Traffic Congestion Research Articles

An Adaptive Framework for Traffic Congestion Prediction using Deep Learning

Aim and background: Congestion on China's roads has worsened in recent years due to the country's rapid economic development, rising urban population, rising private car ownership, inequitable traffic flow distribution, and growing local congestion. As cities expand, traffic congestion has become an unavoidable nuisance that endangers the safety and progress of its residents. Improving the utilization rate of municipal transportation facilities and relieving traffic congestion depend on a thorough and accurate identification of the current state of road traffic and necessitate anticipating road congestion in the city. Methodology: In this research, we suggest using a deep spatial and temporal graph convolutional network (DSGCN) to forecast the current state of traffic congestion. To begin, we grid out the transportation system to create individual regions for analysis. In this work, we abstract the grid region centers as nodes, and we use an adjacency matrix to signify the dynamic correlations between the nodes. Results and Discussion: The spatial correlation between regions is then captured utilizing a Graph Convolutional-Neural-Network (GCNN), while the temporal correlation is captured using a two-layer long and short-term feature model (DSTM). Conclusion: Finally, testing on real PeMS datasets shows that the DSGCN has superior performance than other baseline models and provides more accurate traffic congestion prediction.

Enhancing Urban Mobility: Predicting Traffic Congestion with Optimized ML Model

Abstract Traffic congestion is a critical challenge in urban areas, leading to increased travel times, pollution, and a decreased quality of life. This study addresses this issue by developing a predictive model for traffic congestion using the eXtreme Gradient Boosting (XGBoost) machine learning model, enhanced with hyperparameter optimization and detrending techniques. Key traffic parameters, including waiting times, vehicle angles, latitude, longitude, and speeds, are incorporated to improve the model's accuracy. Compared to ARIMA and LSTM models, the proposed XGBoost model significantly outperforms these approaches. LSTM exhibits much higher prediction errors. The XGBoost model, with its lowest MAE (0.007), MSE (0.008), and an accuracy of 99.5%, demonstrates superior performance and efficiency. Implemented in Python using the SUMO simulator for real-time traffic data, the proposed model showcases robust predictive capabilities, contributing to smarter urban mobility solutions and improved transportation systems. These findings underscore the effectiveness of advanced machine learning techniques in predicting traffic congestion and provide valuable insights for developing smarter urban mobility solutions. The study’s contributions aim to enhance transportation systems and improve the overall quality of urban life.

Comparison of gap-based and flow-based control strategies using a new controlled stochastic cellular automaton model for traffic flow

ABSTRACT Autonomous vehicles are essential to future transportation systems, potentially reducing traffic congestion. This study examines the impact of different vehicle control strategies on traffic flow through simulations. We propose a novel stochastic cellular automaton model, the controlled stochastic optimal velocity (CSOV) model, which incorporates vehicle control effects. Within the CSOV model, two control strategies are implemented: gap-based control (GC), which adjusts vehicle velocity to balance the gaps between adjacent vehicles, and flow-based control (FC), which aims to maintain a consistent local flow between the front and rear vehicles. Results show that both control strategies improve traffic flow. However, under weaker control, the GC sometimes resulted in lower flow compared to no control. In contrast, the FC consistently enhanced flow across control strengths, yielding more robust outcomes. Furthermore, when both strategies achieved comparable flow rates, the FC provided a more stable velocity distribution under varying traffic densities than the GC.

Error and Traffic State Analysis of Floating Car Data

Abstract. Floating car technology is widely applied in traffic information collection and has become a common method for real-time and dynamic monitoring of urban traffic conditions. Using floating car data from Shanghai as the research object, this paper analyzes the positioning error of floating cars and finds that most errors are within 15 meters. The error is significantly affected by vehicle speed but less by time, and the error decreases as the road grade increases. Speed and travel time ratio are used as parameters to assess the spatiotemporal traffic state, exploring the spatial distribution and temporal evolution of speed, as well as the spatiotemporal distribution of traffic congestion. In the spatial distribution of speed, clustering analysis is used to deduce the temporal distribution pattern of speed on the same road segment. Based on this, corresponding traffic management measures can be applied to different road segments and at different times.

