A Spatio-temporal Analysis of Traffic Flow Dynamics using UAV and GIS Integration at Elkanemi Intersection, Maiduguri, Borno State, Nigeria

A Spatio-temporal Analysis of Traffic Flow Dynamics using UAV and GIS Integration at Elkanemi Intersection, Maiduguri, Borno State, Nigeria

Ali Garga Bukar,

Department of Civil and Water Resources Engineering, University of Maiduguri

Mustapha Shettima

Department of Geography, University of Maiduguri, Borno State

Msc Student, Department of Environmental Science, Integral University Lucknow, India

ABSTRACT

Rapid urbanization and increasing vehicle ownership have intensified traffic challenges particularly at critical intersections with poor design and limited regulation across many African cities and Nigeria is not exceptional. This study assessed the spatiotemporal dynamics of traffic flow at Elkanemi Intersection in Maiduguri, Borno State, Nigeria using an integrated Unmanned Aerial Vehicle (UAV) and Geographic Information Systems (GIS) approach. UAV-captured videos, observations, and GPS coordinates, were collected during morning, afternoon, and evening peak hours. The data were analyzed using spatial analysis tools within ArcGIS 10.8. Findings revealed significant temporal and spatial variation in traffic flow, with Mogaram Road recording the highest volume during the morning peak and Ahmadu Bello Way Westbound showing evening dominance. Tricycles emerged as the most prevalent mode across all corridors and time periods, accounting for over 55% of traffic, followed by private cars, particularly in the evening. Buses and trucks had minimal representation, while bicycle usage spiked in the afternoon, correlating with school closures. Gap acceptance analysis showed modal differences, with trucks requiring the highest gaps and tricycles exhibiting aggressive merging behavior. The study highlights the utility of UAV and GIS in urban traffic assessment and recommends integrated transport planning that accounts for modal heterogeneity, peak-hour demand, and intersection performance to improve urban mobility in rapidly growing cities.

Keywords: – Spatio-temporal, Traffic Flow Dynamics, UAV, GIS, Gap acceptance

INTRODUCTION

The continuous rise in traffic flow has emerged as a major challenge for municipalities and governments across the world due to the rapid pace of urbanization, motorization, and infrastructure (Bukar et al 2025). According to the United Nations more than 55% of the global population resides in urban areas and projected to reach over 68% by 2050 (Rodriguez-Nikl, 2022; López and Flores-Garcí 2023; Saavedra et al., 2024). This urban growth exerts significant pressure on road infrastructure, resulting in traffic congestion, increased travel times, deteriorate atmosphere and increase the road accidents (Vrandani & Mathur, 2022; Faheem et al., 2024). These conditions are pronounced at most of the urban intersections, which often serve as critical nodes for intra-city transportation but are prone to inefficiencies as a result of poor design and inadequate traffic control and weak integration of data in planning processes (Kelly & Gupta, 2024; Rajesh et al., 2024; Malakshah & Amini, 2025)

Technological advancements have introduced new tools, notably Unmanned Aerial Vehicles (UAVs) and Geographic Information Systems (GIS) for traffic monitoring, congestion detection, and managing urban traffic conditions (Quamar et al., 2023; Raj et al., 2024). UAVs provide cost-effective, high-resolution aerial imagery and real-time video data, making them invaluable for observing traffic dynamics, vehicle counts, and movement trajectories ((Elloumi et al., 2019; Bashar, 2023) and GIS enables spatial visualization and analysis of transportation networks, facilitating the identification of traffic patterns, congestion hotspots, and spatial-temporal trends in road usage (Aghajani et al., 2017; Nayak & Goyal, 2024; Journal, 2024). Thus, UAV alongside with GIS technologies Provide a robust foundation for comprehensive traffic flow analysis and evidence-based urban planning (Zhang et al., 2019; Williams-Kelly, 2023; Raj et al., 2024; Kumarasinghe & Pushpakumara, 2024; Wang & Zhang, 2024; Zaiats, 2024; Bukar et al. 2025).

