Delhi Traffic

By ranjeetverma3084 · January 26, 2026

Data Source :

The analysis shows how different weather conditions affect average driving speeds across all trips. A bar chart has been created that displays the average speed for each weather condition, with color coding from red (slower) to green (faster) to make patterns easy to spot. The chart also includes error bars showing the variability in speeds for each condition.

The analysis successfully identified the routes that maintain the highest average speeds for trips over 20 kilometers. A visualization and detailed data table were generated showing the top-performing routes ranked by their average speed.

I've created two visualizations that show how average speed varies throughout different times of the day. The first chart displays the average speeds with error bars showing the variation, while the second chart shows the full distribution of speeds for each time period using box plots.

I've analyzed the trip volumes across different start areas and created a visualization showing the top 10 locations. The chart displays trip counts for each area, color-coded by their average speeds to help you identify both high-volume locations and their typical traffic speeds.

I've identified the routes experiencing chronic congestion by analyzing average speeds across all start-to-end area combinations. The analysis reveals 15 routes with significantly low average speeds, indicating persistent traffic problems.

The analysis identified the routes that experience the steepest speed reductions when comparing adverse weather conditions to clear conditions. A visualization and detailed data tables were generated showing the top 10 most affected routes.

The analysis reveals clear differences in average speeds and traffic density patterns between weekdays and weekends. Two visualizations were created: a box plot showing the distribution of average speeds by day of week, and a grouped bar chart displaying traffic density levels (such as low, medium, and high) across different days.

The analysis successfully identified the top 10 most popular end area destinations and mapped out their typical traffic conditions. Two comprehensive visualizations were created: one showing the trip volume to each destination, and another displaying the distribution of traffic density levels (like low, medium, high) at these popular locations.

Highway delivers the best average speed performance with 39.06 km/h. The analysis compared different road types to identify which one achieves the highest speeds relative to the distances traveled.

There is a strong negative correlation between traffic density and average speed across all road types. As traffic density increases from Low to High, average speeds decrease significantly. Low density traffic maintains speeds of 46.9 km/h, while high density traffic slows to just 24.2 km/h - a difference of 22.7 km/h. This pattern holds across all road types, with Highways showing the fastest speeds (39.1 km/h average) and Inner Roads the slowest (14.6 km/h average).

The combination of Night time and Weekend days yields the highest average speed at 45.0 km/h. A heatmap visualization was created showing average speeds across all time-of-day and day-of-week combinations, making it easy to identify the fastest travel periods.

During high traffic density conditions, highways are significantly faster than local roads. Highways maintain an average speed of 34.2 km/h, while inner roads (local roads) average just 12.4 km/h. This means highways are 21.7 km/h faster, representing a 174.7% speed advantage over local roads even when traffic is heavy.

I've analyzed how trip distance relates to average speed across different road types. Two visualizations were created to show these relationships: a scatter plot displaying the distance-speed relationship with trend lines for each road type, and a box plot showing the distribution of speeds across road types.

I've analyzed the area pairs to identify which routes have the most trips and the best speed performance. Two visualizations were created to show both aspects clearly.

The analysis reveals a clear relationship between traffic density and speed. Two visualizations were created: a box plot showing speed distributions across all traffic density levels, and a bar chart identifying the top 10 bottleneck routes with high traffic and lowest speeds.