Mohammed S.Alfawzan1, Arshad Jamal1,*, Md Kamrul Islam2,*, Khurram Shahid Minhas3, Ahmad Aftab4
1 Department of Civil Engineering, College of Engineering, Qassim University, Buraydah 51452, Saudi Arabia; Email: m.alfawzan@qu.edu.sa; a.jamal@qu.edu.sa
2 Department of Civil and Environmental Engineering, College of Engineering, King Faisal, University, Al Ahsa, 31982, Saudi Arabia; E-mail: maislam@kfu.edu.sa
3 Transportation and Traffic Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, Dammam 31451, Saudi Arabia; Email: ksminhas@iau.edu.sa
4Senior Traffic Engineer, SETS International, Al Ashariah Street I, Building 5018, Ash Shati Ash Sharqi, Dammam 32414, Saudi Arabia; Email: aaftab@setsintl.net
Corresponding author: maislam@kfu.edu.sa; a.jamal@qu.edu.sa
Revised: May 6, 2025; Accepted: May 11, 2025; Published: May 12, 2025
Today urban metropolitans worldwide are facing significant mobility challenges, where congestion, delays, and safety concerns hinder quality of life and sustainable growth. Like other developing nations, traffic congestion has emerged as a major socio-economic in main cities around Saudi Arabia. Intersections serve as hotspots for traffic congestion and incidents. To address the operational inefficiencies and safety concerns at intersections, it is essential to evaluate the intersection improvement strategies through data-driven and simulation-based methods. The current study aims to assess the operational and safety performance of various improvement strategies for two critical signalized intersections (Nabiyyah Bridge and Uhud Street) along the Dhahran-Jubail Highway in the city of Qatif, Saudi Arabia. A comprehensive evaluation framework consisting of field data collection through manual observations, advanced video analytics, and microscopic simulation modeling with PTV VISSIM is proposed. In addition to simulating the existing/baseline conditions, three improvement scenarios (optimized signal retiming; targeted geometric modifications, and conversion to a diverging diamond interchange) were tested. Modeling results demonstrated that signal retiming/optimization alone reduced average vehicle delays by up to 35% and improved the Level of Service (LOS) from E/F to C/D. Geometric enhancements consisting of adding exclusive left turns and improved channelization further delivered reductions in queue length and average vehicle delay. The third scenario of conversion to diverging diamond interchange (DDI) yielded only marginal additional benefits primarily due to local traffic patterns and spatial constraints. Safety analysis and crash estimation through Highway Safety Manual (HSM) methodologies revealed that operational improvements were correlated with a noticeable reduction in predicted crash rates. Findings of current study offer valuable insights for decision-makers and transportation planners, signifying how microsimulation can effectively guide intersection design improvements and control to improve traffic flow, enhance safety, and support evidence-based urban mobility planning in the study area and similar urban jurisdictions.
Keywords: Traffic congestion, sustainable urban mobility, traffic simulation, VISSIM, Saudi Arabia
- Introduction
Rapid urbanization and the growing prevalence of private vehicles are significant factors in the global issue of traffic congestion [1,2]. The growth of urban areas and increasing population levels lead to heightened demand on existing transportation infrastructure, often surpassing the capacity of road networks, particularly at critical locations such as signalized intersections [3,4]. Signalized intersections play a crucial role in managing traffic flow, enhancing safety, and supporting the effectiveness of urban mobility systems. Inadequate management or optimization of these intersections may result in significant delays, congestion, and safety hazards [5,6]. Intersection congestion has various adverse impacts. Extended delays at signalized intersections diminish road users’ level of service (LOS) and lead to heightened fuel consumption, greenhouse gas emissions, and driver frustration [7,8]. Intersections often represent significant sites for traffic accidents, particularly when signal timing does not correspond with current traffic patterns or when the geometric design does not sufficiently accommodate turning movements and pedestrian flows [9,10]. The economic implications of urban traffic congestion are considerable, encompassing diminished productivity, elevated vehicle operating costs, and heightened health risks associated with air pollution [11]. Recent advances in traffic flow prediction and control have been extensively explored through novel deep learning architectures and intelligent systems, including the parallel convolution residual network [12,13], visual feature fusion techniques [14], dynamic trend fusion modules [15], and bilinear spatiotemporal fusion networks [16]. Further developments involve spatiotemporal graph transformers [17], 6G-based intelligent traffic control systems [18], and unified spatial-temporal neighbor attention networks [19].
Traditional approaches to alleviating intersection congestion, such as expanding roadways and constructing new infrastructure, encounter increasing constraints due to limited urban space, rising construction costs, and the imperative to minimize environmental and social repercussions [20,21]. As a result, there is a growing emphasis on improving the efficiency of existing intersections by applying intelligent transportation systems (ITS), sophisticated traffic signal control methods, and data-informed scenario analysis [22,23]. Microsimulation modeling has demonstrated its efficacy in evaluating the operational performance of intersections under different improvement scenarios. Microsimulation models can be used to assess the impact of geometric changes at intersections. For instance, a study in Islamabad, Pakistan, demonstrated that modifying the geometric features of a four-legged intersection led to a 47% reduction in emissions and fuel consumption, and a 40% reduction in delays, improving the Level of Service (LOS) from ‘E’ to ‘B’ [24]. Microsimulation enables transportation engineers and planners to mimic actual traffic scenarios effectively, assess the impacts of various signal timing strategies, geometric modifications, and demand management approaches, and predict outcomes like average delays, queue lengths, and safety indicators [25].