Despite global advancement in geospatial traffic monitoring, the application of UAV and GIS in traffic management and monitoring remain limited across many African urban centers. In Nigeria, the most populous country in Africa, the surge in urban migration and vehicle ownership has outpaced the development of transportation infrastructure, leading to worsening congestion and increased safety challenges (Buhari et al., 2020; Otuoze et al., 2021; Faiyetole, 2023; Ogunkunbi et al., 2023; Oludele, et al 2024). This situation is also evident in Maiduguri which has undergone significant urban expansion in recent years due to internal migration and reconstruction effort. The key intersections, such as the Elkanemi Roundabout, have emerged as major traffic nodes, yet suffer from chronic congestion, unregulated vehicular flow, and poor design standards (Bukar and Kundiri, 2025)

Several studies have demonstrated the utility of UAVs in traffic monitoring, especially for extracting vehicle trajectories, estimating traffic density, and analyzing intersection performance (Butila & Boboc, 2022; Zong et al., 2022; Espadaler-Clapés et al., 2023; Sarkar & Chatterjee, 2024). Similarly, GIS-based spatial analysis has proven effective in understanding traffic flow, road network capacity, and land-use interactions (Reuben & Ejikeme, 2024; Journal, 2024 (Iranmanesh et al., 2024; Jiang et al., 2024; Khallef et al., 2025). However, the integration of UAV and GIS technologies has not been well explored in Maiduguri. Therefore, this study aims to assess the spatial analysis of traffic flow dynamics at Elkanemi Intersection in Maiduguri, using UAV and GIS techniques.

THE STUDY AREA AND METHODOLOGY

STUDY AREA

Maiduguri, the capital of Borno State, is the largest city in northeastern Nigeria. Geographically, it is situated between latitudes 11°43N and 11°52N and longitudes 13°05E and 13°15E (Figure 1b) The city covers a total area of approximately 543 km² and lies at an altitude of 345 meters above sea level (Kodiya et al. 2023. Maiduguri is characterized by a semi-arid climate, marked by low annual precipitation and high temperatures throughout much of the year.The study area, Elkanemi Intersection, is located between latitudes 11°4950N and 11°5065N and longitudes 13°90E and 13°925E. (Figure 1a) It is one of the key intersections encircling the Maiduguri Central Market.

Figure: Study Araea Description: (a) Elkanemi Intersection (b) MMC Showing the Elkanemi Intersection (c) Borno State Showing MMC (d) Nigeria Showing Borno State

MATERIALS

MATERIALS AND METHODS

Vol. 14 Issue 07, July – 2025

Hardware: The UAV used for capturing and acquiring the live traffic flow, direction and coverage in the study area. Also, a laptop computer for processing the acquired images from the UAV, GPS data and Android Phone. The Unmanned Aerial Vehicles (DJI Phantom 4, is shown in Plate 1.

Plate 1: DJI Phantom 4

The characteristics of the UAV DJI Phantom 4 used includes flight duration of 30 minutes (One fully charged), transmission distance of 3.5km, altitude of 1000m above mean sea level, camera with resolution of 12.0 mp. To replace table 1

Type DJI Phantom 4

Flight time – 25mins (one full battery) Transmission distance 3.5km Altitude – 1000m above sea level

Camera – 12.0mp

Specification

Table 1: Specification of the Unmanned Aerial Vehicle used

Software: Google Earth Pro Engine for capturing optical remotely sensed data, with GP. The ArcGIS version 10.8 environment were used to perform GIS Operations which incorporate the digital image processing, spatial analyst and interpolation tools to analyse the traffic flow parameters. Microsoft Excel and Microsoft word were used for plotting charts and word-processing for the presentation respectively.

DATA SOURCE AND COLLECTION PROCEDURE

The study utilized both primary and secondary data sources. Primary data comprised UAV-based videography and photography for assessing traffic composition, high-resolution GeoEye satellite imagery (0.45 m) for accurate georeferencing, GPS-collected point coordinates and field observations conducted during peak hours. Unmanned

Aerial Vehicles (UAVs) were specifically employed to capture traffic flow along the selected corridors during morning, afternoon, and evening peak periods within the study area. Each peak period was recorded over a two-hour duration 7:00 a.m. to 9:00 a.m., 12:00 p.m. to 2:00 p.m., and 4:00 p.m. to 6:00 p.m. Secondary data comprised traffic reports, topographic and thematic road maps, published literature, and internet-sourced information. The sets of data used for this study is presented in Table 1

Table 1: Datasets used for the Study

(Source: Researcher, 2023)

Figure 1: Methodological Flow Chart

DATA PROCESSING AND ANALYSIS

The videography data from the memory card of the UAV was played on a laptop computer and vehicle types were identified (cars, tricycles, buses, trucks and bicycles), their composition and Gap acceptance. The classification of the vehicles and conversion to a common standard format was performed using the Microsoft Excel before being carried forward for GIS Operations in ArcGIS 10.8 Environment to achieve geospatial mapping. The ArcGIS 10.8 was used to digitize the study areas road network using Geo Eye High-Resolution satellite image with 0.35m spatial resolution and the traffic flow parameter were analyze using geoprocessing and Spatial Analyst tools.

