Measuring of the COVID-19 Based on Time-Geography

At the end of 2019, the COVID-19 pandemic began to emerge on a global scale, including China, and left deep traces on all societies. The spread of this virus shows remarkable temporal and spatial characteristics. Therefore, analyzing and visualizing the characteristics of the COVID-19 pandemic are relevant to the current pressing need and have realistic significance. In this article, we constructed a new model based on time-geography to analyze the movement pattern of COVID-19 in Hebei Province. The results show that as time changed COVID-19 presented an obvious dynamic distribution in space. It gradually migrated from the southwest region of Hebei Province to the northeast region. The factors affecting the moving patterns may be the migration and flow of population between and within the province, the economic development level and the development of road traffic of each city. It can be divided into three stages in terms of time. The first stage is the gradual spread of the epidemic, the second is the full spread of the epidemic, and the third is the time and again of the epidemic. Finally, we can verify the accuracy of the model through the standard deviation ellipse and location entropy.

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Understanding Gap Acceptance Behavior at Unsignalized Intersections using Naturalistic Driving Study Data

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211007140 ◽

2021 ◽

pp. 036119812110071

Author(s):

Yingfeng (Eric) Li ◽

Haiyan Hao ◽

Ronald B. Gibbons ◽

Alejandra Medina

Keyword(s):

Road Traffic ◽

Wait Time ◽

Study Data ◽

Gap Acceptance ◽

Factors Affecting ◽

Unsignalized Intersections ◽

Research Activities ◽

Stop Sign ◽

Naturalistic Driving Study ◽

Naturalistic Driving

Even though drivers disregarding a stop sign is widely considered a major contributing factor for crashes at unsignalized intersections, an equally important problem that leads to severe crashes at such locations is misjudgment of gaps. This paper presents the results of an effort to fully understand gap acceptance behavior at unsignalized intersections using SHPR2 Naturalistic Driving Study data. The paper focuses on the findings of two research activities: the identification of critical gaps for common traffic/roadway scenarios at unsignalized intersections, and the investigation of significant factors affecting driver gap acceptance behaviors at such intersections. The study used multiple statistical and machine learning methods, allowing a comprehensive understanding of gap acceptance behavior while demonstrating the advantages of each method. Overall, the study showed an average critical gap of 5.25 s for right-turn and 6.19 s for left-turn movements. Although a variety of factors affected gap acceptance behaviors, gap size, wait time, major-road traffic volume, and how frequently the driver drives annually were examples of the most significant.

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Analysis of Driver's Characteristics Based on Internet of Vehicles

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.505-506.1148 ◽

2014 ◽

Vol 505-506 ◽

pp. 1148-1152

Author(s):

Jian Qun Wang ◽

Xiao Qing Xue ◽

Ning Cao

Keyword(s):

Traffic Accidents ◽

Road Traffic ◽

Classification Model ◽

Economic Losses ◽

Lane Changing ◽

Internet Of Vehicles ◽

Factors Affecting ◽

The Road ◽

The University ◽

Driver’S Characteristics

The road traffic accidents caused huge economic losses and casualties, so it had been focused by the researchers. Lane changing characteristic is the most relevant characteristic with safety. The intent of lane changing was discussed. Firstly, the factors affecting the intent were analyzed, the speed satisfaction value and the space satisfaction value were proposed; then the data from the University of California, Berkeley was extracted and the number of vehicles changed lane more often and the vehicle ID were obtained; the BP neural network classification model was established, it was trained and testified by actual data. The results shown the method could predict the intent accurately.

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Assessing the built environment of neighborhood in the winter city from the perspective of pupils' commuting safety

Proceedings of the 56th ISOCARP World Planning Congress ◽

10.47472/hswz2399 ◽

2020 ◽

Author(s):

Hong Leng ◽

◽

Huimin Zhao ◽

Chunyu Zou ◽

◽

...

Keyword(s):

Built Environment ◽

Influencing Factors ◽

Open Space ◽

Road Traffic ◽

Thermal Environment ◽

Gated Communities ◽

Environment Design ◽

Signal Identification ◽

Factors Affecting ◽

Social Management

Safety commuting environment can promote children’s walking and cycling, thus reducing the risk of obesity and other diseases. Most of the existing studies on children’s safety focus on open space, but pay little attention to children's commuting environment. Moreover, few studies pay attention to the differences between open blocks and gated communities in winter city. Taking Harbin, a winter city in China, as an example, this study uses the optimized IPA method to explore the built environment factors affecting pupils’ commuting safety from three aspects: environment design, social management and road traffic. The results show that the influencing factors of road traffic have the highest impact on pupils‘ commuting safety. In addition, the occupation management in social management also has a great impact. In terms of satisfaction, the satisfaction with gated communities is generally higher than that with open blocks, but the satisfaction of open block is higher in neighbourhood relationship and street thermal environment. By coupling the importance and satisfaction of influencing factors, it is found that safety guardrail, signal identification, occupation management are in urgent need of renovation.

