International Journal of Biometeorology,2015年59(1):99-108 ISSN：0020-7128
[Dong, Liang; Zeng, YuLang] Southwest Jiaotong Univ, Sch Architecture, West Sect, Chengdu 611756, Peoples R China.
[Dong, Liang] Southwest Jiaotong Univ, Sch Architecture, West Sect, Chengdu 611756, Peoples R China.
Outdoor human comfort;Human perception;Pedestrian street;Hot and humid regions
The outdoor thermal environment of a public space is highly relevant to the thermal perception of individuals, thereby affecting the use of space. This study aims to connect thermal human biometeorological conditions and subjective thermal sensation in hot and humid regions and to find its influence on street use. We performed a thermal comfort survey at three locations in a pedestrian precinct of Chengdu, China. Meteorological measurements and questionnaire surveys were used to assess the thermal sensation of respondents. The number of people visiting the streets was counted. Meanwhile, mean radiant temperature (T (mrt)) and the physiological equivalent temperature (PET) index were used to evaluate the thermal environment. Analytical results reveal that weather and street design drive the trend of diurnal micrometeorological conditions of the street. With the same geometry and orientation, a street with no trees had wider ranges of meteorological parameters and a longer period of discomfort. The neutral temperature in Chengdu (24.4 A degrees C PET) is similar to that in Taiwan, demonstrating substantial human tolerance to hot conditions in hot and humid regions. Visitors' thermal sensation votes showed the strongest positive relationships with air temperature. Overall comfort level was strongly related to every corresponding meteorological parameter, indicating the complexity of people's comfort in outdoor environments. In major alleys with multiple functions, the number of people in the street decreased as thermal indices increased; T (mrt) and PET had significant negative correlations with the number of people. This study aids in understanding pedestrian street use in hot and humid regions.
Yang, Hongtai;Yang, Jianjiang;Han, Lee D.*;Liu, Xiaohan;Pu, Li;Chin, Shih-miao;Hwang, Ho-ling
PLOS ONE,2018年13(4):e0195957 ISSN：1932-6203
Han, Lee D.
[Chin, Shih-miao; Hwang, Ho-ling] Center for Transportation Analysis, Oak Ridge National Laboratory, Cherahala Boulevard, Knoxville, TN, United States of America;[Han, Lee D.] Department of Civil & Environmental Engineering, the University of Tennessee, Knoxville, TN, United States of America;[Yang, Jianjiang] Model Risk Management, Bank of America, Charlotte, NC, United States of America;[Liu, Xiaohan; Yang, Hongtai] National United Engineering Laboratory of Integrated and Intelligent Transportation, School of Transportation and Logistics, Southwest Jiaotong University, Hi-Tech Industrial Development Zone, Chengdu, Sichuan, China;[Pu, Li] School of Architecture and Design, Southwest Jiaotong University, Hi-Tech Industrial Development Zone, Chengdu, Sichuan, China
[Han, Lee D.] Univ Tennessee, Dept Civil & Environm Engn, Knoxville, TN 37996 USA.
Along with the rapid development of Intelligent Transportation Systems, traffic data collection technologies have progressed fast. The emergence of innovative data collection technologies such as remote traffic microwave sensor, Bluetooth sensor, GPS-based floating car method, and automated license plate recognition, has significantly increased the variety and volume of traffic data. Despite the development of these technologies, the missing data issue is still a problem that poses great challenge for data based applications such as traffic forecasting, real-time incident detection, dynamic route guidance, and massive evacuation optimization. A thorough literature review suggests most current imputation models either focus on the temporal nature of the traffic data and fail to consider the spatial information of neighboring locations or assume the data follow a certain distribution. These two issues reduce the imputation accuracy and limit the use of the corresponding imputation methods respectively. As a result, this paper presents a Kriging based data imputation approach that is able to fully utilize the spatiotemporal correlation in the traffic data and that does not assume the data follow any distribution. A set of scenarios with different missing rates are used to evaluate the performance of the proposed method. The performance of the proposed method was compared with that of two other widely used methods, historical average and K-nearest neighborhood. Comparison results indicate that the proposed method has the highest imputation accuracy and is more flexible compared to other methods.