Plate 1 Traffic situation in the study area at afternoon Source: UAV 2021

RESULTS AND DISCUSSION

TRAFFIC FLOW

The analysis of average traffic flow across the study area reveals significant spatial and temporal variations in traffic dynamics (Figure 3). Among the observed locations, Mogaram Road recorded the highest average traffic flow during the morning peak 2249 vehicles (Figure 3a), indicating that it serves as a major entry corridor into the central Market and accommodating commuters heading towards workplaces and schools (Huang et al., 2021; Zhou et al., 2022). While Ahmadu Bello Way (ABW) eastbound recorded the lowest morning traffic flow 1715 vehicles (Figure 3a), demonstrating that this corridor is not a primary route during early hours. This is as a result of its function as a feeder road to the Maiduguri Central Market which remains closed during the early morning period (Omole et al., 2013).

In the afternoon, the highest traffic was observed along the Shehu Sanda Kura (SSK) corridor 2136 vehicles (Figure 3b), attributed to market-related activities occurring during midday hours ( Mulíek & Osman, 2018; Ayo-Odifiri, 2023). Conversely, Ahmadu Bello Way (ABW) westbound had the lowest afternoon traffic 1866 vehicles (Figure 3b), this reflecting reduced of mid-day traffic generators along that corridor.

During the evening peak, Ahmadu Bello Way Westbound experienced the highest traffic flow 2190 vehicles (Figure 3c), highlighting its significance as a major outbound for commuters leaving the central Market and nearby institutions. While Ahmadu Bello Way eastbound again showed relatively low traffic 1836 (Figure 3c), corroborating the observation that this corridor remains consistently less trafficked throughout the day.

Figure 3: Traffic Flow Map: (a) Morning (b) Afternoon (c) Eveving

TRAFFIC COMPOSITION

The results of the average of traffic composition are depicted in Figure 4-6 and the average Proportion presented in Figure 7-10. The traffic in the area mainly consisted of Tricycles, Cars, Buses, Bicycles and Trucks This observation agreed with research findings by (Adams et al 2014; Bukar & Kundiri, 2025).

Figure 4: Traffic compositions at Elkanemi intersection during morning peak hour

Figure 5: Traffic compositions at Elkanemi intersection during afternoon peak hour

Figure 6: Traffic compositions at Elkanemi intersection during evening peak hour

The analysis of traffic composition across the four selected corridors of the intersection reveals a clear dominance of tricycles across all time periods. Tricycles accounted for more than 55% of the total traffic and peaking at 69% (1400 tricycles) along ABW West Bound in the morning (Figures 4 and 8). This pattern reflects the increasing reliance on tricycle due to limited transportation options and comfort and flexibility, especially for short-distance intra-urban movement (Toanchina, 2019; Kpacha et al., 2022). These findings are consistent with Adams et al. (2014); Jing et al. (2019) who reported that tricycles and bicycles dominate intersections.

1

Cars emerged as the second most common vehicle type, typically comprising 2430% of traffic, with higher percentage observed in the evening (Figures 7,8 and 10) indicating their greater use for private, home-bound commuting. These findings are consistent with previous studies that highlight the growing reliance on personal automobiles for urban commuting (Xiao et al. 2022; Gao & Zhu, 2022; Saeidizand & Boussauw, 2023; Lv et al., 2020). Notably, bicycle use was relatively low but showed a temporary surge in the afternoon especially on Shehu Sanda Kura Road and Mogaram Road, reaching up to 18% (Figure 0). This increase corresponds with school closures where bicycles are a common mode of transport for students and pupils returning home. This pattern supports findings by Mokitimi & Vanderschuren, (2017); Risimati et al., (2021); Okoro & Lawani, (2022) who emphasized the functional importance of non-motorized transport in African cities where modal diversity and infrastructure limitations force co-use of roads by multiple transport types.

Buses and trucks were minimally represented, generally contributing less than 5%( Figures 7-10) of the total traffic volume. These patterns support the findings of Joubert (2010) Dawda, (2024); Bukar and Kundiri (2025).

