The project's source code is available on the PieceX Marketplace.

predicting impact of disruptions to urban rail

Time and Date: The time and date of the disruption, which can help identify patterns in disruptions based on the day of the week, time of day, and season. Weather Conditions: Weather data such as temperature, precipitation, and wind speed, which can impact the likelihood and severity of disruptions. Location: The location of the disruption within the transit system, which can help identify areas that are more prone to disruptions. Type of Disruption: The type of disruption, such as track maintenance, signal failure, or unplanned incidents like accidents or medical emergencies. Historical Data: Historical data on past disruptions, including their duration and impact, which can help identify trends and patterns. Service Information: Information about the affected rail lines, stations, and services, which can help determine the extent of the disruption. External Factors: Other external factors that could impact the transit system, such as events, holidays, or road closures. Infrastructure Data: Data on the condition of the rail infrastructure, such as track age, maintenance history, and recent repairs. Operational Data: Data on train schedules, capacity, and other operational factors that could affect the transit system's ability to recover from disruptions.

Agro Advisory Services provide timely and accurate weather forecast and miscellaneous agricultural operations to be carried out for better crop growth. Agro Advisory Services (AAS) were formed to be helpful to the farmers in managing climate risks effectively for sustainable and profitable agricultural production.

I want a introduction,functional and non functional requirements, er daigram, schema daigram, usecases,flowchart, architecture,test case, conclusion of this mini project..