, Xiamen, China. The results indicate that the proposed method can successfully identify the most popular scenic spots and major transportation corridors for tourists. The feature extraction, identification, and spatiotemporal analysis presented in this paper are of great significance for analyzing the urban tourism demand, managing the urban space, and mining the tourist behavior.
Keyword ：
Mobile phone signaling data Mobile phone signaling data Human mobility Human mobility Tourists behavior Tourists behavior K-means clustering method K-means clustering method
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| GB/T 7714 | Sun, Haodong , Chen, Yanyan , Lai, Jianhui et al. Identifying Tourists and Locals by K-Means Clustering Method from Mobile Phone Signaling Data [J]. | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS , 2021 , 147 (10) . |
| MLA | Sun, Haodong et al. "Identifying Tourists and Locals by K-Means Clustering Method from Mobile Phone Signaling Data" . | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS 147 . 10 (2021) . |
| APA | Sun, Haodong , Chen, Yanyan , Lai, Jianhui , Wang, Yang , Liu, Xiaoming . Identifying Tourists and Locals by K-Means Clustering Method from Mobile Phone Signaling Data . | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS , 2021 , 147 (10) . |
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| GB/T 7714 | Sun, Haodong , Chen, Yanyan , Wang, Yang et al. Trip purpose inference for tourists by machine learning approaches based on mobile signaling data (Aug, 10.1007/s12652-021-03346-y, 2021) [J]. | JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING , 2021 . |
| MLA | Sun, Haodong et al. "Trip purpose inference for tourists by machine learning approaches based on mobile signaling data (Aug, 10.1007/s12652-021-03346-y, 2021)" . | JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING (2021) . |
| APA | Sun, Haodong , Chen, Yanyan , Wang, Yang , Liu, Xiaoming . Trip purpose inference for tourists by machine learning approaches based on mobile signaling data (Aug, 10.1007/s12652-021-03346-y, 2021) . | JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING , 2021 . |
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Abstract ：
为了研究导航播报措辞复杂度对驾驶行为的影响,以普通交叉口为例,将措辞复杂度分为Ⅰ、Ⅱ、Ⅲ、Ⅳ级,通过驾驶模拟方法获取实验数据,构建考虑驾驶人时空和综合行为表现的多维度指标体系,运用方差分析和非参数检验方法研究不同措辞引导下驾驶人行为表现的时空特征,挖掘驾驶人综合行为表现的变化规律。结果表明:导航播报情况下驾驶人普遍从交叉口上游200 m处开始减速;4种措辞的作用效果在交叉口上游100 m至下游100 m区段存在明显差异;Ⅰ、Ⅱ级措辞引导下车辆运行的平稳性较差;与Ⅰ级措辞相比,Ⅱ、Ⅲ和Ⅳ级措辞引导下驾驶人的综合行为表现依次提升15%、20%和40%。
Keyword ：
车辆导航 车辆导航 驾驶行为 驾驶行为 交通工程 交通工程 驾驶模拟技术 驾驶模拟技术 播报措辞复杂度 播报措辞复杂度
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| GB/T 7714 | 杨丽平 , 边扬 , 赵晓华 et al. 导航播报措辞复杂度对驾驶行为的影响 [J]. | 华南理工大学学报(自然科学版) , 2021 , 49 (03) : 139-148 . |
| MLA | 杨丽平 et al. "导航播报措辞复杂度对驾驶行为的影响" . | 华南理工大学学报(自然科学版) 49 . 03 (2021) : 139-148 . |
| APA | 杨丽平 , 边扬 , 赵晓华 , 伍毅平 , 刘小明 . 导航播报措辞复杂度对驾驶行为的影响 . | 华南理工大学学报(自然科学版) , 2021 , 49 (03) , 139-148 . |
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基于最小二乘法将分形表面简化为三角函数的叠加,采用弹塑性有限元方法计算界面的接触刚度,定量表征了法向接触压力、法向接触变形及法向接触刚度的关系,研究结果揭示了粗糙面分形维数和特征尺度参数对法向接触刚度的影响机制。结果表明:存在基体最优建模厚度,可有效提高粗糙面接触刚度的计算效率;法向接触刚度随法向接触变形及法向接触压力的增加呈现非线性增加趋势;表面分形维数和特征尺度参数对法向接触刚度影响显著,法向接触刚度随分形维数增加而增加,但随特征尺度参数增加而减小。
Keyword ：
分形维数 分形维数 界面接触刚度 界面接触刚度 基体最优建模厚度 基体最优建模厚度 粗糙表面 粗糙表面 特征尺度参数 特征尺度参数
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| GB/T 7714 | 范立峰 , 赵璐 , 聂雯 et al. 界面分形参数对法向接触刚度影响的研究 [J]. | 工程力学 , 2021 , 38 (07) : 207-215 . |
| MLA | 范立峰 et al. "界面分形参数对法向接触刚度影响的研究" . | 工程力学 38 . 07 (2021) : 207-215 . |
| APA | 范立峰 , 赵璐 , 聂雯 , 刘小明 . 界面分形参数对法向接触刚度影响的研究 . | 工程力学 , 2021 , 38 (07) , 207-215 . |
