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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]

Macroscopic fundamental diagram (MFD)-based perimeter control has a potential to improve traffic throughput and relieve the overall congestion. In practice, not only the MFD but also the traffic demand suffers from a variety of inherent uncertainties. Hence, this paper aims to contrive a robust control technique to address the perimeter traffic flow control issue in the oversaturated region. To this end, an integral sliding mode control is proposed to drive the traffic state to the desirable condition. The developed strategy deals with practical issues such as finite time stability and range of uncertainty in both MFD and traffic demand. Theoretical analysis verifies that the developed integral sliding mode control approach can guarantee the finite time convergence of the traffic state to the desired one. The performance of the developed scheme is attested by considering various traffic scenarios with uncertain MFDs and traffic demand, in which a comparative performance study with the proportional and integral control method is conducted. It is indicated that the developed scheme can alleviate the congestion in the urban network and improve the throughput of the urban network. Practical Applications: The research presented in this article proposes a novel perimeter control technique designed to enhance traffic flow management in densely populated urban road networks, particularly during peak congestion times. By leveraging the macroscopic fundamental diagram (MFD) and integral sliding mode control theory, the method significantly boosts traffic throughput and alleviates the impact of congestion. This advancement can be seamlessly integrated into current urban traffic management systems, leading to more efficient and convenient urban commutes. Additionally, it addresses the rise in vehicle emissions due to heavy traffic, aiding in the reduction of pollution and supporting sustainable development initiatives. The technique also curtails vehicle fuel consumption and cuts down on costs associated with congestion-related delays, thereby fostering resource optimization and economic growth within urban environments. Moreover, the method is designed to be resilient, taking into account the variability of traffic demand and ensuring the robustness of the traffic control strategy. Overall, the article demonstrates the practical application of advanced control technologies to address real-world traffic challenges, providing a scalable and robust solution adaptable to realistic situations with the uncertainties of urban traffic demand. [ABSTRACT FROM AUTHOR]

Travelers always perform some preference during the decision-making process. The preference will affect the decision results and can be improved by continuously learning. In order to understand the influence of individual preference on travel behavior choice , two individual preferences, including indifference preference and compulsive preference are considered in the paper. Two updating mechanisms of compulsive preference are proposed to obtain the choosing probability of all alternatives. Reinforcement learning models are established integrating the gain stimulating and loss stimulating considering expected utility. Nguyen Dupuis network is adopted for numerical simulation to study the updating process. Simulation results denote that the equilibrium state is much more efficient when preference learning mechanism is considered comparing with the traditional stochastic user equilibrium model, and can decrease the total travel time greatly, which can be applied for urban traffic management. Personalized traffic guidance is the effective solution to traffic congestion in the future [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]

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]

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]

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]

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]

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]

Urban traffic problems are one of the major challenges in the development of smart cities. To effectively alleviate traffic congestion and improve urban traffic management, a distributed big data analytics platform based on the MapReduce computing framework is investigated to process urban traffic data containing millions of traffic flow, speed and timestamp records. The study proposes an improved Long Short-Term Memory (LSTM) neural network algorithm and describes the network structure, training process and parameter tuning in detail. The effectiveness of the improved algorithm was verified through comparative analysis with the traditional ARIMA, linear regression and support vector regression algorithms. The results showed that the improved LSTM algorithm achieved an accuracy of 97.62% in traffic flow prediction, which was significantly better than other algorithms. The results demonstrate that the method can provide reliable technical support and decision-making basis for traffic management in smart cities. [ABSTRACT FROM AUTHOR]

The growing urban population and traffic congestion underline the importance of building pedestrian-friendly environments to encourage walking as a preferred mode of transportation. However, a major challenge remains, which is the absence of such pedestrian-friendly walking environments. Identifying locations and routes with high pedestrian concentration is critical for improving pedestrian-friendly walking environments. This paper presents a quantitative method to map pedestrian walking behavior by utilizing real-time data from mobile phone sensors, focusing on the University of Moratuwa, Sri Lanka, as a case study. This holistic method integrates new urban data, such as location-based service (LBS) positioning data, and data clustering with unsupervised machine learning techniques. This study focused on the following three criteria for quantifying walking behavior: walking speed, walking time, and walking direction inside the experimental research context. A novel signal processing method has been used to evaluate speed signals, resulting in the identification of 622 speed clusters using K-means clustering techniques during specific morning and evening hours. This project uses mobile GPS signals and machine learning algorithms to track and classify pedestrian walking activity in crucial sites and routes, potentially improving urban walking through mapping. [ABSTRACT FROM AUTHOR]

