Istanbul, the largest city in Türkiye, is infamous for its heavy traffic. The traffic density problem in Istanbul is a complex issue that stems from several factors such as population growth, urban sprawl, inadequate public transportation, and a high number of private vehicles on the roads. With over 15 million residents and growing, the city’s roads are often gridlocked, particularly during peak hours.
A study conducted by Yeditepe University revealed that in 2017, roughly 55% of the travel time for both drivers and passengers was wasted due to heavy traffic. What should have been a 20-minute journey in lighter traffic often stretched to approximately 45 minutes. Furthermore, during periods of heavy traffic, the average driving speed was found to be a mere 36 kilometers per hour.
Some of the data from the study are as follows:
– Istanbulites are sacrificing an average of 3.5 years of their lives for the delays caused by the intensity of the city traffic.
– Drivers moving on the arterial roads of Istanbul in 2017 moved at an average speed of 37 km / h during the day, and 54% of their time was lost due to traffic congestion.
– In the same arterial roads, the average speed of the drivers was measured at 26 km / h during the weekday mornings. 67% of the time the drivers spent on the road traveling in the morning was caused by traffic intensity.
– These values increased even more during weekday evenings. Drivers moved at an average speed of 22 km / h and lost 71% of their time due to traffic congestion.
The Turkish government has initiated several infrastructure projects and transportation improvements to alleviate the traffic density in Istanbul, however, the issue still remains a huge problem for the residents.
Predictive AI can provide valuable insights into traffic patterns and enable authorities to reduce traffic density in Istanbul proactively. It could optimize traffic flow in real-time, provide personalized travel recommendations, and ultimately lead to a more efficient and sustainable transportation system for the city.
– Setting a project outline
– Data Collection
– Data Preprocessing and Exploratory Data Analysis
– Feature Extraction
Model Development and Training
Time Series Forecasting, Data Extraction, API, Predictive Analysis, Data Visualization