Despite the availability of sophisticated modeling tools, various persistent challenges hinder the effective optimization of signalized intersections [26]. Urban travel demand demonstrates considerable variability, influenced by time-related factors like peak and off-peak periods, changes in land use, and evolving mobility patterns [5,27,28]. This necessitates continuous updates to signal timing strategies and intersection layouts to uphold efficiency and safety. The incorporation of new data collection technologies, such as video analytics, automated turning movement counts, connected vehicle data, and intersection fog-based distributed routing strategies [29], varies significantly across different jurisdictions, frequently due to constraints in resources or resistance to change. The geometric complexity of urban intersections, marked by numerous approach lanes, expansive medians, and complex turning movements, requires tailored solutions that consider local conditions rather than relying solely on standardized international practices [4,30]. Given these challenges, it is crucial to perform comprehensive studies using sophisticated microsimulation tools and robust data analytics to evaluate and improve the operational efficiency of signalised intersections.
Like other developing nations, Saudi Arabia has experienced significant urbanization and rapid economic growth, leading to marked increases in vehicle traffic, particularly in major cities and their surrounding regions. This study intends to use a microsimulation technique to fully assess traffic flow improvements in the Qatif area at two signalized crossings (those of the Nabiyyah Bridge and Uhud Street intersection). The specific objectives of the study are:
- Quantify existing traffic conditions at the two intersections using observed traffic volumes, queue lengths, delays, and signal timing data, with a focus on identifying the root causes of congestion and inefficiency.
- Develop and simulate alternative improvement scenarios-including signal retiming, geometric modifications, and the potential conversion to a diverging diamond interchange (DDI)-using state-of-the-art microsimulation software (PTV Evaluate the impacts of these scenarios on key performance indicators such as average delay, queue length, and LOS.
- Provide evidence-based recommendations for municipal stakeholders, emphasizing cost-effective and context-sensitive strategies for congestion mitigation and safety enhancement.
This study is significant due to its methodological rigor, the application of advanced data analytics and simulation tools, and its direct relevance to the urgent transportation challenges encountered by rapidly urbanizing areas in Saudi Arabia. The study provides actionable insights for practitioners and policymakers, contributing to the objectives of sustainable urban mobility, improved traffic safety, and enhanced quality of life in Qatif and comparable urban environments.
The remainder of this paper is structured as follows: Section 2 provides a concise review of the literature regarding the performance evaluation of various intersection improvement strategies in complex urban environments and modeling methodologies widely employed. Section 3 present an overview of the study area and data collection. Section 4 outlines the details of analytic methodology, i.e., simulation model setup and description of simulation scenarios and performance evaluation indicators. Section 5 provides the results obtained and related discussion. Finally, section 6 provides a summary of key conclusions, policy implications, and recommendations for future works.
- Literature Review
Signalized intersections are vital elements in urban transportation networks, affecting traffic safety, user experience, and overall corridor effectiveness. Safety concerns, congestion, and delays are the increasing challenges to intersections alongside the continued increase in vehicle ownership and population growth [4,9]. To address such challenges, advanced intersection configurations, implementing geometric modifications, and signal optimizations have been developed and implemented by transportation professionals and researchers, aimed at enhancing the performance of intersections [31,32]. Implementing these modifications in real life can involve potential safety risks to road commuters and economic costs. To mitigate this, microsimulation modeling has become a significant tool in transportation research, offering optimized performance results, more information for decision-making, and minimizing hostile impacts by a thorough evaluation of highways and interaction scenarios before the deployment in real-time [33]. The following sections provide a review of earlier studies investigating different intersection improvement strategies aimed at improving traffic flows and enhancing safety.
- Intersection Improvement Through Signal Timing Optimization
To enhance the operational effectiveness of signalized intersections, there is a viable approach acknowledged as the most effective and economical, known as Signal Timing Optimization (STO) [34]. Systematically adjusting the parameters of the intersection signals, such as cycle length, green time distributions, and phase sequencing, to enhance existing traffic demand patterns, improve the Level of Services (LOS), and reduce vehicle delays and queue length comes under the fundamental principles of signalized intersection optimization based on HCM [4].
In literature, several studies have underscored the advantages of signal retiming and optimization [34,35]. For example, multiple-intersection research directed by Stevanovic shows that in comparison to conventional fixed-time signal control, adaptive signal control systems provide a 25 % reduction in the average delays at intersections [36]. Study’ findings emphasized the significance of utilizing adaptive signal strategies and data collection in real-time, predominantly in the traffic corridors with variable traffic demands. A similar study, conducted by Al-Ahmadi in Saudi Arabia investigated intersections by retiming and optimization of signal control and reported a considerable reduction in accidents and a reduction of 15 % in violations of red light at the intersection [37]. Furthermore, the research investigated and highlighted the advantages of synchronizing the signals with real-time traffic flow patterns. Despite these benefits, challenges persist and many urban intersections still operate under out-of-date fixed-time signal strategies, which do not depict the actual traffic patterns, resulting in less optimal performance characterized by insufficient utilization of intersection capacity and extreme delays [38]. For better decision-making in real-time traffic with regard to signal optimization, accuracy in the process of data collection is essential, which is a resource-intensive task. Recent developments in the field of automatic data collection technologies and video analytics have addressed these challenges using the Artificial Intelligence (AI) model [39–42]. Recently, with the advent of big data and enhancement in computational power, AI-based metaheuristics techniques have been implemented to optimize signal timing with noticeable improvement in intersection performance [43–45]. Moreover, the lack of intersection development strategies, such as demand management and geometrical changes, and synchronization of signal timing optimization. when deployed in an integrated manner, these have the significant potential to enhance the intersection performance [46,47].