Air quality monitoring system based on low power wide area network technology at public transport stops

<span>Mass migration from rural areas to urban areas has caused problems of traffic congestion, high industrial concentration and inequity in the distribution of housing in the world's capitals, generating a significant threat to sustainable development and public health due to air pollution air. In the Peruvian context, the importance of real-time monitoring of air quality is highlighted according to the standards established by the government. Several studies propose real-time environmental monitoring systems using internet of thing (IoT) technologies, electrochemical and optical sensors to measure pollutants, highlighting the need for data analysis. The objective of the paper is to show the implementation of IoT devices called sensor nodes, with long range wide area network (LoRaWAN) transmission technology for continuous monitoring of polluting gas concentrations. In addition, they are integrated into a central node called gateway to perform real-time monitoring through a web application. As an initial result, IoT devices demonstrated their effectiveness for real-time monitoring. Despite being a prototype-level result, the next stage involves its deployment at public transport stops in Lima. Overcoming the limitations of the solution, this paper establishes the foundation for future research on pollution and public health.</span>

Hybrid deep learning model with VMD-BiLSTM-GRU networks for short-term traffic flow prediction

Accelerating urbanization and the rapid development of intelligent transportation systems have rendered short-term traffic flow prediction an important research field. Accurate prediction of traffic flow is beneficial for the optimization of traffic planning, improvement of road utilization, reduction of traffic congestion, and reduction in the incidence of traffic accidents. However, data pertaining to traffic flow are typically influenced by a multitude of factors, resulting in data that exhibit a considerable degree of nonlinearity and complexity. To address the issue of noise in raw traffic flow data, this study proposes a hybrid model that combines variational modal decomposition (VMD), a bidirectional long short-term memory network (BiLSTM), and a gated recurrent unit (GRU) for short-term traffic flow prediction. To validate the effectiveness of the model, an experimental validation was conducted based on traffic flow data from UK highways, and the performance of the model was compared with common benchmark models. The experimental results demonstrate that the proposed method yields superior prediction results in terms of mean absolute error, coefficient of determination, and root-mean-square error compared to existing prediction techniques, thereby substantiating its efficacy in short-term traffic flow prediction.

An AI-driven Approach to Traffic Congestion Prediction in Port Harcourt City Using Naïve Bayes

Four important intersections Location Junction, Rumuokoro Junction, GRA Junction, and Rumuokwuta Junction will be the focus of this study's investigation into the persistent problem of traffic congestion in Port Harcourt, Nigeria. In order to provide insights for improved traffic management, the project also aims to construct a predictive model that forecasts traffic congestion on certain routes using the Naïve Bayes algorithm. The study used both exploratory and descriptive research techniques to gather information about Port Harcourt's traffic congestion. Field surveys were carried out at the four intersections between 7 a.m. and 7 p.m. to collect traffic counts and vehicle footage. The investigation also included data from Rivers State Command, a division of the Federal Road Safety Corps (FRSC). Using this data to find patterns and trends in traffic flow, a Naïve Bayes algorithm was developed to classify and predict the degree of traffic congestion. With a 97% accuracy rate, the algorithm accurately classified 1164 out of 1200 traffic data points, predicting traffic congestion. The predictive algorithm used bar charts to show the traffic conditions; tall, moderate, and tiny bars, respectively, indicated high, medium, and low traffic congestion. The model's efficacy in real-time traffic forecasting was reinforced by the close match between the forecasts and observed traffic patterns. According to the study's findings, the Naïve Bayes algorithm can accurately forecast traffic congestion and provides road users and urban planners with useful information. The model can greatly enhance road efficiency by lowering travel times, relieving congestion, and improving traffic management on Port Harcourt's major arteries by accurately predicting traffic conditions.

Hundreds of myosin 10s are pushed to the tips of filopodia and could cause traffic jams on actin

Myosin 10 (Myo10) is a motor protein known for its role in filopodia formation. Although Myo10-driven filopodial dynamics have been characterized, there is no information about the absolute number of Myo10 molecules during the filopodial lifecycle. To better understand molecular stoichiometries and packing restraints in filopodia, we measured Myo10 abundance in these structures. We combined SDS-PAGE densitometry with epifluorescence microscopy to quantitate HaloTag-labeled Myo10 in U2OS cells. About 6% of total intracellular Myo10 localizes to filopodia, where it enriches at opposite cellular ends. Hundreds of Myo10s are in a typical filopodium, and their distribution across filopodia is log-normal. Some filopodial tips even contain more Myo10 than accessible binding sites on the actin filament bundle. Live-cell movies reveal a dense cluster of over a hundred Myo10 molecules that initiates filopodial elongation. Hundreds of Myo10 molecules continue to accumulate during filopodial growth, but accumulation ceases when retraction begins. Rates of filopodial elongation, second-phase elongation, and retraction are inversely related to Myo10 quantities. Our estimates of Myo10 molecules in filopodia provide insight into the physics of packing Myo10, its cargo, and other filopodia-associated proteins in narrow membrane compartments. Our protocol provides a framework for future work analyzing Myo10 abundance and distribution upon perturbation.

