Improving public transportation and traffic by big data analysis

Joint Event on 5^th World Machine Learning and Deep Learning Congress and World Congress on Computer Science, Machine Learning and Big Data

August 30-31, 2018 Dubai, UAE

Hisham Airan

University of Utara Malaysia, Malaysia

Posters & Accepted Abstracts: J Comput Sci Syst Biol

Abstract :

Dubai is known for its heavy traffic and traffic congestion. The government has taken many initiatives to improve traffic flow but there is no significant difference in the traffic condition. The Road and Transport Authority (RTA) has doubled the network of roads from 8715 lane-km to 13594 lane-km in 10 years but the condition is still not controlled. Big data and database analysis can be used in solving public transportation issues such as by predicting passengers behavior, heat maps and demand areas where it needs to add more public transportation services or parking lots or any other transportation means, this is where database analysis and big data can really add value to public transportation. This will help countries to use historical data for planning their next transportation projects, transportation infrastructure, future smart city planning. Public transportation users have their own motivations, desires and understanding their actions is all part of databases and big data normalization analytics is about. By studying these actions, we can understand why people choose these routes, or why they choose to use a taxi not a bus, or vice versa. Based on these findings, cities can plan where to focus on future projects to respond to the needs of transportation users. Getting accurate data can be a challenge, but many cities, such as Dubai, started collecting such data which have details of when and where people are traveling, how long it took them to reach final destination, what was the cost, how often they do this and other useful data.

Biography :

E-mail: hisham301@gmail.com