scholarly journals Analyzing Temporal Changes in an Urbanized Area Using Densely Staked Image Classification and Multinomial Logistic Regression (MLR) Technique

2020 ◽  
Author(s):  
Abhinav Wadhwa ◽  
Pavan Kumar Kummamuru
Keyword(s):  
Road Network ◽  
Smart Cities ◽  
Multinomial Logistic Regression ◽  
Land Use Land Cover ◽  
Multinomial Regression ◽  
Causative Factors ◽  
Major Transition ◽  
Spatio Temporal ◽  
Urban Extent ◽  
Urbanization Pattern

Abstract Monitoring transformation of non-built-up area to urban spread via densely-stacked Land-Use-Land-Cover (LULC) classification offers a catalogue of spatio-temporal statistics to evaluate discrepancies instigated by transition factors. Impacts of major transition apparatuses in an area persuading the haphazard urbanization pattern are evaluated for Vellore acts a major contribution to Smart city project. Implications of causative factors: i) Population density; ii) proximity from rail-road-network; and iii) commercial areas are scrutinized with respect to urbanization upsurge. Multi-variate correlation is established using trend analysis and Multinomial Regression (MLR) technique for individual and homogeneous amalgamation of the aforementioned factors. Resulting equations obtained is formally used to detect closeness of urban extent from several landscapes. Research outcomes exhibited that the built-up straggling occurs from 30 to 232 m along the landscapes with a maximum of 336 m. Illustration of this study can also be assessed for various social and economic causative factors against urbanization for other smart cities.

Forest path condition monitoring based on crowd-based trajectory data analysis

2020 ◽  
pp. 1-18
Author(s):  
Francisco Arcas-Tunez ◽  
Fernando Terroso-Saenz
Keyword(s):  
Information Acquisition ◽  
Road Network ◽  
Road Networks ◽  
Trajectory Data ◽  
Country Road ◽  
Wide Range ◽  
Sport Activities ◽  
Path Condition ◽  
Spatio Temporal ◽  
The City

The development of Road Information Acquisition Systems (RIASs) based on the Mobile Crowdsensing (MCS) paradigm has been widely studied for the last years. In that sense, most of the existing MCS-based RIASs focus on urban road networks and assume a car-based scenario. However, there exist a scarcity of approaches that pay attention to rural and country road networks. In that sense, forest paths are used for a wide range of recreational and sport activities by many different people and they can be also affected by different problems or obstacles blocking them. As a result, this work introduces SAMARITAN, a framework for rural-road network monitoring based on MCS. SAMARITAN analyzes the spatio-temporal trajectories from cyclists extracted from the fitness application Strava so as to uncover potential obstacles in a target road network. The framework has been evaluated in a real-world network of forest paths in the city of Cieza (Spain) showing quite promising results.

scholarly journals Application of Multinomial Regression Model to Identify Parameters Impacting Traffic Barrier Crash Severity

2019 ◽  
Vol 13 (1) ◽  
pp. 57-64
Author(s):  
Mahdi Rezapour ◽  
Amirarsalan Mehrara Molan ◽  
Khaled Ksaibati
Keyword(s):  
Regression Model ◽  
Multinomial Logistic Regression ◽  
Crash Severity ◽  
Multinomial Regression ◽  
Interstate Highways ◽  
The Road ◽  
Run Off ◽  
Multinomial Regression Model ◽  
Proportional Odds Assumption ◽  
And Performance

Background: Run Off The Road (ROTR) crashes are some of the most severe crashes that could occur on roadways. The main countermeasure that can be taken to address this type of crashe is traffic barrier installation. Although ROTR crashes can be mitigated significantly by traffic barriers, still traffic barrier crashes resulted in considerable amount of severe crashes. Besides, the types of traffic barriers, driver actions and performance play an important role in the severity of these crashes. Methods: This study was conducted by incorporating only traffic barrier crashes in Wyoming. Based on the literature review there are unique contributory factors in different crash types. Therefore, in addition to focusing on traffic barrier crashes, crashes were divided into two different highway classes: interstate and non-interstate highways. Results: The result of proportional odds assumption was an indication that multinomial logistic regression model is appropriate for both non-interstate and interstates crashes involved with traffic barriers. The results indicated that road surface conditions, age, driver restraint and negotiating a curve were some of the factors that impact the severity of traffic barrier crashes on non-interstate highways. On the other hand, the results of interstate barrier crashes indicated that besides types of barriers, driver condition, citation record, speed limit compliance were some of the factors that impacted the interstate traffic barrier crash severity. Conclusion: The results of this study would provide the policymakers with the directions to take appropriate countermeasures to alleviate the severity of traffic barrier crashes.

A Neural Network for Modeling Multicategorical Parcel Use Change

2013 ◽  
pp. 1297-1308
Author(s):  
Kang Shou Lu ◽  
John Morgan ◽  
Jeffery Allen
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Multinomial Logistic Regression ◽  
Slight Variation ◽  
Ann Model ◽  
Temporal Prediction ◽  
Percentage Points ◽  
Spatio Temporal ◽  
Coastal County ◽  
Artificial Neural Network Ann

This paper presents an artificial neural network (ANN) for modeling multicategorical land use changes. Compared to conventional statistical models and cellular automata models, ANNs have both the architecture appropriate for addressing complex problems and the power for spatio-temporal prediction. The model consists of two layers with multiple input and output units. Bayesian regularization was used for network training in order to select an optimal model that avoids over-fitting problem. When trained and applied to predict changes in parcel use in a coastal county from 1990 to 2008, the ANN model performed well as measured by high prediction accuracy (82.0-98.5%) and high Kappa coefficient (81.4-97.5%) with only slight variation across five different land use categories. ANN also outperformed the benchmark multinomial logistic regression by average 17.5 percentage points in categorical accuracy and by 9.2 percentage points in overall accuracy. The authors used the ANN model to predict future parcel use change from 2007 to 2030.

A Neural Network for Modeling Multicategorical Parcel Use Change

2012 ◽  
pp. 131-142
Author(s):  
Kang Shou Lu ◽  
John Morgan ◽  
Jeffery Allen
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Multinomial Logistic Regression ◽  
Slight Variation ◽  
Ann Model ◽  
Temporal Prediction ◽  
Percentage Points ◽  
Spatio Temporal ◽  
Coastal County ◽  
Artificial Neural Network Ann

This paper presents an artificial neural network (ANN) for modeling multicategorical land use changes. Compared to conventional statistical models and cellular automata models, ANNs have both the architecture appropriate for addressing complex problems and the power for spatio-temporal prediction. The model consists of two layers with multiple input and output units. Bayesian regularization was used for network training in order to select an optimal model that avoids over-fitting problem. When trained and applied to predict changes in parcel use in a coastal county from 1990 to 2008, the ANN model performed well as measured by high prediction accuracy (82.0-98.5%) and high Kappa coefficient (81.4-97.5%) with only slight variation across five different land use categories. ANN also outperformed the benchmark multinomial logistic regression by average 17.5 percentage points in categorical accuracy and by 9.2 percentage points in overall accuracy. The authors used the ANN model to predict future parcel use change from 2007 to 2030.

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