Traffic Management Strategies Research Articles

A data-driven optimization-based approach for freeway traffic state estimation based on heterogeneous sensor data fusion

Accurate estimation of freeway traffic states is crucial for designing effective traffic management and operational strategies. The integration of various sensor data, such as data from the Electronic Toll Collection (ETC) system and traffic detectors, can significantly enhance the granularity and coverage of traffic state estimation. This study introduces a data-driven optimization-based approach for estimating freeway traffic states, leveraging the fusion of ETC data with detector data. This methodology capitalizes on the broad coverage provided by ETC data and the fine granularity offered by detector data. The probabilistic interdependence between the traffic state of a segment and its upstream and downstream counterparts is captured from real-world traffic state data. Two optimization models, based on the maximum likelihood and maximin likelihood principles, are developed to accurately depict the distribution patterns of freeway traffic states. To address the computational challenges of large-scale scenarios, the study proposes both a decomposition algorithm and a heuristic algorithm. A case study utilizing real-world data from the G92 freeway in Zhejiang, China, is conducted. The findings indicate that the two optimization models exhibit commendable accuracy, with mean absolute percentage errors of 0.9% and 2.3% during peak hours, and 0.9% and 1.4% during off-peak hours, respectively.

Deep causal inference for understanding the impact of meteorological variations on traffic

Understanding the causal impact of meteorological variations on traffic conditions (e.g., traffic flow and speed) is crucial for effective traffic prediction and management, as well as the mitigation of adverse weather effects on traffic. However, many existing studies focused on establishing associations between meteorological situations and traffic, rather than delving into causal relationships, especially with deep learning techniques. Consequently, the ability to identify specific meteorological conditions that significantly contribute to traffic congestion or delays is still limited. To address this issue, this study proposes the Meteorological-Traffic Causal Inference Variational Auto-Encoder Model (MT-CIVAE) to estimate the causal impact of fine-grained meteorological variations (e.g., rain and temperature) on traffic. Specifically, MT-CIVAE is based on the Variational Auto-Encoder and consists of an encoder to recover the distribution of latent confounders and a decoder to estimate the conditional probabilities of treatments. Transformer encoder layers are incorporated to analyze the spatial and temporal correlations of historical traffic data to further enhance the inference capability. To evaluate the effectiveness of the proposed approach for causal inference, real-world traffic flow and speed datasets collected from California, along with corresponding fine-grained meteorological datasets, are employed. The counterfactual analysis is conducted using artificially generated meteorological conditions as treatments, which allows for the simulation of hypothetical meteorological scenarios and the evaluation of their potential impact on traffic conditions. This study develops deep learning methods for assessing the causal impact of meteorological variations on traffic dynamics, offering explanations and insights that can assist transportation institutions in guiding post-meteorology traffic management strategies.

Optimizing passenger vehicle travel time with model predictive control in multi-region traffic networks

Abstract This study investigates the impact of truck traffic on passenger vehicles in an urban network. Utilizing the Macroscopic Fundamental Diagram (MFD), a methodology to calculate the travel time spent by passenger vehicles has been developed. To address this issue, an optimal control problem was formulated and solved using a Model Predictive Control (MPC) approach. The MPC framework has been applied in a centralized manner, to manage accumulation for various modes. To explore different traffic management strategies, the centralized MPC technique was implemented in two distinct configurations: region-based and vehicle-based approaches. It has been tested for various vehicle mixes and multiple control scenarios to assess the effectiveness in reducing passenger travel time spent and vehicle accumulation. The results demonstrate that the vehicle-based MPC approach tends to minimize the number of vehicles more effectively compared to the region-based approach. However, in terms of reducing passenger travel time, the region-based approach outperforms the vehicle-based strategy. This is attributed to enhanced coordination among traffic flow controllers, highlighting the importance of strategic controller interactions in urban traffic management systems. This research enhances both the theoretical framework for optimizing traffic flow and provides valuable practical insights for city planners and engineers aiming to deploy advanced traffic management strategies. Future studies could explore the scalability of these control systems and their capability to integrate real-time traffic data.

