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The United States has emerged as a prominent actor in the international fight against transnational terrorism in the Sahel region of Africa since 2002, exerting significant political influence on regional affairs. Under the leadership of the United States, a coalition of Sahel countries has collaboratively established security programs, notably including the Trans-Saharan Counterterrorism Partnership (TSCTP). However, the U.S. counterterrorism strategy in the Sahel region has not only proven ineffective in containing the deterioration of regional security, but also has exacerbated the vulnerability to coups in recipient countries. The observed deterioration can be attributed to various driving factors, such as deviations in the willingness and ability to involve and the security traffic jam. We contend that the U.S.-Sahel counterterrorism partnership is fundamentally misaligned and plagued by a securitization problem. Firstly, a significant impediment to international cooperation lies in the United States' pursuit of consolidating its dominant role in Sahel security affairs, which usually overlooks the pragmatic principle of 'African solutions to African problems'. In the counterterrorism process, there is a misalignment between U.S. national interests and those of Sahel states. Furthermore, the United States' extensive reliance on military strategies indicates a significant securitization of non-violent civil affairs in certain Sahel states, inadvertently creating an opportunity for genuine terrorist organizations to recruit members. In addition, U.S. interference in the internal affairs of Sahel states heightens the risk of coups, thereby undermining the legitimacy of U.S. leadership in the region. Currently, with both the withdrawal of the United Nations Stabilization Mission in Mali (MINUSMA) and France withdrawing their security forces from the Sahel region, there has a significant weakening effect on regional security capabilities. Therefore, it is imperative for the international community to collaborate with African nations and adopt more proactive measures to safeguard regional security and maintain order. [ABSTRACT FROM AUTHOR]

Air pollution is a significant and pressing environmental and public health concern in urban areas, primarily driven by road transport. By gaining a deeper understanding of how traffic dynamics influence air pollution, policymakers and experts can design targeted interventions to tackle these critical issues. In order to analyse this relationship, a series of regression algorithms were developed utilizing the Google Project Air View (GPAV) and Dublin City's SCATS data, taking into account various spatiotemporal characteristics such as distance and weather. The analysis showed that Gaussian Process Regression (GPR) mostly outperformed Support Vector Regression (SVR) for air quality prediction, emphasizing its suitability and the importance of considering spatial variability in modelling. The model describes the data best for particulate matter (PM2.5) emissions, with R-squared (R2) values ranging from 0.40 to 0.55 at specific distances from the centre of the study area based on the GPR model. The visualization of pollutant concentrations in the study area also revealed an association with the distance between intersections. While the anticipated direct correlation between vehicular traffic and air pollution was not as pronounced, it underscores the complexity of urban emissions and the multitude of factors influencing air quality. This revelation highlights the need for a multifaceted approach to policymaking, ensuring that interventions address a broader spectrum of emission sources beyond just traffic. This study advances the current knowledge on the dynamic relationship between urban traffic and air pollution, and its findings could provide theoretical support for traffic planning and traffic control applicable to urban centres globally. [ABSTRACT FROM AUTHOR]

This paper introduces a two-echelon optimization model for the integrated routing of an electric vehicle (EV) and a traditional internal combustion engine vehicle (ICEV) in an urban environment. The scientific context of this study is sustainable urban logistics. The case study focuses on the distribution of pharmaceuticals in the metropolitan area of Rome. Distributing pharmaceuticals in large cities presents significant challenges, including heavy traffic congestion, the need for strict temperature control, and the maintenance of the integrity and timely delivery of sensitive medications. Furthermore, the complexity of urban logistics and adherence to regulatory requirements introduce additional layers of difficulty. Therefore, the implementation of fast and sustainable distribution mechanisms is crucial in this context. Specifically, the model seeks to minimize both total CO2 emissions and transportation costs while optimizing the use of an EV and an ICEV, all while ensuring that service level requirements are met. Computational results demonstrate the effectiveness of the proposed method in improving the sustainability of pharmaceutical distribution. [ABSTRACT FROM AUTHOR]

The aim of this study was to compare the head displacement of the KPSIT C50 dummy, representing a 50th percentile male, with the KPSIT C5 dummy, representing a 5th percentile female, during low-speed collisions. Low-speed collisions, such as those occurring in urban traffic jams, are increasingly common. The research was conducted on a dedicated educational workstation designed to measure forces in seat belts. This study is part of a comprehensive research project on crash tests involving both volunteers and physical KPSIT dummies. The tests were conducted at a speed of 20 km/h to simulate real-world low-speed collision scenarios. The findings demonstrate that using a sports bucket seat with four-point or five-point harnesses significantly reduces head displacement compared with standard car seats. Such seating configurations enhance safety by minimizing the risk of head injuries, which can occur when airbags do not deploy during low-speed collisions. Moreover, the study highlights that standard three-point seat belts allow for greater forward head movement, increasing the risk of head contact with the vehicle's interior during collisions at speeds too low to trigger airbag deployment. [ABSTRACT FROM AUTHOR]

Due to the country's rapidly growing population and economy, transportation has become an essential part of Egyptian life. Air quality has decreased as a direct result of the rapid increase in urban areas' vehicle use due to human activities. In Egypt, almost 48.1% of all trips are taken by private automobiles, whereas public transportation is utilized only sometimes. Heavy congestion is the direct result of people's reliance on personal automobiles. More than 98% of the world's population in 2019 was located in regions where air quality did not meet World Health Organization (WHO) criteria. About 32% of Egypt's 2019 emissions came from the transportation sector. Different aspects that define the causes and possible alternatives to the air pollution problem at intersections are addressed in this research. Three intersections in Zagazig are the focus of this project, which looks into ways to reduce emissions from moving cars. PTV VISSIM generates a realistic peak-hour traffic simulation of the current traffic situation. Emissions are calculated using the data collected from the vehicles in the traffic simulation as input to the emission modelling software COPERT. [ABSTRACT FROM AUTHOR]

With the rapid increase in the number of vehicles on the road, minor traffic accidents have become more frequent, contributing significantly to traffic congestion and disruptions. Traditional methods for determining responsibility in such accidents often require human intervention, leading to delays and inefficiencies. This study proposed a fully intelligent method for liability determination in minor accidents, utilizing collision detection and large language models. The approach integrated advanced vehicle recognition using the YOLOv8 algorithm coupled with a minimum mean square error filter for real-time target tracking. Additionally, an improved global optical flow estimation algorithm and support vector machines were employed to accurately detect traffic accidents. Key frames from accident scenes were extracted and analyzed using the GPT4-Vision-Preview model to determine liability. Simulation experiments demonstrated that the proposed method accurately and efficiently detected vehicle collisions, rapidly determined liability, and generated detailed accident reports. The method achieved the fully automated AI processing of minor traffic accidents without manual intervention, ensuring both objectivity and fairness. [ABSTRACT FROM AUTHOR]

