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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]

Structural models of traffic congestion, such as the bottleneck model, are used to answer important, policy‐relevant questions. However, existing models typically assume that no travelers are inframarginal regarding when to travel; that is, given equilibrium travel times, no travelers strictly prefer their ex ante departure time to all others. In this article, I address this shortcoming by incorporating inframarginal travelers into these models. This change significantly improves these models' ability to fit the data and changes policy prescriptions. In the case of congestion pricing, it typically changes the optimal toll by at least 25% and significantly worsens the distributional impacts. [ABSTRACT FROM AUTHOR]

Data transmission from sensor nodes is the biggest problem for IoT networks. Overusing communication power shortens node lifespans. Thus, network issues including QoS, security, network heterogeneity, congestion avoidance, reliable routing, and energy savings must be addressed. Routing protocols are essential for delivering data between organizations. Information gathering and consolidation require data aggregation to minimize traffic congestion, operating expenses, energy usage, and network lifespan. IoT data aggregation makes route planning dependable, energy-efficient, and difficult. Disjoint & Scalable Multipath Routing (D &SMR) is a new routing system developed using NS2 simulation. The method estimates delivery success using decision trees and neural networks. We evaluate characteristics such as (D &SMR) routing scheme predictability, node popularity, power consumption, speed, and location while training the model. Simulation results show that (D &SMR) outperforms a reliable routing system in terms of delivery success, lost messages, overhead, and hop count. The proposed hybrid routing method involves cluster construction and intra- and inter-cluster routing. The study found that (D &SMR) beats previous research in network resilience, packet transmission efficiency, end-to-end latency, and energy usage. [ABSTRACT FROM AUTHOR]

The venture is intended to foster a thickness based unique traffic light framework. The sign timing changes consequently on detecting the traffic thickness at the intersection. Gridlock is an extreme issue in many significant urban communities across the world and it has turned into a bad dream for the workers in these urban areas. Traditional traffic signal framework depends on fixed time idea allocated to each side of the intersection which can't be changed according to fluctuating traffic thickness. Intersection timings apportioned are fixed. Some of the time higher traffic thickness at one side of the intersection requests longer green time when contrasted with standard allocated time. The item identification in the traffic light is handled and changed over into test system then, at that point, its limit is determined in view of which the shape has been attracted request to ascertain the quantity of vehicles present nearby. In the wake of working out the quantity of vehicles we will came to realize in which side the thickness is high in light of which signs will be distributed for a specific side. On account of its high recognition rate, CNN can be used to realize various computer vision tasks. Tensor Flow is used to implement CNN. In the German data sets, we are able to identify the circular symbol with more than 98.2% accuracy. [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]

This paper is a response to the roundtable on Metamodernism: The Future of Theory, focusing on the work’s methodological implications and theoretical advancements. Using traffic jams as a case study, Jason Ānanda Josephson Storm illustrates how his approach both incorporates and transcends the insights of discourse studies. He abridges a version of his theory of process “social kinds,” which he describes as dynamic, socially constructed clusters of properties, defined by the anchoring processes – dynamic-nominalist, mimetic, and ergonic – that uniquely contribute to their properties. He shows how focusing on these anchoring processes challenges conventional methodologies and could herald new research paths in religious studies and other fields, advocating for a shift towards a more inclusive and dynamic interpretation of social phenomena. [ABSTRACT FROM AUTHOR]

Summary: Recently, wireless sensor networks (WSNs) have been used for monitoring, sensing, processing, and communication purposes in real‐time applications. It is employed with a routing protocol that performs an effective data transmission process. However, while transmitting large data, there occurs an over fitting issue, which leads to determining a huge data leakage. Also, the delay is increased with heavy congestion in the network. Hence, a novel method is proposed to diminish the network congestion regarding distributed networks as well as cloud edge computing. Moreover, it diminished the data loss from an overloaded condition. However, the proposed technique controls congestion that resists the traffic in the network through lightweight, ultra‐dense label‐less federation and incorporates adaptive multi‐agent Markov reinforcement learning. Furthermore, a distributed energy‐efficient delay‐aware routing protocol is employed to analyze and regulate congestion control in the network. Also, it varies the network dynamically by adjusting the routing protocol that optimizes the congestion and implements the traffic mechanism. Moreover, the congestion in WSNs overwhelms the nodes and channels distributed in the packets. The evaluation of the proposed method is determined by various metrics such as queuing delay, network lifetime, energy efficiency, throughput, and packet delivery ratio. The experimental results revealed that the proposed method attained an enhanced performance by maximizing energy efficiency and packet delivery ratio by 94% as well as 89% and reducing the delay by 55%, respectively. [ABSTRACT FROM AUTHOR]

