Yasmine Waleed, Rola wafi, Celiusty Naguib, Hagar Maged , Amal Khaled
Traffic congestion is unavoidable in large and growing cities across the world. Life will be easier if
there is an application to guide you when to go out tomorrow to reach your meeting on time, or say to
you to avoid a certain route because it will be clogged due to heavy rains next Tuesday. Traffic Prediction
system will make life simpler specially here in Egypt. There are predictable factors that cause traffic like
weather and traffic crashes. Our project’s goal is to predict the traffic congestion before months or days
using traffic data-set. Traffic prediction can help in guiding the best route to take and manage traffic
congestion. The algorithm used in this system is “SVR” and achieved high accuracy .
Although a driver cannot always avoid traffic, precise traffic projections can help him make better decisions that save money, time, and improve overall road safety. Heavy rains, Storms, accidents, and rush hours have a significant impact on traffic congestion .Traffic prediction system for long-term period will make a major change in society and in individual’s life.
We have realized that other applications that related to traffic in general, detects the traffic at the current
moment but it can’t predict anything, and also when they collect information on one feature such as( weather condition only) but doesn’t combine all the possible features such as( weather condition, rain, fog, accidents events).So our application’s goal is to predict traffic on long term and to combine all the possible features that affects the traffic flow. The problem occurred when people realized that staying in traffics for a long time when there are any events or car accidents or weather conditions. Our purpose is to collect any information which might cause traffic congestion in general and long term and try to alert them ahead of time. Our work is motivated by the previous work of “Crosstown traffic -supervised prediction of the impact of planned special events on urban traffic” has collected information about events and their location and population in Germany.
“From Twitter to traffic predictor: Next-day morning traffic prediction using social media data” used Twitter to collect some data on morning traffic flow on the and made the prediction based on these data.
“Short-Term Traffic Flow Prediction with Weather Conditions: Based on Deep Learning Algorithms and Data Fusion” used the weather to predict traffic flow.
“Predicting the Spatial Impact of Planned Special Events ” predicting an event that might take place in the next day or month or in the future.
Our goal is to predict a traffic prediction based on events in the long term which helps drivers to take short routes and save time. From the economist’s perspective side, using the accuracy of predictions of accident rates which collects all the information that might affect the traffic requires as part of a comprehensive cost- benefit analysis of major transportation investments or policy changes. Using traffic prediction from the economics side to check the safety of transportation planners and engineers in their design of transportation and self-safety and for the result of public policy. Focusing on good forecasts prediction in the long term helps creating a road safety and s urface public transportation
1.3 Problem Statement
Traffic prediction may be affected by many reasons such as important events, Heavy rains that began in late June and peaked on July 6 and 7, 2018, resulting in disastrous landslides and severe transportation network disruptions in the routs and most cities. Also rush hours for the leaving of workers and students from their jobs and universities. All of these may let the driver stay for 2 hours or more at the same spot. So, this application aims to collect all the information that might make traffic congestion and integrate with Google Maps API in order to predict the traffic ahead, make it easy for the user to choose the best and the shortest route to take. Avoiding traffic congestion might save time, money, and might save lives. In addition might also help in decreasing traffic congestion