Statistical approach to the diagnosis of the dynamics of urban mobility under the influence of the road congestion situation in the city of Douala, Cameroon

Frédéric Laurent  Esse Esse; Cyrille Mezoue  Adiang; Moussa  Sali; Fabien  Kenmogne; Blaise Ngwem  Bayiha; Gilbert  Tchemou; Emmanuel Yamb  Bell

doi:10.59400/issc2356

Frédéric Laurent Esse Esse Department of Civil Engineering, Advanced Teachers Training College of the Technical Education, University of Douala, Douala 1872, Cameroon; Doctoral School of Basic and Applied Sciences, Doctoral Training Unit for Engineering Sciences, Mechanical Laboratory, University of Douala, Douala 1872, Cameroon
Cyrille Mezoue Adiang National Higher Polytechnic School of Douala, University of Douala, Douala 2701, Cameroon
Moussa Sali Department of Civil Engineering, Advanced Teachers Training College of the Technical Education, University of Douala, Douala 1872, Cameroon; Laboratory of Materials, Mechanics and Civil Engineering, National Higher Polytechnic School of Maroua, Maroua 46, Cameroon
Fabien Kenmogne Department of Civil Engineering, Advanced Teachers Training College of the Technical Education, University of Douala, Douala 1872, Cameroon
Blaise Ngwem Bayiha Department of Civil Engineering, Advanced Teachers Training College of the Technical Education, University of Douala, Douala 1872, Cameroon
Gilbert Tchemou Department of Civil Engineering, Advanced Teachers Training College of the Technical Education, University of Douala, Douala 1872, Cameroon
Emmanuel Yamb Bell Department of Civil Engineering, Advanced Teachers Training College of the Technical Education, University of Douala, Douala 1872, Cameroon

Article ID: 2356

DOI: https://doi.org/10.59400/issc2356

Keywords: traffic congestion; dynamics of urban mobility; monocentrism; polycentrism

Abstract

Smooth movement is an essential function and an indicator of a healthy city. Cities being engines of growth, congestion is a real cancer for the country’s economy. In this sense, linking travel habits and the increase in the level of congestion in a city is very important. The objective of this work is to diagnose, using a statistical approach, the dynamics of urban mobility influenced by the traffic congestion situation in the city of Douala. For this, the study focuses on two aspects; the first aspect concerns the multivariate descriptive analysis of motorists’ travel habits. The methods used involve first submitting surveys to motorists in vehicle technical inspection centers and processing the data in IBM SPSS statistical software. Follow-up of the analysis of the correlation between the congestion level and some required solutions. The second aspect focuses on the principal component analysis (PCA) that is performed. The determination of Kaiser-Meyer-Olkin (KMO) indices, the Bartlett significances, and the Pearson correlation coefficients are also done. The results show that the travel habits of motorists create a massive use of roads at certain specific time slots, in addition to extra-municipal trips mainly oriented towards the city center and the administrative district due to the monocentric situation of the city, which contributes to the increase in the level of congestion in the city. The correlation shows that there is significance between the level of congestion and the solutions considered, but this correlation is more or less moderate, which shows that the solutions considered can be used in the short term to alleviate congestion in the city.

References

[1]Jang J, Ko J. Factors associated with commuter satisfaction across travel time ranges. Transportation Research Part F: Traffic Psychology and Behaviour. 2019; 66: 393-405. doi: 10.1016/j.trf.2019.09.019

[2]Jin Z, Yang X, Li C, et al. Modeling urban resident travel satisfaction during the morning and the evening peak hours: A case study in Beijing. International Journal of Transportation Science and Technology; 2024.

[3]Yang JS. A study of travel time modeling via time series analysis. In: Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005; August 2005. pp. 855-860.