This paper focuses on the shopping behavior of Gen Z consumers while shopping in Jakarta MRT station. MRT has become an alternative solution for transportations in Jakarta to get over traffic jam and tends to have punctual schedule. Each MRT stations have different type of brand and retail stores that varies from food and beverages, convenience store and others. Gen Z often use MRT as their preferred transportations and in this study their response towards MRT stations retail and shopping behavior was analyzed during a short interview with the respondents. Analysis in retail shop and how consumers behave towards shopping including their motivations and preferences. In applying a qualitative approach, the study brings forth the Gen Z passengers' preference for punctual, fast travel, brand loyalty, and quick shopping. The results from the research indicate that Gen Z consumers tend to shop at retail shops near the city transit stations in the morning, providing that they love speed and efficiency. It also shows that brand play key role in the Gen Z consuming behavior and how they have planned ahead before actually shopping. The research also indicates that retail firms need to blend the digital way of doing business and social responsibility in order to reach this popular demographic. The dynamics of Gen Z consumers contribute significantly to the choice of products, place, time, and retail shops. Through a deep understanding of these elements, a business can succeed in the trends of the consumers in the urban transit setting. [ABSTRACT FROM AUTHOR]

The growth of motor vehicles in Surakarta from year to year always increases while road capacity does not develop. Therefore, there are often traffic jams everywhere. The discipline of every road user also plays an important role in overcoming congestion on the highway. Stages carried out from problem formulation, research objectives, and literature review, Then preliminary surveys, geometric surveys, vehicle volume surveys, and vehicle speed surveysMonday saturation degrees (DS) decreased after changing the cycle time. This means that the traffic volume at the Mlipahan intersection has decreased due to the resetting of traffic lights on Monday Road capacity: north 1,647 smp / hour, south 1,610 smp / hour, west 786 smp / hour, east 1,348 smp / hour. Saturation Degrees: North 1.15, South 1.12, West 1.15, and East 1.13. Queue Length: North 203 m, South 195 m, West 93 m, and East 145 m. obtained a value of 210 sec/smp. Sunday obtained Road capacity: North 1,763 smp / hour, South 1,549 smp / hour, West 818 smp / hour, and East 1,050 smp / hour. Saturation Degree: North 0.54, South 0.51, West 0.95, and East 0.91. Queue Length: North 92 m, South 89 m, West 87 m, and East 104 m. And the delay was obtained at 36 sec/smp. With PTV-VISSIM software, the queue length is obtained: North 97 m, South 129 m, West 198 m, and East 83 m. The delay was obtained at 72 sec/skr. And Service Level Intersection F or poor. [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]

Despite the needs of assessing the time-varying queue length for incident traffic management, such imperative information has not yet been available in practice, due partly to the lack of reliable sensor data on many highways plagued by nonrecurrent congestion. Hence, this study first presents an incident queue impact assessment system that allows highway agencies to perform reliable estimate of the queue distance during the incident clearance period with available detector data, and then discusses two alternative models for supporting approximating such impacts on roadway networks without traffic sensors. The proposed system’s major model, grounded in the classical shockwave theory, has augmented the formulations for queue propagation dynamics with the impacts from incoming drivers’ perceptions and responses to the progressively constrained traffic conditions. To cope with the lack of real-time traffic flow information, the first alternative model integrates the increasing available speed data from probe vehicles or other sources with lane blockage patterns to approximate the discharge flow rate of the incident. The second alternative model centers on the methodology and calibration methods for constructing a set of speed– flow rate relationships for different times of day with archived traffic data to approximate the arriving flow rate. With such information, responsible agencies can then proceed the approximation of incident queue variation length and take necessary actions. Performance evaluation of the proposed system with both field incident data and simulated scenarios has confirmed its promising properties, especially for highway networks without reliable traffic surveillance systems. [ABSTRACT FROM AUTHOR]