Rainfall has a significant impact on urban population mobility, posing great challenges to traffic management and urban planning. An understanding of this influence from multiple perspectives is urgently needed. In this study, we devised a multiscale comparative research framework to explore the spatiotemporal effects of rainfall on taxi travel patterns, aiming to provide a new perspective on the investigation of rainfall's impact on urban human mobility. More specifically, at the macroscopic scale, we computed taxi travel indicators across the entire study area and used kernel density estimates to observe the spatiotemporal distribution patterns influenced by rainfall. Subsequently, complex traffic networks were constructed by considering urban road intersections as nodes and combined with visualization methods to understand changes in taxi travel patterns visually at the microscopic level. We selected Wuhan City, a typical urban area in southern China with frequent rainfall, as the study area and used meteorological data along with a large volume of taxi spatiotemporal trajectory data for investigation. Results indicated a 4.16% decrease in weekly travel volume due to rainfall, with a 3.96% decrease on workdays and a 4.64% decrease on weekends. However, nighttime rainfall between 19:00 and 22:00 on weekdays increased the demand for taxi travel. Furthermore, the impact of rainfall on weekends exceeded that on workdays, restricting people's mobility and leisure activities, resulting in reduced travel to recreational tourist spots and commercial pedestrian streets. Rainfall altered residents' travel preferences to some extent, with more residents choosing taxis during rainy weather, which led to decreased transportation efficiency and increased traffic congestion. These findings contribute to a deeper understanding of the complex relationship between population mobility patterns and the urban ecological environment, providing valuable insights for planning resident travel and taxi dispatching under adverse weather conditions. [ABSTRACT FROM AUTHOR]

The study concentrates on two different genetic programming approaches for determining passenger car equivalent (PCE) values and observing the impact on capacity estimation at urban unsignalized intersections. Considering heterogeneous traffic conditions, a new PCE value is introduced to encompass sustainable modes of public transit vehicles, specifically slow-moving three-wheelers (SM3W), commonly known as E-Rickshaws. Since PCE value is considered an important parameter for capacity calculations, the present study considered 14 unsignalized intersections located in Ranchi city of India. An automatic plate recognition system is employed to have the count of vehicular traffic. The methodologies include age-layered population structure genetic programming (ALPSGP), and the offspring selection genetic programming (OSGP) approach that incorporates static and dynamic variables. Based on the significance test and ranking of the genetic programming (GP) models, the OSGP model is recommended as the most appropriate model for heterogeneous traffic. Sensitivity analysis reported that lagging headway () is the most contributing factor in PCE estimation. The PCE value of SM3W is found to be 0.81 and that could be incorporated as a new classification of vehicles in Indo-HCM. It is observed that evaluated capacity based on PCE values of OSGP performed admirably in both normal and congested traffic situations. [ABSTRACT FROM AUTHOR]

Urban road traffic congestion prediction holds significant research importance as it enables timely response to traffic flow patterns, optimizes urban planning, and enhances transportation efficiency. In order to improve the accuracy and recall of predicting urban road traffic congestion, a knowledge graph-based intelligent prediction approach is provided in this paper. This method first analyzes the measurement indicators of urban road traffic congestion status, and uses vehicle speed as the evaluation standard to develop a measurement standard for congestion degree. Secondly, a spatiotemporal knowledge graph of urban transportation networks was constructed using multi-source spatiotemporal data. Finally, based on the constructed spatiotemporal knowledge graph and the correlation of dynamic traffic, a novel prediction model for urban road traffic congestion was constructed using graph convolutional neural networks to obtain traffic congestion prediction results. The experimental results validate the effectiveness of the proposed approach, and indicate its significant potential for practical applications. [ABSTRACT FROM AUTHOR]

For the dynamic takeout delivery vehicle routing problem, which faces fluctuating order demand and time-varying speeds, this study presents a novel approach. We analyze the time distribution of takeout orders and apply a Receding Horizon Control (RHC) strategy to convert the dynamic challenge into a static one. The driving speed of delivery vehicles on different roads at different times is determined based on the subdivision criteria of the urban road network and a traffic congestion measurement method. We propose a dynamic takeout delivery vehicle routing optimization model and a time-varying subdivision road network is established to minimize the total delivery cost. We validated the model through simulation examples. The optimization results show that the total distribution cost is reduced after considering the time-varying subdivision road network, with the penalty cost decreasing by 39%. It is evident that considering the subdivision of the road network can enhance order delivery efficiency and optimize the overall dining experience. The sensitivity analysis of various parameters reveals that the delivery platform must appropriately determine the time domain and allocate the number of delivery personnel based on order scale to avoid escalating delivery costs. These findings provide theoretical guidance for vehicle routing planning in the context of delivery platforms. [ABSTRACT FROM AUTHOR]