Hundreds of myosin 10s are pushed to the tips of filopodia and could cause traffic jams on actin.

Myosin 10 (Myo10) is a motor protein known for its role in filopodia formation. Although Myo10-driven filopodial dynamics have been characterized, there is no information about the absolute number of Myo10 molecules during the filopodial lifecycle. To better understand molecular stoichiometries and packing restraints in filopodia, we measured Myo10 abundance in these structures. We combined SDS-PAGE densitometry with epifluorescence microscopy to quantitate HaloTag-labeled Myo10 in U2OS cells. About 6% of total intracellular Myo10 localizes to filopodia, where it enriches at opposite cellular ends. Hundreds of Myo10s are in a typical filopodium, and their distribution across filopodia is log-normal. Some filopodial tips even contain more Myo10 than accessible binding sites on the actin filament bundle. Live-cell movies reveal a dense cluster of over a hundred Myo10 molecules that initiates filopodial elongation. Hundreds of Myo10 molecules continue to accumulate during filopodial growth, but accumulation ceases when retraction begins. Rates of filopodial elongation, second-phase elongation, and retraction are inversely related to Myo10 quantities. Our estimates of Myo10 molecules in filopodia provide insight into the physics of packing Myo10, its cargo, and other filopodia-associated proteins in narrow membrane compartments. Our protocol provides a framework for future work analyzing Myo10 abundance and distribution upon perturbation.

Mudik Assistance Application Using Android-Based Scrum Method

Homecoming is an activity undertaken by nomads and migrant workers to return to their hometowns. In Indonesia, going home is synonymous with annual traditions starting from religious and national holidays such as Eid al-Fitr, Eid al-Adha, Christmas and New Year. However, going home is often accompanied by traffic jams and confusion, which can be a nuisance for travelers. Therefore, a possible solution to overcome this problem is to develop a mobile application to support homecoming. This research focuses on developing an Android-based homecoming assistance application using the Scrum methodology. The aim is to create technological solutions that make it easier for Indonesian people to return home every year. The development process is divided into sprints, and each sprint lasts two weeks. Each sprint includes planning, developing, and evaluating features such as real-time maps, traffic information, stop locations, and emergency response systems. The development team works closely with stakeholders to ensure that the application meets user needs. The research results show that the use of the Scrum methodology increases development efficiency and improves the quality of the final product. The resulting application prototype received a positive response in user testing. 90% of participants found the app useful when returning home. In conclusion, the development of homecoming assistance applications using the Scrum methodology has produced an effective and user-friendly solution to help travelers. This research opens up opportunities for further development and large-scale implementation in the future.

CATOM : Causal Topology Map for Spatiotemporal Traffic Analysis with Granger Causality in Urban Areas.

The transportation network is an important element in an urban system that supports daily activities, enabling people to travel from one place to another. One of the key challenges is the network complexity, which is composed of many node pairs distributed over the area. This spatial characteristic results in the high dimensional network problem in understanding the 'cause' of problems such as traffic congestion. Recent studies have proposed visual analytics systems aimed at understanding these underlying causes. Despite these efforts, the analysis of such causes is limited to identified patterns. However, given the intricate distribution of roads and their mutual influence, new patterns continuously emerge across all roads within urban transportation. At this stage, a well-defined visual analytics system can be a good solution for transportation practitioners. In this paper, we propose a system, CATOM (Causal Topology Map), for the cause-effect analysis of traffic patterns based on Granger causality for extracting causal topology maps. CATOM discovers causal relationships between roads through the Granger causality test and quantifies these relationships through the causal density. During the design process, the system was developed to fully utilize spatial information with visualization techniques to overcome the previous problems in the literature. We also evaluate the usability of our approach by conducting a SUS(System Usability Scale) test and traffic cause analysis with the real-world data from two study sites in collaboration with domain experts.