- Intersection Improvement Through Geometric Interventions
Changing the physical configuration of an intersection, including the reconfiguration of alignment of approach, addition or reduction in the turning movements, and channelization of the traffic approaches at an intersection, falls under geometrical modification. These modifications are typically implemented to reduce conflict points, improve the safety for commuters, and increase the capacity to improve LOS [48,49].
In literature, researchers have consistently sought the impact of the geometrical modifications on the overall performance of the intersection, which can also lead to potential operational advantages. A study by Alfawzan evaluated the influence of exclusive left turns in the Qassim region, Saudi Arabia, and found that these interventions led to 85% reduction in average delay and 70% reduction in queue length [50]. Also, there was a notable improvement in LOS. Similarly, other studies reported an 8-12 % reduction in emissions and a significant improvement in the traffic flow at the approach when integrating the geometrical changes with signal optimization [51,52].
Despite their benefits, there are some challenges when it comes to geometrical modification. Physical limitations, such as existing land use, limited feasibility of adding new lanes and right of way, presence of adjacent developments, and reconfiguration approaches, especially in densely populated urban areas [9]. Furthermore, geometric changes can cause partial and full temporary disturbances during the construction period, and more importantly, they often require capital investment. A study by Qin et al. indicated that if demand management strategies are not being utilized efficiently, increasing capacity can source additional traffic demands that could reduce the long-term benefits [53]. Fundamentally, the efficiency of these changes solely hinges on the context of the approach that considers land use characteristics, safety priorities, and the behavior of local commuters. The past researchers highlighted that to ensure that the deployment of changes is effective and sustainable over time, integration of advanced simulation modeling, geometrical modification, and signal optimization is essential [32,54]
- Intersection Improvement Through Innovative Designs
In recent years, to address safety challenges at high-volume intersections and congestion, unconventional intersection configurations such as Continuous Flow Intersection (CFI), Diverging Diamond interchange (DDI), and modern roundabouts have been gradually implemented. These innovative designs aim to streamline pedestrian and vehicle traffic by optimizing traffic flow and reducing accident points [49,55].
In recent literature, the impact of innovative intersection designs has been increasingly investigated on the safety and operational efficiency of intersections, particularly, the DDI has attracted substantial attention [56–58]. Studies report that there is a 15 to 40 % reduction in crashes at DDI constructed in the United States [59,60]. These developments make signal phasing more efficient and eradicate accidents at the left turn. A recent study by Chlewicki [61] indicated the operational advantages of DDIs, such as a reduction in delays, particularly for heavy vehicles’ left-turning approaches. Similarly, another study reported that DDIs in comparison to the conventional intersection witnessed improvement in operational efficiency both in terms of average delay and conflict points [62]. Despite their promising outcomes, these changes come with some challenges, especially in urban limited right-of-way and higher deployment construction costs [49]. Furthermore, a lack of familiarity among pedestrians and drivers can initially result in confusion or minor accidents, pointing to the significance of public awareness before deployment [61].
- Microsimulation Modelling
In recent years, microsimulation modeling has emerged as a promising tool to optimize and evaluate intersection performance and efficiency. In comparison to macroscopic and mesoscopic models, this approach offers a comprehensive dynamic interaction among vehicles, the traffic signal control system, and non-motorized road users [63,64]. The advantages of microscopic traffic simulations have been highlighted by many researchers [64,65]. In 2010, Barcelo stated that this modeling approach permits traffic planners to investigate a broad range of scenarios, including modification in demand patterns, cycle length, and geometric design, before implementing in the real world [25]. This methodology supports data-driven decision-making, mitigates safety risks, and enables early detection of accidental consequences. Industry-standard tools such as PTV VISSIM, Synchro, and AIMSUN offer comprehensive proficiencies for multimodal modeling interactions, network traffic behavior, and signal optimization.
The current advancement in the field of data science has elevated the utilization of microsimulation modeling. Cloud-based data infrastructure and video analytics incorporation support the automated extraction of the vehicles, turning movement counts, and origin-destination matrices, along with enhancing the model’s accuracy and precision while reducing the labor and cost of data collection) [66]. Recent empirical studies have assessed the values of microsimulation in evaluating the adaptive signal control, different multi-model intersections, and assessing the network resilience under projected future growth scenarios [67,68]. However, challenges persist. The microsimulation process is hectic, resource-intensive, and time-consuming, especially for complex networks [69]. Furthermore, the accuracy of the simulation model is heavily influenced by the assumption embedded in the driver behavior model and the quality of data inputs [70,71]. Due to its inherent advantages in modeling driver behavior, and configuring various traffic control and roadway conditions, the microscopic traffic simulation tool VISSIM has been widely used in recent years [72–74].
In summary, a critical review of existing literature suggests the importance of context-sensitive and data-driven approaches to intersection efficiency and safety improvement. The reviewed literature highlighted that signal timing optimization and geometric interventions have yielded substantial improvement based on key performance measures such as travel time and delay reduction, improvement in level of service, and crash frequencies, however, the effectiveness of such strategies can be validated through field deployment and periodic-revaluation. It was also revealed that microscopic simulation together with advanced data integration strategies could provide cost-effective solutions and can help to deploy specific strategy based on evidence-based decision making. However, there remains gaps and dearth of literature focused on evaluating the combined effect of signal improvement, geometric interventions, and operational changes on the network performance, specifically in the context of developing countries like Saudi Arabia. The current study addresses such gaps by applying a rigorous microscopic traffic simulation model to evaluate the impact of multiple intersection improvement scenarios, leveraging modern data analytics and consultation of stakeholders.
