Hypothesis: At rush hour, the unregulated intersection will be severely blocked because people force their way through and nobody waiting their turn. Roundabouts, especially the United Nations Avenue/Ruaka Road one, will work more effectively, up to a certain level of traffic, after which the same problem will arise.
Over the past few years, traffic in Nairobi, the capital of Kenya, has increased tremendously due to the rapid increase in the ownership of cars. This has led to problems such as congestion, depletion of green space and noise largely contributing to the overwhelming swell in environmental and urban stress. Nairobi’s geography, which is made up of numerous crests and troughs, has made it very difficult to build convenient connecting roads in order to eliminate some of the congestion problems.
A person who works in the city centre coming from outside of Nairobi, therefore, has a very long trip to work, which is mostly spent sitting in their car, wishing they had woken up a little earlier to escape the mess. Nairobi’s poorly designed infrastructure and lack of cooperation between politicians, largely because of corruption, only adds to the chaos.
Drawn from the syllabus section: Urban Environments, the field study compares various measures of congestion at two types of intersections, which are common in Nairobi, roundabouts and unregulated intersections, in order to establish their effectiveness. Both intersections are on important commuter routes and therefore allow for effective data collection.
Cars using the roundabout on United Nations Avenue and Ruaka Road come in from two directions, both from the Runda residential area where it is located and from the outer suburbs such as Kiambu District, funneling towards United Nations Avenue on their way to work. The intersection at General Mathenge Drive and Peponi Road has cars regularly coming in from all four directions, as the Westlands area where it is located is a bottleneck on the way to downtown Nairobi. Traffic is one of the largest contributors to early-morning frustration and hence, urban stress.
*Attached maps show Kenya, Nairobi and each area of study individually
Part B Methods of investigation
Our class decided to all do the same field study in order to be able to gather results more quickly and efficiently. We split up into different groups at each station (A, B, C, and D) to observe and record flow rates and transit times, starting with the roundabout of Ruaka Road and United Nations Avenue. The first thing we did was measure 25 meters from the center of the roundabout out each of the four roads entering the intersection. We marked that point with a line of cornstarch across each road. We followed the same procedure at the unregulated intersection.
Two people measured flow rates, taking into account two roads entering the four-way intersections. Their responsibility was only to count the number of vehicles that passed their designated cornstarch lines when entering the intersections each minute, without noting their exit points or times.
The rest of the students were measuring transit times, using a stopwatch to time vehicles from the time they passed a line of corn starch entering the intersection until crossing another line of corn starch leaving the intersection. The transit times were recorded by the data recorders who stood either in the center of the roundabout or off to the side of the unregulated intersection, documenting the times that were being shouted out by the transit time observers, with their stopwatches. The data collectors were also responsible of keeping time, ensuring that when every new minute started this would be recorded. Any anomalies that affected the transit times were noted.
After having recorded flow rates and transit times for 30 minutes at each intersection, students compiled all of the data, making an excel spreadsheet to share with the rest of the class. Additionally, students were responsible of taking pictures at each intersection.
Figure 2: Unregulated intersection
Figure 1: roundabout
Figure 3: data collection
Part C/D Data Collection and Processing/ Written Analysis
* All Black lines represent line of best fit
The increase in transit times, without taking the anomalies into account, shows the movement toward rush hour. With more cars starting to shuffle around the roundabout, the transit times show that over time, the roundabout did become slightly more congested and that this would only increase as more people headed to work.
Transit times at the unregulated intersection gradually become faster, although still relatively slow and with several fluctuations, which in this case actually meant that more cars were able to drive from one line of cornstarch across another line of cornstarch exiting the intersection faster. Although congestion was still clearly visible at this intersection, the overall trend is moving toward less congestion, and therefore, faster movement. Note particularly the peak at 8:26, which happened because one vehicle stopped in the center of the intersection for several minutes, which raised the average transit times for vehicles passing through the intersection during that minute.
Over time, more and more cars started to enter the intersection. This is shown by the positive trend of the number of vehicles on the graph. As several of Nairobi’s expatriates work on United Nations Avenue and many of these live within Runda, the indicated increase was most probably due to more people heading to work, as well as their staff arriving at their homes on bicycles and commercial vehicles. The anomalies at 7:02 (sudden decline) and 7:18 (sharp increase) were most likely due to sudden changes in congestion levels during those one-minute periods. As flow rates are not determined by the exit of the vehicles from the intersection, random changes in the amount of cars that are able to come into or do not arrive at the intersection can only explain the anomalies.
Although slight fluctuations are visible, the graph clearly shows that the number of vehicles entering the unregulated intersection stayed very similar throughout the observation period. The single anomaly in the very first observation at 8:00 am is not repeated. This may mean that traffic further down the roads has become more congested, which means that the vehicles don’t have anywhere to go. This hypothesis would need to be tested with a longer period of investigation.
The increasing number of cars coming into Nairobi is shown by the extreme difference in the percentage of cars versus other types of vehicles at both intersections. As Kenya becomes more economically developed, so do its inhabitants, especially those living in Nairobi. Becoming economically developed involves a higher employment rate, which in turn means more people wanting cars to get to those workplaces and being able to afford them. A large majority of relatively well-off Kenyans own more than one car, simply because they can afford to. Of course there are still millions of people with lower-level working jobs, who take commercial vehicles, public transportation or bicycles to work, yet at our observation points we chose to study most of the vehicles were private cars.