Real-Time Urban Congestion Monitoring in Jeddah, Saudi Arabia, Using the Google Maps API: A Data-Driven Framework for Middle Eastern Cities

Abstract

Rapid urban growth in Middle Eastern cities has intensified congestion-related challenges, yet traffic data-based decision making remains limited. This study leverages crowd-sourced travel time data from the Google Maps API to evaluate temporal and spatial patterns of congestion across multiple strategic routes in Jeddah, Saudi Arabia, a coastal metropolis with a complex road network characterized by narrow, high-traffic corridors and limited public transit. A real-time Congestion Index quantifies traffic flow, incorporating free-flow speed benchmarking, dynamic profiling, and temporal classification to pinpoint congestion hotspots. The analysis identifies consistent peak congestion windows and route-specific delays that are critical for travel behavior modeling. In addition to congestion monitoring, the framework contributes to urban sustainability by supporting reductions in traffic-related emissions, enhancing mobility equity, and improving economic efficiency through data-driven transport management. To our knowledge, this is the first study to systematically use the validated, real-time Google Maps API to quantify route-specific congestion in a Middle Eastern urban context. The approach provides a scalable and replicable framework for evaluating urban mobility in other data-sparse cities, especially in contexts where traditional traffic sensors or GPS tracking are unavailable. The findings support evidence-based transport policy and demonstrate the utility of publicly accessible traffic data for smart city integration, real-time traffic monitoring, and assisting transport authorities in enhancing urban mobility.

1. Introduction

Urban traffic congestion remains a pressing challenge for rapidly growing cities worldwide, with far-reaching consequences for economic performance, public health, and environmental sustainability. In car-dependent regions such as Saudi Arabia, congestion is particularly acute due to rapid urbanization, limited public transit infrastructure, and increasing motorization rates. Jeddah, one of the Kingdom’s largest and fastest-growing cities, exemplifies this issue, experiencing daily congestion that undermines mobility and strains urban systems. Despite the significance of the problem, traffic studies in Saudi Arabia and the broader Middle East region still rely heavily on static, pre-collected data (e.g., manual traffic counts, periodic surveys). Such approaches fall short of capturing the real-time dynamics of urban traffic, limiting their usefulness for proactive traffic management or dynamic policy interventions. Moreover, high-resolution traffic sensor networks remain limited or cost-prohibitive in many developing cities. This study addresses these limitations by introducing a scalable, data-driven framework for monitoring urban congestion in real time, using crowd-sourced data from the Google Maps API. By collecting travel time data across major corridors in Jeddah at high temporal resolution, this research offers a fine-grained view of congestion behavior that has not previously been documented in the region. Through the application of exploratory data analysis (EDA) and unsupervised clustering, the study identifies route-level congestion hotspots, characterizes their temporal variability, and examines systematic differences across day types (weekdays vs. weekends). Critically, it also validates the reliability of Google Maps API data against GPS-based observations, strengthening the case for leveraging such publicly available tools in cities lacking formal traffic-monitoring systems. To our knowledge, this is the first peer-reviewed study to use validated, real-time Google Maps API data to quantify route-specific congestion dynamics in a Middle Eastern urban context. The methodology presented offers a replicable framework for other data-scarce cities seeking to harness low-cost data sources for evidence-based urban mobility planning. The specific contributions of this study are fourfold:

• It identifies route-level congestion hotspots and peak hours in Jeddah, providing granular insights for targeted traffic interventions.
• It analyzes intra-day temporal patterns of congestion to better understand commuter behavior and bottlenecks.
• It evaluates the reliability of Google Maps travel time estimates by benchmarking them against observed GPS data.
• It proposes a scalable, replicable framework for real-time congestion analysis in data-limited urban environments.

To guide this investigation, the study addresses the following research questions:

• How can real-time crowd-sourced data be harnessed to systematically quantify urban congestion at fine temporal resolutions?
• What temporal congestion patterns emerge across different day types, and how can these insights support more adaptive and responsive traffic management strategies?

Subsequent sections review the literature, detail the methodology, discuss findings on temporal congestion patterns and hotspot identification, and conclude with policy implications and future research directions. This case study highlights the value of integrating real-time analytics into traffic-monitoring practices, offering a model for similar urban environments across the Kingdom.

2. Literature Review

Crowd-sourced traffic data is derived from GPS-enabled devices, mobile navigation applications, connected vehicles, and social media platforms, allowing researchers and policymakers to access real-time traffic insights [1]. This real-time approach improves situational awareness and enables adaptive traffic management strategies [2]. Studies show that user-reported incidents and vehicle trajectories significantly enhance road safety monitoring by identifying high-risk areas, hazardous road conditions, and congestion hotspots [3]. Additionally, passively collected data from ride-sharing platforms and fleet management systems can supplement traditional traffic models, particularly in areas where official transportation data is sparse or outdated [1]. Moreover, studies have shown that integrating GPS probe data with social media inputs significantly enhances congestion detection in data-sparse environments. For instance, Wang et al. (2017) [4] developed a hybrid framework that effectively computes urban traffic congestion using sparse GPS data enriched with geotagged tweets. Building on this, Wang et al. (2019) [5] proposed a Parallel Coupled Hidden Markov Model to fuse heterogeneous data sources for accurate and scalable traffic estimation.

The integration of real-time event detection with historical congestion patterns enables researchers to predict and mitigate traffic bottlenecks, contributing to safer and more efficient urban mobility [6]. Artificial intelligence (AI) and machine learning algorithms have further enhanced the effectiveness of crowd-sourced data by refining congestion prediction models, enabling dynamic rerouting, and improving incident response coordination [7]. Research suggests that crowd-sourced traffic enforcement data, such as reports on speed limit violations and police presence, can influence traffic speed distributions and improve congestion forecasting [8]. Moreover, data obtained through crowd-sourcing methodologies has demonstrated significant utility in the examination of the robustness of urban transportation networks amidst severe meteorological phenomena [9]. The Google Maps API has emerged as a powerful tool for urban traffic analysis, offering a comprehensive platform for real-time traffic monitoring, route optimization, and congestion management [10]. Automated data extraction tools can process real-time Google Maps traffic information to pinpoint consistently slow-moving areas, helping local governments implement targeted congestion relief strategies [11]. Similarly, visualization techniques such as GIS-based traffic modeling allow urban planners to compare congestion levels across different cities and assess their correlation with socioeconomic factors [12]. The API is widely utilized in smart city initiatives, supporting real-time traffic control systems, ride-sharing services, and public transportation planning [13]. For instance, studies show that Google Maps JavaScript API 3.65-used for traffic predictions can assist in developing adaptive signal control algorithms, dynamically adjusting green light durations based on live traffic flow [10]. Moreover, researchers have explored the potential of integrating Google Maps data with AI-based traffic forecasting models to improve transportation resilience [13]. Given its accuracy, scalability, and accessibility, the Google Maps API represents an indispensable resource for modern traffic analysis. GIS-based traffic modeling visualizes congestion, enabling urban planners to compare levels across cities and assess correlations with socioeconomic factors [12].