CARS TRICYCLES BICYCLES BUSES TRUCKS

70

61

60

56.2

57

50

40

29

30

30

28.1

20

10

11

4.1

0.7

6

3

9

1

4

1

0

MORNING AFTERNOON EVENING

CARS TRICYCLES BICYCLES BUSES TRUCKS

80

69

70

66

62

60

50

40

30

30

24.2

26

20

10

3.82.7

0.6

5 2

1

5 2

1

0

MORNING AFTERNOON EVENING

CARS TRICYCLES BICYCLES BUSES TRUCKS

70

63

60

60

60

50

40

30

27.5

28

24.8

20

13

10

6.4

2.40.7

9

2 1

2 1

0

<>MORNING AFTERNOON

EVENING

CARS TRICYCLES BICYCLES BUSES TRUCKS

70

61

60

58.2

55

50

40

30

30

24.2

25.8

20

15

18

10

6

2.10.5

1 1

2 1

0

MORNING AFTERNOON EVENING

Percentage

Percentage

Percentage

Percentage

Figure 7:Traffic Composition along ABW EB Figure 8; Traffic Composition along ABW WB

Figure 9: Traffic Composition along SSK Figure 10: Traffic Composition along

Mogaram Road

GAP ACCEPTANCE

Gaps are measured in time (Seconds). However, due to heterogeneous and indiscipline driver behavior, there exist a wide variation of accepted gaps (Pal & Chunchu, 2019; Mohan & Chandra, 2021; Bhatt, & Shah, 2022; Li et al., 2024). Figure 11, present the gap acceptance values across different vehicle types and road corridors at the El-Kanemi Intersection during morning, afternoon, and evening peak hours. Gap acceptance reflects the time, and space a driver is willing to wait, with higher values often indicating low-moving traffic behavior (Vinchurkar et al., 2020; Das et al., 2020; Deepthi & Ramesh, 2021).

Trucks consistently yield the highest gap acceptance across all roads during morning hour (Figure 11a), with 10.20 seconds on Ahmadu Bello Way (Eastbound) and 8.42 seconds on the Westbound, showing their need for larger merging space due to their size and slower acceleration (Sarvi & Kuwahara, 2008; Bhatt, & Shah, 2022). Bicycles and buses also show relatively higher gap acceptance on Mogaram Road at 7.21 seconds (Figure 11a) indicating a cautious

approach. While tricycles exhibit the lowest gap acceptance values indicating they often engage in more aggressive merging maneuvers. (Doddapaneni et al., 2018; Schleinitz et al., 2020; Vinchurkar et al., 2020). In the afternoon, a slight decline in gap acceptance is observed across most vehicle types, particularly among tricycles on Ahmadu Bello Way Westbound, where the value drops to just 1.25 seconds (Figure 11b). This trend indicates a higher level of risk- taking, possibly due to midday time pressure. Despite this, trucks maintain relatively high gap acceptance value of

10.17 seconds on Mogaram Road (Figure 11b). During the evening hours, gap acceptance tends to rise again, especially for trucks, which reach their peak at 13.17 seconds on Mogaram Road, the highest recorded across all periods (Figure 11c). While tricycles and cars remain fairly stable.

Figure 11: Gap Acceptance at Elkanemi intersection: (a)Morning (b)Afternoon (c)Evening CONCLUSION

The study revealed a significant spatiotemporal variation in traffic dynamics at Elkanemi Intersection, Maiduguri, with Mogaram Road recording the highest morning flow due to commuter and market-bound traffic while Shehu Sanda Kura Road and Ahmadu Bello Way Westbound experienced peak volumes in the afternoon and evening, respectively. Tricycles dominated traffic composition across all corridors and periods showing their importance in short-distance urban mobility amid limited public transport options. Private cars constituted the second most prevalent mode, particularly during evening hours, consistent with home-bound commuting trends., while bicycle usage, though generally limited, exhibited a marked increase in the afternoon which attributable to student movement during school closure periods. However, buses and trucks were marginal contributors across all corridors, reflecting limited formal transit and freight activities in the area. Gap acceptance behavior further revealed modal differences, with trucks exhibiting the highest accepted gaps due to operational constraints, and tricycles the lowest which indicates an increased in risk-taking during congested hours. These findings align with previous researches in urban centers of developing nations and substantiate the necessity for integrated transport planning that accounts for modal heterogeneity, time-based demand fluctuations, and the operational characteristics of dominant vehicle types to modernize the mobility.

