Traffic congestion is a significant problem in urban areas globally, leading to increased travel time, higher fuel consumption, and elevated levels of pollution [1]. The presence of large vehicles and increased transport activity on the urban roads contribute significantly to traffic congestion [2]. Pakistan has the highest proportion of compact automobiles in the world, which contributes to traffic congestion [3]. Traffic safety difficulties in Pakistan may be attributed to increased traffic, a lack of planning, reckless driving behavior, no traffic separation, speeding, ignorance of traffic regulations, poor vehicle condition, terrible road conditions, and a large number of motorcycles on the road [4, 5]. Over 96% of freight transport in Pakistan has shifted from rail to road, exacerbating the issue of road traffic congestion [6]. This shift has resulted in escalating logistics costs, putting additional pressure on the economy [7].

Numerous studies have found several factors that involve traffic congestion. According to Smith et al. (2017) [8], inadequate road infrastructure with the occurrence of conflicting traffic flows and deficient signalization can increase congestion [9]. Ineffective road operations and high traffic demand also trigger traffic congestion [10]. Population increase, land-use alignments, and deviations in travel demand have also been detected as critical concerns affecting traffic congestion [11]. Previous research studied the traffic issues along the corridor which contain less than positive traffic movement, and an adverse level of delays along with the environmental conditions inferred by noise and emissions produced from the vehicles [12]. Another case study revealed numerous problems encountered in the analysis of congestion in application. The significance of using demand flow data under congested conditions and the result of queue spillback in reducing saturation flow rates were assessed [13].

The study focuses on Peshawar, one of the country's most polluted and traffic-congested cities [14]. Peshawar, the capital of Khyber Pakhtunkhwa (KPK), is experiencing rapid population growth as people migrate from various regions for education and employment opportunities. This population influx, coupled with inadequate infrastructure and poor traffic management, has led to severe traffic congestion in the city [15]. The population of Peshawar in 2024 is estimated to be 2,481,000, which shows a 2.86% increase in population from 2023 [16]. Traffic management in Peshawar is a major concern in the province [17]. Certain locations in Peshawar experience particularly high levels of congestion, leading to significant delays and negatively impacting air quality [18].

In the past, numerous research studies have been carried out around the world to identify the various reasons for traffic congestion [19]. Basri Said & Syafey, (2021) [20] Studies the parking issues, Khanorkar et al., (2014) [21] identifies the impacts of Lane width of road, Fadare & Ayantoyinbo, (2010) [22] studies the effects of traffic congestion on freight movement, [23] findings the impact of BRT service on traffic congestion etc. In the lack of sufficient data, neither road operators nor travelers can measure how acutely the road system is functioned [10]. Assessing traffic congestion levels using social media data can provide valuable insights into these levels, and this method shows potential for real-time monitoring and management of congestion [24].

GIS technologies are increasing in popularity for transportation planning because they allow for the visualization, analysis, and optimization of geographical data, allowing policymakers to build integrated public transportation networks [25]. In London, for example, GIS has been critical to the development of the Oyster Card system, which analyses passenger movement patterns to optimize routes and timetables, providing fair accessibility [26]. Similarly, Singapore's transport network, which includes buses, trains, and even pedestrian walkways, uses GIS to offer real-time traffic updates, reducing interruptions and delays [27]. These worldwide results demonstrate GIS' revolutionary potential in tackling urban transportation difficulties, particularly in densely populated places where multimodal integration is critical to reduce reliance on private automobiles [28].

Despite numerous traffic flow and signal timing studies in Pakistani cities, GIS based Important Zones Analysis has not been used to rank congestion on Peshawar’s arterial routes. This study aims to identify and analyze traffic congestion along the primary route in Peshawar using Important Zones Analysis in ArcGIS. After identifying congestion hotspots on this route, detailed traffic studies, speed studies, capacity analysis, Level of Service (LOS) calculations, and questionnaire surveys were conducted to assess the primary factors contributing to congestion. This research also proposes effective remedial measures to reduce traffic congestion. The findings offer practical traffic management strategies for Peshawar and a replicable framework for similar urban settings.

