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A2: PAVEMENT CONDITION MONITORING USING SMART PHONE SENSORS
A2: PAVEMENT CONDITION MONITORING USING SMART PHONE SENSORS
Evaluating the Roughness condition of pavements is an expensive, labour intensive, and time consuming process. Many traditional road evaluation methods utilize measurements taken in situ along with visual examinations and interpretations. Roughness is one of the significant road conditions, as it directly affects the safety of the users as well as the vehicle costs. Road roughness is generally measured using expensive or time-consuming. This project discusses a low-cost easy method using smartphone sensors for road roughness measurement. Smartphone based pavement condition assessment is a non-destructive remote sensing method.
A6: STUDY ON PARKING DEMAND AND ITS CHARACTERISTICS USING STRUCTURAL EQUATION MODELLING
A6: STUDY ON PARKING DEMAND AND ITS CHARACTERISTICS USING STRUCTURAL EQUATION MODELLING
Rapid economic growth and ineffective policies have led to more people using private vehicles in cities, which is causing parking to become a major issue for transportation and traffic management globally. Understanding the complex interplay of factors influencing parking demand is crucial for effective urban planning and transportation management. The study tries to investigate the use of Structural Equation Modelling (SEM) to analyze the relationships between various factors that influence parking demand in urban areas. SEM is all about creating a visual representation that shows how different factors are connected. This model will use symbols to represent variables, relationships between variables, and errors in model. The SEM process typically commences with the formulation of a theoretical framework, aiming to examine relationships among constructs of interest. These relationships are then delineated into a schematic diagram, serving to express the hypotheses. The research begins by selection of a study area where parking is a matter of concern. After identification of study area, a secondary and primary data collection including questionnaire survey and establishment survey is done to identify the factors that influence parking demand. SEM helps to build a model that captures the relationships among these variables. This study showcases solving the complex relationships between factors affecting parking demand in urban areas and also emphasizes the importance of considering multiple factors simultaneously when designing effective parking management strategies and urban planning initiatives.
A10: GIS BASED SPATIO TEMPORAL ANALYSIS FOR ROAD TRAFFIC CRASHES
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