REFERENCES

1. Adams, C. A., Zambang, M. A. M., & Opoku-Boahen, R. (2014). Passenger Car Unit values for urban mixed traffic flow at signalised intersections on two-lane dual carriageways in the Tamale Metropolis, Ghana. International Refereed Journal of Engineering and Science (IRJES), 3(5), 4149.

2. Aghajani, M. A., Dezfoulian, R. S., Rezaee Arjroody, A., & Rezaei, M. (2017). Applying GIS to Identify the Spatial and Temporal Patterns of Road Accidents Using Spatial Statistics (case study: Ilam Province, Iran). Transportation Research Procedia, 25, 21262138. https://doi.org/10.1016/J.TRPRO.2017.05.409

3. Ayo-Odifiri, S. O. (2023). Impact of market patronage on the traffic situation in benin metropolis, nigeria. Fudma Journal of Sciences, 6(6), 280288. https://doi.org/10.33003/fjs-2022-0606-1179

4. Bashar, M. (2023). Enhancing roadway traffic management with uavs. International Research Journal of Modernization in Engineering Technology and Science. https://doi.org/10.56726/irjmets44503

5. Bhatt, K., & Shah, J. (2022). An impact of gap acceptance on road safety: A critical systematic review. Journal of Sustainable Development of Transport and Logistics, 7(1), 6-22. doi:10.14254/jsdtl.2022.7-1.1.

6. Buhari, S., Aponjolosun, M. O., Oni, B. G., & Sam, M. W. (2020). Sustainable urban mobility: An approach to urbanization and motorization challenges in Nigeria, a case of Lagos state. 5(2), 9097. https://doi.org/10.14254/JSDTL.2020.5-2.8

7. Bukar, A. G., & Kundiri, A. M. (2025). Analysis of vehicular traffic in the Central Business District of Maiduguri using Unmanned Aerial Vehicle technology. IOSR Journal of Engineering (IOSRJEN), 15(4, Series-1), 2329. http://www.iosrjen.org

8. Bukar, A. G., Shettima, M., Kunduri, A. M., & Sambo, G. H. (2025). Monitoring of Traffic Flow Parameters Using Geospatial Technology for Effective Traffic Management Around Maiduguri Central Business District. International Journal for Research in Applied Science & Engineering Technology, 13(1), 1854-1860 https://www.ijraset.com

9. Butila, E. V., & Boboc, R. G. (2022). Urban Traffic Monitoring and Analysis Using Unmanned Aerial Vehicles (UAVs): A Systematic Literature Review. Remote Sensing, 14(3), 620. https://doi.org/10.3390/rs14030620

10. Das, A., Khan, N., Ahmed, M. M., & Ahmed, M. M. (2020). Nonparametric Multivariate Adaptive Regression Splines Models for Investigating Lane-Changing Gap Acceptance Behavior Utilizing Strategic Highway Research Program 2 Naturalistic Driving Data. Transportation Research Record, 2674(5), 223238. https://doi.org/10.1177/0361198120914293

11. Dawda, N. H. (2024). Towards a comprehensive framework for public transport system planning in India. Social Science Research Network. https://doi.org/10.2139/ssrn.5044961

12. Deepthi, M. S., & Ramesh, A. (2021). A Model for Estimation of Critical Gap and Its Distribution Behaviour at Un Signalised Intersections (pp. 537549). Springer, Singapore. https://doi.org/10.1007/978-981-15-5644-9_41

13. Doddapaneni, A., Brahmankar, D. P., & Ravishankar, K. V. R. (2018). Multi Vehicle-Type Right Turning Gap-Acceptance and Capacity Analysis at Uncontrolled Urban Intersections. Periodica Polytechnica Transportation Engineering, 48(2), 99108. https://doi.org/10.3311/PPTR.9744

14. Elloumi, M., Dhaou, R., Escrig, B., Idoudi, H., Saidane, L. A., & Fer, A. (2019). Traffic Monitoringon City Roads Using UAVs (pp. 588600). Springer, Cham. https://doi.org/10.1007/978-3-030-31831-4_42

15. Espadaler-Clapés, J., Fonod, R., Barmpounakis, E. N., & Geroliminis, N. (2023). Continuous Monitoring of a Signalized Intersection Using Unmanned Aerial Vehicles. 53385343. https://doi.org/10.1109/itsc57777.2023.10422673