This study aims to estimate the driving times of drivers who prefer electric scooter vehicles. In general, e-scooters reduce the loss of time caused by traffic jams thanks to their smaller size and maneuverability, therefore, these vehicles provide rapid progress in urban journeys. E-scooters also offer an advantage in finding a parking space and easy parking thanks to their more compact structure. In this study, ML algorithms were used to predict the driving times of drivers who prefer e-scooter vehicles. The AB model performed well with a low Mean Square Error (MSE) value (0.005). The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values were also relatively low (0.069 and 0.039, respectively), indicating that the model's predictions were close to the actual values. Also, the high R-squared-Coefficient of Determination (R²) value (0.947) suggested that this model explained the data quite well, and its predictions approached the actual values with high accuracy. On the other hand, the GB algorithm performed poorly compared to different algorithms, with its high margin of error and low accuracy rate. These results provide an advantage in time management by estimating the travel time a driver will make with the e-scooter. As a result, e-scooters offer drivers the opportunity to save time and manage their daily mobility more effectively, driving these vehicles attractive for transportation. [ABSTRACT FROM AUTHOR]

The consequences of sustainable and greener mobility have been demonstrated previous year by the pandemic situation. One significant benefit was to reduce the level of noise, dust and air pollution from the traffic in many European countries due to reduced mobility most of the population. Positive phenomena was also recorded in the Slovak Republic. Today's urban mobility offers new, more sustainable ways of transport for residents, especially app-based and shared-ride services, which have become increasingly widespread and very popular. Undoubtedly offer a level of convenience unseen before in the urban mobility systems throughout the world. Although new forms of transport are developed in most Slovak cities, their operation is not very rational and therefore individual car transport still dominates at the expense of sustainable ways of transport. In our paper, we analyze the traffic situation in the city of Nitra, as the large number of trips in the course of the peak-hours often results in severe traffic congestion. Multimodality is one of the solutions to improve the current traffic situation, as it allows urban residents to choose from a range of alternative travel options. The main aim of this paper is to determinate the possibilities of multimodal urban mobility in the city of Nitra in addition to analyze how the potential change in the variability of choice of means of transport affects the urban mobility behaviour. In order to meet stated objectives, a marketing research was conducted. The research findings point to the fact that the current urban infrastructure and overall possibilities are limited, the consequence of which is that travel behaviour of citizens does not demonstrate the features of sustainable urban mobility. Our results point to major differences in attitudes of Nitra´s residents from different urban and sub-urban areas. [ABSTRACT FROM AUTHOR]

Massive amounts of real-time streaming network data are generated quickly because of the exponential growth of applications. Analyzing patterns in generated flow traffic streaming offers benefits in reducing traffic congestion, enhancing network management, and improving the quality of service management. Processing massive volumes of generated traffic poses more challenges when data traffic encryption is raised. Classifying encrypted network traffic in real-time with deep learning networks has received attention because of their excellent performance. The substantial volume of incoming packets, characterized by high speed and wide variety, puts real-time traffic classification within the domain of big data problems. Classifying traffic with high speed and accuracy is a significant challenge in the era of big data. The real-time nature of traffic intensifies deep learning networks, necessitating a considerable number of parameters, layers, and resources for optimal network training. Until now, various datasets have been employed to evaluate the effectiveness of previous methods for classifying encrypted traffic. The primary objective has been to enhance accuracy, precision, and F1-measure. Presently, encrypted traffic classification performance depends on pre-existing datasets. The learning and testing phases are done offline, and more research is needed to investigate the feasibility of these methods in real-world scenarios. This paper examines the possibility of a tradeoff between evaluating the model's effectiveness, execution time, and utilization of processing resources when processing stream-based input data for traffic classification. We aim to explore the feasibility of establishing a tradeoff between these factors and determining optimal parameter settings. This paper used the ISCX VPN-Non VPN 2016 public dataset to evaluate the proposed method. All packets from the dataset were streamed continuously through Apache Kafka to the classification framework. Numerous experiments have been designed to demonstrate the efficacy of the proposed method. The experimental results show that the proposed method outperforms the baseline methods by 11% in the F1-measure when the number of workers is two and by 25% when the number of workers is equal to 32. [ABSTRACT FROM AUTHOR]