Analyzing air pollution caused by traffic conditions requires appropriate indicators. Currently, air pollution indicators are approximated by the number of vehicles and gas station density. However, this approach cannot provide information at a smaller level. This study aims to identify traffic congestion distribution from Google Maps data as an alternative air pollution indicator at smaller level using map digitization method. In addition, this study examines its relationship with the existing indicator called gas station density. The results show that the digitization method can map the traffic congestion distribution where most areas in West, North, and Central Jakarta are classified as high traffic. In addition, this study found that there is a strong and significant relationship of 0.58277 between traffic congestion distribution and gas station density. Thus, traffic congestion distribution and gas station density data from Google Maps can be used as an indicator of traffic-related air pollution, especially land transportation. Furthermore, this research is expected to serve as a basis for the government in determining mitigation strategies related to traffic congestion and the resulting emissions. [ABSTRACT FROM AUTHOR]

This study employs a regression discontinuity design to systematically examine the governance effect of bike-sharing on urban traffic congestion, utilizing city-level data from Beijing, Shanghai, and Wuhan in China between 2016 and 2018. We discover that the introduction of bike-sharing services significantly mitigates traffic congestion in the short term. Our heterogeneity analysis reveals that the initial deployment of shared bicycles primarily alleviates urban congestion, while additional deployments have a limited impact. Further, mechanism test analysis demonstrates that bike-sharing leads to increased metro ridership in these cities, effectively explaining the reduction in road congestion. This study underscores the pivotal role of bike-sharing services in easing urban traffic congestion and provides vital policy insights for enhancing traffic management strategies in Chinese cities. [ABSTRACT FROM AUTHOR]

This paper formulates a cooperative traffic control methodology that integrates traffic signal timing and ramp metering decisions into an optimization model to improve traffic operations in a corridor network. A mixed integer linear model is formulated and is solved in real time within a model predictive controller framework, where the cell transmission model is used as the system state predictor. The methodology is benchmarked in a case study corridor in San Mateo, CA, in VISSIM with two optimization scenarios, namely, optimal metering and optimal signal control, and two simulation scenarios with a preset metering plan and no metering. The numerical results show that integrated traffic signal and ramp metering control reduces delays, stops, and travel times of the corridor by up to 33.1%, 36%, and 16.4%, respectively, compared to existing benchmark conditions. With appropriate weights prioritizing freeway or arterial street operations, the integrated control balances traffic congestion between the arterial street and the freeway. [ABSTRACT FROM AUTHOR]

Jaundice is a symptom of high blood bilirubin levels affecting about 80% of neonates. In neonates fed with breast milk, jaundice is particularly prevalent and severe, which is likely multifactorial. With the development of genomics and metagenomics, a deeper understanding of the neonatal gut microbiota has been achieved. We find there are accumulating evidence to indicate the importance of the gut microbiota in the mechanism of jaundice. In this paper, we present new comprehensive insight into the relationship between the microbiota and jaundice. In the new perspective, the gut is a crucial crossroad of bilirubin excretion, and bacteria colonizing the gut could play different roles in the excretion of bilirubin, including Escherichia coli as the main traffic jam causers, some Clostridium and Bacteroides strains as the traffic police, and most probiotic Bifidobacterium and Lactobacillus strains as bystanders with no effect or only a secondary indirect effect on the metabolism of bilirubin. This insight could explain why breast milk jaundice causes a longer duration of blood bilirubin and why most probiotics have limited effects on neonatal jaundice. With the encouragement of breastmilk feeding, our perspective could guide the development of new therapy methods to prevent this side effect of breastfeeding. [ABSTRACT FROM AUTHOR]

The increased usage of navigation technologies has caused conflicts in local traffic management, resulting in congested residential areas among other challenges for residents. This paper uses content analysis to investigate such negative social externalities within local communities and neighbourhoods. Through a corpus of 90 news articles about traffic incidents caused by navigation technologies, we identified negative traffic and safety-related externalities, including congestion, damage, pollution, and accidents. We also report on countermeasures by local communities and governments, including street closures, speed limit reduction, and turn bans. Based on our results, we discuss the implications for designing mobile navigation technologies that reduce negative social externalities. [ABSTRACT FROM AUTHOR]

New Zealand is considering implementing congestion-charging technologies to improve traffic flows and reduce emissions in city centres. This article reviews current congestion-charging technologies against New Zealand's Privacy Act 2020 and identifies varying privacy risks with these technologies. In particular, using global navigation satellite (GNS) systems and an on-board unit can pose a risk of collecting data that exceeds congestion management requirements. Additional issues arise from data processing for purposes other than congestion charging. Overall, the findings indicate the need for stricter control over who can process what type of personal data and the use and retention of such data. [ABSTRACT FROM AUTHOR]

With the rapid growth of population and vehicles, issues such as traffic congestion are becoming increasingly apparent. Parking guidance and information (PGI) systems are becoming more critical, with one of the most important tasks being the prediction of traffic flow in parking lots. Predicting parking traffic can effectively improve parking efficiency and alleviate traffic congestion, traffic accidents, and other problems. However, due to the complex characteristics of parking spatio-temporal data, high levels of noise, and the intricate influence of external factors, there are three challenges to predicting parking traffic in a city effectively: (1) how to better model the nonlinear, asymmetric, and complex spatial relationships among parking lots; (2) how to model the temporal autocorrelation of parking flow more accurately for each parking lot, whether periodic or aperiodic; and (3) how to model the correlation between external influences, such as holiday weekends, POIs (points of interest), and weather factors. In this context, this paper proposes a parking lot traffic prediction model based on the fusion of multifaceted spatio-temporal features (MFF-STGCN). The model consists of a feature embedding module, a spatio-temporal attention mechanism module, and a spatio-temporal convolution module. The feature embedding module embeds external features such as weekend holidays, geographic POIs, and weather features into the time series, the spatio-temporal attention mechanism module captures the dynamic spatio-temporal correlation of parking traffic, and the spatio-temporal convolution module captures the spatio-temporal features by using graph convolution and gated recursion units. Finally, the outputs of adjacent time series, daily series, and weekly series are weighted and fused to obtain the final prediction results, thus predicting the parking lot traffic flow more accurately and effectively. Results on real datasets demonstrate that the proposed model enhances prediction performance. [ABSTRACT FROM AUTHOR]

