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Vehicular Communication becomes growing research field due to continuously increasing vehicles density and that causes traffic jams, road accidents etc. These problems can be overcome up to some limit by proper shearing of information about their location, speed and acceleration through the periodic messages transmission among vehicles but as the vehicle density increases number of transmitted messages through channel increase and that cause's channel congestion. To improve the channel performance a risk aware data congestion limiting technique (RA-DCLT) is implemented. This technique uses the concept of machine learning with data transmission controlling mechanism and provides the efficient result from the existing congestion techniques by evaluating packet delivery ratio, information loss and throughput. RA-DCLT technique works on the principle of K Mean algorithm with some modification to grouping vehicles and concept of channel busy ratio (CBR). R-DCLT works in three sections, first it groups the nearest vehicles on road by K Mean algorithm with some modifications then in second section it identified the congestion on channel by correlating the instant data load on channel with bandwidth then in last section all the vehicles select data rate by keeping very less risk possibility to minimised congestion on channel. For the evaluation of RA-DCLT, road network is designed by the SUMO software, the proposed idea for congestion minimization is implemented through MATLAB and performance matrix of RA-DCLT is calculated through NS2. The results are the evidence that RA-DCLT is risk aware efficient congestion limiting technique then existing congestion control technique. [ABSTRACT FROM AUTHOR]

As urbanization accelerates, traffic congestion and frequent accidents have become prominent issues, prompting the development of intelligent transportation systems. This paper focuses on the research of vehicle and pedestrian detection algorithms to improve detection accuracy in complex traffic environments. Considering the limitations of traditional object detection algorithms in complex situations, this study adopts the deep learning-based YOLOv8 algorithm and introduces the Coordinate Attention (CA) module to enhance the model's feature extraction and localization capabilities. Experimental results show that the improved YOLOv8 network achieves a 1.1% increase in detection accuracy while maintaining its original speed. Furthermore, this paper constructs a vehicle and pedestrian dataset suitable for Chinese traffic scenes, providing an effective solution for autonomous driving assistance systems. Overall, this study holds significant reference value for vehicle and pedestrian detection in the field of intelligent transportation. [ABSTRACT FROM AUTHOR]

In recent years, the rapid growth of vehicles has imposed a significant burden on urban road resources. To alleviate urban traffic congestion in intelligent transportation systems (ITS), real-time and accurate traffic flow prediction has emerged as an effective approach. However, selecting relevant parameters from traffic flow information and adjusting hyperparameters in intelligent algorithms to achieve high prediction accuracy is a time-consuming process, posing practical challenges in dynamically changing traffic conditions. To address these challenges, this paper introduces a novel prediction architecture called Multiple Variables Heuristic Selection Long Short-Term Memory (MVHS-LSTM). The key innovation lies in its ability to select informative parameters, eliminating unnecessary factors to reduce computational costs while achieving a balance between prediction performance and computing efficiency. The MVHS-LSTM model employs the Ordinary Least Squares (OLS) method to intelligently reduce factors and optimize cost efficiency. Additionally, it dynamically selects hyperparameters through a heuristic iteration process involving epoch, learning rate, and window length, ensuring adaptability and improved accuracy. Extensive simulations were conducted using real traffic flow data from Shanghai to evaluate the enhanced performance of MVHS-LSTM. The prediction results were compared with those of the ARIMA, SVM, and PSO-LSTM models, demonstrating the innovative capabilities and advantages of the proposed model. [ABSTRACT FROM AUTHOR]

The increasing popularity of vehicles has led to traffic congestion and frequent traffic accidents. Intelligent transportation technology is an effective solution to this problem. In order to improve the accuracy and effectiveness of vehicle detection and tracking, this paper combined the improved YOLOv5s model with the optimized DeepSORT tracking algorithm to detect and track vehicles on traffic roads. Firstly, in the detection model of YOLOv5s, the Attention-based Intra-scale Feature Interaction (AIFI) module is introduced to detect vehicles more quickly and accurately. Secondly, the Kalman filtering (KF) algorithm of DeepSORT is optimized to improve the accuracy of predictions of the vehicle state by using the width to replace the length-to-width ratio of the vehicle prediction box in the original KF algorithm. Finally, in the re-recognition network of DeepSORT, the original Convolutional Neural Network (CNN) model is replaced by an improved ResNet36 as the backbone network for feature extraction. The experimental results show that, compared with the original algorithm, when examining the performance of the improved algorithm in terms of target detection, the recall rate, average accuracy (mAP), and detection speed, are increased by 7.7%, 15.5%, and 14.2%, respectively; in terms of multi-object tracking performance, such as multi-object tracking precision (MOTP) and multi-object tracking accuracy (MOTA), improvements of 14.84% and 9.62%, respectively, are obtained and the total number of times a trajectory is fragmented (Frag) is reduced by 32.52%.These results indicate that the proposed algorithm can meet the requirements of accuracy, real-time detection, and stable vehicle detection and tracking on traffic roads. [ABSTRACT FROM AUTHOR]