16. Faheem, H., El Shorbagy, A. M., & Gabr, M. (2024). Impact Of Traffic Congestion on Transportation System: Challenges and Remediations – A review. Magallat Kulliyyat Dar Al-ulum, 49(2). https://doi.org/10.58491/2735-4202.3191

17. Faiyetole, A. A. (2023). Urban expansion and transportation interaction: Evidence from Akure, southwestern Nigeria. Environment And Planning B: Urban Analytics And City Science, 239980832311694. https://doi.org/10.1177/23998083231169427

18. Gao, Y., & Zhu, J. (2022). Characteristics, Impacts and Trends of Urban Transportation. Encyclopedia, 2(2), 11681182. https://doi.org/10.3390/encyclopedia2020078

19. Huang, Z., Long, J., Szeto, W. Y., & Liu, H. (2021). Modeling and managing the morning commute problem with park-and-ride- sharing. Transportation Research Part B-Methodological, 150, 190226. https://doi.org/10.1016/J.TRB.2021.06.004

20. Iranmanesh, A., Kara, C., & Tülbentçi, T. (2024). Mapping the relationship between traffic accidents, road network configuration, and urban land use. International Journal of Injury Control and Safety Promotion, 114. https://doi.org/10.1080/17457300.2024.2409638

21. Jing, Y., Dzoagbe, N. E. K., Amouzou, E. K., & Ayivi, W. (2019). The impact of tricycles on transportation and economic activities in Ghana: A case study of the Ho Municipality. International Journal of Economics, Commerce and Management, 7(12), 633648. http://ijecm.co.uk/

22. Joubert, J. W. (2010). A provincial comparison of commercial vehicle movement. In Proceedings of the 29th Southern African Transport Conference (SATC 2010) (pp. 705713). Pretoria, South Africa: CSIR Built Environment.

23. Journal, I. (2024). Urban Mobility Analysis Using Geospatial Analysis. Indian Scientific Journal Of Research In Engineering And Management. https://doi.org/10.55041/ijsrem28008

24. Kelly, T., & Gupta, J. (2024). Predicting Traffic Congestion at Urban Intersections Using Data-Driven Modeling. arXiv.Org, abs/2404.08838. https://doi.org/10.48550/arxiv.2404.08838

25. Khallef, B., Daoudi, B., & Kerkour, A. (2025). Spatial analysis of traffic accidents using geographic information systems: case study of the wilaya of Sétif (Algeria). Geomatics, Landmanagement and Landscape, 4. https://doi.org/10.15576/gll/194033

26. Kodiya, M.,A Shettima, M., Modu, M., Modu, M., & Yusuf, F. (2023). The Socio-Economic and Environmental Benefits of Waste Scavenging in Maiduguri, Borno State. International Journal of Science for Global Sustainability, 9(1), 150-161. https://doi.org/10.57233/ijsgs.v9i1.408

27. Kpacha, D. S., MAMA, A. Y. A. R., Alda, Y. S. A., Samuel4, A., & Dine, I. (2022). Urban transportation with tricycles in parakou (benin). International Journal of Agriculture, Environment and Bioresearch, 07(03), 1730. https://doi.org/10.35410/ijaeb.2022.5722

28. Kumarasinghe, L., & Pushpakumara, T. D. C. (2024). Identification of traffic hotspots in CMC area and study of flow dynamics around them using GIS &amp; RS techniques. 1820. https://doi.org/10.31705/trf.2024.6

29. Li, G., Zhao, L., Wu, L., Jiang, C., & Tang, W. (2024). Investigating gap acceptance behavior based on correlated random parameter survival model with heterogeneity in means. Transportation Letters: The International Journal of Transportation Research. https://doi.org/10.1080/19427867.2024.2335742

30. López, E. V., & Flores-García, M. (2023). Urbanization in the context of global environmental change. In A. Sharifi & A. R. Khavarian- Garmsir (Eds.), Urban climate adaptation and mitigation (pp. 122). Elsevier. https://doi.org/10.1016/B978-0-323-85552-5.00008-7

31. Lv, J., Zheng, L., Ye, Y., & Ye, C. (2020). Understanding Travel Patterns of Commuting Private Cars using Big data of Electronic Registration Identification of Vehicles. Software Engineering and Knowledge Engineering, 4348. https://dblp.uni- trier.de/db/conf/seke/seke2020.html#ZhengYYL20