- Study area and Data Collection
- Study area
Study area is located along Dhahran Jubail highway and bounded on the East by Nabiyyah, on the South by Dammam, on the North by Jubail, and West side by Anak and Qatif, in the Eastern province of Saudi Arabia. Two signalized intersections located in an urbanized corridor between Dammam and Qatif along the Dhahran-Jubail Highway in the Eastern province were selected for data collection and analysis. This highway serves as a vital corridor, connecting the industrial hubs of Dammam, and providing access to the populus neighboring residential districts of Nabiyyah, Saihat, and Anak. During the past two decades, the area has witnessed rapid population growth, intensifying the pressure on existing road networks. The two signalized intersections (shown in Figure 1) examined in this study are Nabiyyah Bridge and Uhud Street intersections. Street network in this neighborhood is classified as main Streets that aggregate in two directions and are separated by medians. Surrounding land use is of mixed type, comprising residential-commercial areas. The rationale behind selecting this intersection includes its strategic importance in managing access to the Dhahran-Jubail Highway and observed congestion and operational challenges. Field observations and stakeholders’ input identified that these locations were experiencing significant delays, long queues, and outdated signal timings, making them suitable candidates for traffic impact assessment and assessment of various intersection improvement strategies.
Figure 1. Location of study area intersections in Qatif neighborhoods
3.2. Data collection and processing
For assessing the operational performance of selected intersections, a robust and multi-faceted data collection strategy was adopted. Data collection was facilitated using both traditional and modern data collection techniques to ensure efficiency, accuracy, robustness and comprehensiveness. Prior to data collection, the study team visited the site for a preliminary survey and selected points for camera installations. Once the locations were finalized, the team installed the video cameras using a hydraulic mast.
Collecting traffic volumes is one of the key aspects of evaluating the operational characteristics of a given traffic system. Such evaluation may include issues related to delays, level of service appraisals, queue length estimations, and more. The traffic volume data is also helpful in validating traditional travel demand models, and in calibration of simulation frameworks for and in such cases; these data are valuable for evaluating traffic conditions and may serve as input in finding suitable countermeasures to alleviate traffic congestion. Traffic volume data including the turning movement counts (TMCs) were collected during the morning peak period (6:30-8:30 am) using high-definition video cameras at strategic vantage points, ensuring precise counting of all the vehicles. Video footages were later processed using advanced video analytics and cloud-based software platforms. Figure 2 presents the volume and TMCs data collected at the study’s sites. The ramp from Dammam (NB)and 15th Street (EB) from Saihat were identified as critical approaches at the Nabiyyah Bridge Intersection. The Uhud Street intersection Uhud Street from Qatif (EB) and Ramp from
Dammam (NB) were identified critical approaches as well.
Figure 2: Turning Movement Counts Nabiyyah Bridge Intersection (left); and Uhud Street Intersection (right)
Accurate modeling of traffic in the microsimulation environment is largely dependent on signal phasing and timing data, in addition to volume data. Signal timing data were collected through a combination of manual stopwatch observations and video recordings. The observed cycle length at the Nabiyyah Bridge intersection was found to be 240 seconds, with the 15th Street (EB) approach receiving the longest green time (70 seconds). On the other hand, the Uhud Street intersection had a cycle length of 260 seconds with the Uhud Street (EB) approach receiving the longest green split. Amber and all-red intervals were similarly 3 seconds each at both intersections. Figure 3 provides the specific details of signal timings and phasing data gathered at both intersections.
Figure 3: Signal timing/phasing Data Nabiyyah Bridge Intersection (left); and Uhud Street Intersection (right)
Another important parameter for the accurate modeling of signalized intersections is queue length, which was facilitated through manual measurements and observations at the study area intersections. Like volume data, this data was also collected during the morning peak. At the Nabiyyah Bridge intersection, the longest observed queue was 135 meters from the 15th Street (WB) approach from Saihat, while the ramp from Dammam (NB) also exhibited significant queues (an average of 115 meters). At the Uhud Street intersection, the longest queue (60 meters) was observed from Uhud Street (WB) approach from Qatif. Collected data on queue length along both intersections is shown in Table 1. It may be noted that Uhud Street exhibited shorter queue lengths, primarily due to lower approach volumes and longer signal cycles, rather than improved operational efficiency. Delay remained high at all approaches.
Table 1. Observed queue length at both intersections
| Nabiyyah Bridge Intersection | Uhud Street Intersection | ||
| Approach | Average Queue Length (m) | Approach | Average Queue Length (m) |
| 15th Street WB | 135 | Uhud from Qatif WB | 60 |
| Ramp NB | 115 | Uhud towards Qatif EB | 20 |
| 15th Street EB | 64 | Ramp from Jubail SB | 40 |
| Ramp SB | 72 | Ramp from Dammam NB | 55 |
- Methodology
4.1. Simulation model development
PTV VISSIM, a microscopic simulation tool was selected for analysis due to its robust capabilities of modeling detailed intersection dynamics and multimodal transport operations. It is widely recognized for its ability to replicate detailed, real-world traffic operations at the vehicle (microscopic) level. The microscopic framework of the software suit allows for the simulation of individual vehicle trajectories, their interactions with other vehicles, and responses to signal control strategies, making it ideal for capturing and visualizing the dynamics of congestion, delay and queuing at complex urban intersections.
