Nairobi is among the fastest urbanizing cities in Africa — and while urbanization is critical to unlocking economic growth, it also comes with a host of challenges. Like many urban cities globally, it is also facing worsening congestion due to rapid urbanization and lack of infrastructure. With private vehicular traffic estimated to double by 2030, this issue will certainly escalate unless an intervention is made immediately.
Although it is correlated with economic growth, traffic congestion also imposes economic, health, and environmental costs on a city. Local officials have estimated that Nairobi loses $500,000 in productivity every day due to time residents spend in traffic (Honan, 2016). However, according to the World Bank’s Kenya Urbanization Review (World Bank 2016), increasing average commuting speeds from the average recorded 13.5kph to 20 kph would save $54.1 million USD per year and decrease time spent traveling by 30 percent.
Nairobi’s nucleus, centered around the Central Business District or ‘city centre’ is growing. More areas in the vicinity of the CBD are showing tremendous commercial growth accompanied by massive construction activities and real estate development. However, this mono-centric growth with high concentration of employment opportunities adds an interesting spatial facet to the congestion conundrum.
In order to properly weigh the impacts of traffic congestion in Nairobi and evaluate the effects of changes to the road network infrastructure, basic data on traffic speeds throughout the city are needed. In the following analysis, we demonstrate how Speeds data (now freely and publicly available through Uber’s Movement tool) can be used to build a more robust picture of congestion in Nairobi.
Understanding congestion in the City Centre through the lens of Speeds
Using the Speeds tool, we are able to observe the overall distribution of average speeds in Nairobi between October and mid-December 2018 (see Figure 2 below). It is immediately apparent that traffic congestion is pervasive throughout the City, with most road segments visible in the map averaging anywhere between 20–60% below the free-flow speed.
Free-flow Speed: The term used to describe the average speed of traffic in the absence of congestion or other adverse conditions (such as bad weather)
Color Scale: Green represents no deviation from free-flow speed, and speeds worsen from yellow to red
In Figure 3 below, we zoom in on one segment of a major thoroughfare, Mombasa Road. Through Speeds charting function, it is evident that (i) Speeds are significantly lower during daytime hours on this segment and (ii) Weekday speeds are worse compared to weekends. This trend is observed on almost all major segments in and around the central part of the city.
Next, restricting the time window to peak hours during weekdays, we can observe the spatial patterns more clearly across broad regions of the city (Figure 4).
From Figures 4 and 5, it’s clear that rush-hour traffic on major and minor arterials moves substantially below the free-flow speeds - in many cases 60–80% slower (or even worse). As expected in a city where employment is concentrated in the central area, traffic follows a pattern where segments to and from the nucleus are densely congested during AM & PM Peaks respectively.
Important corridors like Mombasa Road, Uhuru highway and Waiyaki Way are badly congested, with most of their segments reporting average speeds more than 50% below free-flow speeds during peak commuting hours.
Evaluating the impact of the new Bypass roads
The Southern and Eastern bypasses are major achievements in Nairobi road infrastructure. When we observe these peripheral roads, we can see that most of their segments operate at close to free flow speeds during peak hours, suggesting that there is still substantial available capacity for traffic to divert this way.
This analysis using Movement Speeds data indicates that congestion remains a serious problem in Nairobi. Congestion significantly impacts mobility between the city centre and outlying areas (especially during typical commute times) -but this is not consistent for all roads. While this is a cursory look at congestion in Nairobi, we look forward to partnering with the city and researchers to dive deeper into ways that speed data can be used to better understand congestion in Nairobi and create innovative public policy solutions to mitigate the impacts.