32. Malakshah, M. R., & Amini, Z. (2025). Towards an Operationally Resilient Urban Traffic Network: An Intersection Importance Assessment Framework Through MCDM Methods. https://doi.org/10.2139/ssrn.5064047

33. Mohan, M., & Chandra, S. (2021). Investigating the influence of conflicting flows composition on critical gap under heterogeneous traffic conditions. International Journal of Transportation Science and Technology, 10(4), 393401. https://doi.org/10.1016/J.IJTST.2021.01.004

34. Mokitimi, M. M., & Vanderschuren, M. (2017). The Significance of Non-Motorised Transport Interventions in South Africa A Rural and Local Municipality Focus. Transportation Research Procedia, 25, 47984821. https://doi.org/10.1016/J.TRPRO.2017.05.491

35. Mulíek, O., & Osman, R. (2018). Rhythm of urban retail landscapes: Shopping hours and the urban chronotopes. Moravian Geographical Reports, 26(1), 213. https://doi.org/10.2478/MGR-2018-0001

36. Nayak, A., & Goyal, K. (2024). Traffic modeling and accidental data analysis using GIS: A Review. https://doi.org/10.1088/1755- 1315/1327/1/012028

37. Ogunkunbi, G., Oguntayo, D. O., Adeleke, O. O., & Salami, L. O. (2023). Its Based Demand Management Model for Congestion Mitigation on an Urban Traffic Corridor in Ilorin, Nigeria. 1, 19. https://doi.org/10.1109/SEB-SDG57117.2023.10124558

38. Okoro, C. S., & Lawani, K. (2022). Optimising sustainable mobility: A performance assessment of non-motorised transport infrastructure in Johannesburg, South Africa. Joernaal van Die Suid-Afrikaanse Instituut van Siviele Ingenieurswese, 64(2), 110. https://doi.org/10.17159/2309-8775/2022/v64no2a6

39. Oludele, S., Ademiluyi, I., & Solanke, M. (2024). Road Transport Infrastructure and Traffic Density in Kano Metropolis, Nigeria. NIU Journal Of Social Sciences, 10(4), 43-53. doi:10.58709/niujss.v10i4.2028

40. Omole, F. K., Lukman, Y., & Baki, A. I. (2013). Analysis of market typology and functions in the development of Osun state, Nigeria. International Journal of Development and Sustainability, 3(1), 5569.

41. Otuoze, S. H., Hunt, D. V. L., & Jefferson, I. (2021). Monitoring Spatial-Temporal Transition Dynamics of Transport Infrastructure Space in Urban Growth Phenomena: A Case Study of LagosNigeria. 2. https://doi.org/10.3389/FFUTR.2021.673110

42. Pal, D., & Chunchu, M. (2019). Modeling of lateral gap maintaining behavior of vehicles in heterogeneous traffic stream. 11(7), 373

    381. https://doi.org/10.1080/19427867.2017.1369633

43. Quamar, Md. M., Al-Ramadan, B. M., Khan, K., Shafiullah, Md., & Ferik, S. E. (2023). Advancements and Applications of Drone-

    Integrated Geographic Information System TechnologyA Review. Remote Sensing. https://doi.org/10.3390/rs15205039

44. Raj, E. F. I., Mahadevan, B., Pushparaj, T. L., & Thiyaharajan, D. (2024). Traffic Managemnt in Smart Cities Using Unmanned Aerial Vehicles (pp. 218247). IGI Global. https://doi.org/10.4018/979-8-3693-2093-8.ch013

45. Raj, E. F. I., Mahadevan, B., Pushparaj, T. L., & Thiyaharajan, D. (2024). Traffic Management in Smart Cities Using Unmanned Aerial Vehicles (pp. 218247). IGI Global. https://doi.org/10.4018/979-8-3693-2093-8.ch013

46. Rajesh, R., Ajimon, M. A., Bose, S., Surya, S., Pillai, V. G., & Sandeep, U. P. (2024). Unscrambling traffic congestion and increasing sustainability in special urban intersection. E3S Web of Conferences, 529, 04011. https://doi.org/10.1051/e3sconf/202452904011

47. Reuben, E. C., & Ejikeme, J. O. (2024). Gis-Based assessment Of Traffic System In Osisioma Ngwa L.G.A,Abia State. 6(11), 430437. https://doi.org/10.35629/5252-0611430437