Recent studies underscore the growing use of crowd-sourced traffic data and AI techniques for congestion analysis in urban environments. In Casablanca, Morocco, researchers employed the Waze API (Waze Transport SDK version 4.43) to calculate a Travel Time Index (TTI) and used K-means clustering to classify congestion levels, linking them to land use patterns [14]. Ji et al. (2021) [15] validated the reliability of Google Maps traffic data in Montgomery County by comparing it with sensor-based ground truth data, finding strong correlations. Dao et al. (2020) [16] introduced a 3DCNN-based deep learning model to predict congestion on spatial meshcodes, enabling efficient pattern recognition through temporal databases. Similarly, in Tokyo, Arp et al. (2020) [17] applied a metaheuristic optimization framework to crowd-sourced mobility data to dynamically reallocate traffic and reduce travel times. These studies highlight scalable, real-time, and data-rich approaches to urban mobility planning, though challenges remain in data accessibility, cost, and context-specific adaptability.

Recent studies emphasize the need for intelligent transportation systems in Saudi Arabia, advocating for the integration of AI-driven congestion monitoring, smart traffic signals, and culturally sensitive urban planning [18]. A GIS-based study conducted at King Abdulaziz University highlights the importance of spatial data in prioritizing road infrastructure investments, particularly in high-traffic areas [19]. Additionally, researchers have developed IoT-based adaptive traffic control algorithms using convolutional neural networks (CNNs) and real-time sensor data, demonstrating their potential to reduce congestion and waiting times [20]. The primary factors contributing to congestion in Jeddah include high population density, infrastructure limitations, and inefficient traffic management strategies [21]. While urban expansion has increased reliance on private vehicles, technological advancements in real-time traffic monitoring could offer scalable solutions to mitigate congestion [22].

By harnessing data from GPS tracking, public transit systems, ride-sharing platforms, and IoT-enabled traffic sensors, researchers can detect congestion patterns, identify mobility bottlenecks, and develop smarter transportation networks [23]. Predictive modeling techniques, including machine learning algorithms and real-time traffic data integration, have demonstrated strong potential for congestion forecasting and dynamic traffic rerouting [24]. For instance, studies utilizing edge-cloud computing frameworks have shown how taxi fleets and ride-hailing services can optimize passenger pickups and minimize traffic congestion in dense urban areas [25]. Additionally, process mining techniques applied to open transportation allow researchers to uncover commuter behavior trends and detect urban mobility inefficiencies [26]. These findings underscore the importance of real-time data integration in traffic management.

Recent works in 2023–2025 have increasingly emphasized the role of real-time, crowd-sourced, and AI-integrated traffic data in urban mobility planning [1,3]. However, these studies remain concentrated in North American, European, and East Asian settings. Also, recent research has demonstrated diverse applications of the Google Maps API in traffic analysis across different contexts. Muñoz-Villamizar et al. (2021) [27] applied the Google Maps API to evaluate urban traffic congestion in Boston, demonstrating how real-time travel time data can be used to construct congestion indices and analyze spatial–temporal mobility patterns. Hasabi et al. (2024) [28] mapped congestion patterns in urban Jakarta using digitized Google Maps data and linked them to gas station density as an emerging pollution proxy.

However, most applications of the Google Maps API remain concentrated in Western urban contexts, with limited attention to data-scarce regions such as the Middle East. Systematic application and validation of the Google Maps API remain largely unexplored in Middle Eastern contexts. Our contribution, therefore, lies in extending these methods to a data-scarce environment (Jeddah), validating the API against GPS trajectories and proposing a scalable congestion-monitoring framework that can be adapted to similar urban settings. This study analyzes real-time, crowd-sourced traffic data from the Google Maps API in Jeddah, Saudi Arabia, to monitor congestion. Using exploratory data analysis, clustering, and congestion index modeling, it offers insights for urban planners and policymakers to improve mobility.

3. Methodology

This section details the data collection process, the computation of a congestion index, and the analytical techniques—such as cluster analysis—used to examine temporal and spatial variations in traffic conditions. Collectively, these methods establish a practical framework for real-time congestion monitoring and data-driven decision making in urban mobility planning.

3.1. Study Area and Data Collection

Jeddah, the second-largest city in Saudi Arabia, is a crucial economic, commercial, and transportation hub. Situated along the eastern coast of the Red Sea, Jeddah plays a pivotal role in the national economy and serves as the primary gateway for millions of pilgrims traveling to the holy cities of Mecca and Medina. However, rapid urbanization, population growth, and increasing vehicle ownership have significantly strained the city’s road infrastructure, leading to chronic traffic congestion, particularly along its major arterial roads and urban corridors.

The road network in Jeddah is composed of a combination of highways, expressways, arterial roads, and local streets, each serving distinct purposes in facilitating urban mobility [29,30,31,32]:

• King Abdulaziz Road: Parallel to the coast; links residential, commercial, and industrial areas.
• King Fahd Road: Major commercial route intersecting King Abdulaziz Road; handles heavy business traffic.
• Madinah Road: Connects Jeddah to Medina; passes urban districts, malls, and industrial areas.
• Prince Majed Road: Key east–west corridor intersecting King Abdulaziz Road; links city sectors.
• King Abdullah Road: Crucial for central traffic; intersects King Fahd Road.
• Corniche Road: Coastal route for transport and leisure; busy on weekends/holidays.
• Sari St: Commercial corridor intersecting King Abdulaziz Road.
• Ghernata Street: Links Prince Mohammed Road to Palestine Road; supports local traffic.
• Hera Street: Dense business area; intersects King Fahd Road.
• Tahlia Street: Major shopping/entertainment hub; high peak-hour traffic.

