Analysing Road Traffic Situation in Lilongwe: An Agent Based Modelling (ABM) Approach

Khetwayo B Sibale; Kondwani G  Munthali

doi:10.21467/ajgr.10.1.3-15

Authors

Khetwayo B Sibale Computer Science Department, Chancellor College, University of Malawi https://orcid.org/0000-0002-5425-436X
Kondwani G Munthali Computer Science Department, Chancellor College, University of Malawi

DOI:

https://doi.org/10.21467/ajgr.10.1.3-15

Abstract

A 15, 451 km road network forms the main mode of transport for Malawi with 26 % paved. With increasing number of vehicles and elongated travel times during rush hour the study analysed the traffic situation on the M1 road between Mchinji and Area 18 roundabouts in Lilongwe City using an agent-based model (ABM). The methodology used game theory’s traffic grip model to analyse traffic flow by controlling traffic variables such as lights, speed limits and the number of vehicles. Each intersection was treated as non-cooperative game where each agent tried to minimize its queue resulting into Q−Nash’s equilibrium as the solution. The ABM tested the empirical relationships of traffic flow parameters in terms of density, ﬂow, acceleration, deceleration, speed, time lost in traffic congestion and fuel consumption. The model was calibrated using traffic data collected through observing 1,312 vehicles sampled against 24,977. The observation results from the road junctions reveal that on average, a vehicle takes 20 mins 18 seconds, 37 minutes 6 seconds, 44 minutes 21 seconds and 58 minutes 53 seconds to exit Chitukuko, Bwandilo, Chilambula roads and Area 18 roundabout respectively upon entering the M1 at Mchinji roundabout. This data was then used to calibrate the business-as-usual model for the peak hour scenario for the road junctions. The model results show that a selected vehicle entering Chitukuko junction travels at an average speed of 22.60 km/hr, until it exits that junction. On average the selected motorists spend 2.52 l/km with a traffic density of 72 v/km. If dualized average speeds improved to 41.54 km/hr while the traffic density declined to 54.42 v/km, saving motorists MK 3,921,624.00 annually. The predictive model of the dual carriage informed that by 2021, commuters will spend MK 5,187,168 on fuel more than single-lane business as usual scenario of 2019.

Keywords:

agent based modelling, fuel consumption, Traffic model, Road construction

Downloads

Download data is not yet available.

References

W.-X. Wang, R.-J. Guo, and J. Yu, “Research on road traffic congestion index based on comprehensive parameters: Taking Dalian city as an example,” Advances in Mechanical Engineering, vol. 10, no. 6, p. 1687814018781482, Jun. 2018, doi: 10.1177/1687814018781482.

N. Tsanakas, J. Ekström, and J. Olstam, “Estimating Emissions from Static Traffic Models: Problems and Solutions,” Journal of Advanced Transportation, vol. 2020, p. 5401792, Feb. 2020, doi: 10.1155/2020/5401792.

T. Reed, “INRIX Global Traffic Scorecard,” 2019, [Online]. Available: https://trid.trb.org/view/1456836.

D. Transport, “Department Of Transport Vote NO. 35 Annual Report 2018/2019 Financial Year,” 35. [Online]. Available: https://www.gov.za.

M. N. S. Office, 2018 Population and Housing Census: Main Report. National Statistical Office, 2019.

V. Shikhin, “Introductory Chapter: Major Automatic Control Challenges in Multi-Agent Systems,” Multi-Agent Systems - Control Spectrum, Jan. 2019, doi: 10.5772/intechopen.83450.

K. J. Murphy, S. Ciuti, and A. Kane, “An introduction to agent-based models as an accessible surrogate to field-based research and teaching,” Ecology and Evolution, vol. 10, no. 22, pp. 12482–12498, 2020, doi: https://doi.org/10.1002/ece3.6848.

H. Wang, J. Zhang, and W. Zeng, “Intelligent simulation of aquatic environment economic policy coupled ABM and SD models,” Science of The Total Environment, vol. 618, pp. 1160–1172, Mar. 2018, doi: 10.1016/j.scitotenv.2017.09.184.

K. Kuusemäe, M. von Thene, T. Lange, E.K. Rasmussen, M. Pothoff, A.I. Sousa, and M.R. Findt, “Agent Based Modelling (ABM) of eelgrass (Zostera marina) seedbank dynamics in a shallow Danish estuary,” Ecological Modelling, vol. 371, pp. 60–75, Mar. 2018, doi: 10.1016/j.ecolmodel.2018.01.001.

K. G. Munthali, “Modelling deforestation in dzalanyama forest reserve, Lilongwe, Malawi: using multi-agent simulation approach,” Un published Phd Thesis-the University of Tsukuba, 2013.

E. Lindkvist, N. Wijermans, T.M. Daw, B. Gonzalez-Mon, A. Giron-NAva, A.F> Johnson, I. van Putten, X. Basurto, and M. Schuter, “Navigating Complexities: Agent-Based Modeling to Support Research, Governance, and Management in Small-Scale Fisheries,” Front. Mar. Sci., vol. 6, 2020, doi: 10.3389/fmars.2019.00733.