Traffic flow prediction is one of the challenges in the development of an Intelligent Transportation System (ITS). Accurate traffic flow prediction helps to alleviate urban traffic congestion and improve urban traffic efficiency, which is crucial for promoting the synergistic development of smart transportation and smart cities. With the development of deep learning, many deep neural networks have been proposed to address this problem. However, due to the complexity of traffic maps and external factors, such as sports events, these models cannot perform well in long-term prediction. In order to enhance the accuracy and robustness of the model on long-term time series prediction, a Graph Attention Informer (GAT-Informer) structure is proposed by combining the graph attention layer and informer layer to capture the intrinsic features and external factors in spatial–temporal correlation. The external factors are represented as sports events impact factors. The GAT-Informer model was tested on real-world data collected in London, and the experimental results showed that our model has better performance in long-term traffic flow prediction compared to other baseline models. [ABSTRACT FROM AUTHOR]

This article examines the operational functionality of intelligent transport systems to enhance smart cities by reducing traffic congestion. Given the increasing populations of smart cities, there is a growing demand for public transit systems to address the issue of traffic congestion. Therefore, the suggested system is developed using a few parametric design models, which combine point-to-point protocol and mode control optimization. The multi-objective parametric design for a smart transportation system is conducted using min–max functions to minimize the waiting time period for end users. Furthermore, customers are given the option to utilize a line following mechanism that offers suitable connectivity, along with independent identification and revitalize functions. The predicted model effectively eliminates the delay produced by transportation devices when positioning units are involved, ensuring that individual messages are delivered without any interruptions. In order to evaluate the results of the proposed system model, four different scenarios were examined. A comparison analysis revealed that the suggested method achieves a suitable directional flow for 96% of smart transport units. Additionally, it reduces delays and waiting periods by 2% and 6% respectively, while increasing energy consumption by 29%. [ABSTRACT FROM AUTHOR]

Particulate matter is one of the most dangerous air pollutants, especially in urban areas, due to its significant adverse health effects. Traditionally, air quality monitoring relies on fixed reference stations, which often have a low temporal and spatial resolution. To address this limitation, a low-cost, portable air quality monitoring device with a rapid measurement response was used to assess particulate matter concentration levels in the afternoon hours in central Thessaloniki, Greece. This approach enabled the identification of local hotspots directly related to human activities. Statistical analysis and spatial mapping were employed, and data collected were categorized using k-means clustering. The findings of the study suggest that data acquired via portable low-cost sensors can describe the local variability of PM2.5 concentrations. The results indicate that local activities, such as increased human accumulation, traffic congestion at traffic lights, market working hours, together with meteorological parameters, can significantly impact air quality in specific urban locations. They also highlight the differences between data recorded in colder and warmer periods, with the concentrations of PM2.5 in the first period being 3.7 μg/m3 greater on average than in the second. These differences are also identified via the k-means clustering method, which suggest that higher concentrations appear mostly during the colder period of the study. [ABSTRACT FROM AUTHOR]

The rapid growth of urban traffic has intensified daily congestion, affecting both traffic flow and parking. Accurate parking prediction plays a vital role in effectively managing limited parking resources and is essential for the successful implementation of advanced intelligent systems. In an effort to comprehensively assess the latest developments in parking prediction, we curated a dataset of 639 articles spanning from 2010 to the present, using the Scopus database. Initially, we performed a bibliometric analysis utilizing VOSviewer software. These findings not only illuminate emerging trends within the parking prediction field but also provide strategic guidance for its progression. Subsequently, we categorized advancements in three focal areas: behavior prediction, demand prediction, and parking space prediction. A comprehensive overview of the present research status and future directions was then provided. The findings underscore the substantial progress achieved in current parking prediction models, achieved through diverse avenues like multi-source data integration, multi-variable feature extraction, nonlinear relationship modeling, deep learning techniques application, and ensemble model utilization. These innovative endeavors have not only pushed the theoretical boundaries of parking prediction but also significantly heightened the precision and applicability of predictive models in practical scenarios. Prospective research should explore avenues such as processing unstructured parking datasets, developing predictive models for small-scale data, mitigating noise interference in parking data, and harnessing potent platform fusion techniques. This study's significance transcends guiding and catalyzing advancement in academic and practical domains; it holds paramount relevance across academic research, technological innovation, decision-making support, business applications, and policy formulation. [ABSTRACT FROM AUTHOR]

Due to the limited bandwidth and transmission congestion of the vehicle platoon's communication, it is inevitable to induce time delay, which significantly degrades the control performance of the vehicle platoon, even resulting in instability. This paper focuses on analyzing the internal stability under generic communication topologies and presents a method of computing the exact time delay margin (ETDM). The proposed method can offer a necessary and sufficient internal stability condition with no conservatism. Firstly, to reduce the analytical complexity and computational burden elegantly, we decompose the closed-loop platoon dynamics into a set of individual subsystems via similarity transformation and matrix factorization. This decomposition approach is applicable for any general communication topology. Secondly, an explicit formula is deduced to compute the ETDM by surveying the characteristic roots' distribution of all these individual subsystems. It is further demonstrated that only the positive purely imaginary roots need to be considered to compute the ETDM. Finally, simulations are conducted to demonstrate the effectiveness of the theoretical claims. [ABSTRACT FROM AUTHOR]

The detection and characterisation of electromagnetic signals within a specific frequency range, known as spectrum sensing, plays a crucial role in Cognitive Radio Networks (CRNs). The CRNs aim to adapt their communication parameters to the surrounding radio environment, thereby improving the efficiency and utilisation of the available radio spectrum. Spectrum sensing is particularly important in device‐to‐device (D2D) communication when operating independently of the cellular network infrastructure. The Medium Access Control (MAC) protocol coordinates device communication and ensures interference‐free operation of the CRN coexisting with the primary cellular network. A spectrum sensing strategy at the MAC layer for cognitive D2D communication. The strategy focuses on reducing the overall sensing period allocated at the MAC layer by having each Cognitive D2D User (cD2DU) sense a smaller subset of available channels while maintaining the same sensing time for cellular user detection at the physical layer. To achieve this, the concept of concurrent groups of D2D devices is introduced in proximity, which are formed by using unique IDs of cD2DUs during the device discovery stage. Each concurrent group senses a specific portion of the cellular user band in a shorter time, resulting in a reduced overall sensing period. In addition to mitigating traffic congestion through data diversion from the cellular network, the proposed strategy facilitates the concurrent sensing of multiple channels by cD2DUs within the underutilised cellular user band. This leads to extended data transmission periods, increased network throughput, and effective offloading of the cellular network. The effectiveness of the proposed work is evaluated by considering factors, such as network throughput and transmission time. Simulation results confirm the effectiveness of the approach in improving spectrum utilisation and communication efficiency in multi‐channel Cognitive D2D Networks (cD2DNs). [ABSTRACT FROM AUTHOR]

