shiv1143 / traffic_prediction

Using Junction data to predict congestion on particular junction with time and compare the performance of different models

traffic_prediction

Using Junction data to predict congestion on particular junction with time and compare the performance of different models

Traffic congestion is rising in cities around the world. Contributing factors include expanding urban populations, aging infrastructure, inefficient and uncoordinated traffic signal timing and a lack of real-time data.

The impacts are significant. Traffic data and analytics company INRIX estimates that traffic congestion cost U.S. commuters $305 billion in 2017 due to wasted fuel, lost time and the increased cost of transporting goods through congested areas. Given the physical and financial limitations around building additional roads, cities must use new strategies and technologies to improve traffic conditions.

About Dataset

This dataset contains 48.1k (48120) observations of the number of vehicles each hour in four different junctions:

1. DateTime
2. Juction
3. Vehicles
4. ID

The sensors on each of these junctions were collecting data at different times, hence you will see traffic data from different time periods. Some of the junctions have provided limited or sparse data requiring thoughtfulness when creating future projections.