IDEAS home Printed from https://ideas.repec.org/p/cdl/itsrrp/qt6dv195p7.html
   My bibliography  Save this paper

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

Author

Listed:
  • Gonzales, Eric J.
  • Chavis, Celeste
  • Li, Yuwei
  • Daganzo, Carlos F.

Abstract

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.

Suggested Citation

  • Gonzales, Eric J. & Chavis, Celeste & Li, Yuwei & Daganzo, Carlos F., 2009. "Multimodal Transport Modeling for Nairobi, Kenya: Insights and Recommendations with an Evidence-Based Model," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6dv195p7, Institute of Transportation Studies, UC Berkeley.
  • Handle: RePEc:cdl:itsrrp:qt6dv195p7
    as

    Download full text from publisher

    File URL: https://www.escholarship.org/uc/item/6dv195p7.pdf;origin=repeccitec
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daganzo, C. F. & Li, Yuwei & Gonzales, Eric J. & Geroliminis, Nikolas, 2007. "City-Scale Transport Modeling: An Approach for Nairobi, Kenya," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt7hk8d77b, Institute of Transportation Studies, UC Berkeley.
    2. Daganzo, Carlos F., 2007. "Urban gridlock: Macroscopic modeling and mitigation approaches," Transportation Research Part B: Methodological, Elsevier, vol. 41(1), pages 49-62, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ding, Heng & Di, Yunran & Feng, Zhongxiang & Zhang, Weihua & Zheng, Xiaoyan & Yang, Tao, 2022. "A perimeter control method for a congested urban road network with dynamic and variable ranges," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 160-187.
    2. Daganzo, Carlos F. & Gayah, Vikash V. & Gonzales, Eric J., 2011. "Macroscopic relations of urban traffic variables: Bifurcations, multivaluedness and instability," Transportation Research Part B: Methodological, Elsevier, vol. 45(1), pages 278-288, January.
    3. Ding, Heng & Qian, Yu & Zheng, Xiaoyan & Bai, Haijian & Wang, Shiguang & Zhou, Jingwen, 2022. "Dynamic parking charge–perimeter control coupled method for a congested road network based on the aggregation degree characteristics of parking generation distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    4. Gayah, Vikash V. & Daganzo, Carlos F., 2010. "Exploring the Effect of Turning Maneuvers and Route Choice ona Simple Network," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6kg0d8ds, Institute of Transportation Studies, UC Berkeley.
    5. Daganzo, Carlos F. & Gayah, Vikash V. & Gonzales, Eric J., 2010. "Macroscopic Relations of Urban Traffic Variables: An Analysis of Instability," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt7qd590bv, Institute of Transportation Studies, UC Berkeley.
    6. Niu, Xiao-Jing & Zhao, Xiao-Mei & Xie, Dong-Fan & Liu, Feng & Bi, Jun & Lu, Chaoru, 2022. "Impact of large-scale activities on macroscopic fundamental diagram: Field data analysis and modeling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 161(C), pages 241-268.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Richard Connors & David Watling, 2015. "Assessing the Demand Vulnerability of Equilibrium Traffic Networks via Network Aggregation," Networks and Spatial Economics, Springer, vol. 15(2), pages 367-395, June.
    2. Fiems, Dieter & Prabhu, Balakrishna & De Turck, Koen, 2019. "Travel times, rational queueing and the macroscopic fundamental diagram of traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 412-421.
    3. Choi, T.S. & To, Kiwing & Wong, K.Y. Michael, 2024. "The dynamics of traffic congestion: Data from a freeway Electronic Toll Collection system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    4. Daganzo, Carlos F & Lehe, Lewis J, 2014. "Distance-dependent Congestion Pricing for Downtown Zones," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt9vz1b9rs, Institute of Transportation Studies, UC Berkeley.
    5. Xu, Guanhao & Gayah, Vikash V., 2023. "Non-unimodal and non-concave relationships in the network Macroscopic Fundamental Diagram caused by hierarchical streets," Transportation Research Part B: Methodological, Elsevier, vol. 173(C), pages 203-227.
    6. Haddad, Jack & Zheng, Zhengfei, 2020. "Adaptive perimeter control for multi-region accumulation-based models with state delays," Transportation Research Part B: Methodological, Elsevier, vol. 137(C), pages 133-153.
    7. Zhang, Lele & Garoni, Timothy M & de Gier, Jan, 2013. "A comparative study of Macroscopic Fundamental Diagrams of arterial road networks governed by adaptive traffic signal systems," Transportation Research Part B: Methodological, Elsevier, vol. 49(C), pages 1-23.
    8. Nikolas Geroliminis & David M. Levinson, 2009. "Cordon Pricing Consistent with the Physics of Overcrowding," Springer Books, in: William H. K. Lam & S. C. Wong & Hong K. Lo (ed.), Transportation and Traffic Theory 2009: Golden Jubilee, chapter 0, pages 219-240, Springer.
    9. Haddad, Jack & Geroliminis, Nikolas, 2012. "On the stability of traffic perimeter control in two-region urban cities," Transportation Research Part B: Methodological, Elsevier, vol. 46(9), pages 1159-1176.
    10. Daganzo, Carlos F., 2011. "On the macroscopic stability of freeway traffic," Transportation Research Part B: Methodological, Elsevier, vol. 45(5), pages 782-788, June.
    11. Zhong, R.X. & Huang, Y.P. & Chen, C. & Lam, W.H.K. & Xu, D.B. & Sumalee, A., 2018. "Boundary conditions and behavior of the macroscopic fundamental diagram based network traffic dynamics: A control systems perspective," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 327-355.
    12. Guardiola, I.G. & Leon, T. & Mallor, F., 2014. "A functional approach to monitor and recognize patterns of daily traffic profiles," Transportation Research Part B: Methodological, Elsevier, vol. 65(C), pages 119-136.
    13. Gonzales, Eric Justin, 2011. "Allocation of Space and the Costs of Multimodal Transport in Cities," University of California Transportation Center, Working Papers qt7s28n4nj, University of California Transportation Center.
    14. Saeedmanesh, Mohammadreza & Geroliminis, Nikolas, 2017. "Dynamic clustering and propagation of congestion in heterogeneously congested urban traffic networks," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 193-211.
    15. Batista, S.F.A. & Leclercq, Ludovic & Geroliminis, Nikolas, 2019. "Estimation of regional trip length distributions for the calibration of the aggregated network traffic models," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 192-217.
    16. Kenneth Small, 2015. "The Bottleneck Model: An Assessment and Interpretation," Working Papers 141506, University of California-Irvine, Department of Economics.
    17. Jin, Wen-Long, 2020. "Generalized bathtub model of network trip flows," Transportation Research Part B: Methodological, Elsevier, vol. 136(C), pages 138-157.
    18. Paipuri, Mahendra & Leclercq, Ludovic, 2020. "Bi-modal macroscopic traffic dynamics in a single region," Transportation Research Part B: Methodological, Elsevier, vol. 133(C), pages 257-290.
    19. Yildirimoglu, Mehmet & Sirmatel, Isik Ilber & Geroliminis, Nikolas, 2018. "Hierarchical control of heterogeneous large-scale urban road networks via path assignment and regional route guidance," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 106-123.
    20. Bao, Yue & Verhoef, Erik T. & Koster, Paul, 2021. "Leaving the tub: The nature and dynamics of hypercongestion in a bathtub model with a restricted downstream exit," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cdl:itsrrp:qt6dv195p7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Lisa Schiff (email available below). General contact details of provider: https://edirc.repec.org/data/itucbus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.