To analyze real-time traffic congestion trends across Jeddah’s Road network, this study utilizes crowd-sourced data from the Google Maps API, which aggregates live traffic information from GPS-enabled devices, navigation apps, and connected vehicles. A total of 44 key routes were selected for this study, covering roads with historical congestion trends, high traffic volumes, and strategic importance. Among these, four major intersections, known for experiencing persistent congestion, were chosen for in-depth analysis: (1) Palestine Road and King Fahd Road, (2) Palestine Road and Prince Majid Road, (3) Madinah Road and Palestine Road, and (4) King Fahd Road and Ghernata Street. Figure 1 is a visual representation of these intersections on the city map.

To ensure a comprehensive dataset, traffic data was collected over a nine-day period starting on 23 May 2023, capturing a variety of commuting behaviors and congestion patterns: (a) five regular workdays (WDs) to study peak-hour congestion under normal weekday conditions, (b) two weekend days (WEs) to observe leisure-related traffic fluctuations, and (c) two long weekend days (LWEs) to evaluate how extended holidays influence traffic patterns. To gather real-time traffic data, the Google Maps API was leveraged, which compiles live traffic information from millions of GPS-enabled devices, navigation apps, and connected vehicles. A Python script (python 3.10 running on a Google Cloud Virtual Machine) was developed to automatically retrieve average speed and travel time estimates for each selected route at 15 min intervals, ensuring a continuous and comprehensive dataset. The script was deployed on a Google Cloud Virtual Machine (VM) to automate data collection without interruptions. For each query, the API provided the timestamp (date and time of retrieval), route ID (unique identifier for each road segment), current traffic speed (km/h), expected travel time (minutes), and estimated free-flow speed (FFS). To ensure accuracy, each route’s start and end coordinates were predefined, and only verified road segments were included in the study. The details of the selected routes, including coordinates, segment lengths, and directions, are presented in Appendix A.

Once collected, the data underwent structuring and cleaning to ensure consistency and reliability. This involved (1) synchronizing timestamps across all routes to maintain uniform data intervals, (2) standardizing data formats to ensure compatibility across different analytical tools, and then (3) checking for missing or duplicated entries to maintain dataset integrity. This refined dataset serves as the foundation for the congestion analysis, allowing us to examine traffic flow variations across different time periods and urban zones.

3.2. Congestion Index Calculation

Traditional measures such as the Travel Time Index (TTI), Speed Reduction Index (SRI), or delay-based measures focus primarily on deviations in travel time or average speed relative to free-flow conditions. While useful, these indices often overlook spatial variability and may underrepresent congestion severity in multi-corridor settings. The Congestion Index (CI) proposed in this study provides a more comprehensive measure by directly benchmarking observed speeds against route-specific free-flow speeds, thereby reflecting both the intensity and persistence of congestion across time and space. Compared with TTI, which normalizes delays as a ratio of peak-to-free-flow travel times, our CI offers finer temporal resolution (15 min intervals) and explicit alignment with widely understood Google Maps’ color-coded thresholds, enhancing both interpretability and policy relevance. Previous studies that employed the Google Maps API for congestion measurement, such as Muñoz-Villamizar et al. (2021) [27] and Hasabi et al. (2024) [28], demonstrate the practicality of travel time-based indices but did not emphasize route-level benchmarking or temporal clustering, as presented here. Accordingly, the CI facilitates hotspot identification and real-time applicability in data-scarce environments, making it a robust and scalable alternative for congestion monitoring in Middle Eastern cities.

Addressing congestion effectively requires a quantifiable measure that enables urban planners and traffic management authorities to assess road performance and mobility trends. In this study, the Congestion Index (CI) is employed as a key metric to evaluate the severity of traffic congestion across Jeddah’s major roadways. The Congestion Index (CI) serves as a quantitative indicator, representing the degree to which real-time traffic speeds deviate from ideal, uncongested conditions. This deviation is assessed by comparing observed vehicle speeds to a reference benchmark, commonly defined as the free-flow speed (FFS): the maximum attainable speed under optimal conditions with minimal external disruptions. Higher CI values indicate severe congestion and diminished traffic efficiency, whereas lower values suggest smooth vehicular movement and minimal delays.

Urban traffic management authorities rely on the Congestion Index to monitor, analyze, and optimize transportation networks, enabling the development of data-driven congestion mitigation strategies. This study employs a methodology that systematically compares real-time traffic speeds against historically recorded free-flow conditions (Nair et al., 2019 [12]). The Congestion Index (CI) is calculated using the following formula:

$${CI}_{nt}={\displaystyle \frac{\left(\frac{1}{V_{nt}}-\frac{1}{V_{fn}}\right)}{\frac{1}{V_{fn}}}}$$

where

• ${CI}_{nt}$: Congestion Index for a specific road segment n at time t.
• $V_{nt}$: Actual traffic speed (km/h) at time t.
• $V_{fn}$: Free-flow speed (km/h), representing the highest speed recorded when the road is uncongested.

This formula effectively captures the relative speed reduction experienced during congestion: when CI approaches zero, the road operates near its free-flow capacity; when CI increases beyond 0.54, severe congestion is present.

Google Maps employs a color-coded scheme: green, yellow (orange), and red, each corresponding to specific CI thresholds, effectively categorizing road segments based on congestion intensity. The Congestion Index (CI) measures traffic conditions, aligning with Google Maps’ color-coded system to represent congestion severity:

• Green (Free-Flowing Traffic): CI ≤ 0.18 indicates minimal congestion, with traffic moving at 85–100% of free-flow speed.
• Yellow (Moderate Congestion): CI between 0.18 and 0.54 reflects moderate traffic, with speeds at 65–85% of free-flow speed, and mild delays.
• Red (Heavy Congestion): CI > 0.54 indicates significant congestion, with speeds below 65% of free-flow speed, causing severe delays and potential route changes.

This system offers a quick, visual understanding of traffic conditions for commuters and planners, especially during severe slowdowns, stop-and-go movement, and extended travel delays, requiring route adjustments or traffic interventions to improve flow.

The Congestion Index operates on a continuous gradient, where higher values indicate worsening congestion levels. As CI approaches zero, the actual travel speed is nearly equivalent to free-flow speed, classifying the road segment as green. Conversely, higher CI values (above 0.54) indicate severe congestion, triggering a red classification. This dynamic color representation is particularly useful for real-time navigation, as it enables both daily commuters and transportation planners to identify congestion hotspots and make informed travel decisions.

Table 1. Congestion Index and Google Maps traffic classification.

	CI	Traffic Condition
Green	0–0.18	Free-flow traffic
Yellow	0.18–0.54	Moderate congestion
Red	greater than 0.54	Heavy congestion

presents the specific Congestion Index thresholds corresponding to Google Maps’ navigation colors, providing a structured reference for interpreting congestion levels.