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Abstract ：
随着城市化进程的不断推进,迅猛增加的轨道交通客流对客运组织管理提出了更高的要求.目前,虽然多样化的视频采集设备已广泛应用于地铁客流监测中,但是对视频监测范围缺乏统一的标准规范导致监控设备布设随意性大、客流采集无法满足监测需求,鉴于此,开展了对轨道交通车站客流状态数据采集范围的研究.首先,阐述了客流状态采集范围的概念和影响因素;其次,分析轨道交通车站不同功能区域的数据采集参数类型,以全面性及精度最优为目标,构建了不同功能区域的采集范围模型,并给出模型中参数权重的计算方法和模型求解方法;最后,以北京西直门地铁站为研究对象进行实例分析,给出其不同功能区客流状态数据的最优采集范围.研究结果可为交通流数据的获取提供有效的技术支持和保障.
Keyword ：
采集范围 采集范围 采集精度 采集精度 客流状态 客流状态 轨道交通车站 轨道交通车站
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| GB/T 7714 | 宋程程 , 陈艳艳 , 陈宁 et al. 城市轨道交通车站客流状态采集范围 [J]. | 科学技术与工程 , 2021 , 21 (27) : 11836-11842 . |
| MLA | 宋程程 et al. "城市轨道交通车站客流状态采集范围" . | 科学技术与工程 21 . 27 (2021) : 11836-11842 . |
| APA | 宋程程 , 陈艳艳 , 陈宁 , 刘小明 . 城市轨道交通车站客流状态采集范围 . | 科学技术与工程 , 2021 , 21 (27) , 11836-11842 . |
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Abstract ：
导航播报措辞复杂度对驾驶行为的影响
Keyword ：
车辆导航 车辆导航 驾驶行为 驾驶行为 交通工程 交通工程 播报措辞复杂度 播报措辞复杂度 驾驶模拟技术 驾驶模拟技术
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| GB/T 7714 | 杨丽平 , 边扬 , 赵晓华 et al. 导航播报措辞复杂度对驾驶行为的影响 [J]. | 杨丽平 , 2021 , 49 (3) : 139-148 . |
| MLA | 杨丽平 et al. "导航播报措辞复杂度对驾驶行为的影响" . | 杨丽平 49 . 3 (2021) : 139-148 . |
| APA | 杨丽平 , 边扬 , 赵晓华 , 伍毅平 , 刘小明 , 华南理工大学学报:自然科学版 . 导航播报措辞复杂度对驾驶行为的影响 . | 杨丽平 , 2021 , 49 (3) , 139-148 . |
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Abstract ：
In China, F-type-5 m intersections are not uncommon. One approach of these intersections usually includes a driveway closely followed by an intersecting street, and the driveway and the intersecting street are parallel and approximately 5 m apart. Nowadays, drivers often rely on the navigation systems for directions. However, it is found that the navigation systems sometimes mislead or confuse drivers to make wrong turns or miss their turns at such F-type-5 m intersections. This study proposed to employ driving simulation to identify the appropriate prompt message delivered at the right prompt timing to help drivers navigate through such F-type-5 m intersections. First, a within-subjects two-factor experiment was designed. One factor was the Prompt Timing Mode (PTM), representing a set of three sequential messages broadcast by the navigation system at varying distances to the intended intersection; the other factor was the Prompt Message Type (PMT), representing various sets of three sequential messages broadcast by the navigation system. Three Prompt Timing Modes were used: PTM1 = {- 400 m, -200 m, - 30 m}, PTM2 = {- 300 m, - 150 m, - 30 m}, and PTM3 = {- 200 m, - 100 m, - 30 m}. Three Prompt Message Types were defined: PMT-A = {Turn right at the traffic light XXm ahead; Turn right at the traffic light XXm ahead; Turn right}, PMT-B = {Turn right at the traffic light XXm ahead, enter YY street; Turn right at the traffic light XXm ahead, enter YY street; Turn right}, PMT-C = {Turn right at the traffic light XXm ahead, enter YY street, and please use the second right turn lane; Turn right at the traffic light XXm ahead, enter YY street, and please use the second right turn lane; Turn right}. The combinations of the two factors generated nine experimental intersections which were randomly assigned to three experimental routes. Then, a total of 37 drivers were recruited, and participated in the driving simulation experiment from which vehicle operation data were collected under different prompt timing modes and message types. Next, the repeated Analysis of Variance (rANOVA) was performed to examine the effects of different prompt timing modes and prompt message types on vehicle operation indicators, such as Driving Time, Standard Deviation of Speed, Absolute Value of