Sana'a - Aden intercity travel corridor: Tavel Vs. Impediment Densities [OC] Comparison

What This Visualization Shows

This data visualization displays "Sana'a - Aden intercity travel corridor: Tavel Vs. Impediment Densities [OC]" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on **Introduction:**

The ongoing civil war in Yemen has had a major effect on domestic infrastructure. Since the closure of Sana'a International Airport to standard commercial flights, the route linking Sana'a to Aden has emerged as the essential gateway for millions of citizens seeking international travel and/or official IDs/passports since the internationally recognized government moved from Sana'a to Aden. This dependency forces civilians to endure a stressful, multi-jurisdictional road trip across opposing active frontlines.

To evaluate the structural impediments during this transit corridor, I collected primary data during a single continuous road trip using a private car. The total distance of 349.3 km was split into 9 distinct sections bounded by well-known landmarks such as major intersections or town centers. Physical impediment - namely speedbumps and armed checkpoints—were manually logged alongside precise time entries.

**Methodology:**

The log recorded passage durations, speedbumps that the vehicle had to slow down to 10 km/hour or lower, and police/military check point across three prominent political control zones:

I. Houthi Militia Territory (Sections 1–3): Spans from the initial point of departure in Sana'a southern suburbs through an aggregate distance of 138.0 km. (Represented in the graph by diagonal striped blocks)

II. Temporary Ceasefire Border Zone (Section 4): Covers a 31.2 km transition corridor between the opposing militia lines.

III. Southern Separation Forces Territory (Sections 5–9): Encompasses the final 180.1 km leading into Northern suburbs of Aden. (Represented in the graph by checkered blocks)

[Raw data logging sheet](https://docs.google.com/spreadsheets/d/1q1DFkI5dodEDy_d1z3RyyTRCZtbEa6ofDNq2useB0PE/edit?gid=0#gid=0)

**Mind-blowing stats:**

\- There were a total of **391** speedbumps, which comes to **1.12 speedbump every 1 km** or **1.81 every 1 mile**.

\- There were a total of **65** checkpoints, which comes to **1.86 every 10 km** or **3 every 10 mile**.

\- The average speed during the whole distance was only **44.44 km/hour** or **27.6 mph.**

\- The most offending section was section 6 were there were a total of **82 speedbumps**, **14** **checkpoints** in a **38.3 km** or **23.8 miles** distance. (Just imagine the torture).

Limitations:

This data was collected during a single trip from Sana'a to Aden. In order to draw a well-reprsented picture of accurate duration through each section, a simultaneous trip on the opposite direction needs to be collect. It would be natural to conclude that travel time in the early morning would be faster than in the afternoon. Multiple data collection exercises would improve accuracy.

Final thoughts:

It is critical to note that the data in this exercise represents an optimal, best-case scenario achieved via an unburdened private automobile. For the general public, the realities are much more difficult. Most Yemeni citizens cannot afford private cars and must rely on tightly packed public minibuses. These public transit vehicles encounter vastly longer delays, as they are systematically subjected to detailed passenger screening, identity verification, and luggage offloading at a significant number of the 65 checkpoints. A journey that requires roughly 8 hours in a private car routinely takes 12 to 16 hours for a minibus., which allows us to understand complex relationships and insights within the data through visual storytelling.

Deep Dive into the Topic

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Data Analysis and Insights

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Significance and Applications

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