[4]Almeida A, Brás S, Sargento S, et al. Exploring bus tracking data to characterize urban traffic congestion. Journal of Urban Mobility. 2023; 4: 100065. doi: 10.1016/j.urbmob.2023.100065

[5]Lindov O, Omerhodžić A, Mujić A, et al. Sustainable Urban Mobility Living Lab: Case study Sarajevo. Transportation Research Procedia. 2022; 64: 25-33. doi: 10.1016/j.trpro.2022.09.004

[6]Ravazzoli E, Torricelli GP. Urban mobility and public space. A challenge for the sustainable liveable city of the future. The Journal of Public Space. 2017; 2(2): 37. doi: 10.5204/jps.v2i2.91

[7]Tyrinopoulos Y, Antoniou C. Factors affecting modal choice in urban mobility. European Transport Research Review. 2012; 5(1): 27-39. doi: 10.1007/s12544-012-0088-3

[8]Organisation des Nations Unies. World urbanization prospects: the 2003 revision population database. Organisation des Nations Unies (ONU); 2007.

[9]Costa PB, Neto GCM, Bertolde AI. Urban Mobility Indexes: A Brief Review of the Literature. Transportation Research Procedia. 2017; 25: 3645-3655. doi: 10.1016/j.trpro.2017.05.330

[10]Patrão C, Moura P, Almeida AT de. Review of Smart City Assessment Tools. Smart Cities. 2020; 3(4): 1117-1132. doi: 10.3390/smartcities3040055

[11]Papadakis DM, Savvides A, Michael A, et al. Advancing sustainable urban mobility: insights from best practices and case studies. Fuel Communications. 2024; 20: 100125. doi: 10.1016/j.jfueco.2024.100125

[12]Esse FLE, Cyrille MA, Kenmogne F, et al. Estimation of Measurement Parameters of Urban Traffic Congestion: Case of a Road Section of the Douala 3rd District Commune in Cameroon. ResearchGate; 2024.

[13]Ali MS, Adnan M, Noman SM, et al. Estimation of Traffic Congestion Cost-A Case Study of a Major Arterial in Karachi. Procedia Engineering. 2014; 77: 37-44. doi: 10.1016/j.proeng.2014.07.030

[14]Galiano G, Forestieri G, Moretti L. Urban sprawl and mobility. Urban and Maritime Transport XXVII. 2021; 1: 245-255. doi: 10.2495/ut210201

[15]Aliu IR. Unpacking the dynamics of intra-urban residential mobility in Nigerian cities: Analysis of low-income families in Ojo Lagos. Cities. 2019; 85: 63-71. doi: 10.1016/j.cities.2018.12.005

[16]Shi F. Improving urban non-motorized mobility for public affairs trips: A survey and analysis of innovative official bicycles in Nanjing city, China. Journal of Urban Management. 2019; 8(3): 396-407. doi: 10.1016/j.jum.2019.03.005

[17]Ceccato R, Deflorio F, Diana M, et al. Measure of urban accessibility provided by transport services in Turin: a traveller perspective through a mobility survey. Transportation Research Procedia. 2020; 45: 301-308. doi: 10.1016/j.trpro.2020.03.020

[18]Wei J, Ju Y. Research on optimization method for traffic signal control at intersections in smart cities based on adaptive artificial fish swarm algorithm. Heliyon. 2024; 10(10): e30657. doi: 10.1016/j.heliyon.2024.e30657

[19]Wang H, Liu R, Wang P, et al. Intelligent optimization of dynamic traffic light control via diverse optimization priorities. International Journal of Intelligent Systems. 2021; 36(11): 6748-6762. doi: 10.1002/int.22567

[20]Li Z, Xu C, Zhang G. A deep reinforcement learning approach for traffic signal control optimization. arXiv; 2021.

[21]Wang P. Traffic signal optimization control in five-road intersection based on artificial fish swarm algorithm. Control Engineering of China. 2019; 26(7): 1284.

