scholarly journals Identification and Analysis of Weather-Sensitive Roads Based on Smartphone Sensor Data: A Case Study in Jakarta

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2405
Author(s):  
Chao-Lung Yang ◽  
Hendri Sutrisno ◽  
Arnold Samuel Chan ◽  
Hendrik Tampubolon ◽  
Budhi Sholeh Wibowo
Keyword(s):  
Traffic Congestion ◽  
Sensor Data ◽  
Infrastructure Development ◽  
Weather Factors ◽  
Real World Data ◽  
Clustering And Classification ◽  
Traffic Speed ◽  
Weather Changes ◽  
Smartphone Sensor ◽  
Weather Change

Weather change such as raining is a crucial factor to cause traffic congestion, especially in metropolises with the limited sewer system infrastructures. Identifying the roads which are sensitive to weather changes, defined as weather-sensitive roads (WSR), can facilitate the infrastructure development. In the literature, little research focused on studying weather factors of developing countries that might have deficient infrastructures. In this research, to fill the gap, the real-world data associating with Jakarta, Indonesia, was studied to identify WSR based on smartphone sensor data, real-time weather information, and road characteristics datasets. A spatial-temporal congestion speed matrix (STC) was proposed to illustrate traffic speed changes over time. Under the proposed STC, a sequential clustering and classification framework was applied to identify the WSR in terms of traffic speed. In this work, the causes of WSR were evaluated based on the variables’ importance of the classification method. The experimental results show that the proposed method can cluster the roads according to the pattern changes in the traffic speed caused by weather change. Based on the results, we found that the distances to shopping malls, mosques, schools, and the roads’ altitude, length, width, and the number of lanes are highly correlated to WSR in Jakarta.

Community Development and Role of Tourism: A Case Study of Tourist Destination Manali in Himachal Pradesh (India)

2016 ◽  
Vol 2 (1) ◽  
Author(s):  
Sunil
Keyword(s):  
Traffic Congestion ◽  
Local Community ◽  
Negative Impact ◽  
Himachal Pradesh ◽  
Tourism Industry ◽  
Cultural Development ◽  
Infrastructure Development ◽  
Tourism Sector ◽  
The Government ◽  
Agriculture Products

Tourism sector has a significant role in the economic development of our country. Tourism sector has contributed 6.88 percent to the GDP and has 12.36 percent share in employment (direct and indirect) in the year 2014. It has also a significant share in foreign exchange earnings. The benefit of tourism mostly goes to the local community (Sonya & Jacqueline, Mansour E. Zaei & Mahin E. Zaei, 2013). In this paper, an attempt has been made to assess how the tourism industry has created an opportunity for the economic, political, social and cultural development of the local community at Manali in Himachal Pradesh (India) and also tried to study the problems that are associated with the tourism in the region. The study found that the tourism industry has been extending its contribution for the development of local community at Manali. It has been providing employment, business and investment opportunities, revenue generation for the government, encouraging the community to promote and preserve its art, culture and heritage, raising the demand of agriculture products, provided opportunities for local people to run and work in the transport business and by promoting MSMEs in the region. Besides the opportunities, the tourism industry has also added many problems to the local community. Traffic congestion, increase in water and air pollution, solid waste generation, degradation of the cultural heritage, ecological imbalances, rise in cost of living, increase in crime, noise and environment pollution, migration of people to the region, negative impact on local culture, and extra pressure on civic services during the tourists season, are the problems associated with the tourism. The study suggest that effective management of natural resources, dissemination of environment protection information, involvement of local community in decision making, professionalization in the working of local administration, extending the support of government in sponsoring the events, infrastructure development, tracking records of migrants with the help of local community to curb the crime rate, promotion and preservation of art, culture and heritage, involvement of NGOs, compliance of the rules can make tourism more beneficial in the development of local community.

Statistical estimation of container condensation in marine transportation between Far East Asia and Europe

2021 ◽  
pp. 1-24
Author(s):  
Ping Chi Yuen ◽  
Kenji Sasa ◽  
Hideo Kawahara ◽  
Chen Chen
Keyword(s):  
Statistical Estimation ◽  
Far East ◽  
Estimation Method ◽  
Measured Data ◽  
Probability Estimation ◽  
Container Transportation ◽  
Marine Transportation ◽  
Weather Changes ◽  
Sea Area ◽  
Weather Change

Abstract Condensation inside marine containers occurs during voyages owing to weather changes. In this study, we define the condensation probability along one of the major routes for container ships between Asia and Europe. First, the inside and outside air conditions were measured on land in Japan, and a correlation analysis was conducted to derive their relationship. Second, onboard measurements were conducted for 20,000 twenty-foot equivalent unit (TEU) ships to determine the variation in outside air conditions. Complicated patterns of weather change were observed with changes in latitude, sea area, and season. Third, condensation probability was estimated based on a multi-regression analysis with land and onboard measured data. The maximum condensation probability in westbound or eastbound voyages in winter was found to be approximately 50%. The condensation probability estimation method established in this study can contribute to the quantification of cargo damage risks for the planning of marine container transportation voyages.