The base map for VISSIM’ model was constructed employing high-resolution satellite imagery from Google Earth by extracting the geometric layout of each intersection, including lane configurations, approach alignments, median widths, and channelization features. Next, during the network coding, extracted geometries were digitized within VISSIM, confirming that all physical attributes-such as number of lanes, lane widths, and intersection radii-matched field/site conditions. It is worth mentioning that special attention was paid to incorporating the features of large medians since these can significantly influence turning movements and vehicle storage. Traffic flow data including the TMCs, cycle lengths, and queue lengths were fed to the model as inputs. Model calibration was accomplished by adjusting driver behavior parameters and traffic flow characteristics to ensure that simulated volumes and queues closely matched observed/field data. To validate the model, simulated outputs (traffic volumes and queue lengths) were also compared to field measurements, thereby minimizing the discrepancies through iterative adjustments. Model validation ensured that the developed micro-simulation VISSIM network model closely reflected the real-world/field conditions and allowed for precise scenario testing. Figure 4 presents the research methodology followed to achieve the research objectives, while Figure 5 shows the typical process and steps followed to develop the VISSIM traffic simulation model.
Figure 4. Methodology flowchart
Figure 5. Process of Micro-Simulation inside PTV VISSIM
4.2. Scenario Development and Testing
To mitigate the traffic congestion issue and to improve the operational efficiency and safety of the chosen intersection, the scenario development process was thoroughly grounded by analysis of field data, stakeholder consultations, and a review of literature regarding best practices in intersection management. Intersection improvement/intervention strategies that could feasibly be implemented within the institutional constraints of the Qatif area were considered. Simulation-based scenarios were designed to target the main sources of traffic inefficiency including, geometric constraints, outdated signal timing, and suboptimal lane usage. In this regard, three main scenarios proposed were; i) Signal Retiming; ii) Geometric modifications; and iii) Conversion to the intersection design to Diverging Diamond Interchange (DDI).
Scenario-I consisting of signal retiming was aimed to optimize the intersection performance by adjusting the signal timing plans, specifically the cycle length and green splits to better match the currently prevailing traffic demand. Field data indicated that that both intersections were currently operating with excessively long and pre-timed cycle times (with 240 seconds at Nabiyyah Bridge, 260 seconds at Uhud Street intersections). The revised signal timing plans were incorporated into the VISSIM simulation model and simulations were run for AM peak period employing the observed traffic volume, key performance measures, queue length, delay, and level of service (LOS) were extracted to compare them with base scenario (existing conditions).
Scenario-II involved geometric modifications/interventions aiming to improve the operational efficiency and safety of selected intersections. Analysis of TMCs indicated that certain approaches were experiencing heavy turning flows and blockages due to insufficient lane assignments. Hence, scenario II considers adding exclusive turning lanes, adjusting lane configuration, and improving channelization on heavily congested approaches. The feasibility of this proposed intervention was ensured by site visits and Google Earth imagery (land availability). For scenario-II the modified geometric layout was coded into the VISSIM model, maintaining the optimized signal timing plan from scenario-I.
Scenario-III involved reconfiguration of existing intersection geometries to a Diverging Diamond Interchange (DDI) layout to assess if can lead to improved operational efficiency at the given sites/intersections. DDI is specifically known for better handling of high-left turning traffic and to reduce the potential for conflicts. Feasibility of this major transformative was assessed in terms of land availability and widths of existing medians at the studied intersection sites. The DDI scenario was simulated under the same driver behavior and demand assumptions as previous scenarios (I and II). A comparative assessment of various outputs including delay, LOS, queue length, and predicted crash rates was then compared with the base scenario (existing conditions).
4.3. Performance Evaluation
A multi-dimensional and rigorous performance evaluation framework was central to this study, to ensure that the impact of each scenario on the intersection operational and safety performance was objectively and comprehensively assessed. Such a detailed evaluation process was designed to offer actionable insights for both policymakers and practitioners, focusing on key operational and safety indicators and the effectiveness of proposed intervention strategies at both urban intersections. Selection of chosen performance criteria was based on established engineering principles and best practices, as well as guidelines of the Highway Capacity Manual (HCM) and other relevant international literature across diverse jurisdictions. Specifically, the following key performance indicators (KPIs) were used to assess the intersection performance under each improvement scenario compared with base (existing) conditions. For instance, the average vehicle delay was chosen as one of the KPIs. It refers to the mean time (in seconds) spent by a vehicle waiting to cross the intersection and is a direct measure of user experience and operational efficiency. Queue length is another widely used KPIs that was used for monitoring congestion severity and the risk of spillback at both intersections. Likewise, LOS was also one of the key performance indicators used to assess the quality of operating conditions for each approach on both intersections. In addition, other KPIs measure “throughput and capacity utilization”, referring to the number of vehicles served per cycle, and the utilization of available capacity were also monitored, particularly for approaches with high demand. To assess the safety performance of selected intersections, metric crash reduction for proposed scenarios was estimated using empirical safety models and local traffic and road geometry data.