Under the context of rapid development of the Internet of vehicles and vehicle-road collaboration system, active traffic management (ATM) becoming the mainstream means of road traffic control and developing toward refinement. In this paper, to study the high-precision lane-level dynamic induction control strategy in different scenarios, based on the NaSch model of cellular automata and combined with the characteristics of the failure section area, a fuzzy lane-changing bypass vehicle-following model considering lane-changing pressure in multi-lane failure scenarios was built. The simulation results show that (i) if the lane failure occurs on the middle lane, the lane should be induced in advance, and the induced lane change effect is the best at about 100 m. When the lane failure occurs in the left lane and right lane, the prompt is best at about 250 m. (ii) The induced distance should be based on actual traffic conditions, free combination of different early warning distances between 100 and 300 m can save about 20–30 s congestion time. (iii) The lane-level dynamic coordinated guidance control measures can effectively improve the road traffic efficiency compared with the static unified control measures, improve the traffic efficiency of road performance, and alleviate traffic congestion time. The conclusion of this paper can provide some reference for dynamic active control management and achieve higher accuracy of traffic flow lane-level control. [ABSTRACT FROM AUTHOR]

Traffic congestion is a phenomenon that has been extensively explored by researchers due to its impact on reliability and safety. This research is focused on proactively detecting and mitigating congestion on freeways by fuzing conventional traffic data obtained from radar and loop detectors with newer sources, such as Bluetooth and connected vehicles (CV). Data-driven and signal-processing techniques are explored to develop algorithms that use near- or real-time traffic measurements to predict the onset and intensity level of traffic congestion. The developed algorithm can be applied to both conventional and low penetration CV-based datasets to identify four types of congestion, that is, normal, recurring, other non-recurring, and incident. This research also demonstrates the advantage of using CV-based travel time estimates to calibrate microsimulation models over fixed point-based derivations of travel time from spot speeds. Finally, a set of mitigation strategies consisting of speed harmonization and dynamic rerouting are implemented in the calibrated simulation network to demonstrate their effectiveness in proactively reducing recurring and non-recurring congestion. The final derived algorithm is effective in proactively predicting the onset of congestion and its intensity level, with an overall mean prediction error of 30.2%. A limitation to the algorithm's methodology is that it cannot disentangle the type of congestion when two or more are occurring simultaneously and only predicts/classifies the anticipated highest level. However, this does not impair the user's ability to readily deploy appropriate mitigation strategies to alleviate the predicted intensity of congestion. [ABSTRACT FROM AUTHOR]

Ride-sharing has transformed people's travel habits with the development of various ride-sharing platforms, which can enhance the utilization of transportation resources, alleviate traffic congestion, and reduce carbon emissions. However, the development of a general and efficient matching framework is challenging due to the dynamic real-time conditions and uncertainty of ride-sharing problems in the real world. Additionally, previous research has identified limitations in terms of model practicability and algorithmic solution speed. To address these issues, a two-stage dispatching approach for one-to-many ride-sharing with sliding time windows is proposed. The dynamic ride-sharing problem is formally defined, and an integer programming model is constructed to solve it. A multi-rider distance and time constraint algorithm uses a distance matrix and sliding time windows to preprocess data before matching is proposed, thereby optimizing data quality and improving computational efficiency. The ride-sharing process is divided into a reservation order matching stage based on path similarity and a real-time order matching stage based on path distance degree. A two-stage collaborative mechanism is designed to guide the collaboration of the two stages. Furthermore, numerical experiments are conducted using two real-world datasets from developing and developed country regions to verify the efficiency and practicability of the proposed approach. [ABSTRACT FROM AUTHOR]

The current traffic signal light timing scheme cannot effectively solve the problem of traffic congestion. This paper studied the intelligent control of signal lights based on infrared sensors. Firstly, the traffic flow was detected by infrared sensors; then, the future traffic flow was predicted using a back-propagation neural network; an intelligent control algorithm was designed based on the prediction results, and data were collected at the actual intersection for simulation experiments. It was found that the maximum and minimum root-mean-square error (RMSE) value of the BPNN prediction were 72.33 and 45.77, respectively, i.e., BPNN could predict the traffic flow accurately. Then, in the comparison of timing schemes, the maximum average queuing delay time of the BPNN was 0.55 s, and the maximum average queuing length was 92 m in the case of large traffic flow, both of which were better than the traditional fixed timing scheme. The results prove the reliability of the proposed signal light intelligent control algorithm. The algorithm can be further applied in practice. [ABSTRACT FROM AUTHOR]