Congestions and spectral transitions in time-lagged correlations of motorway traffic

The understanding of congestions contributes to the development of effective traffic management strategies. The propagation of congestions from a motorway section to the neighbouring ones results in correlations. Here, we study symmetrized time-lagged correlation matrices and show how their spectral properties reveal congestion durations. We first carry out an empirical analysis of velocities for two local motorway networks, then set up a numerical simulation for indicator time series of traffic phases, and further propose a simplified, analytical model capturing various scenarios of congestion durations. Our empirical analysis reveals a transition behavior for the dominant eigenvalue as function of time lags, reflecting changes in traffic dynamics. Furthermore, both the numerical simulations and the analytical model disclose a nonlinear relation between the spectral transition and the congestion duration.

Traffic Management on The Cikereteg-Pancawati Road in The Caringin District Tourism Area Bogor District

Traffic as part of transportation planning is a vital indicator of tourism development. Bogor Regency is one of the favorite tourist destinations in Jabodetabek. One of the tourist attractions in Bogor Regency is natural tourism around the Puncak Bogor area. This study aims to analyze land use around Jalan Cikereteg-Pancawati, analyze the performance and level of traffic service on the road, and conduct a SWOT analysis for traffic management strategies. The study used GIS to analyze changes in land use and analyzed the level of road service on Jalan Cikereteg-Pancawati. The traffic performance analysis was conducted by analyzing the volume values and degrees of saturation on Sundays and Thursdays. The land use analysis showed that the area is dominated by forests, plantations, and rice fields, indicating great potential as a tourist area. The traffic performance analysis revealed that Sundays have higher volume values and degrees of saturation than Thursdays, with the highest degree of saturation occurring on Sunday segment 3. The study concludes that the traffic management strategy for Jalan Cikereteg-Pancawati should focus on managing pavement repairs and road widening, providing bus and car parking, and utilizing available human resources and supporting regulations for the development of Bogor Regency as a natural tourism area. The study's findings suggest that effective traffic management is crucial for the sustainable development of natural tourism in Bogor Regency. The proposed traffic management strategies can help alleviate traffic congestion and improve the overall tourist experience, ultimately contributing to the region's economic growth and social well-being.

A crash occurrence risk prediction model based on variational autoencoder and generative adversarial network

Traffic crash risk prediction is pivotal for proactive traffic safety management but faces challenges due to the extreme imbalance between crash and non-crash data. This paper proposes integrating variational autoencoder into a generative adversarial network (VAE-GAN) for crash data augmentation without information loss. VAE-GAN generates higher-quality data due to its superior deep generative capabilities. For the crash occurrence risk prediction task, we utilize a convolutional neural network (CNN) trained on the balanced datasets generated by VAE-GAN. Two kinds of category determinations are tested for better prediction results by using prediction probability maximum and selecting threshold. The prediction model integrating variational autoencoder into a deep convolutional generative adversarial network (VAE-DCGAN) exhibits the best performance. Furthermore, we leverage Shapley additive explanations (SHAP) to interpret the key features and patterns impacting prediction results. This analysis gains insights into the underlying mechanisms of crash occurrences, and helps improve proactive traffic management and control strategies.

Networkwide Traffic State Forecasting Using Exogenous Information: A Multi-Dimensional Graph Attention-Based Approach

Traffic-state forecasting is crucial for traffic management and control strategies, as well as user- and system-level decision-making in the transportation network. While traffic forecasting has been approached with a variety of techniques over the last couple of decades, most approaches simply rely on endogenous traffic variables for state prediction, despite the evidence that exogenous factors can significantly affect traffic conditions. This paper proposes a multidimensional spatiotemporal graph attention-based traffic-prediction approach (M-STGAT), which predicts traffic based on past observations of speed, along with lane-closure events, temperature, and visibility across a large transportation network. The approach is based on a graph attention network architecture, which learns based on the structure of the transportation network on which these variables are observed. Numerical experiments are performed using traffic-speed and lane-closure data from the Caltrans Performance Measurement System (PeMS) and corresponding weather data from the National Oceanic and Atmospheric Administration (NOOA) Automated Surface Observing Systems (ASOS). The numerical experiments implement three alternative models which do not allow for multidimensional input, along with two alternative multidimensional models, based on the literature. The M-STGAT outperforms the five alternative models in validation and testing with the primary data set, as well as for one transfer data set across all three prediction horizons for all error measures. However, the model’s transferability varies for the remaining two transfer data sets, which may require further investigation. The results demonstrate that M-STGAT has the most consistently low error values across all transfer data sets and prediction horizons.