- Results and Discussion
- Model Validation
Model validation is a critical component for any simulation-based study to ensure their fidelity in accurately representing the real-world scenario. It is established that driving behavior and traffic characteristics can vary significantly between different drivers across various locations, hence, an initial model calibration is inevitable. An uncalibrated model may yield biased or unreliable outputs. The process involves adjusting the default simulation parameters to closely reflect the local traffic characteristics and driving conditions [7,75]. Following calibration, the next step is to assess the the validity of proposed model to ascertain its predictive capabilities and practical applicability. Validation of the microscopic traffic simulation model is typically conducted using an array of different performance measures such as travel time, speed, delay, queue lengths and turning movement counts at selected locations/sites in comparison with actual field observation/data collected from the same sites. A general rule of thumb based on relevant literature suggests that a deviation of less than 10% between actual and model-simulated measures is considered acceptable [76].
Figure 7. Validation based on queue lengths
5.2. Evaluation of Base baseline operational performance
The baseline analysis has been established by field observation and calibrated PTV VISSIM simulation which reveals that two intersections under study Nabiyyah Bridge and Uhud Street, are operating under severe congestion, especially during morning peak hours (6:30-8:30 AM). Table 2 (section 3) shows the details of queue length, which is an important indicator of performance and congestion at any intersection. The westbound approach from Saihat (15th street) at Nabiyyah Bridge shows the longest average queue up to 135 meters, while the northbound ramp from Damam often shows an average queue of up to 115 meters. At Uhud Street, the westbound approach from Qatif shows the longest average queue up to 60 meters, while the ramp from Damam often shows an average queue close to 55 meters (shown in Table 2 above). The queues at Uhud Street are shorter than those at Nabiyyah Bridge, the difference in queue length is because of the longer cycle length at Uhud Street (260 sec) compared to Nabiyyah Bridge (240 sec), and the slightly lower approach volume. These results align with the literature [4,36], which points out that longer cycle times might conceal operational inefficiencies by temporarily shortening queue length, even when overall delays are still significant.
Table 2 shows baseline Level of Service (LOS) rated as E or F for Nabiyyah Bridge and Uhud Street intersections, respectively indicating failure or near failure conditions. At the Nabiyyah Bridge intersection, the existing condition of the intersection shows that all the approaches have a level of service (LOS) F. Analysis shows that the average delay at the intersection is nearly 2 minutes as shown in Table2 below. Most delayed approach appears to be 15th Street coming from Nabiyyah (WB) with an average delay of more than 100 seconds. This is due to the long waiting times for this approach. 15th street from Saihat (EB) shows higher queue length due to the highest entering volume but with a longer green split than other approaches, all the queue dissipates at every signal cycle. Similarly, at the Uhud street intersection, the existing condition of the intersection reveals that all the approaches have level of service (LOS) F. Analysis shows that the average delay at the intersection is nearly 90 seconds as shown in Table 2 below. Most delayed approach appears to be Uhud Street coming from Qatif (EB) with an average delay of more than 100 seconds. This is due to the high entering volume at this approach. Lower average values of queue lengths is an indicator of longer green splits (Long Cycle time) and low number of entering volumes. These findings are in line with global research showing that intersections with outdated signal timings and high demand frequently experience delays, longer queues, and lower user satisfaction [38,77].
Table 2. Result of Average delay condition for Existing Condition at both intersections
| Intersection | Movements /Approaches | Average Vehicle Delay (sec) | LOS | Avg. Queue Length |
| Nabiyyah Bridge Intersection | 15th Street from Saihat EB | 85 | F | 124 |
| 15th Street from Nabiyyah WB | 106 | F | 54 | |
| Ramp from Jubail SB | 101 | F | 70 | |
| Ramp from Dammam NB | 88 | F | 87 | |
| Intersection total | 94 | F | 84 | |
| Uhud Street Intersection | Uhud from Qatif WB | 39 | D | 40 |
| Uhud towards Qatif EB | 104 | F | 16 | |
| Ramp from Jubail SB | 98 | F | 30 | |
| Ramp from Dammam NB | 116 | F | 43 | |
| Intersection total | 89 | F | 32 |
- Evaluation of simulated scenarios
The first improvement scenario (Scenario-I) concentrated on optimizing the signal timing plan, specifically by shortening cycle length and reallocating green splits to better match observed demand. In this scenario, signal timings are fixed and do not respond to changes in traffic. Cycle time plan and green splits are improved with respect to volumes. Cycle times for both intersections was reduced to 200 seconds from the previously used 240 seconds (Nabiyyah Bridge) and 260 seconds (Uhud Street) intersections. Table 3 and Table 4 show a comparison of average vehicle delays and queue length across scenarios for a clear demonstration of these changes at the Nabiyyah Bridgeintersection, and Uhud Street intersection, respectively. Prior studies have also demonstrated the positive impact of optimizing signal timing plans from the perspective of reduction in average delay and queue lengths at signalized intersections [78,79].
The second improvement scenario (Scenario-II) introduced Geometric improvements, such as more turn lanes and optimized approach channelization. The results, shown in Table 3 (Nabiyyah Bridge) and Table 4 (Uhud Street), show more reductions in both delays and queue lengths. At Nabiyyah Bridge, the average delay fell by an additional 10% compared to Scenario 1, and the maximum queue length decreased to about 75 meters. Uhud Street saw similar improvements, with the LOS improving to C on most approaches. Recent studies have also found that geometric adjustments, particularly the addition of an exclusive turn lane, can reduce queues and delays by 20-30% [48,49]. The fundamental mechanism is that by separating conflicting emotions with dedicated lanes, more vehicles can pass the intersection during a single cycle and reduce the chances of blockages. A dense urban environment can limit the feasibility of adding new lines, such as limited right of way or the presence of new adjacent development. So, it is important to identify that geometric changes are not applicable everywhere. In this study, ensuring that the modifications were practical and doable, the study evaluated the land availability using both Google Earth imagery and site visits.