A Review of Expert Systems Integration in Signal Plan Optimisation

In urban networks, periodic peak traffic congestion often occurs during the day, namely in the morning and afternoon hours. Due to spatial constraints and the inability to increase capacity through physical road expansion, modern traffic management increasingly relies on Intelligent Transport Systems (ITS) solutions. One such solution is the integration of automatic licence plate recognition, an expert system and microsimulation tools aimed at optimising the network performance of signalised intersections within a network. Based on real-time and historical data on individual vehicle trajectories, the system predicts the route of each vehicle through the observed segment of the traffic network, determines the network load and proposes optimal signal plans. This paper provides an overview of conducted research related to the optimisation of signal plans utilising expert systems. Mathematical models for capacity and load determination, as well as computational intelligence-based systems used for signalised intersection management strategies, are described. Finally, the paper proposes a basic framework and guidelines related to the suggested system, highlighting open questions and potential challenges in its development.

Metaheuristic optimized complex-valued dilated recurrent neural network for attack detection in internet of vehicular communications

The Internet of Vehicles (IoV) is a specialized iteration of the Internet of Things (IoT) tailored to facilitate communication and connectivity among vehicles and their environment. It harnesses the power of advanced technologies such as cloud computing, wireless communication, and data analytics to seamlessly exchange real-time data among vehicles, road-side infrastructure, traffic management systems, and other entities. The primary objectives of this real-time data exchange include enhancing road safety, reducing traffic congestion, boosting traffic flow efficiency, and enriching the driving experience. Through the IoV, vehicles can share information about traffic conditions, weather forecasts, road hazards, and other relevant data, fostering smarter, safer, and more efficient transportation networks. Developing, implementing and maintaining sophisticated techniques for detecting attacks present significant challenges and costs, which might limit their deployment, especially in smaller settings or those with constrained resources. To overcome these drawbacks, this article outlines developing an innovative attack detection model for the IoV using advanced deep learning techniques. The model aims to enhance security in vehicular networks by efficiently identifying attacks. Initially, data is collected from online databases and subjected to an optimal feature extraction process. During this phase, the Enhanced Exploitation in Hybrid Leader-based Optimization (EEHLO) method is employed to select the optimal features. These features are utilized by a Complex-Valued Dilated Recurrent Neural Network (CV-DRNN) to detect attacks within vehicle networks accurately. The performance of this novel attack detection model is rigorously evaluated and compared with that of traditional models using a variety of metrics.

Inovasi Parkir Cerdas: Prediksi Posisi Kendaraan dengan Teknologi RFID dan Arduino Mega 2560 R3

Urban parking management is an urgent challenge in an ever-increasing vehicle mobility era. This research develops an intelligent parking system that integrates RFID (Radio-Frequency Identification) technology and Arduino Mega 2560 R3 to predict the vehicle's position in real time. The initial literature study identified various approaches to parking management, but none comprehensively combined RFID and Arduino Mega 2560 R3. Therefore, this study fills this gap. The research method involves literature study, system design, hardware and software development, and field testing. In the proposed system, vehicles are equipped with RFID cards, and the parking infrastructure is equipped with RFID readers. When the vehicle enters the parking area, the system reads the RFID card and predicts available parking positions. The results of field tests show an increase in the efficiency of parking space use of around 37% to 46%, and the accuracy of parking position prediction is above 90%. This research provides effective solutions for urban parking management, reducing traffic congestion and air pollution. This system also increases the driver's comfort in finding a suitable parking space. Thus, this research has the potential to support more sustainable urban development by optimizing the use of parking lots and reducing the negative impact of motorized vehicles in urban areas.