Impact of Aggressive HGV Platoons and Human-Driven Heavy Goods Vehicles on Signalized Intersections Performance

This paper presents a study on the performance and environmental impacts of aggressive heavy goods vehicle (HGV) platoons in comparison to human-driven HGVs at a random signalized intersection under varying traffic volumes between 500 and 1500 HGVs. A total of 12 scenarios have been developed, 6 for each of the vehicular behaviors, to quantify the emissions (CO, NOX, VOC) and fuel consumption, travel time, and delays. The analysis is implemented using the PTV VISSIM microscopic traffic flow model. Realization that the majority of the differences in the performance of the two vehicular controls seems to be different as the traffic volume increases was the realization. In most cases, aggressive HGV platoons were found to have lower emissions, in comparison to fuel consumption, while the flow and delay of aggressive HGV platoons were comparatively better against the case with human-driven HGVs. The results have thus provided new avenues for the incorporation of aggressive HGV platoons into urban traffic systems, more so in scenarios that involve a high level of traffic at intersections, as a potentially effective tool for augmenting the efficiency of an intersection and cutting down its environmental impacts. The present study strongly recommends the advancement of traffic management strategies that can capture the dynamics between the two heterogenous traffic flows induced by autonomous and semi-autonomous vehicle technologies.

Modeling dynamic spatio-temporal correlations and transitions with time window partitioning for traffic flow prediction

Predicting traffic flow represents a critical undertaking within the domain of Intelligent Transportation Systems (ITS), given its pivotal role in optimizing traffic management strategies. Nevertheless, this endeavor is fraught with challenges, stemming from the inherent complexity and dynamic nature of traffic systems. Existing methods still suffer from the following three main limitations: (i) modeling the spatio-temporal correlations in a static way, restricting the ability to learn dynamic correlations; (ii) only capturing short-term local temporal features while neglecting long-term temporal patterns; (iii) cannot learn the latent relationships between traffic nodes at different time steps. Therefore, we propose a novel method named InOutformer based on In-Time-Window self-attention and Out-Time-Window self-attention to predict traffic flow. This method can effectively capture both short-term local temporal features and model long-term global temporal patterns, while also capturing dynamic spatial correlations between nodes. Specifically, we first propose a novel time window partitioning mechanism to replace the conventional sliding time window approach, in order to capture dynamic temporal features. Second, two specially designed temporal self-attention modules are used to learn short-term local temporal features and model long-term global temporal patterns, respectively. Then, a 2D-attention method is devised to capture the spatial correlations of nodes at different time steps. Moreover, by exploiting transition flow data, the detrimental effects due to spatial information propagation delays are effectively mitigated. Extensive experiments on four real-world traffic datasets demonstrate that the proposed model InOutformer outperforms the baselines, including several state-of-the-art methods.

Bi-scale car-following model calibration based on corridor-level trajectory

The precise estimation of macroscopic traffic parameters, such as travel time and fuel consumption, is essential for the optimization of traffic management systems. Despite its importance, the comprehensive acquisition of vehicle trajectory data for the calculation of these macroscopic measures presents a challenge. To bridge this gap, this study aims to calibrate car-following models capable of predicting both microscopic measures and macroscopic measures. We conduct a numerical analysis to trace the cumulative process of model prediction errors across various measurements, and our findings indicate that macroscopic measures encapsulate the accumulation of model errors. By incorporating macroscopic measures into vehicle model calibration, we can mitigate the impact of noise on microscopic data measurements. We compare three car-following model calibration methods: MiC (using microscopic measurements), MaC (using macroscopic measurements), and BiC (using both microscopic and macroscopic measurements)—utilizing real-world trajectory data. The BiC method emerges as the most successful in reconstructing vehicle trajectories and accurately estimating travel time and fuel consumption, whereas the MiC method leads to overfitting and inaccurate macro-measurement predictions. This study underscores the importance of bi-scale calibration for precise traffic and energy consumption predictions, laying the groundwork for future research aimed at enhancing traffic management strategies.