The paper looks at certain vehicle-level re-routing issues within urban road networks, and related network-level traffic management issues. These arise mostly when the traffic along a route of significance is hindered, slowed down, or even blocked because of some – possibly minor – unexpected, nonrecurring traffic incident at a sensitive road location, or road section. Considerations for planning routes in urban areas – routes that are in some sense robust against such incidents – are presented herein. Also, the on–the–spot detection of traffic queues by an ego-vehicle – relying on data streams from on-board visual line-of-sight (LoS) exteroceptive sensors watching, scanning and monitoring the ego-vehicle's road environment, and by some on-board dedicated real-time detection systems processing and analyzing the incoming data streams – is touched upon. However, this traffic congestion avoidance and mitigating approach – effectuated either by individual autonomous vehicles, or by human drivers – presumes availability of alternative routes, which is not the case for a good portion of the route considered. A route planning approach that could be used for routes with such critical sections is proposed and motivated through an example of an urban route of significance. [ABSTRACT FROM AUTHOR]

Congestion free mobility has become nearly impossible in most of the metropolitan cities of India especially during peak hours. The understanding of factors inducing congestion demands huge amount of data pertaining to urban traffic. The developed countries have adopted different kinds of automatic data collection systems such as loop detectors, surveillance cameras and radars for the data collection of road traffic condition. In developing countries like India, the collection and monitoring of data related to movement of traffic stream are mostly manual, very time consuming and expensive. In India, Intelligent Transport System (ITS) has been implemented to Mysore City public transport in the year 2012. This study makes use of Global Positioning System (GPS) data of Mysore ITS. The major objective of the present study is to evaluate the congestion on urban roads using public transit GPS data with the help of visualization techniques. Spatio-temporal visualization-based analysis has been carried out to evaluate the traffic congestion patterns of urban roads. Initially, the comparison of traffic states on urban street and arterial road has been carried out. Later, the difference in congestion patterns before and after the operation of grade separator and the impact of route diversion on the congestion patterns have been evaluated. This study shows that public transit GPS data can be a potential data source to evaluate the traffic state or congestion, especially when there are limited sources of traffic data. [ABSTRACT FROM AUTHOR]

Urbanization has led to accelerated traffic congestion, posing a significant obstacle to urban development. Traditional traffic signal scheduling methods are often inefficient and cumbersome, resulting in unnecessary waiting times for vehicles and pedestrians, exacerbating the traffic situation. To address this issue, this article proposes a dynamic traffic signal scheduling system based on an improved greedy algorithm. Unlike conventional approaches, we introduce a reward function and a cost model to ensure fair scheduling plans. A constraint function is also established, and the traffic signal scheduling is iterated through the feasible matrix using the greedy algorithm to simplify the decision-making process and enhance solution efficiency. Moreover, an emergency module is integrated to prioritize special emergency vehicles, reducing their response time during emergencies. To validate the effectiveness of our dynamic traffic signal scheduling system, we conducted simulation experiments using the Simulation of Urban Mobility (SUMO) traffic simulation suite and the SUMO traffic control interface Traci. The results indicate that our system significantly improves intersection throughput and adapts well to various traffic conditions, effectively resolving urban traffic congestion while ensuring fair scheduling plans. [ABSTRACT FROM AUTHOR]

Compared to terrestrial transportation systems, the expansion of urban traffic into airspace can not only mitigate traffic congestion, but also foster establish eco-friendly transportation networks. Additionally, unmanned aerial vehicle (UAV) task allocation and trajectory planning are essential research topics for an Urban Air Mobility (UAM) scenario. However, heterogeneous tasks, temporary flight restriction zones, physical buildings, and environment prerequisites put forward challenges for the research. In this paper, multigene and improved anti-collision RRT* (IAC-RRT*) algorithms are proposed to address the challenge of task allocation and path planning problems in UAM scenarios by tailoring the chance of crossover and mutation. It is proved that multigene and IAC-RRT* algorithms can effectively minimize energy consumption and tasks' completion duration of UAVs. Simulation results demonstrate that the strategy of this work surpasses traditional optimization algorithms, i.e., RRT algorithm and gene algorithm, in terms of numerical stability and convergence speed. [ABSTRACT FROM AUTHOR]