Proposed Improvement of Trans Semanggi Suroboyo Services Using IPA and QFD Methods

The high intensity of private vehicle usage has led to uncontrolled road congestion. To encourage residents to switch to public transportation, the Surabaya City Government introduced Trans Semanggi Suroboyo. Trans Semanggi Suroboyo experienced a decrease in passengers in 2023, with a decline of 324,711 passengers. Based on a preliminary survey with direct interviews conducted with 35 Trans Semanggi Suroboyo passengers, frequent complaints were related to service and facilities. Therefore, it is important to analyze and propose improvements to the services of Trans Semanggi Suroboyo as public transportation in Surabaya. To enhance the quality of services and facilities, it is essential to identify and evaluate passenger perceptions using service quality dimensions based on the Decree of the Director General of Land Transportation Number SK.5923/AJ.005/DRJD/2016. The most important service quality priorities for passengers are measured using the Importance Performance Analysis (IPA) method. This research identified 9 critical attributes that require improvement. Subsequently, a service quality improvement design was developed using the Quality Function Deployment (QFD) approach through the House of Quality (HoQ) matrix, resulting in 5 priority technical responses. Based on these findings, a plan for improving the service and facilities of the Trans Semanggi Suroboyo bus was proposed to maintain the quality of public transportation services.

A Study of Mixed Non-Motorized Traffic Flow Characteristics and Capacity Based on Multi-Source Video Data.

Mixed non-motorized traffic is largely unaffected by motor vehicle congestion, offering high accessibility and convenience, and thus serving as a primary mode of "last-mile" transportation in urban areas. To advance stochastic capacity estimation methods and provide reliable assessments of non-motorized roadway capacity, this study proposes a stochastic capacity estimation model based on power spectral analysis. The model treats discrete traffic flow data as a time-series signal and employs a stochastic signal parameter model to fit stochastic traffic flow patterns. Initially, UAVs and video cameras are used to capture videos of mixed non-motorized traffic flow. The video data were processed with an image detection algorithm based on the YOLO convolutional neural network and a video tracking algorithm using the DeepSORT multi-target tracking model, extracting data on traffic flow, density, speed, and rider characteristics. Then, the autocorrelation and partial autocorrelation functions of the signal are employed to distinguish among four classical stochastic signal parameter models. The model parameters are optimized by minimizing the AIC information criterion to identify the model with optimal fit. The fitted parametric models are analyzed by transforming them from the time domain to the frequency domain, and the power spectrum estimation model is then calculated. The experimental results show that the stochastic capacity model yields a pure EV capacity of 2060-3297 bikes/(h·m) and a pure bicycle capacity of 1538-2460 bikes/(h·m). The density-flow model calculates a pure EV capacity of 2349-2897 bikes/(h·m) and a pure bicycle capacity of 1753-2173 bikes/(h·m). The minimal difference between these estimates validates the effectiveness of the proposed model. These findings hold practical significance in addressing urban road congestion.

Evaluation of traffic management at Futa Northgate, Akure Ondo state, Nigeria

Due to increasing usage of automobiles, traffic congestion in most cities is becoming an issue and slowing down the day-to-day activities of human and also deteriorating the quality of the road due to the constant congestion. The movement of products and services between locations has become essential and integral to both global and metropolitan economic existence, and modern enterprises, industries, trades, and general activities are dependent on transportation and transportation infrastructure, hence this project is to evaluate the traffic management on the FUTA North Gate Road and propose recommendations to improve traffic flow and reduce congestion. The research identified the causes of traffic congestion, proffered a solution to traffic congestion for the single carriageway under study and analyzed the implications of the proffered traffic management strategy. Data collection on the road was done during peak days of the week (Tuesday – Friday) and also during peak hours of the day i.e. early hours of the morning and in the evening using the traffic count method. The study revealed that the existing single carriageway has a dimension of 3.75-meter width per each side, with a 2.7-meter shoulder, showed 2,585 as the maximum number of vehicles that ply the road causing traffic congestion as a result of poor road conditions, lack of proper traffic management systems, increased population and vehicle ownership, inadequate road infrastructure, and poor driving habits exceeding the capacity. The study concludes that implementing traffic management strategies which includes promoting the use of public transportation, improving the design of intersections and roundabouts, investing in road infrastructure, and educating drivers on safe and responsible driving habits can help improve the traffic flow and reduce congestion on busy roads. While some recommendations may require significant investment, the benefits of a more efficient and sustainable transportation system will far outweigh the costs in the long run. It is important that all stakeholders work together to implement these recommendations and create a transportation system that benefits everyone. Overall, this project highlights the importance of having an efficient and effective traffic management system, and the need to consider all possible impacts before implementing road expansion projects. By adopting a holistic approach to traffic management and implementing the recommended measures, we can create a more efficient and sustainable transportation system that benefits both motorists and the environment giving a long-term solution going ahead.