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Environmental factors affecting the frequency of road traffic accidents: a case study of sub-urban area of Pakistan

Environmental Science and Pollution Research ◽

10.1007/s11356-019-04752-8 ◽

2019 ◽

Vol 26 (12) ◽

pp. 11674-11685 ◽

Cited By ~ 20

Author(s):

Hafiz Mohkum Hammad ◽

Muhammad Ashraf ◽

Farhat Abbas ◽

Hafiz Faiq Bakhat ◽

Saeed A. Qaisrani ◽

...

Keyword(s):

Environmental Factors ◽

Urban Area ◽

Traffic Accidents ◽

Road Traffic ◽

Road Traffic Accidents ◽

Factors Affecting

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QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis

Sensors ◽

10.3390/s19224882 ◽

2019 ◽

Vol 19 (22) ◽

pp. 4882 ◽

Cited By ~ 3

Author(s):

Fernando Terroso-Saenz ◽

Andres Muñoz ◽

José Cecilia

Keyword(s):

Social Network ◽

Data Analysis ◽

Online Social Networks ◽

Road Traffic ◽

Online Social Network ◽

Network Data ◽

Regression Problem ◽

Factors Affecting ◽

Demand Prediction ◽

Social Network Data

Road traffic pollution is one of the key factors affecting urban air quality. There is a consensus in the community that the efficient use of public transport is the most effective solution. In that sense, much effort has been made in the data mining discipline to come up with solutions able to anticipate taxi demands in a city. This helps to optimize the trips made by such an important urban means of transport. However, most of the existing solutions in the literature define the taxi demand prediction as a regression problem based on historical taxi records. This causes serious limitations with respect to the required data to operate and the interpretability of the prediction outcome. In this paper, we introduce QUADRIVEN (QUalitative tAxi Demand pRediction based on tIme-Variant onlinE social Network data analysis), a novel approach to deal with the taxi demand prediction problem based on human-generated data widely available on online social networks. The result of the prediction is defined on the basis of categorical labels that allow obtaining a semantically-enriched output. Finally, this proposal was tested with different models in a large urban area, showing quite promising results with an F1 score above 0.8.

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Factors Affecting Response to Road Noise

Environment and Planning A Economy and Space ◽

10.1068/a090585 ◽

1977 ◽

Vol 9 (5) ◽

pp. 585-597 ◽

Cited By ~ 6

Author(s):

S M Taylor ◽

F L Hall

Keyword(s):

Noise Exposure ◽

Road Traffic ◽

Traffic Noise ◽

Individual Response ◽

Road Traffic Noise ◽

Residential Areas ◽

Factors Affecting ◽

Planning Decisions ◽

Impact Reduction ◽

Noise Impact

Investigation of the factors affecting individual response to noise provides an improved basis for the selection and implementation of noise impact reduction policies. This investigation is necessary because the cause and effect relationship between the level of noise exposure and noise response is confounded by personal and situational variables. Examination of the effects of these variables on response to road traffic noise with the use of data collected at residential sites in Southern Ontario suggests the following points for residential planning decisions. Arguments for taking no action to reduce noise impact are not supported. The sex, age, and socioeconomic compositions of residential areas are not important considerations for implementing measures to reduce noise impact. Life-style characteristics of residents on the other hand do affect response to noise. Methods to reduce noise must be effective indoors and outdoors to have a significant effect on attitudes: air conditioning alone is inadequate. Noise barriers appear to be more effective for improving attitudes than their noise reduction properties would suggest.

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Analysis of the Risk Factors Affecting the Severity of Traffic Accidents on Spanish Crosstown Roads: The Driver’s Perspective

Sustainability ◽

10.3390/su12062237 ◽

2020 ◽

Vol 12 (6) ◽

pp. 2237 ◽

Cited By ~ 9

Author(s):

Natalia Casado-Sanz ◽

Begoña Guirao ◽

Maria Attard

Keyword(s):

Traffic Accidents ◽

Injury Severity ◽

Road Traffic ◽

Fatal Outcome ◽

Policy Support ◽

Health Concern ◽

Contributing Factors ◽

Factors Affecting ◽

Traffic Volumes ◽

New Vision

Globally, road traffic accidents are an important public health concern which needs to be tackled. A multidisciplinary approach is required to understand what causes them and to provide the evidence for policy support. In Spain, one of the roads with the highest fatality rate is the crosstown road, a particular type of rural road in which urban and interurban traffic meet, producing conflicts and interference with the population. This paper contributes to the previous existing research on the Spanish crosstown roads, providing a new vision that had not been analyzed so far: the driver’s perspective. The main purpose of the investigation is to identify the contributing factors that increment the likelihood of a fatal outcome based on single-vehicle crashes, which occurred on Spanish crosstown roads in the period 2006-2016. In order to achieve this aim, 1064 accidents have been analyzed, applying a latent cluster analysis as an initial tool for the fragmentation of crashes. Next, a multinomial logit (MNL) model was applied to find the most important factors involved in driver injury severity. The statistical analysis reveals that factors such as lateral crosstown roads, low traffic volumes, higher percentages of heavy vehicles, wider lanes, the non-existence of road markings, and finally, infractions, increase the severity of the drivers’ injuries.

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