The public transport system is vital in any country and gives an idea of how well cities are organized. Recently, Saudi Arabia has turned its attention toward developing a new public transport system, as issues such as low mobility, traffic congestion, and environmental pollution have arisen. In Riyadh City (Saudi Arabia) in 2012, an effort was made to decrease the reliance on private cars by developing travel demand management strategies and a public transport system. This study aims to determine the new public transport system's impact on the economy, environment, and social practices. Qualitative semistructured interviews have been taken with Riyadh City public transport officers. Then, NVivo was used for data analysis. Interviews were transcribed verbatim and analyzed using a thematic approach. The findings of this study showed that the new public transport system would have significant environmental, social, and economic benefits, such as greenhouse gas (GHG) emissions, reduced traffic accidents, and economic opportunities. The Riyadh transportation system would promote flexibility, reduce driving time, and provide more job opportunities, significantly impacting the city's social and economic aspects. Barriers that are associated with the utilization of public transport include social norms and culture, higher reliance on private vehicles, and low fuel prices. This study's findings demonstrated that public transport would motivate people to use public transport. Strategies such as cleanliness, safety, strict gender segregation, and comfortable seats would encourage people to use public transport. Overall, introducing a new transport system would significantly change traffic congestion and positively influence the lives of citizens by reducing the time wasted in traffic and improving air quality. During the last few decades, Saudi Arabia, particularly Riyadh City, has witnessed a significant increase in motorization and urban development, which causes traffic congestion. This huge dependence on vehicles has undoubtedly brought about vehicle proprietorship, congestion, and pollution. Many researchers highlight the rapid increase in private vehicle possession, dangers to the human environment, and traffic safety. Therefore, the author conducted this study to determine the impact of the new public transport system on environmental, social, and economic aspects and the barriers and relevant measures to promote public transport use among Riyadh City residents. Qualitative semistructured interviews have been conducted with Riyadh City public transport officers. The main findings of this study are that the new public transport system would have significant environmental, social, and economic benefits, such as reduced greenhouse gas (GHG) emissions and traffic accidents, and economic opportunities. The major barrier to promoting public transport in Riyadh is changing people's attitude toward using public transport instead of private vehicles. This study could help the government and other authoritative departments to plan an effective public transport system for Riyadh and related interventions to improve urban quality of life and mobility. [ABSTRACT FROM AUTHOR]

With the rise in vehicle ownership, traffic congestion has emerged as a major barrier to urban progress, making the study and optimization of urban road capacity exceedingly crucial. The research on the medium and long-term free-flowing capacity and queue emission rate of roads takes an in-depth exploration of this issue from a cutting-edge perspective, aiming to find solutions adaptable to the progression of the times. The purpose of this study is to understand and predict the road capacity and queue emission rate more accurately, thus improving the urban traffic condition. Existing literature primarily focuses on short-term forecasts of road capacity, leaving a notable void in the research of medium and long-term road capacity and queue emission rate. This gap often results in a lack of sufficient foresight when urban traffic planning faces practical issues. To fill this void, this study undertook an in-depth examination of the road capacity and queue emission rate over the medium and long term (10 years) based on big data analysis and artificial intelligence theories. This paper employs a Radial Basis Function (RBF) neural network, combined with twelve other parameters that could potentially impact road capacity, such as traffic volume, road width, number of lanes, traffic signal control methods, etc., to analyze the relationship between each parameter and free-flow traffic and queue emission rate. These analyses are grounded in extensive road data, encompassing not only the city's main roads but also secondary roads and community roads. The study results show a continuous downward trend in the free-flowing capacity of roads and a slight upward trend in the queue emission rate over the past decade. Further analysis reveals the extent of impact each factor has on the free-flow traffic and queue emission rate, providing a scientific basis for future urban traffic planning. [ABSTRACT FROM AUTHOR]