Traffic congestion and roadside emissions are severe and common problems in metropolitans. As a promising and sustainable solution to mitigating these vehicle externalities, shared mobility reduces the required vehicle fleet size for serving a given level of demand by sharing a vehicle among travelers with similar schedules and itineraries. Public acceptance is the key to the success of shared mobility development. This study investigates the acceptance of drivers and passengers of two typical competing shared mobility modes, car-pooling and taxi ride sharing, taking Hong Kong as a case study. For an empirical evaluation, an on-street stated preference survey was conducted, and 829 respondents, including 257 private car owners and 572 non-private car owners were interviewed about their travel preferences in three given hypothetical scenarios. In total, 2,487 observations were collected for calibrating two proposed logit-based discrete choice models for drivers and passengers. The model results show that the out-of-pocket cost, in-vehicle travel time, and out-of-vehicle time are key factors influencing travelers' decisions toward car-pooling and taxi ride-sharing. An equilibrium model was proposed and an iteration solution procedure was applied to obtain a convergent solution to balance the demand and supply of drivers and passengers for car-pooling services. Furthermore, sensitivity analyses were carried out to examine the effects of variations in proportions of travel cost and taxi fare shared by passengers for car-pooling and taxi ride-sharing, and to assist in the formulation of relevant transport policies. [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]

The conventional intelligent prediction methods for urban road traffic congestion suffer from drawbacks including limited prediction accuracy and prolonged prediction times. To address these issues, this paper introduces a novel approach for predicting urban road traffic accident losses, leveraging a variable weight TOPSIS method in conjunction with a modified entropy weight scheme. By refining the entropy weight calculation to determine the significance of factors influencing urban road traffic accident losses, and integrating this with the variable weight TOPSIS method, we achieve effective screening of these factors. Subsequently, utilizing these identified factors and the XGBoost algorithm, we conduct predictions of urban road traffic accident losses. Experimental results demonstrate that our method attains a maximum precision of 98.67% in selecting influential factors, and a peak prediction accuracy of 98.26% for urban road traffic accident losses. Furthermore, the prediction time ranges efficiently between 0.29s and 0.71s, underlining the reliability and practicality of our proposed method. [ABSTRACT FROM AUTHOR]

The intelligent prediction of urban road traffic congestion is of great significance for improving traffic efficiency, reducing congestion, and optimizing resource allocation, thereby improving the overall mobility and quality of life of the city. In order to overcome the shortcomings of traditional prediction methods for urban road traffic congestion, such as poor prediction accuracy and long prediction time, an intelligent prediction method of urban road traffic congestion based on knowledge graph technology is proposed in this paper. Urban road data are collected in a static and dynamic way, and urban road traffic data are clustered by improved K-means clustering algorithm. Using stepwise regression method to repair urban road traffic data, combining knowledge graph technology to mine the influencing factors of urban road traffic congestion, and combining hidden Markov model to realize intelligent prediction of urban road traffic congestion. The experimental results show that the average absolute error of this method is 0.378, the mean root mean square error is 1.284, and the time formula of urban road traffic congestion intelligent prediction is below 0.32s. It prove that new the prediction method has the characteristics of high precision and high efficiency. [ABSTRACT FROM AUTHOR]

During the construction of concrete bridges, vehicle traffic capacity frequently experiences significant disruptions. To assess this impact, this article conducts a new comprehensive evaluation method. Initially, we calculate the equivalent speed and density of traffic on the construction section, subsequently analyze the functional relationship between these two parameters. By categorizing traffic into three distinct flow states, we compute the traffic congestion index. Furthermore, we establish a multi-lane traffic capacity model to estimate the vehicle traffic capacity during concrete bridge construction. This estimated capacity is then compared with pre-construction traffic capacity data. Experimental results reveal that the traffic capacity reduction rate and traffic flow change rate calculated using new methodology align well with actual values, thereby validating the high accuracy of new evaluation approach in assessing the impact of construction on vehicle traffic capacity. [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]

The issue of traffic congestion at signalized intersections is a concern in transportation systems due to the growth of urban areas and increased vehicular transportation. To study the evolution of congestion and evaluate the traffic performance operation of signalized intersections under problematic congested and improved conditions, the microscopic simulation VISSIM software is utilized. The objectives of this paper are to evaluate operational techniques, build a simulation model, and produce a well-calibrated and validated model. The methodology procedure to evaluate the signalized intersection involves the application of a traffic simulation model to observe real-time delays and stopped vehicles. Using the VISSIM software Version 9 to create an intersection model and redesign geometry with an exclusive right turn to enhance the intersection functionality and reduce delay. Our research focused on the Al-Nakhala signalized intersection located in the southern part of Palestine urban street in Baghdad city. This intersection is one of the busiest along the corridor due to significant land-use changes in the study area, including residential, educational, or commercial areas generating daily pressure from additional trips and saturating the absorptive capacity of the intersections. The proposed scenario of an exclusive right– turn could reduce the queue length and vehicle delay at the signalized intersection, resulting in a more efficient traffic operation. As a result of the reduction in vehicle delay, the Level of service (LOS) for the north, west, and east approaches improved from F to D. However, there was only a slight improvement for the south approach, with the LOS changing from E to F. Nonetheless, there was a noticeable reduction in queue length and vehicle delay ranging from 25% to 50%. [ABSTRACT FROM AUTHOR]