Emission calibrated traffic model for environmental-sensitive decision making in Sri Lanka: Review

Air pollution from road transportation is a growing concern in urban areas, particularly in Sri Lanka, where vehicle emissions account for a significant portion of air pollutants. The increase in the number of vehicles on the road and the resulting traffic congestion exacerbate this problem. Therefore, accurate prediction of emissions and air quality related to road traffic is essential for informed decision-making in traffic planning and management. While Sri Lanka has made progress in developing driving cycles and traffic flow simulations tailored to its unique context, there remains a gap in calibrating these models with local emission values, which are influenced by specific driver behaviours and operational modes. An emission-calibrated traffic model specific to the Sri Lankan context can integrate both traffic-related and environmental factors, providing a comprehensive tool for analyzing the impact of traffic management strategies on emissions and air quality. By addressing the discrepancies between global manufacturer data and local conditions, this model can enable more environmentally friendly decisions in urban transportation planning, contributing to the reduction of air pollution. The study identifies, through a comprehensive review, the need for an emission-calibrated traffic model to address the unique challenges posed by Sri Lanka's evolving traffic landscape. While traffic modelling has made strides in Sri Lanka, this study identifies a significant research gap in modelling the emissions generated by these traffic models, especially considering the unique characteristics of the region's roads and vehicles. Further, the paper concludes that an emission-calibrated traffic model specifically tailored to the Sri Lankan context is essential to guide environmentally friendly traffic planning decisions and bridge the gap between traffic data and its environmental consequences.

Comprehending particulate matter dynamics in transit-oriented developments: Traffic as a generator and design as a captivator

In Transit-Oriented Development (TOD), the close integration of residential structures with community activities and traffic heightens residents' exposure to traffic-related pollutants. Despite traffic being a primary source of particulate matter (PM), the compact design of TODs, together with the impact of urban heat island (UHI), increases the likelihood of trapping emitted PM from traffic, leading to heightened exposure of TOD residents to PM. Although PM originates from two distinct sources in road traffic, exhaust and non-exhaust emissions (NEE), current legislation addressing traffic-related PM from non-exhaust emissions sources remains limited. This paper focuses on two TOD typologies in Manchester City—Manchester Piccadilly and East Didsbury—to understand the roles of TOD traffic as a PM generator and TOD place design as a PM container and trapper. The investigation aims to establish correlations between street design canyon ratios, vehicular Speed, and PM10/PM2.5, providing design guidance and effective traffic management strategies to control PM emissions within TODs. Through mapping the canyon ratio and utilising the Breezometer API for PM monitoring, the paper revealed elevated PM levels in both TOD areas, exceeding World Health Organization (WHO) recommendations, particularly for PM2.5. Correlation analysis between canyon configuration and PM2.5/PM10 highlighted the importance of considering building heights and avoiding the creation of deep canyons in TOD design to minimise the limited dispersion of PM. Leveraging UK road statistics and the PTV Group API for vehicle speed calculations, the paper studied the average speeds on the TOD roads concerning PM. Contrary to conventional assumption, the correlation analyses have revealed a noteworthy association shift between vehicular speed and PM concentrations. A positive correlation existed between speed increase and PM increases on arterial roads. However, a negative correlation emerged on main, collector, and local streets, indicating that PM levels rise for both PM10 and PM2.5 as Speed decreases. These findings challenge the traditional assumption that higher Speed leads to increased emissions, highlighting the potential impact of NEE on PM concentrations. This paper calls for thorough design considerations and traffic management strategies in TOD, especially in dense areas, considering building height, optimising traffic flow, and enhancing compromised air quality associated with vehicular emissions.

Predicting travel mode choice with a robust neural network and Shapley additive explanations analysis

AbstractPredicting and understanding travellers’ mode choices is crucial to developing urban transportation systems and formulating traffic demand management strategies. Machine learning (ML) methods have been widely used as promising alternatives to traditional discrete choice models owing to their high prediction accuracy. However, a significant body of ML methods, especially the branch of neural networks, is constrained by overfitting and a lack of model interpretability. This study employs a neural network with feature selection for predicting travel mode choices and Shapley additive explanations (SHAP) analysis for model interpretation. A dataset collected in Chengdu, China was used for experimentation. The results reveal that the neural network achieves commendable prediction performance, with a 12% improvement over the traditional multinomial logit model. Also, feature selection using a combined result from two embedded methods can alleviate the overfitting tendency of the neural network, while establishing a more robust model against redundant or unnecessary variables. Additionally, the SHAP analysis identifies factors such as travel expenditure, age, driving experience, number of cars owned, individual monthly income, and trip purpose as significant features in our dataset. The heterogeneity of mode choice behaviour is significant among demographic groups, including different age, car ownership, and income levels.