This research establishes an emergency evacuation time model specifically designed for subway stations with complex structures. The model takes into account multiple factors, including passenger flow rate, subway facility parameters, and crowd density, to accurately assess evacuation times. It considers the impact of horizontal walking distance, flow rate, subway train size, and stair parameters on the overall evacuation process. By identifying bottleneck points such as gates, car doors, and stairs, the model facilitates the evaluation of evacuation capacity and the formulation of effective evacuation plans, particularly in multiline subway transfer stations. The good consistency is achieved between the calculated evacuation time and simulated results using the Pathfinder software (with the relative error of 5.4%). To address urban traffic congestion and enhance subway station safety, the study recommends implemented measures for emergency diversion and passenger flow control. Additionally, the research presents characteristic mathematical models for various evacuation routes by considering the structural and temporal characteristics of metro systems. These models provide valuable guidance for conducting large-scale passenger evacuation simulations in complex environments. Future research can further enhance the model by incorporating psychological factors, evacuation signage, and strategies for vulnerable populations. Overall, this study contributes to a better understanding of evacuation dynamics and provides practical insights to improve safety and efficiency in subway systems. [ABSTRACT FROM AUTHOR]

Traffic congestion is a globally widespread problem that causes significant economic losses, delays, and environmental impacts. Monitoring traffic conditions and analyzing congestion factors are the first, challenging steps in optimizing traffic congestion, one of the main causes of which is regional spatiotemporal imbalance. In this article, we propose an improved spatiotemporal hierarchical analysis method whose steps include calculating road carrying capacity based on geospatial data, extracting vehicle information from remote sensing images to reflect instantaneous traffic demand, and analyzing the spatiotemporal matching degree between roads and vehicles in theory and in practice. First, we defined and calculated the ratio of carrying capacity in a regional road network using a nine-cell-grid model composed of nested grids of different sizes. By the conservation law of flow, we determined unbalanced areas in the road network configuration using the ratio of the carrying capacity of the central cell to that of the nine grid cells. Then, we designed a spatiotemporal analysis method for traffic congestion using real-time traffic data as the dependent variables and five selected spatial indicators relative to the spatial grids as the independent variables. The proposed spatiotemporal analysis method was applied to Chengdu, a typical provincial capital city in China. The relationships among regional traffic, impact factors, and spatial heterogeneity were analyzed. The proposed method effectively integrates GIS, remote sensing, and deep learning technologies. It was further demonstrated that our method is reliable and effective and enhances the coordination of congested areas by virtue of a fast calculation speed and an efficient local balance adjustment. [ABSTRACT FROM AUTHOR]

The millimeter‐wave radar sensor is widely used for urban traffic surveillance because of its weather resistance and high detection accuracy. Methods such as fuzzy theory, pattern recognition, and artificial neural networks have been integrated into the research of traffic state discrimination. However, research on systematically describing the fusion of sensors and traffic state discrimination algorithms to alleviate urban road congestion is still lacking, especially based on millimeter‐wave radar. Thus, the authors propose an urban traffic congestion alleviation system framework. First, the design and deployment of the millimeter‐wave radar system, including waveforms, signal processing flow, and target tracking, are demonstrated to achieve vehicle information acquisition and output. Then, the appropriate traffic parameters are obtained by analysing traffic state influencing factors and the radar data characteristics. Finally, a traffic conditions identification algorithm combining spectral clustering and neural network algorithm is presented to realise road congestion level classification. The system is applied to real urban intersections rather than simulation or approximate real simulation. According to the current road congestion level, regulate the traffic light state to achieve road vehicle driving command. Experiments show that the proposed system can effectively reduce road congestion by 20% compared to the current fixed traffic light system. [ABSTRACT FROM AUTHOR]

Summary: In recent days, VANET is considered as the main hopeful equipment in the system of transportation since traffic congestion arises regularly and thus occurred road accidents very easily. Besides, the network traffic is increasing due to the huge quantity of information generated in region of urban. Therefore, one of the primary challenges faced by Intelligent Transportation System (ITS) is ensuring the accurate transmission of information in both vehicle‐to‐vehicle (V2V) and vehicle‐to‐road (V2R) sensing unit communications. So, here, an adaptive routing controller (ARC) is utilized to improve the data transmission between vehicle and road sensing unit (RSU) and to minimize the traffic density in VANET, and the self‐adaptive search optimization is developed in VANET clustering to prioritize vehicles in the lane, in which the multi‐objective function is intended depending on the energy of the node, acceleration, jitter, priority, velocity, and trust factors. The traffic light control is done to facilitate the effective communication in the network. Thus, the proposed technique is calculated in terms of throughput, jitter, quadratic mean of acceleration (QMA), and spatially distributed travel time (SDTT), which acquired the values of 0.53 s, 36.07 kmph, 3.472 s, and 43.572%, respectively, while using 50 vehicles at 50 s. [ABSTRACT FROM AUTHOR]