The increasing number of private cars, public transportation vehicles, and pedestrians, as well as the absence of adequate space for these ground amenities, are one of the primary causes of traffic congestion and accidents in the Kathmandu Valley. Investigations have indicated that the Kathmandu Valley has the greatest traffic accidents despite the heavy presence of the government and its agencies there. Most teens and young adults suffer injuries while using motor vehicles. The study's primary objective is to foresee and prevent such complications by planning for sufficient subsurface infrastructure (a cut‐and‐cover rectangular tunnel) for the Kathmandu Valley's transportation network. The overlying pressure, lateral earth pressure, live load, uplift pressure, and live surcharge are some of the forces acting on the tunnel, creating unique stress and moment zones. The tunnel meets the following geometric requirements: (a) Each of the tunnel's two cells has a clear span of 10 m and a clear height of 5.5 m. The side walls, inner walls, top slab, and bottom slab are all 700 mm thick. Soil has built up to a height of 4 m over the tunnel's roof. The analytical method is used in the tunnel segment's analysis. Furthermore, the designed tunnel has been evaluated for stability, considering the deflection and shear resistance. The analysis indicates that the tunnel meets the stability requirements. This implies that the structure is capable of withstanding the applied forces without excessive deflection. Non‐linear dynamic time history analyses of the El Centro earthquake and the Gorkha earthquake were computed. From the El Centro earthquake, the maximum displacement was 23.63 mm at 10.59 s, and from the Gorkha earthquake, the maximum displacement was 16 mm at 0.19 s for the modeled structures. [ABSTRACT FROM AUTHOR]

We investigate the formation of dust traffic jams in polar-aligning circumbinary discs. We use 3D smoothed particle hydrodynamical simulations of both gas and dust to model an initially highly misaligned circumbinary disc around an eccentric binary. As the circumbinary disc evolves to a polar configuration (perpendicular to the binary orbital plane), the difference in the precession between the gas and dust produces dust traffic jams, which become dense dust rings. We find the formation of dust rings exists for different Stokes number, binary eccentricity, and initial disc tilt. Dust rings are only produced while the circumbinary disc is misaligned to the binary orbital plane. When the disc becomes polar aligned, the dust rings are still present and long-lived. Once these dust rings are formed, they drift inward. The drift time-scale depends on the Stokes number. The lower the Stokes number, the faster the dust ring drifts near the inner edge of the disc. The dust rings will have an increased mid-plane dust-to-go ratio, which may be a favourable environment for the steaming instability to operate. [ABSTRACT FROM AUTHOR]

Ride-hailing services have grown rapidly, presenting challenges such as increased traffic congestion, inefficient driver workload distribution, and environmental concerns like higher fuel consumption and emissions. This study develops a non-linear ride-hailing assignment model addressing these issues by considering service level, driver workload, and fuel consumption. A piecewise linear method was employed to handle a non-linear programming model, and the method was modified to function autonomously without operator intervention. The model's performance was evaluated using a publicly accessible dataset of taxi trips in Manhattan, focusing on indicators such as passenger waiting time, driver workload distribution, and fuel consumption. Numerical simulations demonstrated significant improvements: a 15% reduction in average passenger waiting time, a 20% improvement in balancing driver workloads, and a 10% decrease in overall fuel consumption, contributing to reduced emissions and environmental impact. The modified piecewise linear method proved effective in optimizing ride-hailing assignments, providing a more efficient and sustainable solution. The model also showed robustness in handling large datasets, ensuring scalability and applicability to various urban settings. These findings highlight the model's potential to enhance operational efficiency and promote sustainability in ride-hailing services. By integrating considerations for service level, driver workload, and fuel consumption, the model offers a holistic approach to addressing the key challenges faced by the ride-hailing industry. This study provides valuable insights for future ride-hailing development and implementations of ride-hailing systems, promoting practices that are both efficient and environmentally friendly. [ABSTRACT FROM AUTHOR]

Optimizing travel routes in urban transportation networks is essential for enhancing traffic efficiency in major cities. This study focuses on optimizing the combined subway and taxi travel routes from Beijing Union University to Beijing South Railway Station, a critical route for students and staff in Beijing. To address this, we propose three genetic algorithms: the Standard Genetic Algorithm (SGA), the Multipoint Crossover and Elitist Selection Genetic Algorithm (MPEGA), and the Improved Dynamic Crossover and Mutation Rate Genetic Algorithm (IDCMGA). Experimental results indicate that MPEGA reduces the mean travel cost by 15.21%, the variance by 81.72%, and the standard deviation by 57.25% compared to SGA. Additionally, IDCMGA reduces the average by 1.76%, the variance by 63.16%, and the standard deviation by 39.30% compared to MPEGA. The improved IDCMGA algorithm demonstrates significant advantages over the other two algorithms in terms of global optimization capability, convergence speed, and stability, and is more adept at adapting to new traffic conditions to identify the optimal route. Optimizing this route not only reduces commuting time and costs but also alleviates traffic congestion, thereby enhancing the overall efficiency of the urban transportation system. [ABSTRACT FROM AUTHOR]

