Lessons in Traffic: Nairobi’s School Term and Equity Challenges

Paper by Charles Reuven Starobin Hatfield et al: “The study set out with several key objectives:
1. To analyze traffic congestion during school terms versus holidays—responding to anecdotal evidence suggesting school-term congestion is a problem despite the absence of formal analysis
2. To assess the impact of school commutes on overall citywide congestion, and
3. To explore the broader equity and economic implications of this congestion.
 
The analysis utilized the Uber Movement dataset from 2019, which covers 98% of Nairobi’s motorways, primary, secondary, and trunk roads; 88.7% of tertiary roads; and 9.5% of residential roads. The study focused on three school terms and three corresponding holiday periods, intentionally excluding public holidays and weekends to isolate the school-related traffic impact. The primary temporal focus was the morning rush hour, defined as 6 a.m. to 9 a.m.
 
The approach relied heavily on Uber Movement data for both exploratory and in-depth analysis of congestion during morning hours. The analytical steps included hourly and daily traffic analysis, binomial analysis of the most and least congested roads, travel time loss modeling, statistical evaluation, and interpretation supported by local knowledge. The results from the exploratory hourly analysis showed significant morning rush hour congestion during school terms, with sharp speed declines in the early morning hours, pointing to capacity challenges in the road network.
 
Daily traffic pattern analysis revealed distinct seasonal variations, varied congestion patterns during school terms, and elevated travel speeds on weekends and holidays. The binomial analysis highlighted an unequal distribution of congestion across Nairobi, with structural overburdening observed on arterial roads, while motorways and primary roads appeared less affected. Statistical testing confirmed that differences between school term and holiday periods were statistically significant—even after controlling for spatial and temporal autocorrelation…(More)”.