Study on Building Materials and Building Color Attribute Changes in Cold Regional for Weather Factors and Distance

2014 ◽  
Vol 1014 ◽  
pp. 263-266
Author(s):  
Jie Liu ◽  
Qi Wang
Keyword(s):  
Visual Perception ◽  
Physical Properties ◽  
Building Materials ◽  
Sensory Experience ◽  
Cold Region ◽  
Weather Factors ◽  
Observation Distance ◽  
Weather Changes ◽  
Distance Conditions ◽  
Color Attribute

The sensory experience of visual perception and quantification of physical properties of colors are combined in this paper, and with the colors commonly used in buildings materials in the cold region of China as an example, based on the visual perception principle, the changes in such color attributes of buildings as chromaticness, blackness and hue in vision in different weather and observation distance conditions are analyzed. The result shows that the stimulus degree of chromaticness and blackness decreases with the increase in observation distance, directly related to weather changes, while hue basically remains unchanged. Keywords: cold regional, color attribute, weather factors

scholarly journals Evaluation of Sensor and Environmental Factors Impacting the Use of Multiple Sensor Data for Time-Series Applications

2018 ◽  
Vol 10 (11) ◽  
pp. 1678 ◽  
Author(s):  
Rajagopalan Rengarajan ◽  
John Schott
Keyword(s):  
Time Series ◽  
Forest Canopy ◽  
Vegetation Index ◽  
Deciduous Forest ◽  
Spectral Response ◽  
Sensor Data ◽  
Landsat 8 ◽  
Real World Data ◽  
Large Variability ◽  
Atmospheric Compensation

Many remote sensing sensors operate in similar spatial and spectral regions, which provides an opportunity to combine the data from different sensors to increase the temporal resolution for short and long-term trend analysis. However, combining the data requires understanding the characteristics of different sensors and presents additional challenges due to their variation in operational strategies, sensor differences and environmental conditions. These differences can introduce large variability in the time-series analysis, limiting the ability to model, predict and separate real change in signal from noise. Although the research community has identified the factors that cause variations, the magnitude or the effect of these factors have not been well explored and this is due to the limitations with the real-world data, where the effects of the factors cannot be separated. Our work mitigates these shortcomings by simulating the surface, atmosphere, and sensors in a virtual environment. We modeled and characterized a deciduous forest canopy and estimated its at-sensor response for the Landsat 8 (L8) and Sentinel 2 (S2) sensors using the MODerate resolution atmospheric TRANsmission (MODTRAN) modeled atmosphere. This paper presents the methods, analysis and the sensitivity of the factors that impacts multi-sensor observations for temporal analysis. Our study finds that atmospheric compensation is necessary as the variation due to the atmosphere can introduce an uncertainty as high as 40% in the Normalized Difference Vegetation Index (NDVI) products used in change detection and time-series applications. The effect due to the differences in the Relative Spectral Response (RSR) of the two sensors, if not compensated, can introduce uncertainty as high as 20% in the NDVI products. The view angle differences between the sensors can introduce uncertainty anywhere from 9% to 40% in NDVI depending on the atmospheric compensation methods. For a difference of 5 days in acquisition, the effect of solar zenith angle can vary between 4% to 10%, depending on whether the atmospheric attenuations are compensated or not for the NDVI products.

Metric-Based Semi-Supervised Fuzzy C-Means Clustering

2011 ◽  
Vol 268-270 ◽  
pp. 166-171
Author(s):  
Xue Song Yin ◽  
Qi Huang ◽  
Liang Ming Li
Keyword(s):  
Real World ◽  
Side Information ◽  
Data Sets ◽  
Membership Degree ◽  
Real World Data ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Clustering And Classification ◽  
Classification Tasks ◽  
Fuzzy C Means Algorithm

This paper presents a metric-based semi-supervised fuzzy c-means algorithm called MSFCM. Through using side information and unlabeled data together, MSFCM can be applied to both clustering and classification tasks. The resulting algorithm has the following advantages compared with semi-supervised clustering: firstly, membership degree as side information is used to guide the clustering of the data; secondly, through the metric learned, clustering accuracy can be greatly improved. Experimental results on a collection of real-world data sets demonstrated the effectiveness of the proposed algorithm.

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