The effect of on street parking in urban commercial areas can cause a degradation of road performance. Therefore, the development of an integrated parking system is crucial to be carried out in commercial areas so that the performance of road sections does not degrade and does not cause heavy congestion. This study aims to provide recommendations for integrated parking locations based on existing parking conditions in the study area, so that they can be integrated and combined in one location. The method used is a primary survey that refers to the guidelines of the Indonesian Road Capacity Manual (IRCM) 1997 in analyzing traffic performance, and refer to the Guidelines for the Implementation of Parking Facilities 1997 in analyzing parking conditions. Meanwhile, for recommendations in determining alternative parking locations using SWOT analysis. The result of this research is the proposed location for integrated parking at Pasar Balong with the recommendation of a 4-floor building. The SWOT analysis showed that its strengths are its strategic location and high parking demand, its weaknesses are land limitations and lack of integrated parking management, its opportunities are potential for revenue and increased traffic flow, and its threats are resistance from local businesses and significant investment needs. The implication of this study is to provide a solution to the problem of roadside parking in commercial areas that interfere with traffic flow, improve the efficiency and accessibility of parking facilities for the community, and become a reference for further research on integrated parking management in other commercial areas. [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]

Road traffic accidents are the leading cause of death worldwide for people aged 2–59. Nearly all deaths are due to human error. Automated vehicles could reduce mortality risks, traffic congestion, and air pollution of human-driven vehicles. However, their adoption depends on consumer acceptance, among other factors. In a nationally representative sample of Americans (N = 580) and direct replication (N = 193), we find consumers prefer lower levels of vehicle automation for themselves than for others. This difference is mediated by self-enhancing comparative evaluations. Relative to automated vehicles, consumers believe they are safer and more trustworthy drivers than other drivers. In a second experiment (N = 803), enhanced assessments of self, not different assessments of automated vehicle capabilities, explained different preferences for self and others. Our findings show how biased self-evaluations reduce the acceptance of automated vehicles. This yields practical insights for policymakers and firms seeking to increase acceptance of automated vehicles. [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]

On-ramp control is an effective approach for alleviating traffic congestion on highways. However, there is still a lack of on-ramp control approaches applicable to large regional highway networks. Here, we develop a targeted on-ramp control approach applicable to regional highway networks by taking advantage of the vehicle source information, which pinpoints the on-ramps contributing major traffic flow to the highway bottleneck. Furthermore, a combined and tunable controlling index is proposed to enhance the equity of the generated traffic control scheme. The proposed on-ramp control approach is validated on an actual large highway network using actual travel demand data. Results indicate that the proposed approach can well mitigate the traffic congestion of highway bottleneck while at the same time enhancing the equity and practicability of the generated traffic control scheme. [ABSTRACT FROM AUTHOR]

Rapid growth of urban population and unplanned urbanization are reducing the number of urban parking spaces and increase traffic congestion. In China, due to the tripling of the number of motor vehicles in the last decade, the problem of finding a parking space has become very pressing. Increased difficulties are observed with short-term parking near public urban locations such as cinemas, shopping centers, universities and hospitals. In the telecommunications industry and other related fields, learning automata have been widely employed as adaptable devices for making decisions in unknowable settings. Their use in choosing a parking spot for a little time in cities hasn't been properly researched, nevertheless. The usage of learning automata in the created LAPS (Learning Automata Parking System) parking algorithm is discussed in this study in contrast to other algorithms. More specifically, the study will gain insight into the impact of factors such as vehicle count, space between vehicle arrivals in the parking lot, time of day, and the parking area that can be used for reservations on the average waiting time for a parking space and the likelihood of finding one. The paper presents the research findings determining the parking system's effectiveness based on learning automata in the urban areas of China. The experimental part of the study was performed using Arena simulation software on the parking lot model, with the maximum parking time under simulation conditions not exceeding 4 h. The paper evaluated such parking efficiency metrics as the average parking space waiting time and the probability of getting one, measuring the influence on these indicators on such factors as the number of motor vehicles, interval between the arrival of vehicles in the parking lot, time of day, as well as the parking area that can be used for reservations. The research findings suggested that the LAPS algorithm can be recommended for development of smart packing systems software in urban areas in China and other countries. [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]