Improving the spatial-temporal balance between the supply and demand of urban transportation and alleviating traffic congestion are important ways to build sustainable cities. The travel reservation strategy (TRS) is more flexible and refined than traditional traffic demand management methods. This study aims to determine the optimal reservation volume (ORV) for urban roads and verify the effectiveness of the TRS. First, we employed the sustained flow index to estimate the ORV from the degree of trade-off between the road breakdown probability and capacity. Then, a bilevel programming model based on ORV constraints was established to analyse the effectiveness of the TRS. The results indicated that the ORV range is 0.79–0.89 times the road capacity. The TRS can achieve the best steady benefit when the demand for reservation travel reaches at least 40%. Selecting the most congested critical roads in the network to implement TRS is more effective than on a large area. The driver default behavior will increase the V/C ratios and travel costs of all roads in the network. It has been proven that the reservation transportation mode will promote the spatial-temporal balance between supply and demand to alleviate traffic congestion. [ABSTRACT FROM AUTHOR]

To explore the mechanism of the end-to-end transmission delay of the communication network on the collaborative driving process for traffic flow in the vehicle-to-vehicle communication environment, based on the idea of the car-following model, this paper introduces characteristic parameters characterizing the end-to-end transmission delay of the network into Newell's following model and proposes a CD and OV model by considering the time delay characteristics of the collaborative driving process from information transmission to control decision and then to physical execution. To determine the cooperative driving system's stability criterion, the stability analysis of the new model is examined. By using the reductive perturbation approach, the spatiotemporal evolution mechanism of the traffic flow around the critical stability point under the influence of various transmission delays is analyzed. The resulting modified Korteweg-de Vries (mKdV) equations and density wave solutions are derived. The results show that the end-to-end transmission delay of the network has a significant shock effect on the stability of the vehicle-vehicle cooperative driving system, and the stability of the traffic flow and the ability to suppress traffic congestion becomes worse with the increase in the end-to-end transmission delay. [ABSTRACT FROM AUTHOR]

Traffic congestion has been a hot topic of research in the field of intelligent transportation, which can be alleviated by efficient route navigation. Most of the existing route planning methods are non-negotiated algorithms, which do not take into account the route conflicts and collaborative relationships between multiple vehicles. Also, most negotiated algorithms have not been comprehensively considered dynamic route collaboration between vehicles, large-scale efficient computation, environmental pollution, etc. Therefore, an ecological multivehicle real-time route selection model (EMR2SM) for urban road networks is firstly proposed in this paper, which combines real-time traffic conditions of the road network with travel time, distance, and exhaust emissions as optimization indicators. In order to solve the large-scale computation problem of traditional negotiated algorithms, an adaptive multiswarm bee colony (AMSBC) algorithm is designed, which efficiently solves the multivehicle dynamic route selection problem. AMSBC searches the optimal route for each vehicle in parallel through multiple population division and self-adaption mechanism, to make multivehicle route selection reach Nash equilibrium. Compared with three non-negotiated optimization algorithms based on swarm technology, EMR2SM is verified by experiments that it improves the efficiency and accuracy of the optimal route selection for multiple vehicles and reduces vehicle emissions, which can effectively reduce traffic congestion and environmental pollution. [ABSTRACT FROM AUTHOR]

With advancing vehicular technology, there are challenges related to the computing capabilities of the deployed infrastructure. In a dense vehicular network, the system performance quickly degrades due to the scarcity of computing capacity and the heavy workload on the coordinating nodes. In situations of a road accident or slow-moving traffic, many trigger messages are generated to enable situational awareness. Aforesaid events may lead to network congestion across the vehicular environment resulting in higher packet loss. Moreover, emergency messages incur increased service delays rather than getting preference for servicing. In this work, we propose a clustering-based re-routing framework for network traffic congestion avoidance on urban vehicular roads. We use queue optimization to categorize and execute tasks based on their priority level at each fog node. Moreover, threshold-based congestion detection is used to determine congested nodes, which, alongside clustering-based suitable node selection, is used for workload sharing. Furthermore, a re-routing mechanism is implemented to decrease packet loss, improving the delivery rate. The simulation results show that the proposed technique is reliable, effective, and robust in terms of packet loss, throughput, and delivery of emergency messages. [ABSTRACT FROM AUTHOR]

