Toronto is unique among North American cities for having a legacy streetcar network as an integral part of its transit system. The streetcars are icons of the city and make a unique contribution because of their passenger capacity and zero emissions. However, because most of the streetcar network shares streets with other vehicles, and because unlike buses, streetcars cannot move around obstacles or be quickly towed away if they break down, streetcar delays have a disproportionate effect on gridlock.
Mark Ryan and Alina Li Zhang demonstrate an approach that uses time series forecasting and deep learning to predict streetcar delays. You’ll learn the economic impact of the streetcar-trigged gridlock on Canada’s largest city and the development environment Mark and Alina used to tackle the problem. They outline the source data and cleansing process with a focus on the challenges and opportunities of dealing with wild data and share the essential points of time series focusing as they applied it in conjunction with deep learning. You’ll see the results of their analysis, including practical recommendations for Toronto’s transit authority (the TCC) to resolve the problem of streetcar delays so the city can continue to enjoy the character and soul of its unique street railway for the rest of the 21st century.
Mark Ryan is a leader in the machine learning hub at IBM, where he’s responsible for shepherding customers to a variety of database products, including IBM Integrated Analytics System, which includes a full-blown machine learning environment: DSX. Ever since doing a masters at the University of Toronto in the ‘80s, he’s had an interest in machine learning and artificial intelligence, with a particular focus on deep learning on structured data and natural language processing (NLP).
Alina Zhang is a data scientist at Skylinerunners, where she drives the company to provide AI powered Chatbot for online stores, for example, online restaurants booking, grocery shopping, hotel booking, customer service, FAQ.
Previously, Alina was a data scientist helping startups (Nobul, superQuery) to apply machine learning models on user behaviour analysis, recommendation systems, sentiment analysis, classification, and time series forecasting. She worked as a software developer and WLM component owner of IBM DB2 at IBM. She’s a certified Google Cloud Professional Data Engineer and the author of machine learning articles on firstname.lastname@example.org.
Alina holds a masters degree in computer science from Western University, where her research focused on high-performance computing and truncated Fourier transform.
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