[22]Ma C, He R. Green wave traffic control system optimization based on adaptive genetic-artificial fish swarm algorithm. Neural Computing and Applications. 2015; 31(7): 2073-2083. doi: 10.1007/s00521-015-1931-y

[23]Lu B, Wang Q, Wang Y. An improved artificial fish swarm algorithm for traffic signal control. International Journal of Simulation and Process Modelling. 2019; 14(6): 488. doi: 10.1504/ijspm.2019.106158

[24]Sharke P. Smart Cars. Mechanical Engineering. 2003; 125(03): 50-52. doi: 10.1115/1.2003-mar-2

[25]Bachand-Marleau J, Larsen J, El-Geneidy AM. Much-Anticipated Marriage of Cycling and Transit. Transportation Research Record: Journal of the Transportation Research Board. 2011; 2247(1): 109-117. doi: 10.3141/2247-13

[26]Krizek KJ, Stonebraker EW. Assessing Options to Enhance Bicycle and Transit Integration. Transportation Research Record: Journal of the Transportation Research Board. 2011; 2217(1): 162-167. doi: 10.3141/2217-20

[27]Karimi H, Ghadirifaraz B, Shetab Boushehri SN, et al. Reducing traffic congestion and increasing sustainability in special urban areas through one-way traffic reconfiguration. Transportation. 2021; 49(1): 37-60. doi: 10.1007/s11116-020-10162-4

[28]Liu L, Biderman A, Ratti C. Urban mobility landscape: Real time monitoring of urban mobility patterns. In: Proceedings of the 11th international conference on computers in urban planning and urban management, Citeseer; 2009. pp. 1-16.

[29]République du Cameroun. Information gathering/verification study for the development of the transport network in Douala in the Republic of Cameroon (French). Japan International Cooperation Agency (JICA); 2017.

[30]Kotrlik J, Higgins C. Organizational research: Determining appropriate sample size in survey research appropriate sample size in survey research. Information technology, learning, and performance journal. 2001; 19(1): 43.

[31]Reddy KG, Khan MGM. Constructing efficient strata boundaries in stratified sampling using survey cost. Heliyon. 2023; 9(11): e21407. doi: 10.1016/j.heliyon.2023.e21407

[32]Taherdoost H. Determining sample size; how to calculate survey sample size. International Journal of Economics and Management Systems. 2017; 2.

[33]Ali SA. Sample size calculation and sampling techniques. Journal of the Pakistan Medical Association. 2012; 62(6): 624.

[34]Rea LM, Parker RA. Designing and conducting survey research: A comprehensive guide. John Wiley & Sons; 2014.

[35]Singh AS, Masuku MB. Sampling techniques & determination of sample size in applied statistics research: An overview. International Journal of economics, commerce and management. 2014; 2(11):1 22.

[36]Mustafa A, Basha E. Survey data show the correlation between school attachment dimensions and internet addiction among secondary school students in Kosovo. Data in Brief. 2023; 51: 109638. doi: 10.1016/j.dib.2023.109638

[37]Islam MA, Bhuiyan MAF. Factors affecting citizen safety of urban transportation service in Bangladesh: The case of Pabna municipality. Heliyon. 2024; 10(2): e24697. doi: 10.1016/j.heliyon.2024.e24697

[38]Li W, Feng W, Yuan H. Multimode Traffic Travel Behavior Characteristics Analysis and Congestion Governance Research. Feng Z, ed. Journal of Advanced Transportation. 2020; 2020: 1-8. doi: 10.1155/2020/6678158

[39]Lalinde JDH, Castro JFE, Tarazona MEP, et al. On the proper use of Pearson’s correlation coefficient: definition, properties and assumptions (Spanish). AVFT – Archivos Venezolanos de Farmacología y Terapéutica. 2018; 37(5).

[40]Santiago K, Yanes A, Mercado-Caruso N. Analyzing Correlations in Sustainable Tourism Perception: Statistical Insights from Diverse Caribbean Colombian Tourist Sites. Procedia Computer Science. 2024; 231: 490-495. doi: 10.1016/j.procs.2023.12.239

[41]Rahadian H, Bandong S, Widyotriatmo A, et al. Image encoding selection based on Pearson correlation coefficient for time series anomaly detection. Alexandria Engineering Journal. 2023; 82: 304-322. doi: 10.1016/j.aej.2023.09.070