Severe traffic congestion leads to toxic levels of air pollution. We attempt to explore how traffic congestion affects air pollution, especially PM10 and PM2.5, by focusing on the city of Seoul, Korea, which is one of the largest cities in the world and is plagued by severe traffic congestion. We employ a fixed-effect panel model with various datasets, including air pollution, travel speed, and weather conditions. The findings show that traffic congestion adversely affects air quality by increasing the concentration of PM10 and PM2.5. In particular, the impact of traffic congestion on PM10 and PM2.5 was 1.2 times and 1.7 times greater during the COVID-19 period than during the non-COVID-19 period, respectively. In addition, an increase in one unit of RCI is associated with 0.4μg/m³ increase in PM10 during the peak hours, but 6.7 μg/m³ increase in PM10 during the non-peak hour because of the increased traffic of trucks and motorcycles induced by increased online shopping. To mitigate air pollution, it is suggested that not only reducing vehicle use, but also minimizing traffic congestion is important. [ABSTRACT FROM AUTHOR]

This study developed a travel time congestion index (TTCI) keyed to the differences in actual versus desired travel times for various types of roadways, and found that the TTCI is influenced by traffic and geometric characteristics of the roadway other than volume and capacity. The TTCI provides a continuous scale for assessing the level of congestion in the network. In general, the measurement of the level of service (LOS) across the roadway sections is adopted to measure the level of congestion. However, measuring LOS alone does not provide better insight into the reasons for congestion other than the volume-to-capacity ratio. Therefore, we developed the TTCI by employing traffic and geometric parameters to measure the congestion along the roadways on the urban road network. In addition to the development of the TTCI, level of service is measured and compared with the TTCI values for the selected road sections. The results indicate that employing all the variables together in a single technique yields much better and more-reliable values for congestion indexes. The results indicate that providing grade-separated intersections reduces traffic congestion to a great extent. The results also were found to be in line with the field conditions. This study also proposes using TTCI values to design the cordon lines for delaminating the congestion zones and estimating the level of service of the roadway. The findings of this study can be used by policymakers and transportation planners to design effective countermeasures for decongesting urban roadways. [ABSTRACT FROM AUTHOR]

• We studied the advanced air mobility aircraft scheduling problem at vertiports. • We considered separation rules at touchdown and lift-off pads and blocking constraints. • Two mixed integer linear programming formulations are presented for optimally solving small instances. • We propose two heuristic algorithms for solving real-life sized instances. • The computational results provide insights into vertiport operations. Recent progress in electric vertical take-off and landing (eVTOL) vehicles suggests that soon these vehicles could safely and efficiently transport people and cargo in urban areas. Therefore, advanced air mobility vehicles could become an alternative means of transport to overcome traffic congestion in cities in the upcoming years. There has been enormous interest from companies and governments in recent years in developing such technologies and enabling markets for new air transportation services. Despite the interest in the topic, little research has been done to address the aircraft scheduling problem in advanced air mobility take-off and landing areas (vertiports). The vertiports serve as the airports of eVTOL vehicles and could experience congestion problems similar to those of airports. This work proposes two optimization models for scheduling departing and landing aircraft at the vertiports' common ground taxi routes (taxiways), gates, and touchdown and lift-off (TLOF) pads. The mathematical models include advanced air mobility features such as separation rules and blocking constraints. As scheduling objectives, the first model maximizes the vertiport throughput, and the second model minimizes the deviation from the expected take-off/landing time. In addition, as a solution methodology, we developed two heuristic algorithms that use scheduling rules to assign and sequence the aircraft to the vertiport components. Computational results show that the optimization models find optimal schedules for small-sized instances of up to 10 aircraft, while the heuristic algorithms provide good results in terms of solution quality and computational time for large instances. [ABSTRACT FROM AUTHOR]