The real-time updating of Google Maps’ traffic colors is directly linked to CI calculations, ensuring that users receive accurate, continuously refreshed insights into road conditions. This integration significantly enhances situational awareness, empowering drivers to make data-driven route adjustments while providing urban planners with a robust tool for congestion analysis and intervention strategies. To systematically evaluate congestion levels across Jeddah’s road network, a structured approach was adopted:

• Estimating Free-Flow Speed ($V_{fn}$): The free-flow speed for each route segment was determined by analyzing historical speed data during late-night and early-morning hours (typically between 2:00 a.m. and 6:00 a.m.), when traffic is minimal and unobstructed.
• Collecting Real-Time Speed Data ($V_{nt}$): Using the Google Maps API, traffic speed data was retrieved every 15 min for all selected road segments, ensuring a comprehensive dataset capturing temporal congestion fluctuations.
• Computing the Congestion Index (CI): The CI formula was applied to quantify congestion severity by comparing real-time speeds ($V_{nt}$) with free-flow conditions ($V_{fn}$), identifying delays and efficiency losses across different routes.
• Mapping CI Values to Google Traffic Colors: Each road segment was assigned a congestion level (Green, Yellow, or Red) based on CI thresholds, ensuring alignment with Google Maps’ real-time traffic visualization.

3.3. Free-Flow Speed Analysis

Free-flow speed (FFS) is the ideal travel speed on a road segment under uncongested conditions, serving as a baseline for evaluating traffic delays. Common methods for collecting FFS include (1) field observations under low-traffic conditions, (2) traffic simulations, and (3) historical speed data analysis. In this study, FFS is essential for calculating the Congestion Index (CI) and is estimated using historical speed data from low-traffic hours (2:00–6:00 a.m.). This approach, which avoids fieldwork, captures consistent speed trends and offers a reliable, scalable method suited to Jeddah’s urban environment.

Although closely related, free-flow speed (FFS) and posted speed limits often differ. FFS represents the maximum speed in ideal conditions, while speed limits are influenced by safety, road design, and policy. Key factors influencing speed limit decisions include:

• Road design: Curvature, lane width, and pavement quality determine safe travel speeds.
• Pedestrian/cyclist activity: High-foot-traffic areas may have lower speed limits, even if the road could support higher speeds.
• Traffic control measures: Intersections and stop signs disrupt FFS but may not justify speed limit reductions.
• Accident history/enforcement: High accident rates may lead to lower speed limits for safety.

Comparing FFS and posted limits offers insights into road efficiency:

• FFS > Speed Limit: Indicates overly restrictive speed limits or that the road design could support higher speeds.
• FFS < Speed Limit: Suggests external factors like congestion or poor road conditions are limiting speed, pointing to potential infrastructure improvements.

Understanding these differences helps policymakers adjust regulations to better align speed limits with actual driving conditions while maintaining safety.

3.4. Data Validation: Ensuring Accuracy and Reliability

To establish confidence in the accuracy and reliability of the traffic data collected from the Google Maps API, a validation process was conducted by cross-referencing the extracted dataset with real-time GPS-based travel data. Since crowd-sourced data can be influenced by factors such as user participation density, API constraints, and temporary network anomalies, it was essential to verify that the retrieved traffic estimates reflected actual conditions on Jeddah’s road network. By conducting a comparative analysis between the API-derived values and real-time navigation estimates, this study ensures that any potential discrepancies are minimized, reinforcing the credibility of the dataset for congestion analysis.

To validate the dataset, the validation process followed a structured approach:

• Route Selection: Diverse road segments, including highways, arterial roads, and dense commercial streets, were chosen to represent diverse traffic conditions.
• Comparative Analysis: API-derived speed and travel time estimates were compared with real-time GPS travel estimates obtained through Google Maps navigation.
• Deviation Measurement: A 5% deviation threshold was used as a benchmark for reliability, ensuring that any observed variations remained within an acceptable margin.
• Final Accuracy Assessment: The overall consistency of API-derived traffic data was evaluated to confirm alignment with real-time conditions.

Validation showed discrepancies between API data and GPS estimates stayed below 5%, confirming high accuracy. This supports the use of the Google Maps API for reliable travel speed and congestion estimates in traffic analysis. Consistent results across road types further validate its role in planning and policymaking. However, despite the strong accuracy indicators, certain inherent limitations of crowd-sourced traffic data must be acknowledged:

• User Participation Limitations: Low-traffic areas may yield fewer data points, causing slight inconsistencies.
• No Historical Data: The API provides real-time traffic data only, requiring ongoing collection for long-term analysis.
• Uncaptured Disruptions: Sudden congestion events or harsh conditions (e.g., accidents, road closures, weather changes, or extreme scenarios) may not be fully reflected in real time, which could affect reliability.

Despite minor limitations, validation confirms Google Maps API data as a reliable, scalable, and cost-effective tool for analyzing congestion and mobility in Jeddah. Its low error margin supports the use of real-time crowd-sourced data in urban transport planning.

4. Data Analysis and Discussion

4.1. Overall City Congestion Profile

4.1.1. Overall Congestion Index

By analyzing congestion patterns across different times and traffic conditions, this study offers key insights into Jeddah’s urban mobility. Traffic congestion was assessed using the Congestion Index (CI) across three time periods—morning (02:00–10:00), evening (10:00–18:00), and night (18:00–02:00)—and three day types: Workday (WD), Weekend (WE), and Long Weekend (LWE). Results show that congestion is shaped by commuting and daily activities. On workdays, moderate morning congestion is followed by peak levels in the evening due to return commutes, with relief at night. Weekends show low morning congestion, moderate evening traffic from leisure activities, and slightly elevated night levels. Long weekends exhibit minimal morning traffic, sharp evening peaks from increased outings, and moderate night congestion that exceeds regular weekends.

The analysis reveals that evening congestion consistently peaks across all scenarios, especially during long weekends. Morning congestion remains low on weekends and long weekends, reflecting reduced work-related travel. The average CI is 0.51, with a maximum of 4.5 recorded on route 4.04 (King Fahd (4), NS) and 3.04 (Madinah–Palestine, NW) during long weekend evenings (2:00–6:00 p.m.), marking key traffic bottlenecks. Figure 2 shows CI variations across time periods and day types, highlighting the sharp rise in congestion on long weekend evenings. Further sections explore route-specific patterns and contributing factors.