For the Nabiyyah Bridge intersection, LOS improved from E/F to D/C (Table 3), and there was a 35% decrease in average delay per vehicle approximately compared to the baseline. At Uhud Street, LOS improved to D on most approaches (Table 4), and similar reductions in delay were observed. Figures 8 and 9 show the delay and queue length reduction following improvement scenarios. It was observed that at this site the maximum queue length dropped from 135 meters to under 90 meters. Similarly, average delay (Figure 10) and queue length (Figure 11) were also observed at the Uhud Street intersection. At the Uhud Street intersection, the maximum queue was reduced from 60 meters to 40 meters (Figure 11). A prior study [36] reported that optimized and adaptive signals can minimize delays by 20-25% in congested urban corridors. Reducing waiting time and delays lowers drivers’ frustration and decreasing the likelihood of violating signals can also increase the safety of road users.
The third scenario (Scenario-III) evaluated the intersections’ conversion to a diverging diamond interchange (DDI) layout. The simulation results are presented in Tables 3 (for Nabiyyah Bridge Intersection) and Table 4 (for Uhud Street Intersection). The results show that while the DDI scenario reduced average delay and queue length to some extent, the improvements were not as significant as those made possible by signal optimization and geometric adjustments. Despite it offers considerable safety and operational benefits in high-volumes and heavy left-turn movements, previous studies [80–82] have also reported that the effectiveness of Diverging Diamond Interchanges (DDIs) is constrained by limited land availability and the complexity of traffic patterns. In the context of Qatif, the practical benefit of DDI were diminished by large medians and constrained space for reconfiguration, particularly when compared to gradual improvement provided by signal and geometric optimization. The modeling and micro-simulation analysis conducted on scenarios at both Intersection showed that the scenario II of introducing DDI could significantly reduce the delays. Hence, Scenario 2 should be considered as the preferred scenario.
Table 3. Scenario Comparison for Delay and LOS: Nabiyyah Bridge Intersection
| Sr. No | Scenario Description | Scenario Number | Controller Type | Cycle Time (sec) | Average Vehicle Delay (sec) | LOS | Avg. Queue Length (m) |
| 1 | Existing Situation | Existing | Fixed | 240 | 94 | F | 84 |
| 2 | Optimizing Signalized Intersection | SCN 1 | Fixed | (190-200) | 88 | F | 83 |
| 3 | Diverging Diamond Intersection | SCN 2 | Fixed | (112-130) | 27 | C | 22 |
| 4 | Addling lanes and dedicated right turns | SCN 3 | Fixed | (180-200) | 79 | E | 51 |
Figure 8: Scenario Comparison – Vehicle Delay at Nabiyyah Bridge Intersection
Figure 9: Scenario Comparison – Queue Length at Nabiyyah Bridge Intersection
Table 4. Scenario Comparison for Delay and LOS: Uhud Street Intersection
| Sr. No | Scenario Description | Scenario Number | Controller Type | Cycle Time (sec) | Average Vehicle Delay (sec) | LOS | Avg. Queue Length (m) |
| 1 | Existing Situation | Existing | Fixed | 260 | 89 | F | 32 |
| 2 | Optimizing Signalized Intersection | SCN 1 | Fixed | (180-200) | 73 | E | 31 |
| 3 | Diverging Diamond Intersection | SCN 2 | Fixed | (130-150) | 30 | C | 11 |
| 4 | Addling lanes and dedicated right turns | SCN 3 | Fixed | (180-200) | 55 | E | 22 |
Figure 10: Scenario Comparison – Vehicle Delay at Uhud Street Intersection
Figure 11: Scenario Comparison – Queue Length at Uhud Street Intersection
5.4. Safety and Crash Analysis
GIS-based crash hotspot analysis for study sites in the preceding year (shown in Figure 12) revealed 16 all severity accidents (14 Serious, 2 Fatal) at the Nabiyyah Bridge intersection and 8 all severity accidents (7 Serious, 1 Fatal) at the Uhud Street Intersection.
Figure 12: GIS Crash Data for Nabiyyah and Uhud Intersection in Qatif Region
The crash frequency for each segment and intersection is predicted using an iterative 18-step method mentioned in Chapter 12 of the HSM called “Urban and Suburban Arterials.” In summary, this method consists of initially calculating multiple- and single-vehicle fatal-and-injury and property-damage-only crashes; these values are added to obtain base predicted vehicle crashes. The next step is to adjust the base predicted vehicle crashes with crash modification factors based on the roadway characteristics. If a calibration factor was available, or historical data was available to apply the empirical Bayes method, these two steps would be included. A sample calculation using the base equation for predicted average crash frequency is shown below; Equation 1 illustrates the base equation [83].
where,
Nbi = Predicted average crash frequency for an intersection
Nspf = Predicted average crash frequency for base conditions
CMF1i…CMF6i= Crash modification factors for:
left-turn lanes (CMF1i=0.66); left-turn phasing (CMF2i=0.96); right-turn lanes (CMF3i=0.88); right turn on red (CMF4i=1.00); lighting (CMF5i=0.91); red-light camera (CMF6i=1.00)
C = Calibration factor (C=1.00)
Based on the data collected from the road safety survey and from the recorded suggestions for improvements at both intersections to alleviate the safety concerns. Crash Modification Factors were selected/calculated from the CMF tables provided in Chapter 12 of the Highway Safety Manual (HSM). Table 5 shows the safety analysis of selected intersections based on the application of application of the HSM Predictive Method to estimate the expected benefits of improvement in terms of reduction in crashes over these signalized intersections. Results show that if CMFs from HSM are applied in study locations, around 6 crashes can be avoided at the Nabiyyah intersection, and around 3 crashes can be avoided at the Uhud Street intersections. The safety analysis emphasizes the relationship between operational improvements and safety benefits. Rear-end and angle collision at intersections are high, especially during peak hours when queues are longest as demonstrated by previous studies [84,85]. The proven relationship between congestion mitigation and improved safety was supported by simulated scenarios that decreased delays and queues, which were linked to proportionate decreases in predicted crash rates.
