Multimodal Transport Modeling for Nairobi, Kenya: Insights and Recommendations with an Evidence-Based Model

Traffic congestion is a growing problem in Nairobi, Kenya, resulting from rapidly increasing population and the crowding of motorized traffic onto a limited street network. This report includes analysis of the traffic conditions in Nairobi, the expected effects of further growth in demand, and a set of recommendations for how to improve the performance of the street network. Data describing motorized vehicle traffic was used to build a simulation model of Nairobi’s street network considering cars and matatus. This model was used to analyze traffic conditions at the city-scale under existing conditions and future growth scenarios. The results provide insights for improving the network performance and support recommendations for Nairobi. City-scale analysis of the street network was conducted with the use of the macroscopic fundamental diagram (MFD) which relates the number of vehicles circulating on the street network to the rate at which trips reach their destinations. The results of simulations with different demand patterns show that there is a consistent MFD relating vehicle accumulation to network flow in Nairobi’s central business district (CBD). Therefore, detailed knowledge of demand is not necessary to understand how the network performs, because the MFD depends on the properties of the street network itself. Monitoring and controlling the number of vehicles in the network is sufficient to maintain traffic flow on the city’s streets. As traffic demand grows in the future, the streets will quickly become more congested, so measures should be taken to improve the system. The first recommendations seek to control the accumulation of vehicles in the network so that traffic flow is maximized according to the MFD. One method is to meter the rate at which vehicles can enter the CBD in order to control accumulation so that everyone can reach their destinations sooner. Metering can be effective in the morning when more vehicles are entering the CBD from outside, but during the evening there are many internally generated trips which will tend to jam the network anyway. Policies that reduce the peak travel demand by shifting trips to public transport or spreading the demand across more time can reduce traffic congestion in the evening. A second set of recommendations expand the shape of the MFD itself by increasing the capacity of the streets in the network which is largely dependent on how intersections operate. Traffic circles (roundabouts) are common in Nairobi, but signalized intersections can have greater capacity. Converting intersections will also reduce the congestion effects when queues spill back into upstream intersections. Capacity can be further increased by adding redundancy to the network. An analysis of dedicating lanes to buses and matatus on radial arterials shows that queues in the remaining lanes will grow longer. In the morning, these queues grow away from the center, so matatus experience reduced travel times, but in the evening, the queues back up into CBD increasing delays for everyone. The simulation study provides an illustration representing Nairobi approximately, so results are relevant and qualitatively useful. Further data could be collected to estimate the real MFD for Nairobi and provide more accurate quantitative values. Although Nairobi’s streets are congested and bound to get worse, the network performance can be improved by making strategic investments in the transport network.