M. Schlüter, L.J. Haider, S.J. Lade, E. Lindkvist, R. Martin, K. Orach, N. Wijermans, and C. Folke, “Capturing emergent phenomena in social-ecological systems: an analytical framework,” Ecology and Society, vol. 24, no. 3, Aug. 2019, doi: 10.5751/ES-11012-240311.

K. G. Munthali and Y. Murayama, “Modeling deforestation in Dzalanyama Forest Reserve, Lilongwe, Malawi: a multi-agent simulation approach,” GeoJournal, vol. 80, no. 5, pp. 743–757, Oct. 2015, doi: 10.1007/s10708-014-9592-4.

D. F. Adamatti, Multi-Agent-Based Simulations Applied to Biological and Environmental Systems. Hershey, PA: Information Science Reference - Imprint of: IGI Publishing, 2016.

U. Wilensky, “The NetLogo 6.1.1 User Manual,” https://ccl.northwestern.edu/netlogo/docs/, pp. 1–449, 2019, [Online]. Available: https://ccl.northwestern.edu/netlogo/docs/.

Z. H. Khan and T. A. Gulliver, “A macroscopic traffic model for traffic flow harmonization,” European Transport Research Review, vol. 10, no. 2, p. 30, Jun. 2018, doi: 10.1186/s12544-018-0291-y.

D. Uribe, L. Lugo, and E. Cuan, “Deep Analysis of a Basic Traffic Model,” Res. Comput. Sci., 2016, doi: 10.13053/rcs-118-1-4.

M. Bernas, B. Płaczek, and J. Smyła, “A Neuroevolutionary Approach to Controlling Traffic Signals Based on Data from Sensor Network,” Sensors, vol. 19, no. 8, Art. no. 8, Jan. 2019, doi: 10.3390/s19081776.

B. Płaczek, M. Bernas, and M. Cholewa, “A Credibility Score Algorithm for Malicious Data Detection in Urban Vehicular Networks,” Information, vol. 11, no. 11, Art. no. 11, Nov. 2020, doi: 10.3390/info11110496.

Y. Zhang, Z. Li, and Y. Zhang, “Validation and Calibration of an Agent-Based Model: A Surrogate Approach,” Discrete Dynamics in Nature and Society, vol. 2020, pp. 1–9, Jan. 2020, doi: 10.1155/2020/6946370.

Y. Ali, Z. Zheng, Md. M. Haque, and M. Wang, “A Game Theory-based Approach for Modelling Mandatory Lane-Changing Behaviour in a Connected Environment,” Transportation Research Part C Emerging Technologies, vol. 106, pp. 220–242, Jul. 2019, doi: 10.1016/j.trc.2019.07.011.

A. Navon et al., “Applications of Game Theory to Design and Operation of Modern Power Systems: A Comprehensive Review,” Energies, vol. 13, no. 15, Art. no. 15, Jan. 2020, doi: 10.3390/en13153982.

H. Asienkiewicz and Ł. Balbus, “Existence of Nash equilibria in stochastic games of resource extraction with risk-sensitive players,” TOP, vol. 27, no. 3, pp. 502–518, Oct. 2019, doi: 10.1007/s11750-019-00516-2.

S. Galib, S. A. Forhad, G. M. M. Bashir, and B. Hossain, “Application of Game Theory to Road Traffic Optimisation,” p. 5, 2016, [Online]. Available: https://www.ijcsmc.com/docs/papers/March2016/V5I3201605.pdf.

I. Alvarez, A. Poznyak, and A. Malo, “Urban traffic control problem a game theory approach,” in 2008 47th IEEE Conference on Decision and Control, Dec. 2008, pp. 2168–2172, doi: 10.1109/CDC.2008.4739461.

S. Sun and N. Sun, Management Game Theory. Springer Singapore, 2018.

E. Jupp, “Replacement for notorious A428 ‘Black Cat’ roundabout gets public support,” Motoring Research, Feb. 10, 2020. https://www.motoringresearch.com/car-news/black-cat-roundabout-replacement-public-support/ (accessed Jan. 13, 2021).

R. T. Milam, M. Birnbaum, C. Ganson, S. Handy, and J. Walters, “Closing the Induced Vehicle Travel Gap Between Research and Practice:,” Transportation Research Record, Jan. 2017, doi: 10.3141/2653-02.

J. Speck, “Understand Induced Demand,” in Walkable City Rules: 101 Steps to Making Better Places, J. Speck, Ed. Washington, DC: Island Press/Center for Resource Economics, 2018, pp. 64–65.

E. Leigh, “Reducing Traffic Congestion and Pollution in Urban Areas,” Smarter Cambridge Transport, Dec. 12, 2016. https://www.smartertransport.uk/smarter-cambridge-transport-urban-congestion-enquiry/ (accessed Oct. 19, 2020).