Table 5: Reduction in Crashes per Year in Numbers
| Reduction in Crashes per Year in percentage – Application of HSM | |||||
| Sr. No | Name of the Intersection | N (Calculated from CMFs with Nbase = 1) Crashes/Year | Reduction in crashes per Year (%) | Number of Actual Crashes | Reduction in Number of Crashes if HSM applied |
| 1 | Nabiyyah Bridge Intersection | 0.61 | 39% | 16 | 6 |
| 2 | Uhud Street/Bridge Intersection | 0.61 | 39% | 8 | 3 |
- Conclusions and recommendations
Traffic congestion is a major socio-economic concern faced by urban metropolitans worldwide. Major cities in KSA have witnessed growing congestion and safety concerns in recent years. The current study aimed to comprehensively assess the operational performance of two signalized intersections, Nabiyyah Bridge and Uhud Street-along the Dhahran-Jubail Highway in Qatif, region Saudi Arabia. The analysis is performed by combining advanced data analytics and microscopic traffic simulation modeling methods using VISSIM. Analysis of the baseline/existing scenario during the peak period revealed that both intersections faced extreme delay and queue length due to several factors including outdated signal plan, and geometric deficiencies in the design. Following rigor calibration and validation of the PTV VISSIM microsimulation model, the study proposed three improvement strategies/scenarios; i) signal retiming, ii) geometric modifications, and iii) conversion of existing design to DDI. Simulation results revealed that signal timing optimization alone could reduce the average vehicle delay by up to 35%, leading to improvement of LOS from E/F to C/D, with further improvement in the measure of effectiveness (average delay, and queue length) through targeted geometric interventions such as deployment of additional turn lanes. The DDI scenario, although innovative, yielded only offered only additional benefits in terms of operational efficiency improvement, primarily due to due to local traffic patterns and spatial constraints. Overall, the simulation results demonstrated that signal optimization could be deployed as a rapid and cost-effective strategy in mitigating traffic congestion. Similarly, it was also demonstrated geometric modifications including additional turn lanes and improved channelization can improve the capacity and offer safety benefits. However, such modifications should be planned by careful assessment of feasibility (site-specific constraints) and potential impact on surrounding land uses. Safety analysis of the selected intersections further revealed that operational improvements were accompanied by a quantifiable reduction in predicted crash rates following HSM predictive methodologies, emphasizing the association between congestion mitigation and enhanced intersection safety.
Despite several contributions and robust methodology offering practical insights, the study has several limitations that should be acknowledged. First, the analysis was based on data collected during a specified period, which may not fully capture the seasonal or long-term variations in travel and traffic demand. Second, the proposed simulation model primarily focused on motorized vehicles with limited consideration of other modes, pedestrians, cyclists, etc, which are likely to influence intersection dynamics and safety outcomes. Third, though the simulation model was meticulously calibrated and validated, its accuracy remains inherently dependent on input data quality and the assumptions regarding driver behaviors. Finally, the evaluation of crash reductions was based on modeled predictions using HSM methodologies, rather than long-term actual crash data.
For future research, it is suggested to utilize an extended dataset for prolonged collection periods and multiple seasons, which will enable a more inclusive understanding of demand variability and intersection performance under different scenarios. Incorporating multi-modal traffic including transit movement, bicycles, and pedestrians will provide a more holistic assessment of intersection operational and safety benefits under various geometric intervention scenarios. In addition, emerging data sources such as connected vehicle data and integration of real-time adaptive signal control could enhance the accuracy and responsiveness of simulation models. Similarly, longitudinal studies tracking the long-term actual crash rates and operational performance at the deployment of feasible intersection improvement strategies will be useful to validate the predicted operational benefits and safety targets. Finally, it will be interesting to examine the scalability of the proposed interventions to other intersections and corridors in the region and similar jurisdictions to assess the operational efficiencies and safety objectives.
Author Contributions:
Mohammed Saleh Alfawzan: Conceptualization; methodology; writing—review and editing; resources; Arshad Jamal: Formal analysis; Data curation, writing—original draft preparation: Md Kamrul Islam: Conceptualization; resources, funding, writing—review and editing; Khurram Shahid Minhas: methodology, software, data curation, writing—original draft preparation; Ahmad Aftab: Conceptualization; methodology; formal analysis, writing—review and editing.
Funding Acknowledgment:
This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Grant No. KFU251826].
Acknowledgement:
The authors would like to acknowledge and appreciate the support of the Deanship of Scientific Research, Vice Presidency of Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia, for conducting this research.
Data Availability:
Data used in this research can be obtained from the corresponding author upon reasonable request.
Conflicts of Interest:
“The authors declare no conflicts of interest.”
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