4.1.2. Free-Flow Speeds

The maximum free-flow speeds for all routes is 94 km/hr for route 2.10, and the minimum is 33 km/hr for route 4.08. Free-flow speeds for all routes are presented in Figure 3. The graph highlights discrepancies between free-flow speeds and the universal speed limit of 80 km/h. For routes exceeding 80 km/h, enforcing the speed limit may enhance safety. Conversely, routes with free-flow speeds below the limit suggest the need for improvements in road infrastructure or design to optimize flow. Ranges of speeds for the green, yellow, and red navigation colors, as calculated by Google Maps, are provided in Appendix B. These ranges are affected by the free-flow speed of the route. For route 2.10, the maximum free-flow speed, the green region is between 80 and 94 km/hr, and the red navigation speed is less than 61 km/hr. For route 4.08, the green region is between 28 and 33 km/hr, and the red navigation speed is less than 22 km/hr.

4.2. Daily Congestion Profile

4.2.1. Weekday Congestion Patterns

Traffic congestion in Jeddah’s road network shows clear weekday patterns, with levels varying across time periods. Box-and-whisker plots of the Congestion Index (CI), sampled at 15 min intervals, visualize these trends across four roadway groups (Figure 4). Most routes have median CIs below 2, indicating generally moderate congestion. However, corridors like King Fahd Road (NS) consistently show higher congestion, suggesting persistent traffic issues beyond peak hours. The Interquartile Range (IQR) varies across roads, reflecting different levels of congestion stability. Palestine Street shows narrow IQRs and steady flow, while Prince Majid Road (NS) and King Fahd Road (SN) have broader IQRs, indicating more volatile traffic, likely driven by inconsistent demand or road conditions. Outliers in the plots highlight extreme congestion, especially during peak hours, often caused by incidents or sudden demand surges. Gharnatah Road and Prince Majid Road (NS) display frequent spikes, marking them as high-variability routes. In contrast, Madinah Road (SN) remains smooth and stable, with minimal disruptions. This contrast in congestion behavior underscores the uneven distribution of traffic issues in Jeddah. Identifying unstable routes like King Fahd, Prince Majid, and Gharnatah is crucial for targeted traffic management, helping reduce delays and improve mobility in the city’s expanding urban landscape.

4.2.2. Weekend Congestion Patterns

Traffic congestion in Jeddah follows distinct patterns on weekends, with varying levels of congestion across different road segments throughout the day. Using box-and-whisker plots, the analysis captures fluctuations in the Congestion Index (CI) at 15 min intervals, allowing for a deeper understanding of congestion variability. The weekend traffic analysis is presented for all route groups in Figure 5. Overall, congestion levels remain lower on weekends compared to workdays, with median CI values generally closer to 1, indicating smooth traffic flow for most routes. This trend suggests reduced commuting demand, particularly in the morning hours. However, despite the lower median values, several routes display a wider interquartile range (IQR), indicating greater variability in congestion levels at different times of the day. Certain roads experience sporadic but severe congestion spikes, evident through the presence of outliers in the box plots. Notably, King Fahd Road (NS) and Madinah Road (NS) exhibit pronounced peaks, particularly during the first and second weekends, suggesting recurring congestion surges that could be attributed to increased weekend travel activity, shopping, or leisure-related trips. A comparative analysis of different routes reveals varying congestion characteristics across Jeddah’s urban network. Palestine Road (EW) demonstrates lower congestion variability and fewer extreme outliers, suggesting better traffic flow management or reduced traffic demand on weekends. Conversely, roads such as King Fahd Road (SN) and Prince Majid Road (NS), route groups 2 and 4, exhibit broader whiskers and more frequent outliers, indicating intermittent but intense congestion periods, possibly influenced by localized traffic events or high vehicle volumes in commercial zones.

4.2.3. Long Weekend Congestion Patterns

Observations reveal notable variations in congestion intensity, variability, and extreme congestion instances across the studied routes during long weekends. As illustrated by the box-and-whisker plots in Figure 6, unique congestion behavior across different road segments is spotted. Palestine Road (EW) consistently demonstrates the lowest congestion levels, characterized by a narrow interquartile range (IQR) and minimal outliers. This suggests stable traffic flow with little disruption, likely due to reduced travel demand on long weekends. The minimal fluctuation in congestion levels across all LWE categories indicates that existing traffic conditions on this corridor are relatively smooth and unaffected by peak-hour surges. In contrast, King Fahd Road (NS) and Prince Majid Road (NS) experience moderate congestion with increased variability. The broader IQR and frequent outliers observed in these routes suggest that congestion levels fluctuate more widely during extended weekends, likely influenced by peak-hour travel demand, commercial activity, or special events. These patterns indicate a need for adaptive traffic management measures to address congestion surges effectively. Madinah Road (NS) exhibits the highest variability among the studied routes, with significant shifts in the median Congestion Index (CI) and a wider IQR. This suggests heightened congestion levels that are both intense and unpredictable. The presence of multiple extreme outliers reflects conditions where traffic bottlenecks or sudden congestion spikes occur, potentially due to increased recreational and intercity travel during extended weekends. The findings highlight key differences in how congestion evolves across Jeddah’s Road network during long weekends. While some routes maintain stable traffic conditions, others are more susceptible to sharp congestion spikes and fluctuations. Targeted traffic management strategies should be prioritized accordingly, maintaining current interventions on Palestine Road; implementing peak-hour congestion mitigation measures on King Fahd and Prince Majid Roads; and introducing congestion control mechanisms for Madinah Road, where traffic conditions are most volatile. These measures can help optimize urban mobility and enhance road network efficiency during extended holiday periods.

When comparing across day types, clear differences emerge. Weekdays exhibit the highest overall congestion, with pronounced morning and evening commuter peaks. In contrast, weekends display significantly lower congestion, particularly in the mornings, though short-lived afternoon peaks appear due to leisure activities. Long weekends fall between the two: while morning congestion is minimal, evening congestion becomes sharper and more prolonged than on regular weekends, reflecting increased recreational and intercity travel. These differences confirm that weekday, weekend, and long weekend travel behaviors produce distinct congestion patterns across Jeddah’s network.

4.3. Within-Day Congestion Profile

The within-day congestion analysis highlights significant variations in traffic patterns across weekdays (WDs), weekends (WEs), and long weekends (LWEs). The findings underscore the need for dynamic traffic management strategies tailored to different travel behaviors. While weekday congestion mitigation should focus on peak-hour interventions such as signal optimization and traffic flow management, weekend and long weekend strategies should account for dispersed congestion patterns, particularly in the afternoons. By implementing adaptive traffic control measures, particularly for routes that exhibit persistent congestion across different scenarios, urban planners can enhance mobility and reduce travel delays.