The method is considered one of the novel and attractive approaches in order to evaluate the traffic-related issues in the developing countries' economies, such as transportation capacity and infrastructure effectiveness [39], transit system operation performance [40], and to select transportation modes and routes [41].

Unlike previous studies that examined traffic congestion in Pakistani cities using conventional statistical or simulation-based methods, this study introduces a novel integration of the Shannon Entropy and TOPSIS techniques within a GIS-based Important Zones Analysis framework to evaluate and rank urban congestion hotspots. To the best of the authors’ knowledge, this paper is the first study in Pakistan to combine multi-criteria decision-making (MCDM) with spatial analysis for congestion assessment. The proposed integrated approach not only quantifies congestion severity but also provides a replicable model that can guide data-driven urban mobility planning in other developing cities.

Stage – 4: MCA Overlay Weights Applied

Multi Criteria Analysis allows researchers to systematically compare different criteria by assigning weights to each one based on its importance. In this process, factors like proximity to important zones e.g., educational facilities, markets, roads, and built-up areas etc. are ranked and weighed by their impact on traffic congestion. Using these weights, MCA overlays them in a GIS, creating a map that highlights high-priority areas for congestion management. These weights are shown in Table 2.

Stage – 5: MCA Overlay Weights Resultant Raster

Figure 3 represents the Final Priority Raster, which was generated by using the Weighted Overlay (Spatial Analyst) tool in the ArcMap environment. The tool Overlays several raster’s using a common measurement scale and weights each according to its importance.

The study was undertaken in different steps to evaluate traffic congestion at specified locations. Accordingly, the traffic data from the four locations—Amin Hotel, PC Hotel, Army Stadium, and Jalil Kabab House indicate that motorcycles and cars are the dominant vehicle types at all sites. The morning hours, particularly between 9:00 AM and 12:00 AM, consistently experience the highest traffic volumes. All identified congestion hotspots exceed the road's capacity, with V/C values indicating severe congestion. The V/C ratio is 1.65 at the Amin Hotel, 1.32 at the PC Hotel, 1.35 at the Army Stadium, and 1.50 at Jalil Kabab House. Among these, Amin Hotel exhibits the highest V/C ratio of 1.65 and the highest peak hour P.C.U., indicating significant congestion. In contrast, Army Stadium shows relatively lower traffic volume and better capacity utilization. The Shannon entropy method evaluated the weights of the specified criteria. According to the results, total traffic and the V/C ratio emerge as the most influential criteria, with weights of 0.40 and 0.22 respectively, while the maximum capacity of the road and SF both hold moderate importance (0.14), and P.C.U. and peak hour contributes the least (0.10). The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method was utilized to rank the investigated points. Amin Hotel ranks first with the highest closeness coefficient (C_i = 0.85), confirming it as the most congested location. It is followed by PC Hotel, Jalil Kabab House, and Army Stadium, in descending order of congestion severity. To assess the stability of these rankings, a sensitivity analysis was conducted. These results reveal that the criterion "maximum capacity of road" has the greatest influence on the ranking outcome, with any changes in its weight significantly affecting the results. Conversely, altering the weights of the remaining criteria—SF, V/C, total traffic, and P.C.U. peak hour—does not impact the ranking, indicating the robustness of the analysis and highlighting the critical role of road capacity in urban traffic congestion. Previous study applied the Shannon Entropy and TOPSIS approaches in Karachi to rank congestion locations, highlighting that total traffic volume and V/C ratio are the most significant factors in assessing urban traffic effectiveness [42]. Rehman and Qureshi (2020) examined congestion formations in Lahore and evaluated that intersections near commercial zones undergo excessive delays and unstable flow appearances, with road capacity being the maximum sensitive element inducing congestion severity [43].