Purpose: Companies are adopting innovative methods for responsiveness and efficiency in the public transport sector. The implementation of air-taxi services (ATS) in the transport sector is a move in this direction. Air taxis have a two-pronged advantage as they can reduce travel times by avoiding traffic congestion and have the potential to reduce carbon footprint compared to traditional modes of public transportation. Many companies worldwide are developing and testing ATS for practical applications. However, many factors may play a significant role in adopting ATS in the transport sector. This paper attempts to unearth such critical success factors (CSFs) and establish the interrelationships between these factors. Design/methodology/approach: Fifteen CSFs were identified by systematically reviewing the literature and taking experts' input. An integrated multi-criteria decision-making (MCDM) technique, Decision-Making Trial and Evaluation Laboratory-Analytic Network Process (DEMATEL-ANP [DANP]) was used to envisage the causal relationships between the identified CSF. Findings: The results reveal that Govt Regulations (GOR), Skilled Workforce (SKF) and Conductive Research Environment (CRE) are the most influential factors that impact the adoption of ATS in the transport sector. Practical implications: The research implications of these findings will help practitioners and policymakers effectively implement ATS in the public transportation sector. Originality/value: This is the first kind of study that identifies and explores the different CSFs for ATS implementation in public transportation. The CSFs are evaluated with the help of a framework built with inputs from logistics experts. The study recognizes the CSFs for ATS implementation and provides a foundation for future research and smooth adoption of ATS. [ABSTRACT FROM AUTHOR]

In the future, intelligent transportation systems will rely heavily on vehicle to vehicle (V2V) communication-based cooperative vehicular networks since they can provide several applications for tackling traffic congestion and road safety. Future 6G and New Radio 5G (NR5G) technology will facilitate improving cellular vehicle to everything (CV2X) communication performance. Cellular-assisted V2V communications, in which cellular evolved Node base stations (eNBs) relay the transmission from one vehicle to another, have lately been proposed to overcome the performance restrictions of conventional dedicated short-range communication (DSRC) protocols (Kimura in IEEE Trans Veh Technol 70(4): 3396–3411, 2021. 10.1109/TVT.2021.3063694). These cellular-aided communications have demonstrated the potential for more reliable V2V communications with a larger capacity and longer transmission distances. In this paper, we theoretically examine the effectiveness of cellular vehicle to everything communications, where a vehicle broadcasts a message to its nearby vehicles or user equipment (UEs). It happens in two ways, i.e., direct vehicle to vehicle and via the closest eNB through an uplink. Then the closest eNB forwards the message to the destination vehicle via downlink i.e., vehicle to network (V2N) or vehicle to infrastructure (V2I). To obtain a suitable model for estimating the Bit Error Rate (BER) and the Packet Error Rate (PER), we consider the movement of three vehicles are in a straight line on the roadways with different velocities. Using numerical examples, we assess the analytical findings and show how different system factors affect the effectiveness of CV2X communications. A simulation has been carried out in the network (system) level context. [ABSTRACT FROM AUTHOR]

Knowledge of modern state-of-the-practice traffic flow sensors provides traffic managers, researchers, and students an understanding of the operation, strengths, and limitations of current sensor technologies and enables them to make an informed decision as to which is appropriate for a particular application. Accordingly, this article describes the intrusive and nonintrusive traffic flow sensor technologies in use today, their applications and selection criteria, and typical output data. Furthermore, it provides examples of representative sensor models. The technologies discussed are mature with respect to current traffic management applications, although some may not provide the data or accuracy required for a specific application or may not perform as needed under the operational conditions anticipated at the installation site. Sensors selected for a first-time application should be field tested under conditions that will be encountered in day-to-day operation before large-scale purchases of the devices are made. As alternative traffic data and information sources, such as commercial data vendors, Wi-Fi and Bluetooth sensing of smartphone locations, and connected and automated vehicle data, become increasingly available, they are progressively finding their way into modern traffic management systems as a complement to conventional roadside sensors. [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]