4.3.1. Route Group 1

Hourly congestion profiles across Jeddah’s road network reveal distinct patterns for weekdays (WDs), weekends (WEs), and long weekends (LWEs). Figure 7 illustrates hourly Congestion Index (CI) variations for route group 1, highlighting shifts in traffic intensity. On weekdays, traffic follows a classic commuter trend, with a smaller morning peak (7:00–9:00 a.m.) and a more pronounced evening peak (4:00–6:00 p.m.), aligned with work commutes. Routes like 1.01 and 1.05 frequently exceed CI 3.0 during peak hours, indicating consistent bottlenecks or high demand. Traffic is relatively smooth during late-night and midday hours. On weekends, congestion builds gradually, reaching a single, broader peak between 3:00 p.m. and 7:00 p.m., driven by leisure travel. Overall CI values are lower, especially in the early morning, reflecting reduced demand. For long weekends, patterns resemble regular weekends but with broader and slightly higher peaks in the afternoon and evening—likely due to extended leisure activities. Routes such as 1.01 and 1.07 consistently show elevated CI values across all scenarios. These findings emphasize the need for route-specific management strategies: rush-hour optimizations for weekdays and adaptive controls for weekends. Targeting persistent hotspots—like King Fahd Road and Madinah Road—can improve traffic flow and urban mobility across Jeddah.

4.3.2. Route Group 2

Figure 8 illustrates the hourly Congestion Index (CI) for route group 2, highlighting variations across weekdays, weekends, and long weekends. On weekdays, congestion follows a structured pattern, with two clear peaks: 8:00–10:00 a.m. and 3:00–5:00 p.m., aligned with work commute hours. After the evening peak, CI values decline, reflecting reduced nighttime traffic. While most routes follow this trend, those with higher demand exhibit sharper peaks, underscoring their priority for traffic management. On weekends, congestion patterns shift. Instead of sharp peaks, CI gradually increases, with midday (11:00 a.m.–1:00 p.m.) and afternoon (3:00–5:00 p.m.) peaks driven by leisure travel. Evening congestion declines more sharply than on weekdays, indicating lower night travel. Long weekends exhibit a hybrid pattern: low morning congestion followed by a broad, sustained peak from 1:00 to 4:00 p.m., reflecting increased holiday and recreational travel. Variability across routes also rises, with some—especially those serving leisure areas or city exits—experiencing sharp congestion spikes. These patterns highlight the need for adaptive, day-specific traffic strategies, especially for high-demand routes during long weekends.

4.3.3. Route Group 3

Figure 9 illustrates congestion patterns typical of commuter traffic; weekdays have two prominent peaks: 6:00–9:00 a.m. and 4:00–7:00 p.m., reflecting heavy demand during work commutes. Minimal congestion is observed overnight (12:00–5:00 a.m.). Routes 3.01 and 3.02 consistently show the highest peak CI values, indicating high congestion susceptibility. On weekends, congestion shifts to a single, moderate peak between 12:00 and 3:00 p.m., aligned with leisure travel. Although overall congestion is lower, routes like 3.01 and 3.06 still show elevated CI levels, suggesting localized afternoon hotspots. Long weekends exhibit an extended peak from 4:00 to 7:00 p.m., likely due to recreational travel and return trips. Morning congestion remains low, but CI values begin rising in early afternoon, signaling increased non-commuter traffic. Routes 3.04 and 3.11 show notable congestion surges, marking them as key targets for traffic interventions during holiday periods.

4.3.4. Route Group 4

Figure 10 highlights congestion patterns for Route Group 4, showing clear differences across weekdays, weekends, and long weekends. On weekdays, congestion peaks occur during 8:00–10:00 a.m. and 4:00–6:00 p.m. Route 4.06 (King Fahd–Gharnatah, NW) consistently records the highest congestion, indicating its role as a major traffic corridor. In contrast, routes 4.01, 4.02, and 4.03 maintain relatively low and stable CI levels, suggesting smoother flow. On weekends, overall congestion declines due to reduced commuter traffic, with a moderate peak around 3:00–5:00 p.m., likely driven by leisure and shopping trips. Still, Route 4.06 shows a noticeable spike, reinforcing its high demand. Long weekends feature a broader and more pronounced peak between 3:00 and 7:00 p.m., reflecting increased recreational or holiday travel. Route 4.06 remains the most congested throughout, underscoring its strategic significance, while other routes exhibit minimal variation and maintain lower congestion levels.

As a general observation, Route 4.06 (King Fahd–Gharnatah, NW) consistently records the highest congestion levels across all time periods, marking it as a critical target for traffic management. Long weekends display unique patterns, with extended midday-to-evening peaks, suggesting increased non-commuter travel and requiring adaptive traffic control. Strategic recommendations include the following:

• Implementing congestion mitigation measures on Route 4.06, such as adaptive signal control and traffic flow redistribution.
• Introducing dynamic traffic management during long weekend peaks to manage elevated travel demand.
• Launching public awareness initiatives with real-time congestion updates to support informed route planning and travel time adjustments.

Understanding these congestion dynamics allows planners to deploy targeted, data-driven interventions to enhance traffic flow, reduce delays, and support improved urban mobility in Jeddah’s growing transport network.

As key insights, weekday traffic in Jeddah follows structured, commuter-driven patterns, with distinct morning and evening peaks. In contrast, weekends exhibit more dispersed congestion, driven by leisure travel, while long weekends show greater variability, with extended afternoon and evening peaks. To effectively mitigate congestion, traffic management strategies must align with these distinct travel behaviors:

• Weekdays: Prioritize rush-hour relief through optimized signal timing and flow management.
• Weekends and Long Weekends: Focus on managing afternoon congestion, especially on high-variability routes.

