{"id":39,"date":"2024-12-13T11:00:28","date_gmt":"2024-12-13T11:00:28","guid":{"rendered":"https:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/malcolm-connolly\/?page_id=39"},"modified":"2025-01-21T18:59:15","modified_gmt":"2025-01-21T18:59:15","slug":"page-2","status":"publish","type":"page","link":"https:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/malcolm-connolly\/page-2\/","title":{"rendered":"Coding Projects"},"content":{"rendered":"\n
RTA app<\/strong><\/p>\n\n\n\n I created an R app to visualise UK government data<\/a> on serious and fatal road traffic accidents. All code is available on my Github. In this project I enjoyed getting the maps to interact and learned about shape files and the leaflet library. The link below will take you to shinyapps.io where the application is hosted. <\/p>\n\n\n\n Sprint 1 – Dynamic programming<\/strong><\/p>\n\n\n\n An interesting task in this sprint was to produce a visualisation of an optimal booking control policy for a problem with an airline theme. Consider a network of two legs. Leg 1 services Frankfurt to London with 20 seats capacity. Leg 2 services London to New York with 40 seats capacity. Passengers may book either a single leg journey or a two-leg journey. The time until departure is 100 days, the prices of each of the 3 tickets was detailed in the question, each with a different probability of booking requests being made. At any time we can accept a requested booking or reject the booking. What is the optimal policy to maximise the expected revenue?<\/p>\n\n\n\n It turns out this problem can be solved exactly with the Bellman equation, and I wrote a small app to visualise the optimal policy.<\/p>\n\n\n\n RTA app I created an R app to visualise UK government data on serious and fatal road traffic accidents. All code is available on my Github. In this project I enjoyed getting the maps to interact and learned about shape … Continue reading \n
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