| The final open access version of Lessons in traffic: Nairobi’s school term congestion and equity challenges, is available online here: https://authors.elsevier.com/sd/article/S2950-1962(25)00022-5 |
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. Further, the distribution analysis of congestion across wards indicated notable speed reductions during school terms, a geographic concentration of affected wards, and a disproportionate impact on certain areas. The travel time loss analysis revealed widespread increases in drive times in the northern and southern regions of Nairobi, contrasted by a band of shorter travel times from east to west, with high positive spatial autocorrelation. However, several limitations were noted, including the restricted scope and inherent bias in the Uber data, the lack of comprehensive commute data, challenges posed by the Modifiable Areal Unit Problem (MAUP), and gaps in socio-economic data. In conclusion, the study confirmed that school term congestion is not just anecdotal—it is a measurable and significant phenomenon. There is substantial variability in congestion at the ward level and differentiated impacts based on road types. The findings suggest important future research directions, such as multi-scale spatial analysis, more detailed investigation of peripheral and intra-CBD commuting patterns, and the incorporation of school location and preference data to enhance understanding and inform solutions. |
Journal: African Transport Studies
Title: Lessons in Traffic: Nairobi’s School Term Congestion and Equity Challenges
Corresponding Author: Mr. Charles Reuven Starobin Hatfield
Co–Authors: Anna Kustar; Marcel Reinmuth; Constant Cap; Agraw Ali Beshir; Jacqueline M. Klopp, PhD; Alexander Zipf, PhD; James Rising, PhD; Thet Hein Tun
Manuscript Number: AFTRAN-D-24-00061R1