4.4. Route Reliability

Route reliability refers to the consistency and predictability of traffic conditions along a road segment over time. It indicates how reliably commuters and planners can anticipate travel times and congestion levels. Reliability is influenced by factors such as congestion, incidents, weather, and infrastructure quality. The relationship between the average Congestion Index (CI) and the standard deviation offers key insights into route reliability. Routes with a low average CI and low standard deviation are more stable and reliable, while those with high values exhibit greater variability and require closer management. Figure 11 presents a scatter plot showing the link between the average CI and standard deviation. A positive correlation emerges—higher average CI values tend to be associated with greater variability, indicating less predictable traffic conditions. Notably, a cluster of routes (e.g., 2.04, 2.07, 1.08, 4.01–4.03, 4.08–4.10, 4.12) shows low averages and minimal variability, signaling consistently reliable traffic flow. We observed the following key points:

• Positive Correlation: There is a clear positive correlation between the average CI and its standard deviation. Routes with higher average congestion also exhibit greater variability (higher standard deviation), indicating that high-congestion routes experience more fluctuating traffic conditions.
• Clustered Patterns:
  • Routes with lower average CI values (<0.4) tend to form a dense cluster near the bottom-left corner, with standard deviations under 0.6. These routes represent consistent low-congestion scenarios with minimal variability.
  • Routes with higher average CI values (>0.8), such as 4.04, 3.07, and 3.11, deviate significantly, showing both high congestion and substantial variability, likely representing major thoroughfares or critical routes subject to dynamic traffic conditions.
• Outliers: Routes like 4.04 and 3.11 stand out with exceptionally high standard deviation values (>1.2), despite a relatively moderate-to-high average CI. These outliers suggest that these routes experience extreme variability, possibly due to irregular traffic surges or inconsistent road usage patterns.
• Linearity: The linear trend indicates that as the average CI increases, the standard deviation grows proportionally, suggesting a systemic relationship where high traffic congestion leads to less predictable conditions.

4.5. Distribution of Congestion

The utilization of K-means clustering can be a valuable tool for understanding and categorizing route behavior. Details of the used procedure can be found in Appendix C. The results are visualized in Figure 12. The clusters, numbered from 1 to 7 on the x-axis, capture distinct congestion profiles, with variations across different time periods and day types.

4.5.1. Cluster Variability and Congestion Patterns

Each cluster represents a unique congestion profile, highlighting variations in traffic behavior across the network. Clusters 1, 2, and 3 generally exhibit lower congestion levels, with most CI values remaining below 1.0. These clusters correspond to routes that experience minimal congestion, regardless of the time of day, suggesting stable and efficient traffic flow. Conversely, Clusters 6 and 7 show significantly higher CI values, particularly during the evening hours. This trend is consistent across all day types, indicating that these clusters encompass high-traffic routes prone to severe congestion during peak demand periods. The elevated congestion levels in these clusters suggest recurring bottlenecks, potentially influenced by traffic volume, signal timings, or nearby urban activities.

4.5.2. Temporal and Day-Type Differences in Congestion

Distinct congestion trends emerge when analyzing the data across different time periods:

• Weekday evenings (CI WD Eve) exhibit the highest congestion levels, particularly in Clusters 5, 6, and 7, reinforcing the presence of heavy commuter traffic during rush hours.
• Weekend evenings (CI WE Eve) and long weekend evenings (CI LWE Eve) also display elevated CI values, but they remain slightly lower than weekday evenings, likely due to differences in travel demand patterns.
• Morning congestion (CI WD Mor, CI WE Mor, CI LWE Mor) is generally lower and more consistent, except for Cluster 6, which shows occasional spikes, suggesting localized morning congestion hotspots.
• Nighttime congestion (CI WD Nig, CI WE Nig, CI LWE Nig) remains minimal across all clusters, reflecting low traffic demand during late hours and reduced variability across different routes.

For clusters 1 and 7, CI values for long weekend evenings and weekday evenings are consistently above 1.5, and in some cases, near or above 2.0, indicating severe congestion. Clusters 6 and 7 exhibit the most significant congestion patterns. Cluster 6 shows a wide CI spread, indicating variable congestion, likely caused by external factors such as road design or fluctuating traffic flow. Cluster 7 records extreme CI values during evening hours, identifying it as a high-demand zone, likely covering major arterials or commercial corridors. These clustering results highlight congestion-prone routes and times, enabling targeted mitigation strategies. Clusters 6 and 7, with the highest congestion, require focused evening traffic management.

4.6. Real-Time Urban Congestion-Monitoring Framework

The proposed framework for real-time traffic monitoring in Jeddah integrates three hierarchical layers: Data Acquisition, Data Processing, and Decision Support. Each layer is tailored to capture, analyze, and act upon urban congestion patterns using crowd-sourced data from the Google Maps API (Figure 13). In the Data Acquisition Layer, traffic information is collected in real time from GPS-enabled devices, mobile applications, and connected vehicles through the Google Maps API. The data is validated through cross-verification with real-time GPS navigation records, maintaining an accuracy threshold of ±5%. This layer also includes automated cleaning procedures, timestamp synchronization, and 15 min interval binning to ensure uniform temporal resolution across all road segments.

The Data Processing Layer comprises multiple analytical stages. A key component is the calculation of the Congestion Index (CI), which quantifies traffic severity by comparing actual travel speeds against free-flow conditions derived from low-traffic time windows (typically 2:00–6:00 a.m.). Alongside this, Free-Flow Speed Benchmarking establishes performance baselines for each route. The system applies exploratory data analysis to identify congestion trends across time segments (weekday/weekend/long weekend; morning/evening/night) and integrates K-means clustering to classify key road segments into behavioral groups based on their congestion variability and temporal dynamics.

These insights feed into the Decision Support Layer, which informs real-time traffic management strategies such as adaptive signal control and dynamic rerouting. The framework also enables hotspot prioritization, allowing transportation authorities to focus interventions on high-variability corridors like King Fahd and Madinah Roads. Additionally, it facilitates integration with Smart City initiatives, contributing to policy planning under Saudi Arabia’s Vision 2030 and enhancing public mobility through real-time congestion alerts and predictive traffic modeling. Overall, the framework represents a scalable, data-driven approach to urban traffic governance, balancing technological feasibility with actionable insights for improving city-wide mobility.

Illustrative Case Applications of the Proposed Framework

1. Adaptive Signal Timing at Congested Intersections

A recurrent congestion hotspot was identified along the King Fahd–Gharnatah corridor (Route 4.06), where CI values consistently exceeded 0.54 during evening peaks. Within the proposed framework, such conditions could serve as real-time triggers for adaptive signal control systems. By dynamically extending green phases in the predominant traffic direction, the system would mitigate queuing and improve throughput. This operational pathway illustrates how continuous congestion monitoring can directly inform intersection-level traffic management interventions.

2. Dynamic Rerouting During Long Weekend Peaks

Analysis of long weekend travel patterns revealed pronounced afternoon congestion on Madinah Road (Route 3.11), with CI values displaying high variability. In practice, this information could be integrated with navigation platforms or municipal mobility applications to facilitate dynamic rerouting. Vehicles could be redistributed to parallel arterials with lower CI values, thereby alleviating bottlenecks. The framework thus provides the analytical foundation for demand-responsive route allocation under atypical travel demand conditions.