48. Risimati, B., Gumbo, T., & Chakwizira, J. (2021). Spatial Integration of Non-Motorized Transport and Urban Public Transport Infrastructure: A Case of Johannesburg. Sustainability, 13(20), 11461. https://doi.org/10.3390/SU132011461

49. Rodriguez-Nikl, T. (2022). Systems approaches to the use of underground space in urban environments. Civil Engineering and Environmental Systems, 39(4), 283286. https://doi.org/10.1080/10286608.2022.2153124

50. Saavedra, M., Muñuzuri, A. P., Menéndez, M., & BalsaBarreiro, J. (2024). Analysing macroscopic traffic rhythms and city size in affluent cities: insights from a global panel data of 25 cities. Philosophical Transactions of the Royal Society A, 382(2285). https://doi.org/10.1098/rsta.2024.0102

51. Saeidizand, P., & Boussauw, K. (2023). Patterns of car dependency of metropolitan areas worldwide: Learning from the outliers. International Journal of Sustainable Transportation. https://doi.org/10.1080/15568318.2023.2283462

52. Sarkar, D. R., & Chatterjee, N. (2024). Automatic Traffic Safety Analysis using Unmanned Aerial Vehicle Technology at Unsignalized Intersections in Heterogeneous Traffic. Transportation Research Record. https://doi.org/10.1177/03611981241266838

53. Sarvi, M., & Kuwahara, M. (2008). Using ITS to Improve the Capacity of Freeway Merging Sections by Transferring Freight Vehicles. IEEE Transactions on Intelligent Transportation Systems, 9(4), 580588. https://doi.org/10.1109/TITS.2008.2006812

54. Schleinitz, K., Petzoldt, T., & Gehlert, T. (2020). Drivers gap acceptance and time to arrival judgements when confronted with approaching bicycles, e-bikes, and scooters. Journal of Transportation Safety & Security, 12(1), 316. https://doi.org/10.1080/19439962.2019.1591551

55. Toanchina, A. (2019). Urban electric tricycle. Journal of Industrial Design and Engineering Graphics, 14(2), 3540. http://sorging.ro/jideg/index.php/jideg/article/view/7

56. Vinchurkar, S., Jain, M., Rathva, D., & Dave, S. (2020). Gap Acceptance Behaviour of Vehicles at Unsignalized Intersection in Urban Area (pp. 545556). Springer, Singapore. https://doi.org/10.1007/978-981-32-9042-6_43

57. Vrandani, N. K., & Mathur, D. (2022). Analysis & Prediction of Road Accident Data for NH-19/44. International Journal on Recent Technologies in Mechanical and Electrical Engineering, 9(2), 1333. https://doi.org/10.17762/ijrmee.v9i2.366

58. Wang, S., & Zhang, L. (2024). A Review of GIS Technology Applications in Transportation Planning and Management. Scientific Journal of Technology. https://doi.org/10.54691/e1s89w46

59. Williams-Kelly, K. S. (2023). Drones GIS System in Urban Transport. Springer Proceedings in Energy, 733741. https://doi.org/10.1007/978-3-031-30171-1_78

60. Xiao, Z., Xiao, H., Jiang, H., Chen, W., Chen, H., & Regan, A. C. (2022). Exploring human mobility patterns and travel behavior: A focus on private cars. IEEE Intelligent Transportation Systems Magazine, 14(5), 129146. https://doi.org/10.1109/MITS.2021.3098627

61. Zaiats, O. (2024). Gis for urban safety: crime analysis and prevention strategies, emergency management, traffic flow modeling, and road traffic optimization. Ekonomìnì Gorizonti, 4(29), 5665. https://doi.org/10.31499/2616-5236.4(29).2024.314561

62. Zhang, H., Liptrott, M., Bessis, N., & Cheng, J. (2019). Real-Time Traffic Analysis using Deep Learning Techniques and UAV based Video. Advanced Video and Signal Based Surveillance, 15. https://doi.org/10.1109/AVSS.2019.8909879

63. Zong, S., Chen, S., Alinizzi, M. F., & Labi, S. (2022). Leveraging UAV Capabilities for Vehicle Tracking and Collision Risk Assessment at Road Intersections. Sustainability, 14(7), 4034. https://doi.org/10.3390/su14074034

IJERTV14IS070178

(This work is licensed under a Creative Commons Attribution 4.0 International License.)