In the realm of Intelligent Transportation Systems (ITS), vehicular communication technologies such as Dedicated Short-Range Communications (DSRC), Cellular Vehicle-to-Everything (C-V2X), and LoRa 2.4 GHz play crucial roles in enhancing road safety, reducing traffic congestion, and improving transport efficiency. This article explores the integration of these communication protocols within smart intersections, emphasizing their capabilities and synergies. DSRC, based on IEEE 802.11p, provides reliable short-range communication with data rates up to 27 Mbps and latencies below 50 ms, ideal for real-time safety applications. C-V2X leverages LTE and 5G networks, offering broader coverage up to 10 km and supporting data rates up to 100 Mbps, with latencies as low as 20 ms in direct communication mode (PC5). LoRa 2.4 GHz, known for its long-range (up to 15 km in rural areas, 1–2 km in urban settings) and low-power characteristics, offers data rates between 0.3 and 37.5 kbps, suitable for non-critical data exchange and infrastructure monitoring. The study evaluates the performance and interoperability of these technologies in urban environments, focusing on data latency, transmission reliability, and scalability. Experimental results from simulated and real-world scenarios show that DSRC maintains reliable communication within 1 km with minimal interference. C-V2X demonstrates superior scalability and coverage, maintaining robust communication over several kilometers in high-density urban settings. LoRa 2.4 GHz exhibits excellent penetration through urban obstacles, maintaining connectivity and efficient data transmission with packet error rates below 10%. [ABSTRACT FROM AUTHOR]

With the rapid advancement of autonomous driving and network technologies, future vehicles will function as network nodes, facilitating information transmission. Concurrently, in-vehicle entertainment systems will undergo substantial enhancements. Beyond traditional broadcasting and video playback, future systems will integrate immersive applications featuring 360-degree views and six degrees of freedom (6DoF) capabilities. As autonomous driving technology matures, vehicle passengers will be able to engage in a broader range of entertainment activities while on the move. However, this evolution in video applications will significantly increase bandwidth demand for vehicular networks, potentially leading to bandwidth shortages in congested traffic areas. This paper presents a method for bandwidth allocation for vehicle video applications within the landscape of vehicle-to-everything (V2X) networks. By utilizing a millimeter-wave (mmWave), terahertz (THz) frequency band, and cell-free (CF) extremely large-scale multiple-input multiple-output (XL-MIMO) wireless communication technologies, we provide vehicle passengers with the necessary bandwidth resources. Additionally, we address bandwidth contention issues in congested road segments by incorporating communication methods tailored to the characteristics of vehicular environments. By classifying users and adjusting according to the unique requirements of various multimedia applications, we ensure that real-time applications receive adequate bandwidth. Simulation experiments validate the proposed method's effectiveness in managing bandwidth allocation for in-vehicle video applications within V2X networks. It increases the available bandwidth during peak hours by 32%, demonstrating its ability to reduce network congestion and ensure smooth playback of various video application types. [ABSTRACT FROM AUTHOR]

Taxila intersection is facing severe traffic issues includingcongestion, delays, increased travel times and inadequate infrastructure. The aim of the study is to highlight the real problems at Taxila intersection related to road users, vehicles and road infrastructure. Traffic data was collected during peak hours through traffic observing video cameras at the unsignalized intersection. Subsequently, traffic violation measurements and their comparisons between morning and evening peak hours, as well as between weekend and week days were developed. The study also identifies the involvement in traffic violations by different drivers, age groups and vehicle types. Comparing morning and evening peak hours reveals variations in traffic patterns and congestion levels throughout the day, helping to create customized plans. Similarly, investigating disparities between weekends and weekdays uncovers unique factors influencing traffic conditions on different days. Results showed that highest percentage of violations at the intersection resulted from 'illegal crossing of road by pedestrian' followed by 'illegal parking' at the intersection. While the lowest percentage of violation observed was 'wrong way driving' at the intersection. In conclusion, this research provides a comprehensive understanding of the traffic issues at the Taxila intersection and offers evidence-based strategies to improve traffic flow, mitigate congestion, and enhance road safety in the area. [ABSTRACT FROM AUTHOR]

Traffic flow is a result of the connection between the derived demand and land use. The derived demand varies in space and time, this study explores how traffic congestion is correlated with land use patterns. Historically, statistical models were used to predict and analyze these patterns. The methodology of this study is to investigate this interaction by statistical methods such as linear regression modeling. This analysis was performed using various land use types that could influence the demand. From the regression analysis, the best influence variables that affect the model are land use variables. The strong statistical parameter is commercial land use, which affects traffic volume, and causes the highest traffic congestion. In addition, correlation values are negative, meaning that as commercial land use increases, traffic flow increases and road capacity decreases. When modeling with the commercial land use variable, we conclude the value of R-Squared = 0.87 and that the relationship is an inverse strong relationship between traffic volumes and commercial land use. Mostly, land use govern traffic demand. [ABSTRACT FROM AUTHOR]

The optimization of transportation systems and the integration of autonomous vehicles (AV) are significantly transforming urban mobility and exerting outstanding effects from an environmental perspective. This article examines the possibilities of autonomous vehicles in reducing traffic congestion, emissions, and energy consumption. The optimized driving style of AVs, dynamic route planning, and enhanced intersection systems have a profound impact on emission reduction. The article also delves into current development trends and challenges, encompassing advancements in AV sensing technologies, traffic safety, and cybersecurity. These findings collectively suggest that the deployment of autonomous vehicles brings substantial benefits to sustainable urban mobility; however, further development is necessary to support the widespread adoption of AVs and strengthen societal trust. [ABSTRACT FROM AUTHOR]

With the continuous advancement of the economy and technology, the number of cars continues to increase, and the traffic congestion problem on some key roads is becoming increasingly serious. This paper proposes a new vehicle information feature map (VIFM) method and a multi-branch convolutional neural network (MBCNN) model and applies it to the problem of traffic congestion detection based on camera image data. The aim of this study is to build a deep learning model with traffic images as input and congestion detection results as output. It aims to provide a new method for automatic detection of traffic congestion. The deep learning-based method in this article can effectively utilize the existing massive camera network in the transportation system without requiring too much investment in hardware. This study first uses an object detection model to identify vehicles in images. Then, a method for extracting a VIFM is proposed. Finally, a traffic congestion detection model based on MBCNN is constructed. This paper verifies the application effect of this method in the Chinese City Traffic Image Database (CCTRIB). Compared to other convolutional neural networks, other deep learning models, and baseline models, the method proposed in this paper yields superior results. The method in this article obtained an F1 score of 98.61% and an accuracy of 98.62%. Experimental results show that this method effectively solves the problem of traffic congestion detection and provides a powerful tool for traffic management. [ABSTRACT FROM AUTHOR]