Optimization of ride-sharing services: An investigation of the ride-hailing service providers

This study investigates the optimization of ride-sharing services (RSS) on the ride-hailing service (RHS) providers in Bangladesh. This study employed an explanatory sequential mixed method research design- a qualitative study followed by a quantitative one. Qualitative data were collected through focus group discussions and in-depth interviews with twenty (20) riders and drivers in Bangladesh, and quantitative data were collected from 300 respondents consisting of riders and drivers using a convenience sampling technique. Factor analysis and hierarchical cluster analysis were applied to the data analysis. The qualitative analysis reveals several significant factors associated with RSS and RHS, including cost efficiency, fare, fuel consumption, traffic congestion, carbon emissions, environmental pollution, employment opportunities, business growth, and security. The quantitative results indicate that using RSS is associated with more significant benefits than RHS in various aspects, including cost efficiency, fare, fuel consumption, traffic congestion, carbon emissions, environmental pollution, employment opportunities, and expansion of the automobile industry. The findings may assist policymakers in understanding how RSS can yield more incredible economic, environmental, and social benefits than RHS by analyzing fare sharing among passengers, carbon emissions, fuel consumption, and the expansion of the vehicle markets etc. Therefore, the government can formulate distinct policies for RSS holders due to their contributions to economic, social, and environmental concerns. While RHS services are available in many cities in Bangladesh, this study considered only Dhaka and Sylhet cities. Thus, future studies can consider more respondents from other cities for a holistic understanding.

Comparison of current and proposed travel modes for students considering travelling and sustainable indicators

In the twenty-first century, global warming has become one of the severe challenges. Around one-fourth of the total carbon footprint is contributed by transport vehicles. It is crucial to provide alternative solutions to unsustainable transportation systems. This study compares the present travel modes of students of the Capital University of Science and Technology, Islamabad, with the proposed sustainable travel mode. A campus survey (N = 565) was conducted to understand travel choices, travel times, origin and destinations, and student preferences, considering university buses and private vehicles. A comparison was made between the travel time analysis of university bus users and non-bus users. A sustainability analysis has been conducted to evaluate the effects of the present travel modes on students and the proposed university bus transport system. Five main factors were compared: fuel consumption and associated fuel costs, carbon and vehicular emissions, traffic noise pollution, traffic congestion, and accident ratio. The results show that 38% of students commute by private cars and 18% by motorbikes. Around 25% of the students avoid university buses due to long travel times, and 30% prefer because their parents ask them. On average, university buses take 56.8 min, and non-bus transports take 38.8 min for one-way travel. If all students travel by university busses, they could be 86% more sustainable in fuel costs and carbon emissions than non-bus travel modes. These results give useful policy implications for providing sustainable travel alternatives to educational institutions.

Modeling Population Mobility Flows: A Hybrid Approach Integrating a Gravity Model and Machine Learning

Population mobility between cities significantly affects traffic congestion, disease spread, and societal well-being. As globalization and urbanization accelerate, understanding the dynamics of population mobility becomes increasingly important. Traditional population migration models reveal the factors influencing migration, while machine learning methods provide effective tools for creating data-driven models to handle the nonlinear relationships between origin and destination characteristics and migration. To deepen the understanding of population mobility issues, this study presents GraviGBM, an expandable population mobility simulation model that combines the gravity model with machine learning, significantly enhancing simulation accuracy. By employing SHAPs (SHapley Additive exPlanations), we interpret the modeling results and explore the relationship between urban characteristics and population migration. Additionally, this study includes a case analysis of COVID-19, extending the model’s application during public health emergencies and evaluating the contribution of model variables in this context. The results show that GraviGBM performs exceptionally well in simulating inter-city population migration, with an RMSE of 4.28, far lower than the RMSE of the gravity model (45.32). This research indicates that distance emerged as the primary factor affecting mobility before the pandemic, with economic factors and population also playing significant roles. During the pandemic, distance remained dominant, but the significance of short distances gained importance. Pandemic-related indicators became prominent, while economics, population density, and transportation substantially lost their influence. A city-to-city flow analysis shows that when population sizes are comparable, economic factors prevail, but when economic profiles match, living conditions dictate migration. During the pandemic, residents from hard-hit areas moved to more distant cities, seeking normalcy. This research offers a comprehensive perspective on population mobility, yielding valuable insights for future urban planning, pandemic response, and decision-making processes.