Publications by authors named "Kezhen Hu"

2 Publications

  • Page 1 of 1

Exploring the Relationship between Attitudes, Risk Perceptions, Fatalistic Beliefs, and Pedestrian Behaviors in China.

Int J Environ Res Public Health 2021 03 24;18(7). Epub 2021 Mar 24.

Human Factors Engineering, Transportation Research Group, University of Southampton, Southampton SO16 7QF, UK.

Road safety has become a worldwide public health concern. Although many factors contribute to collisions, pedestrian behaviors can strongly influence road safety outcomes. This paper presents results of a survey investigating the effects of age, gender, attitudes towards road safety, fatalistic beliefs and risk perceptions on self-reported pedestrian behaviors in a Chinese example. The study was carried out on 543 participants (229 men and 314 women) from 20 provinces across China. Pedestrian behaviors were assessed by four factors: errors, violations, aggressions, and lapses. Younger people reported performing riskier pedestrian behaviors compared to older people. Gender was not an influential factor. Of the factors explored, attitudes towards road safety explained the most amount of variance in self-reported behaviors. Significant additional variance in risky pedestrian behaviors was explained by the addition of fatalistic beliefs. The differences among the effects, and the implications for road safety intervention design, are discussed. In particular, traffic managers can provide road safety education and related training activities to influence pedestrian behaviors positively.
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http://dx.doi.org/10.3390/ijerph18073378DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8037076PMC
March 2021

Identifying regional service function from PM mass concentration throughout a city with non-negative tensor factorization approach.

Environ Sci Pollut Res Int 2017 Dec 11;24(35):26893-26900. Epub 2015 Apr 11.

College of Civil Engineering, Tsinghua University, Beijing, 100084, China.

This paper examines the holistic viewpoint on pollution pattern from time, day, and region dimensions based on the public data of fine particle concentrations, which cover 35 ambient monitoring stations in Beijing firstly. According to data driven, non-negative tensor factorization (NTF) method is introduced to distinguish pollution patterns which could identify the area service function. Results show that five patterns are obtained and annotated as traffic, industrial, residential, commercial, and steady ones. Each type owns special characteristics on time basis or day basis. Furthermore, calculating the reconstruction correlation of tensors with respect to sites, time, and days approximately approaches to 0.95-0.96, and it can be employed with high evaluation values of the model. Comparing with the original classifications drew by land use, this method corresponds with the reality well for considering the changes of surrounding sources. Some commendations on public travel and controlling measures based on local pollution presented in this study can be provided for further decrease of fine particle and improvement of air quality.
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http://dx.doi.org/10.1007/s11356-015-4365-2DOI Listing
December 2017