Ridesharing services have been becoming a prominent solution to reduce road traffic congestion and environmental pollution in urban areas. Existing ridesharing services fall apart in ensuring the social comfort of the riders. We formulate a Community aware Ridesharing Group Set (CaRGS) query that satisfies the spatial and social constraints of the riders and finds a set of ridesharing groups with the maximum number of served riders. The CaRGS query utilizes user social data in community levels to ensure user privacy. We show that the problem of finding CaRGS query answer is NP-Hard and propose two heuristic approaches: a hierarchical approach and an iterative approach to evaluate CaRGS queries. We evaluate the effectiveness, efficiency, and accuracy of our solution through extensive experiments using real datasets and present a comparative analysis among the proposed algorithms. [ABSTRACT FROM AUTHOR]

Purpose: This paper presents the Edge Load Management and Optimization through Pseudoflow Prediction (ELMOPP) algorithm, which aims to solve problems detailed in previous algorithms; through machine learning with nested long short-term memory (NLSTM) modules and graph theory, the algorithm attempts to predict the near future using past data and traffic patterns to inform its real-time decisions and better mitigate traffic by predicting future traffic flow based on past flow and using those predictions to both maximize present traffic flow and decrease future traffic congestion. Design/methodology/approach: ELMOPP was tested against the ITLC and OAF traffic management algorithms using a simulation modeled after the one presented in the ITLC paper, a single-intersection simulation. Findings: The collected data supports the conclusion that ELMOPP statistically significantly outperforms both algorithms in throughput rate, a measure of how many vehicles are able to exit inroads every second. Originality/value: Furthermore, while ITLC and OAF require the use of GPS transponders and GPS, speed sensors and radio, respectively, ELMOPP only uses traffic light camera footage, something that is almost always readily available in contrast to GPS and speed sensors. [ABSTRACT FROM AUTHOR]

Traffic management is a critical activity, the population is increasing day by day and so the traffic on the road is also increasing. Traffic jams and long waiting queues of vehicles at the road crossing are now part of everyone's life. The traffic lights used at the crossing to regulate the traffic play a vital role in the smooth functioning of traffic movement. At a crossing of four roads, it has been observed that giving an equal amount of green light to all roads is meaningless since the arrival of traffic on different paths is different. Importantly, the arrival rate is responsible for all traffic jams, long queues, and increased waiting time. Therefore, this paper suggests a green light allocation scheme for all paths i depending on the arrival rate of the vehicles. Thus, the allocation of green light will be dynamic. Further, weight is also computed, where more arrival rate means more weight, thereby assigning more time to the green signal. This will help in reducing the long queue length, residual traffic, and long waiting times. On simulating the traffic with the traffic data, the proposed optimized green light allocation scheme to path i reduces the residue traffic to negligible, allowing smooth traffic flow even during peak hours. The work also provides a proficient optimization of the waiting time of vehicles accumulated during the red light. According to the simulation, the maximum time assigned for the green signal during the peak hour of 9:30 AM to 10:00 AM for paths i , where 1 ≤ i ≤ 4 is 39.96, 33.36, 26.64, and 20.04 seconds respectively. Similarly, during the second rush hour of 5:00 PM to 6:00 PM, the simulation assigns a green signal time of 41.4, 37.2, 24.84, and 16.56 seconds for corresponding paths 1–4. Thus, the proposed work suggests an effective traffic management scheme at the four-road crossing. [ABSTRACT FROM AUTHOR]

Beirut's policies in the last decade or more contributed to the creation of a highly car-dependent city with almost 2 million vehicles dominating the streets and causing high traffic congestion. Yet, Beirut's informal street markets carry the potential for the enhancement of a liveable sustainable city, which previous studies have overlooked. To approach this problem, urban planners need to re-think and restructure the existing street network in Beirut to decrease the distances between the suburbs and the centre of the city. The aim of this research is to formulate a rational system that assigns markets and pedestrian areas to certain streets where cars can have no or limited access. This system adopts a parametric tool based on fuzzy logic which analyzes the current street network and filters out streets that fulfil certain criteria such as parking availability and proximity to public transportation, hence decreasing centrality in the city. This method is applied to both fixed and temporary food markets that are characterized by highly dynamic pedestrian movement. The final results show a series of maps of Beirut with different scenarios identifying streets that fulfil the defined criteria as potential street markets, which can be the blueprint for further analysis of street and transportation planning creating more liveable and sustainable places. [ABSTRACT FROM AUTHOR]

Urban vitality, intricately connected to urban morphology, has long been a cornerstone of urban planning and design. The accelerated pace of urbanization has created abundant living and working spaces, but it has also brought about a series of issues such as traffic congestion, environmental pollution, insufficient public spaces, and uneven urban development, leading to a decline in urban vitality. The spatial distribution patterns of urban vitality and their influencing factors are diverse and vary across different cities, necessitating a multidimensional exploration of the relationship between urban vitality and the built environment. Utilizing the central urban area of Beijing as a case study, this research leverages multi-source urban spatial data to delineate the spatial characteristics of social, economic, cultural, and comprehensive vitality. Furthermore, a comprehensive set of built-environment indicators is developed across five dimensions to analyze their correlation with urban vitality. The results indicate: (1) There is a significant spatial clustering of various vitality types in Beijing, with a pronounced correlation between high-density population aggregation and vigorous economic activities. (2) Subdistricts exhibiting high social vitality display an "n"-shaped distribution around the Second Ring Road. In contrast, those with high economic vitality are concentrated along Chang'an Street and various district commercial centers. High cultural vitality subdistricts are distributed in a northwest–southeast trajectory from the Fourth Ring Road to the Second Ring Road, and subdistricts with high comprehensive vitality exhibit a concentric distribution radiating outwards from the center. (3) Social vitality is most closely related to comprehensive vitality, and the various vitality types in Beijing's central urban area develop relatively evenly. (4) The built environment significantly affects all types of urban vitality. Factors such as floor area ratio, POI density, POI mixing degree, and intersection density are fundamental to enhancing urban vitality, whereas the greening rate somewhat inhibits it. (5) Future spatial planning should utilize the radiating effect of high-vitality subdistricts to optimize population distribution, enhance POI mixing, increase metro station density, and strengthen critical urban structures for synergistic economic and cultural development. This study provides a foundation and promotion strategies for optimizing the layout and enhancing vitality at the subdistrict scale within Beijing's central urban area. [ABSTRACT FROM AUTHOR]