Acceleration, and Standard Deviation of Acceleration. Finally, the grey near-optimal method was adopted to evaluate the effectiveness of three prompt message types under each prompt timing mode. The rANOVA results showed the vehicle operation in the F-type-5 m intersection was affected by prompt timing modes and prompt message types; the evaluation results indicated that PMT-C made drivers perform better in PTM1 and PTM3, while PMT-B made drivers perform better inPTM2. However, the effectiveness of PMT-A was the lowest in each prompt timing mode. The research results provide valuable guidance to design the human machine interface of navigation systems, which can help drivers safely navigate through F-type-5 m intersections. This research also has laid solid foundations for establishing navigation messaging design guidelines.
Keyword ：
Driving simulator Driving simulator m intersections m intersections Grey near-optimal method Grey near-optimal method rANOVA rANOVA Navigation prompt timings Navigation prompt timings F-type-5  F-type-5  Navigation prompt messages Navigation prompt messages
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| GB/T 7714 | Yang, Liping , Bian, Yang , Zhao, Xiaohua et al. Experimental research on the effectiveness of navigation prompt messages based on a driving simulator: a case study [J]. | COGNITION TECHNOLOGY & WORK , 2021 , 23 (3) : 439-458 . |
| MLA | Yang, Liping et al. "Experimental research on the effectiveness of navigation prompt messages based on a driving simulator: a case study" . | COGNITION TECHNOLOGY & WORK 23 . 3 (2021) : 439-458 . |
| APA | Yang, Liping , Bian, Yang , Zhao, Xiaohua , Ma, Jianming , Wu, Yiping , Chang, Xin et al. Experimental research on the effectiveness of navigation prompt messages based on a driving simulator: a case study . | COGNITION TECHNOLOGY & WORK , 2021 , 23 (3) , 439-458 . |
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Abstract ：
It has been gradually recognized that mobile phones can be used as a practical and promising way to identify individual travel trajectories. Researchers have developed various approaches to detecting human mobility and trip characteristics including trip origin-destination, travel modes, trip purposes based on mobile phone data. Among these researches, trip purpose detection has drawn less attention from researchers. This paper presents our work to investigate a set of machine learning approaches to identifying the trip purposes for tourists based on mobile signaling data combined with sampling surveys and point of interest (POI) data. Five machine learning algorithms, including support vector machine, decision tree, random forest, artificial neural network, and deep stacked auto-encoded (DSAE), have been employed to infer trip purposes under multiple scenarios. Four scenarios have been designed by considering the POI information around trip end [a 500 m buffer or Thiessen polygon (the coverage of the base station theoretically)] and training dataset selection (equal probabilities selection or equal proportion selection). The accuracy of trip purpose classification with machine learning algorithms has compared under different scenarios. The highest accuracy of 93.47% for the test dataset is achieved based on DSAE model under the scenario of a trip end 500 m buffer and equal probabilities selection. The experimental results indicate that the methodology developed with machine learning algorithms based on mobile signaling data combined with sample travel survey is expected as an alternative way to traditional travel surveys for trip purposes.