REAL-TIME TRAFFIC LIGHT OPTIMIZATION USING AI AND IOT

In modern cities, the escalating number of vehicles has led to significant traffic issues, affecting road capacity and service quality. Traditional traffic control systems, reliant on fixed signal timers, struggle to adapt to changing traffic dynamics, exacerbating congestion. This paper proposes an innovative approach to traffic management utilizing advanced Computer Vision technology, specifically the YOLOv7 object detection algorithm. By analysing live CCTV footage at intersections, the system dynamically assesses traffic density, identifies vehicle types, and adjusts signal timings in real-time. The system architecture ensures swift processing and response times, with YOLOv7 enabling rapid and accurate vehicle detection. This enables the system to make timely decisions, thereby enhancing overall traffic management efficacy. Leveraging Artificial Intelligence (AI) and Machine Learning techniques, the proposed solution addresses the urgent need for innovative traffic management strategies in urban areas, aiming to alleviate congestion, enhance traffic flow, and reduce environmental impact. Hence, the integration of YOLOv7 technology with adaptive traffic signal switching algorithms represents a promising step towards addressing the complex challenges of urban traffic congestion. Key Words: Computer Vision, Artificial Intelligence, Machine Learning, Traffic Prediction, Adaptive Control, YOLO.

IOT BASED MOVABLE ROAD DIVIDING SYSTEM

Road dividers conventionally serve the purpose of segregating traffic on roads into lanes for ongoing and incoming vehicles, facilitating smooth traffic flow. Typically, an equal number of lanes are designated for each direction of traffic. However, static road dividers present a limitation as they do not adapt to varying traffic demands. Our objective is to devise an automated road divider system capable of dynamically adjusting lane allocation, thereby accommodating the prevailing traffic patterns. The potential benefits of such a system include significant savings in time and fuel consumption, particularly by reallocating lanes to the direction experiencing higher traffic volume. This innovation promises to alleviate traffic congestion effectively and enhance traffic management strategies.

Investigating built environment and traffic flow impact on crash frequency in urban road networks

While numerous studies have examined the factors that influence crash occurrence, there remains a gap in understanding the intricate relationship between built environment, traffic flow, and crash occurrences across different spatial units. This study explores how built environment attributes, and dynamic traffic flow characteristics affect crash frequency by focusing on proposed traffic density-based zones (TDZs). Utilizing a comprehensive dataset from Greater Melbourne, Australia, this research emphasizes on the dynamic traffic flow variables and insights from the Macroscopic Fundamental Diagram model, considering parameters such as shockwave velocity and congestion index. The association between the potential influencing factors and crash frequency is examined using a random parameter negative binomial regression model. Results indicate that the data segmentation based on TDZs is instrumental in establishing a more refined crash model compared to traditional planning-based zones, as demonstrated by improved goodness-of-fit measures. Factors including density (e.g., employment density), network design (e.g., road density and highway density), land use diversity (e.g., job-housing balance and land use mixture), and public transit accessibility (e.g., bus route density) are significantly associated with crash occurrence. Furthermore, the unobserved heterogeneity effects of the shockwave velocity and congestion index on crashes are revealed. The study highlights the significance of incorporating dynamic traffic flow variables in understanding crash frequency variations across different spatial units. These findings can inform optimal real-time traffic monitoring, environmental design, and road safety management strategies to mitigate crash risks.

Teaching Artificial Intelligence Good Air Traffic Flow Management

Air traffic flow managers are continually faced with the decision of when and how to respond to predictions of future constraints. The promise of artificial intelligence, and specifically reinforcement learning, to provide decision support in this domain stems from the ability to systematically evaluate a sequence of potential actions, or strategy, across a range of uncertain futures. As decision support for human traffic managers, the generated recommendations must embody characteristics of a good management strategy; doing so requires introducing such notions to the algorithm. This paper proposes inducing stability into the strategy by dynamically constraining the design space based on upstream design decisions to promote consistency in the recommendations over time, where two such constraint sets are considered. The paper further evaluates the impact of adding a performance improvement threshold that must be overcome to accept a new strategy recommendation. The combination of search constraints and threshold values is evaluated against the agent’s reward function in addition to measures proposed to capture the stability of the strategy. The results show that the more restrictive set of constraints yields the best performance in terms of strategy stability and is more likely to reduce the delay where implementation of the threshold has a minor impact on overall performance. However, for the highest impact day of 8 June 2018, applying the threshold reverses the performance gains in delay but dramatically improves the stability of the resulting traffic flow management strategy from a flight level perspective, implying a potential tradeoff between delay optimization and flight predictability.