Developing country megacities suffer from severe road traffic congestion, yet the level of congestion is not a direct measure of equilibrium inefficiency. I study the peak‐hour traffic congestion equilibrium in Bangalore. To measure travel preferences, I use a model of departure time choice to design a field experiment with congestion pricing policies and implement it using precise GPS data. Commuter responses in the experiment reveal moderate schedule inflexibility and a high value of time. I then show that in Bangalore, traffic density has a moderate and linear impact on travel delay. My policy simulations with endogenous congestion indicate that optimal congestion charges would lead to a small reduction in travel times, and small commuter welfare gains. This result is driven primarily by the shape of the congestion externality. Overall, these results suggest limited commuter welfare benefits from peak‐spreading traffic policies in cities like Bangalore. [ABSTRACT FROM AUTHOR]

In smart factory settings, ensuring the optimal navigation of Automated Guided Vehicles (AGVs) is essential for streamlining material handling operations and enhancing overall efficiency. This paper introduces a novel Sensor-Based Access Point Selection Strategy (SBAPSS) designed specifically to enhance the navigation capabilities of AGVs within smart manufacturing environments. The SBAPSS harnesses a comprehensive array of sensor data, including inputs from laser scanners, vision systems, ultrasonic sensors, and proximity sensors, to dynamically evaluate and select optimal access points for AGV navigation routes. Utilizing real-time sensor information, the SBAPSS algorithm employs sophisticated decision-making mechanisms to identify the most favorable access points based on multiple criteria. These criteria encompass factors such as obstacle detection, proximity to designated loading/unloading stations, traffic congestion, and path optimization. By integrating sensor-driven intelligence into the access point selection process, AGVs can adaptively adjust their navigation paths to circumvent obstacles, avoid collisions, and optimize travel routes in real-time. The effectiveness and reliability of the SBAPSS are demonstrated through extensive simulation studies and experimental validations conducted in representative smart factory environments. Results indicate significant improvements in AGV navigation efficiency, throughput, and safety, thereby validating the efficacy of the proposed strategy. Moreover, the SBAPSS's ability to seamlessly integrate with existing AGV control systems underscores its practical feasibility and scalability for deployment in industrial settings. This innovative sensor-driven approach represents a substantial advancement in AGV navigation methodologies, offering a robust solution tailored to the demands of modern smart manufacturing facilities. By empowering AGVs with intelligent decision-making capabilities, the SBAPSS contributes to the realization of agile, adaptive, and autonomous material handling systems in the industry 4.0 era. [ABSTRACT FROM AUTHOR]

Ensuring road transport safety is a critical imperative for regional development and public welfare. This abstract outline a comprehensive evaluation framework designed to assess the service level of regional road transport safety. The proposed methodology integrates diverse parameters, encompassing infrastructure, technology, policy, and human factors, to provide a holistic understanding of the safety landscape. Using real-time data integration and powerful analytics, the assessment system combines quantitative and qualitative indicators. Technology aspects of infrastructure evaluations centre on the effectiveness of automated transportation systems and their influence on accident reduction; architecture evaluations also cover the layout of roads, advertising, and maintenance requirements. Policy evaluation is examining current laws and the ways in which they are enforced while considering how they affect the behaviour of drivers and public safety. The proposed method uses DCNN method for intelligent road transport safety. Using DCNN algorithms to monitor and regulate traffic congestion in smart cities represents a significant leap in the use of deep learning in traffic management. [ABSTRACT FROM AUTHOR]

With the escalating density of vehicles converging at road intersections, the surge in road accidents, traffic conflicts, and traffic congestion has emerged as a pressing concern. This research paper addresses these challenges by employing MC (manual control) techniques to mitigate encroachment issues at three selected intersections. These intersections were identified through a comprehensive analysis of the Ranking-based Instance Selection (RIS), enabling the design of suitable measures to facilitate smooth traffic flow and minimize the occurrence of crashes. In order to gather pertinent data, the study incorporates various parameters such as traffic volume, peak flow rate (PFR), traffic conflicts, accidents, and intersection inventory. Through the implementation of our proposed approaches, which involve both MC techniques and signalized operation, a supreme level of service (LOS) is attainable. Notably, our findings demonstrate a remarkable reduction in the volume-to-capacity ratio (v/c ratio) of up to 0.62. This paper thus serves as a significant contribution to the field of traffic management, offering practical insights for optimizing intersection design and effectively addressing the challenges posed by increased traffic density. [ABSTRACT FROM AUTHOR]

Time-sensitive networking (TSN) is considered an ideal solution to meet the transmission needs of existing industrial production methods. The traffic scheduling problem of TSN is an NP-hard problem. The traditional traffic scheduling algorithms can lead to issues such as significant computational time consumption and traffic congestion, which are not conducive to the rapid and high-quality deployment of TSN. To simplify the complexity of TSN schedule table generation, the paper studies the scheduling problem of mixed critical traffic in TSN. Using a combinatorial packing traffic scheduling algorithm based on unoccupied space (CPTSA-US), a scheduling table for TSN traffic transmission is generated, proving the feasibility of transforming the TSN traffic scheduling problem into a packing problem. In addition, the initial packing algorithm and traditional traffic scheduling algorithm can cause traffic accumulation, seriously affecting network performance. This paper proposes a mixed-critical traffic scheduling algorithm based on free time domain (MCTSA-FTD), which further partitions the packing space transformed by the time domain. And performs multiple packing of traffic based on the partitioned packing space and generates the TSN schedule table according to the reverse transformation of the packing results. The simulation results show that compared to the CPTSA-US and the traditional traffic scheduling solution algorithm SMT (Satisfiability Modulo Theory), the schedule table generated by the MCTSA-FTD significantly improves the delay, jitter, and packet loss of BE flows in the network while ensuring the transmission requirements of TT flows. This can effectively enhance the transmission performance of the network. [ABSTRACT FROM AUTHOR]

The rapid growth of the automotive industry has exacerbated the conflict between the complex traffic environment, increasing communication demands, and limited resources. Given the imperative to mitigate traffic and network congestion, analyzing the performance of Internet of Vehicles (IoV) mesh networks is of great practical significance. Most studies focus solely on individual performance metrics and influencing factors, and the adopted simulation tools, such as OPNET, cannot achieve the dynamic link generation of IoV mesh networks. To address these problems, a network performance analysis model based on actual switches is proposed. First, a typical IoV mesh network architecture is constructed and abstracted into a mathematical model that describes how the link and topology changes over time. Then, the task generation model and the task forwarding model based on actual switches are proposed to obtain the real traffic distribution of the network. Finally, a scientific network performance indicator system is constructed. Simulation results demonstrate that, with rising task traffic and decreasing node caching capacity, the packet loss rate increases, and the task arrival rate decreases in the network. The proposed model can effectively evaluate the network performance across various traffic states and provide valuable insights for network construction and enhancement. [ABSTRACT FROM AUTHOR]