3. Event-Based Congestion Management

Short-term anomalies, such as those arising during cultural or religious gatherings, can generate sudden and spatially concentrated traffic surges. For instance, localized spikes in CI values along Palestine Road could be detected within the framework’s 15 min monitoring intervals. These anomalies could prompt the deployment of temporary traffic management strategies, including reversible lanes, manual traffic control, or diversion plans. This example highlights the role of the framework in supporting rapid, event-specific decision making.

4. Demand Management via Public Information Systems

The clustering analysis indicated that long weekend afternoons and evenings are associated with systemic congestion across multiple high-variability routes (Clusters 6 and 7). Such predictive insights could be operationalized through demand management policies, for example, disseminating real-time alerts via smart city platforms, social media, or SMS services to encourage staggered departure times or promote the use of park-and-ride facilities. This demonstrates how the framework can extend beyond infrastructure-based measures to influence user behavior through proactive information provision.

5. Conclusions, Limitations, and Future Research Directions

5.1. Concluding Insights

This study has offered a grounded, data-informed view of Jeddah’s traffic congestion landscape, drawing on real-time, crowd-sourced data from the Google Maps API to better understand how mobility patterns shift across time and location. As Jeddah grows and urbanizes, the demand for smarter, more adaptive traffic solutions becomes increasingly urgent. Through a detailed analysis of congestion indices (CIs) across 44 representative road segments and time intervals, this research sheds light on when and where traffic bottlenecks are most severe, and what that means for daily travel in the city. This study contributes a novel, data-driven framework for understanding and managing urban congestion using real-time, crowd-sourced travel time data. By applying this approach to Jeddah, a rapidly urbanizing city with limited access to traditional traffic-monitoring infrastructure, we demonstrate how publicly available data from the Google Maps API can be used to generate high-resolution insights into congestion dynamics. Through a combination of exploratory data analysis, congestion index computation, and unsupervised clustering, we identify temporal patterns and spatial hotspots that are critical for targeted interventions. The validation of Google Maps API data against observed GPS measurements further reinforces the reliability of this low-cost data source in regions where sensor networks and traffic flow models are not readily available.

The contribution of this study also aligns with broader sustainability objectives. By enabling real-time congestion monitoring and supporting data-driven traffic management, the proposed framework can help reduce fuel consumption and vehicle emissions, thereby mitigating the environmental footprint of urban mobility. From a social perspective, improving traffic flow enhances accessibility, reduces travel-related stress, and contributes to public health by lowering exposure to air pollution. Economically, more reliable traffic conditions reduce travel time losses and improve productivity. Collectively, these impacts demonstrate how intelligent congestion management systems can serve as enablers of sustainable urban development, particularly in rapidly growing cities such as Jeddah.

This work moves beyond descriptive reporting by offering a scalable and replicable framework for real-time congestion analysis. It provides a template for data-scarce cities to adopt modern traffic intelligence tools, without the need for expensive infrastructure investments. Ultimately, this study advances the practice of urban mobility planning in the Middle East by operationalizing open data for strategic, evidence-based traffic management. In sum, this case study illustrates how cities like Jeddah can take practical steps toward smarter traffic management using tools already within reach. It reinforces the value of data-driven planning and provides a working model for other urban centers in Saudi Arabia and beyond looking to integrate technology into everyday mobility solutions.

5.2. Discussion of Limitations

While this study offers a practical framework for congestion monitoring using real-time, crowd-sourced data, some limitations must be acknowledged: The study depends on Google Maps API travel time estimates, which are based on proprietary algorithms that lack transparency regarding data sources and computation methods. This may introduce hidden biases in congestion measurements. To address this, future work should incorporate cross-validation with independent GPS or loop detector data to improve reliability and interpretability. Also, the study focused on 44 routes in Jeddah. While these were chosen for their strategic importance, they may not fully capture localized congestion phenomena in secondary roads or residential areas. Future studies should extend data collection to a wider range of road types and over longer periods to improve generalizability. External factors like weather, road construction, or special events were not considered. Integrating incident reports or weather data could improve congestion prediction and event-based traffic management.

Another limitation relates to the duration of the data collection period. Although the nine-day window was designed to capture variability across regular weekdays, weekends, and long weekends, it does not encompass longer-term or seasonal fluctuations, such as those associated with religious festivals or special events. As such, while our framework remains valid for demonstrating congestion dynamics under typical conditions, future studies extending the data collection period would provide additional insights into rare or seasonal congestion scenarios.

While validation results confirmed strong alignment between API-derived estimates and GPS trajectories under normal operating conditions, the study did not explicitly test performance in extreme scenarios (e.g., sudden incidents, weather disruptions, or unusually high event-driven demand). Future studies should incorporate such conditions to enhance the robustness of validation efforts. Also, future research should incorporate additional independent data sources (e.g., loop detectors, mobile GPS traces) to further strengthen the robustness and interpretability, as the use of the Google Maps API entails a certain level of opacity, since its underlying algorithms and data aggregation processes are proprietary and not fully transparent.

5.3. Future Research Directions

Building on the insights gained from this study, several avenues for future research emerge, paving the way for continued advancements in urban mobility and transportation management in Jeddah and beyond.

• Future extensions of this framework could include case-specific applications that translate congestion-monitoring outputs into operational measures, such as adaptive signal timing adjustments, dynamic rerouting strategies, or demand management interventions. Designing such case studies would help illustrate the practical decision-support capabilities of the proposed framework in real-world traffic management scenarios.
• Integration of Machine Learning for Traffic Prediction: By Analyzing historical traffic data alongside real-time variables such as road incidents, weather conditions, and special events.
• Assessing the Impact of Public Transportation Initiatives: Examining how investments in bus networks, metro systems, and ride-sharing services influence traffic congestion in Jeddah could offer valuable insights into transportation strategies.
• Dynamic Traffic Signal Control Systems: By optimizing signal timings based on congestion levels, these systems could significantly improve traffic flow efficiency, particularly at high-traffic intersections identified in this study.
• Community Engagement and User-Centric Traffic Solutions: By incorporating user-reported data on delays, bottlenecks, and alternative routes, transportation authorities could develop more responsive, citizen-centric traffic management policies.
• Future research could build on this framework by incorporating external and contextual factors that influence urban mobility. These include weather conditions, roadworks, and accidents, as well as behavioral and socioeconomic variables such as demographics, public transport usage, and policy interventions.