A multifaceted strategy is needed to solve Peshawar's traffic congestion issue at its core. First and foremost, it is imperative that current traffic laws be enforced more strictly and that new rules that are suited to the demands of modern cities be created. Examples of such regulations include limiting the use of rickshaws on specific city routes and allocating distinct lanes for various vehicle types. Additionally, traffic police and media organizations should actively contribute to cultivating traffic awareness among the public. The physical condition of roadways also demands attention; regular maintenance, including patching and properly constructed manholes, is essential to ensure uninterrupted traffic flow. Adequate parking facilities must be developed at designated locations to prevent random roadside parking, thus allowing roads to be used to their full capacity. Furthermore, electric poles should be installed at a safe distance from roadways to prevent obstruction. Proper road surface markings and the installation of traffic control devices and signals at key points are crucial for regulating traffic flow. In areas where traffic volume exceeds the road's capacity, road stoppers should be installed and traffic diverted to nearby intersections. Additionally, police check posts should be relocated to more appropriate sites to minimize disruption. Finally, where feasible, the width of road lanes should be expanded to at least 12 feet to better accommodate vehicular movement. Based on expected outcomes of the suggested measures it is predicted that the V/C ratio at the designated congested locations could reduce by approximately 15–25%, possibly getting the Amin Hotel Corridor’s V/C ratio down from 1.65 to around 1.25–1.40 under optimized conditions. The applied method can be utilized to other Pakistani cities, as similar traffic situations and infrastructural problems exist. By optimizing routes and timetables, the process can effectively decrease fuel consumption and emissions, aligning with Pakistan’s international environmental commitments such as the Paris Agreement [31].

Future work will employ simulation-based tools such as SUMO or VISSIM to model these interventions more accurately and provide detailed projections of V/C improvements and delay reductions. Moreover, the study suggests to apply advanced naturalistic data collection tools, such as GPS-based tracking, AI-driven monitoring systems, and sensor networks to expand deeper understandings into driver behavior and effectively solve recurring congestion issues.

This study demonstrated that integrating entropy-TOPSIS with GIS hotspot mapping can objectively rank congestion severity in data-scarce contexts. The study identified and analyzed traffic congestion in Peshawar city, through field surveys, speed studies, volume-to-capacity (V/C) ratio analysis, and Passenger Car Unit (PCU) calculations. To identify the significant route in Peshawar, the Important Zones Analysis using ArcGIS, including the Multi-Criteria Analysis Techniques were utilized. The areas of traffic congestion were ranked by applying an integrated approach based on Shannon entropy method and TOPSIS method. It was found that the most important criteria are total traffic and volume to the capacity ratio. The ranking indicates that the area with the biggest traffic congestion of Peshawar is ‘Amin hotel’. The results revealed that all surveyed locations exceed their capacity limits, causing traffic congestion during the peak hours. High ranked factors which are contributing to traffic congestion are Bus Rapid Transit (BRT) service, inadequate parking facilities, poor lane delineation, and the inefficient placement of police checkpoints. Additionally, irregular road geometry, obstructions like electric poles, and unregulated traffic movements further worsen the congestion issues. To address these issues, the study recommends enforcing stricter traffic regulations, improving parking management, enhancing road surface markings, and optimizing traffic signal timings. Infrastructure improvements, such as increasing lane widths where feasible and relocating police checkpoints to less disruptive areas. Implementing these measures helps in improving the traffic flow, reducing delays and enhancing the mobility in Peshawar city. The Study considered the primary traffic data, but the study didn’t focus on the temporal traffic variations, and driver behavior patterns.

The study, however, has few limitations, such as the controlled sample size and less number of corridors, which may not entirely characterize the city’s complete traffic conditions. To further extend the study’s scope, further corridors from several parts of the city should be studied to assess diverse traffic patterns, thereby improving the applicability and consequence of the results. Further recommendations for future work include utilizing advanced traffic simulation and analysis tools such as SUMO or VISSIM to perform a comparative evaluation of the collected data. These tools can enhance the understanding of traffic dynamics, optimize flow management, and provide more accurate predictions for congestion mitigation. Additionally, by expanding the research to other urban corridors and integrating real-time traffic data, the outcomes can be made more comprehensive and applicable to city-wide traffic management planning, considering more efficient mobility strategies and decreased socio-economic costs.