Abstract Air pollution is a significant and pressing environmental and public health concern in urban areas, primarily driven by road transport. By gaining a deeper understanding of how traffic dynamics influence air pollution, policymakers and experts can design targeted interventions to tackle these critical issues. In order to analyse this relationship, a series of regression algorithms were developed utilizing the Google Project Air View (GPAV) and Dublin City’s SCATS data, taking into account various spatiotemporal characteristics such as distance and weather. The analysis showed that Gaussian Process Regression (GPR) mostly outperformed Support Vector Regression (SVR) for air quality prediction, emphasizing its suitability and the importance of considering spatial variability in modelling. The model describes the data best for particulate matter (PM2.5) emissions, with R-squared (R2) values ranging from 0.40 to 0.55 at specific distances from the centre of the study area based on the GPR model. The visualization of pollutant concentrations in the study area also revealed an association with the distance between intersections. While the anticipated direct correlation between vehicular traffic and air pollution was not as pronounced, it underscores the complexity of urban emissions and the multitude of factors influencing air quality. This revelation highlights the need for a multifaceted approach to policymaking, ensuring that interventions address a broader spectrum of emission sources beyond just traffic. This study advances the current knowledge on the dynamic relationship between urban traffic and air pollution, and its findings could provide theoretical support for traffic planning and traffic control applicable to urban centres globally.

The Internet of Things (IoT) is essential in several Internet application areas and remains a key technology for communication technologies. Shorter delays in transmission between Roadside Units (RSUs) and vehicles, road safety, and smooth traffic flow are the major difficulties of Intelligent Transportation System (ITS). Machine Learning (ML) was an advanced technique to find hidden insights into ITSs. This article introduces an Improved Arithmetic Optimization with Deep Learning Driven Traffic Congestion Control (IAOADL-TCC) for ITS in Smart Cities. The presented IAOADL-TCC model enables traffic data collection and route traffic on existing routes for avoiding traffic congestion in smart cities. The IAOADL-TCC algorithm exploits a hybrid convolution neural network attention long short-term memory (HCNN-ALSTM) method for traffic congestion control. In addition, an IAOA-based hyperparameter tuning strategy is derived to optimally modify the parameters of the HCNN-ALSTM model. The presented IAOADL-TCC model effectively enhances the flow of traffic and reduces congestion. The experimental validation was performed using the road traffic dataset from the Kaggle repository. The proposed model obtained an average accuracy of 98.03 % with an error rate of 1.97 %. The experimental analysis stated the superior performance of the IAOADL-TCC approach over other DL methods.

With the continuous development of socio-economics, the rapid increase in the use of road vehicles has led to increasingly severe issues regarding traffic accidents. Timely and accurate detection of road traffic accidents is crucial for mitigating casualties and alleviating traffic congestion. Consequently, road traffic accident detection has become a focal point of research recently. With the assistance of advanced technologies such as deep learning, researchers have designed more accurate and effective methods for detecting road traffic accidents. However, deep learning models are often constrained by the scale and distribution of their training datasets. Presently, datasets specifically tailored for road traffic accident detection suffer from limitations in scale and diversity. Furthermore, influenced by the recent surge in research on intelligent driver assistance systems, datasets from the surveillance perspective (the third-person viewpoint) are fewer than those from the driver's perspective (the first-person viewpoint). Considering these shortcomings, this paper proposes a new dataset, Traffic Accident Detection from the Perspective of Surveillance (TADS). To the best of our knowledge, we are the first to attempt to detect traffic accident under the surveillance perspective with the aid of eye-gaze data. Leveraging the special data components within this dataset, we design the RF-RG model (input: the RGB and optical flow values of the frames; output: the RGB and gaze values of the predicted frame) for detecting road traffic accidents from a surveillance perspective. Comparative experiments and analyses are conducted with existing major detection methods to validate the efficacy of the proposed dataset and the approach. The TADS dataset has been made available at: https://github.com/cyc-gh/TADS/. [ABSTRACT FROM AUTHOR]

This study contributes to the growing body of literature on traffic congestion prediction using machine learning (ML) techniques. By evaluating multiple classifiers and selecting the most appropriate one for predicting traffic congestion, this research provides valuable insights for urban planners and policymakers seeking to optimize traffic flow and reduce jamming and. Traffic jamming is a global issue that wastes time, pollutes the environment, and increases fuel usage. The purpose of this project is to forecast traffic congestion at One of the most congested areas in Amman city using multiple ML classifiers. The Naïve Bayes (NB), stochastic gradient descent (SGD) fuzzy unordered rule induction algorithm (FURIA), logistic regression (LR), decision tree (DT), random forest (RF), and multi-layer perceptron (MLP) classifiers have been chosen to predict traffic congestion at each street linked with our study area. These will be assessed by accuracy, F-measure, sensitivity, and precision evaluation metrics. The results obtained from all experiments show that FURIA is the classifier that presents the highest predictions of traffic congestion where By 100% achieved Accuracy, Precision, Sensitivity and F-measure. In the future further studies can be used more datasets and variables such as weather conditions; and drivers behavior that could integrated to predict traffic congestion accurately. [ABSTRACT FROM AUTHOR]

Abstract The escalation of urban traffic congestion has reached a critical extent due to rapid urbanization, capturing considerable attention within urban science and transportation research. Although preceding studies have validated the scale-free distributions in spatio-temporal congestion clusters across cities, the influence of travel demand on that distribution has yet to be explored. Using a unique traffic dataset during the COVID-19 pandemic in Shanghai 2022, we present empirical evidence that travel demand plays a pivotal role in shaping the scaling laws of traffic congestion. We uncover a noteworthy negative linear correlation between the travel demand and the traffic resilience represented by scaling exponents of congestion cluster size and recovery duration. Additionally, we reveal that travel demand broadly dominates the scale of congestion in the form of scaling laws, including the aggregated volume of congestion clusters, the number of congestion clusters, and the number of congested roads. Subsequent micro-level analysis of congestion propagation also unveils that cascade diffusion determines the demand sensitivity of congestion, while other intrinsic components, namely spontaneous generation and dissipation, are rather stable. Our findings of traffic congestion under diverse travel demand can profoundly enrich our understanding of the scale-free nature of traffic congestion and provide insights into internal mechanisms of congestion propagation.