To study how traffic congestion affects logistics and transportation in terms of cost and carbon emission and countermeasures, we designed congestion-avoidance strategies to reduce vehicle waiting time and vehicle speed. The delivery time in a day is divided into three periods to calculate the roadway travel time under the congestion avoidance strategy. The mathematical models of cold chain logistics route planning with the objectives of minimizing carbon emissions and total costs are constructed, and the congestion-avoidance strategy is applied to plan the cold chain logistics route. An improved ant colony algorithm is proposed to solve the objective model. The reduced speed strategy generates fewer carbon emissions than the waiting strategy. The cost of distribution under different strategies is influenced by the form of distribution and the size of the customer. The results of the study show that the use of congestion-avoidance strategies can reduce the total cost of distribution and carbon emissions. The waiting strategy is more effective in general logistics, and the speed-reduction strategy is more effective in cold chain logistics. By comparing the carbon emissions generated for various average speeds under the reduced-speed strategy, the optimal driving speed is about 45 km/h. [ABSTRACT FROM AUTHOR]

With the rapid growth of trajectory big data, there is a need for more efficient methods to extract, analyze, and visualize these data. However, existing research on trajectory big data visualization mainly focuses on displaying trajectories for a specific period or showing spatial distribution characteristics of trajectory points in a single time slice using clustering, filtering, and other techniques. Therefore, this paper proposes a vector field visualization model for trajectory big data, aiming to effectively represent the inherent movement trends in the data and provide a more intuitive visualization of urban traffic congestion trends. The model utilizes the motion information of vehicles to create a travel vector grid and employs WebGL technology for vector field visualization rendering. The vector field effects are effectively displayed by generating many particles and simulating their movements. Furthermore, this research also designs and implements congestion trend point identification and hotspot congestion analysis, thus validating the practicality and effectiveness of trajectory big data vector field visualization. The results indicate that compared to traditional visualization methods, the vector field visualization method can demonstrate the direction and density changes in traffic flow and predict future traffic congestion. This work provides valuable data references and decision support for urban traffic management and planning. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems need to realize accurate traffic congestion prediction. The spatio-temporal features of traffic flow are essential to analyze and predict congestion. Our study proposes a data-driven model to predict the traffic congested flow. Firstly, the traffic zone/grid method is used to store the local area roads' average speed of the vehicles. Second, the discrete snapshot set is proposed to characterize traffic flow's spatial and temporal features over a continuous period. Third, the evolution of traffic congested flow in various time dimensions (weekly days, weekend days, and one week) is examined by transforming the global urban transportation network into traffic zones. Finally, the data-driven model is constructed to predict urban road traffic congestion by using the extracted spatio-temporal characteristics of traffic zones' traffic flow, the snapshot set of which serves as inputs for this model. The model adopts the convolutional LSTM network to learn the temporal and local spatial features of traffic flow, while utilizing a convolutional neural network to effectively capture the global spatial features inherent in traffic flow. The numerical experiments are conducted on two cities' transportation networks, and the results demonstrate that the performance of the proposed model outperforms traditional traffic flow prediction models. [ABSTRACT FROM AUTHOR]

In Asian megacities undergoing rapid urbanization such as Bangkok, heavy rainfall exacerbates traffic congestion owing to inadequate drainage systems. This study statistically analyzed the extent to which rainfall affects urban traffic speed and how this impact varies depending on regional environmental factors and traffic demand trends, utilizing probe vehicles and rainfall data from 2018 to 2020 in Bangkok. The results clearly indicate that both the intensity of rainfall during driving and previous cumulative rainfall significantly reduce traffic speed. This impact is particularly pronounced during morning and evening rush hours, and in areas with a high proportion of narrow roads or in low-lying areas. On the other hand, areas with rich urban green space, which naturally absorb and retain water, tend to mitigate the speed reduction due to rainfall. This study highlights the fact that the impact of rainfall on traffic varies with time and location, suggesting that the exacerbation of rain-induced congestion can be more effectively mitigated by coordinated improvements in drainage facilities, traffic management and land use. [ABSTRACT FROM AUTHOR]

Generally, urban traffic management evaluates micro-scale projects based on traffic indices, such as travel time, that ignore the sustainability criteria and impacts of changes on adjacent areas. Therefore, the aim is to assess the sustainability of micro-scale traffic congestion removal projects on highways with a wider vision. Five proposed scenarios were evaluated using ten sustainable transportation indicators (STIs) that represent various aspects of sustainability. Scenarios were simulated in Aimsun, and STIs were derived directly and indirectly from the outputs. The results showed that even small changes could improve the operation of the network. Finally, a decision-making method was implemented to prioritize the scenarios. The combination of STIs based on the criteria for the Tehran 2025 horizon as a composite sustainability index (CSI) helped to determine impacts of each scenario. Although all scenarios improved traffic indices of the network, some failed sustainability criteria. [ABSTRACT FROM AUTHOR]