*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.
Part D Written Analysis (continued)
At independence, Kenya took over the road and rail system from the British colonial era. Since then, the infrastructure has not kept pace with the rapid growth in population and economic activity. Kenya is one of the more advanced and internationally recognized African economies, although it is also one of the most corrupt. Resources that could have been put into building better roads have been diverted for personal interests. Nairobi in particular has become a regional hub for businesses, NGO’s and trade partnerships. However, Nairobi’s expansion is leading to increased levels of traffic and to some extent, urban stress, not to mention the environmental damage this economic boom has created. The new bypasses that are being built now are meant to decrease congestion and open up new commuter routes, but their effectiveness is yet to be proven.
A colossal number of new cars are brought onto the streets of Nairobi every day. It is amazing to think that while the distribution of wealth in Kenya is deeply unequal, hundreds of thousands of people are able to afford cars. What the Kenyan government needs to finally realize, however, is that if they want to keep money pouring into their pockets through corruption, tourism and other forms of income, they must also think about the development of their roads, as bizarre as it may seem now. It may just be a profitable source of expenditure.
Strict traffic laws and regulations should be put into place in order to control the growing mass in car ownership. Additionally, specific experiments and tests should be set up by the government to find suitable road types, sizes, locations and even intersections in order to decrease congestion levels, including the transport system needed for deliveries and transport outside of Nairobi.
Even though our class was only sampling Nairobi’s traffic flow, the graphs of our data clearly indicate the reality of Kenya’s poor implementation of infrastructure. The roundabout of United Nations Avenue and Ruaka Road was not nearly as congested as the unregulated intersection of General Mathenge Drive and Peponi Road. The roundabout is located in the center of a residential, suburban neighborhood, whereas the unregulated intersection is a major traffic route for people heading downtown, hence their different congestion levels over the 30 minute observation period, as shown on the graphs.
Figure 4: Unregulated intersection
Part E Conclusion
In conclusion, it was very difficult to establish which intersection is more efficient at regulating traffic flow from the sample we used. However, according to our observations, the roundabout seemed to be a better intersection for low levels of traffic, creating a smooth transit, especially from one side of the roundabout the other. The gradual increase in both the number of vehicles coming into the intersection, as well as the increase in transit times may have reflected the time of observation but it also showed that even with more vehicles, cars were still able to cross the intersection efficiently.
The unregulated intersection was easier to keep track of due to the slow movement of cars, but this also showed that congestion levels are most often higher at unregulated intersections. This was also displayed by the similar results throughout the observation period shown on the graphs for the unregulated intersection.
Therefore, from our sample, roundabouts are more efficient at regulating traffic flow than unregulated intersections.
F Evaluation
Sampling traffic flow in Nairobi, the field study only had us measure flow rates and transit times at two intersections. However, this limited the scope of the study because there are many other intersections that, had we studied them too, would have yielded different and perhaps even better results. Instead, we could have split up the group and had different groups go to different intersections, each of which would be recording both flow rates and transit times for their intersection, in order to get a better range of information. On the other hand, it would have too difficult for our class to split up due to both security and logistic reasons.
Because I was the only recorder of transit times at the roundabout, and this also applied to the unregulated intersection, the other students would have to walk up to me to tell me their data, and meanwhile cars would be driving by unrecorded. This meant that we were missing out on some of the data, which might have not made a big difference to the overall trends at the intersections but still gave us a less accurate account of overall traffic flow. What we could have done to eliminate this problem is have everybody write down their own data and then compile the data for each minute later on.
Another negative aspect of our study is the fact that we measured traffic flow at two different times, which impacted the amount of congestion and also the total number of cars on the roads. We measured flow rates and transit times at the United Nations Avenue/Ruaka Road roundabout from 6:50 to 7:20 am, while observing the unregulated intersection between Peponi Road and General Mathenge Drive from 8:00 to 8:30 am. Although the time difference may not seem great, the differences in traffic flow shown on the graphs above were great due to our first observations being slightly before rush hour and our second observations slightly afterwards. In other cities this may not play a significant role in traffic flow, but here in Nairobi it definitely does! To eradicate this issue we could have measured traffic flow over two days, observing both intersections at the same time. If we were merely measuring congestion levels, we could have also made sure to measure traffic flow at the height of rush hour and then either well before or well after to see a clear difference.