Keyword ：
Trip purpose Trip purpose Point of interest data Point of interest data Machine learning Machine learning Mobile signaling data Mobile signaling data
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| GB/T 7714 | Sun, Haodong , Chen, Yanyan , Wang, Yang et al. Trip purpose inference for tourists by machine learning approaches based on mobile signaling data [J]. | JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING , 2021 . |
| MLA | Sun, Haodong et al. "Trip purpose inference for tourists by machine learning approaches based on mobile signaling data" . | JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING (2021) . |
| APA | Sun, Haodong , Chen, Yanyan , Wang, Yang , Liu, Xiaoming . Trip purpose inference for tourists by machine learning approaches based on mobile signaling data . | JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING , 2021 . |
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Abstract ：
This study proposes an integrated technology acceptance model to investigate the factors that affect drivers' usage intention of mobile navigation applications. The proposed model adds three new constructs (drivers' sense of direction, navigation application affinity and distraction perception) to the original technology acceptance model based on the features of mobile navigation applications. First, a questionnaire was developed and ad-ministered, and data from 384 drivers were collected via an online survey. Second, confirmatory factor analysis was conducted to examine the reliability and validity of the developed scale based on the collected data. Third, a structural equation model was constructed to investigate the interrelationships among these constructs in the conceptual research model and to identify the key factors that affect drivers' acceptance of mobile navigation applications. The proposed model explained 60.50% of the variance in the intention to use mobile navigation applications. In addition to attitude and perceived usefulness, navigation application affinity and distraction perception also significantly affected drivers' intention to use mobile navigation applications. Navigation application affinity and distraction perception affected not only drivers' intention to use but also their perceptions. Sense of direction was a significant individual trait that affected drivers' navigation application affinity, distraction perception, perceived ease of use and perceived usefulness. These findings imply that relevant developers should continually optimize the incorrect and inappropriate use of navigation information and that they should attach importance to the amount and intelligibility of navigation information. Furthermore, the prompt form of navigation information should satisfy the demands and expectations of drivers with different senses of direction. Overall, this study improves our understanding of drivers' acceptance of mobile navigation applications and provides some important practical implications to improve mobile navigation services.
Keyword ：
Navigation application affinity Navigation application affinity Sense of direction Sense of direction Distraction perception Distraction perception Mobile navigation applications Mobile navigation applications Technology acceptance model Technology acceptance model
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| GB/T 7714 | Yang, Liping , Bian, Yang , Zhao, Xiaohua et al. Drivers' acceptance of mobile navigation applications: An extended technology acceptance model considering drivers' sense of direction, navigation application affinity and distraction perception [J]. | INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES , 2021 , 145 . |
| MLA | Yang, Liping et al. "Drivers' acceptance of mobile navigation applications: An extended technology acceptance model considering drivers' sense of direction, navigation application affinity and distraction perception" . | INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES 145 (2021) . |
| APA | Yang, Liping , Bian, Yang , Zhao, Xiaohua , Liu, Xiaoming , Yao, Xianglin . Drivers' acceptance of mobile navigation applications: An extended technology acceptance model considering drivers' sense of direction, navigation application affinity and distraction perception . | INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES , 2021 , 145 . |
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Abstract ：
To explore the effects of navigation broadcast wording complexity on driving behaviors, taking ordinary intersections as an example, this paper divided wording complexity into four levels: , , and . The experiment data were obtained through driving simulation method and a multi-dimension indicator system considering the spatio-temporal and comprehensive driving performance of drivers was constructed. The temporal and spatial characteristics of driving behaviors under different navigation broadcast wordings were studied through variance analysis and nonparametric test methods, and the change law of comprehensive driving performance was excavated. The results show that drivers start to slow down from 200m upstream of the intersection; the effects of broadcast wordings of four levels are obviously different between 100m upstream to 100m downstream in the intersection; under the guidance of broadcast wording Levels and , the stability of vehicle operation is poor; as compared with the comprehensive driving performance under the guidance of broadcast wording Level , the comprehensive driving performance under Levels , and is improved by 15%, 20% and 40%, respectively. © 2021, Editorial Department, Journal of South China University of Technology. All right reserved.
Keyword ：
Navigation Navigation Testing Testing Automobile drivers Automobile drivers Traffic control Traffic control
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| GB/T 7714 | Yang, Liping , Bian, Yang , Zhao, Xiaohua et al. Effects of Navigation Broadcast Wording Complexity on Driving Behaviors [J]. | Journal of South China University of Technology (Natural Science) , 2021 , 49 (3) : 139-148 . |
| MLA | Yang, Liping et al. "Effects of Navigation Broadcast Wording Complexity on Driving Behaviors" . | Journal of South China University of Technology (Natural Science) 49 . 3 (2021) : 139-148 . |
| APA | Yang, Liping , Bian, Yang , Zhao, Xiaohua , Wu, Yiping , Liu, Xiaoming . Effects of Navigation Broadcast Wording Complexity on Driving Behaviors . | Journal of South China University of Technology (Natural Science) , 2021 , 49 (3) , 139-148 . |
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