A Novel Approach of Traffic Congestion and Anomaly Detection with Prediction Using Deep Learning

Traffic congestion and anomalies present significant challenges to urban mobility, impacting economic activity and quality of life. Traditional traffic management systems often fall short in accurately predicting and mitigating these issues due to their reliance on static models and limited data sources. This study introduces a novel deep learning framework designed to enhance traffic congestion and anomaly detection and provide accurate traffic flow predictions. Leveraging a comprehensive dataset encompassing various traffic patterns and conditions, we employ a convolutional neural network (CNN) model, renowned for its efficacy in handling spatial data, combined with long short-term memory (LSTM) networks to capture temporal dependencies. Our approach distinguishes itself by incorporating real-time data and employing advanced feature extraction techniques, enabling the dynamic adjustment of traffic management strategies. The methodology section outlines the data preprocessing steps, model architecture, training process, and evaluation metrics employed. Experimental results demonstrate the model's superior performance over existing methods in terms of accuracy, precision, recall, and computational efficiency. The discussion elaborates on the model's practical implications for smart city initiatives and its potential to revolutionize traffic management systems. This study not only addresses the gaps identified in the literature review but also opens avenues for future research in applying deep learning to urban traffic challenges.

INVESTIGATION OF TRAFFIC EFFICIENCY ON KARBALA ROADS

In this article, we analyze the operation of the road network of the city of Karbala in Iraq, which is characterized by its religious and tourist character, and evaluate the effectiveness of the urban street network in it in order to identify the main steps that need to be taken to take measures to improve traffic efficiency and reduce delays. The scientific novelty of this work lies in the fact that it is the first of its kind, as far as we know, which addresses the problem of traffic efficiency in the city of Karbala as a whole, since the traffic density on the main transport axes in this city varies greatly during normal times and peak hours (morning and evening). The results of the analysis showed that some sectors operate in conditions of congested (stable) traffic (LOS F) in the morning and evening hours, and such sectors require immediate traffic management strategies. While the rest of the sectors are operating at or close to the same capacity (LOSE). Purpose - Analyzed the operation of the Karbala city road network in Iraq and evaluate its effectiveness to identify the main steps that need to be taken to improve traffic efficiency and reduce delays. Methodology. In this work, an experimental analytical methodology was used, in which data were collected, analyzed and compared with relevant information sources. Results. The categories of urban roads were determined based on the speed of free movement (FFS), and the level of service (LOS) is calculated for selected urban streets taking into account the average speed of traffic (ATS). Measurements of speed and travel time showed that traffic jams occurred on Fatima Al-Zahra Street, the second part of Ramadan Street and some sections of Al-Abbas Street in the morning. These areas require immediate traffic management strategies. In the evening, traffic jams were also observed on sections of Fatima Al-Zahra Street, part 2 of Al-Iskan Street and the first section of Ramadan Street. The decrease in traffic speed led to traffic jams on Al-Abbas, Al-Iskan Streets and some sections of Fatima Al-Zahra Street. Low FPS values indicate that the average speed of movement in these areas is low even with a small flow. Practical implications. It is recommended to apply the results obtained to the Traffic Management Department of the city of Karbala.

Traffic Speed and Occupancy Occurrence on the Urban Expressway between Southbound and Northbound

Economic growth in developed nations often leads to a surge in transportation needs, particularly in large cities. This can manifest as increased accident rates, environmental degradation, social strain, and most notably, traffic congestion. Addressing these challenges requires a multifaceted approach, balancing social, environmental, and economic considerations while prioritising public safety and health. The study contributes to the search for solutions by investigating traffic flow patterns on urban expressways. The research compares southbound and northbound lane speeds, analysing the relationship between speed and vehicle occupancy in both directions. By leveraging secondary quantitative data collected over seven consecutive days with Speedlane ProTM Counter Classifier radar technology, the study aims to identify potential causes of congestion. It is expected that the northbound lanes may experience higher traffic volume and greater variability compared to the southbound lanes. This research can inform traffic management strategies and contribute to reduced congestion in urban areas.