Automated vehicles (AVs) hold the potential to reduce road accidents, mitigate traffic congestion, and improve travel experience. However, the possible countervailing impacts from the changes in underserved populations' vehicle travel demand tend to be overlooked. To determine the vehicle travel demand changes that resulted from underserved populations aged between 6 and 80, this paper explores the latent effect of AVs on vehicle kilometers traveled (VKT) in a fully AV environment using person trip survey data from the cities of Sanya, Shijiazhuang, and Shenzhen in China. This paper uses the natural decline hypothesis of travel demand and proposes a regression model to investigate the difference among the cities' latent vehicle travel demand. Results show that the average VKT of the overall population in Sanya, Shijiazhuang, and Shenzhen increased by 33.4%, 47.0%, and 46.8%, respectively. The analysis of the regression model confirms that the current travel behavior of individuals can affect the degree of increase in their average VKT. Integrating AVs into public transport, increasing the acceptance of automated shared mobility options, transforming road space use type, and prototyping AV designs with various features and needs are potential methods to cope with the countervailing impacts. The total VKT of the overall population increased by approximately 10%e25% depending on the city. The conclusions of this paper provide informative insights into the evaluation of VKT for underserved populations and contribute to the deployment of AVs to address equity and inclusion issues. [ABSTRACT FROM AUTHOR]

Self-guided logistics vehicles have become increasingly popular in various industries and warehouses for material lifting and transportation purposes. These automated systems have proven to be valuable in both small-scale production lines and large areas. However, challenges persist in the field of logistics, where industries and factories face difficulties in efficiently transporting materials. In response to these challenges, self-guided vehicles have been employed, but issues such as collisions with obstacles, material falls, and rigid path constraints have been observed. This project aims to address these challenges by implementing hardware safety precautions and incorporating routing and scheduling capabilities to enhance system automation. The article presents a comprehensive analysis of hardware safety precautions and highlights key features including sensors, actuators, and control systems, which contribute to the overall efficiency and smooth operation of the self-guided logistics vehicle system. By integrating these elements, the system can overcome obstacles, ensure material safety, and adapt to changing traffic conditions, thereby optimizing logistics operations in industries and warehouses. [ABSTRACT FROM AUTHOR]

Quality of Service (QoS) is a mechanism used in computer networks to prioritize, classify, and treat packets differently based on certain criteria. This helps the switching devices to schedule and reorder packets if there is congestion in the network. Edge routers experience high traffic congestion as a result of traffic aggregation from the internal network devices. A router can have multiple QoS classes configured, and each class could experience traffic at various rates. However, when a QoS class is underperforming or needs more bandwidth, some bandwidth can be borrowed or leased out to another QoS class to ensure the link is utilized to maximum capacity and the highest throughput is achieved. This article proposes a bandwidth allocation and distribution algorithm that purely uses the flow statistics from the OpenFlow switches to allocate bandwidth to different QoS classes optimally based on their current requirement. The algorithm does not guarantee in advance that the packet loss will be minimized but does guarantee the initial minimum bandwidth allocation. It adjusts the flows' rates with the aim to increase their current throughput. The algorithm uses the Software Defined Networking (SDN) controller's flow monitoring component to query the flow statistics from the switch to first approximate the traffic flow rate and then calculate the optimal bandwidth values to assign to each QoS class. The proposed algorithms will be applied to certain switches in the path with the assumption that all the switches are OpenFlow compatible. The algorithm's performance was compared with the Adaptive Quality of Service (AQoS) algorithm over various traffic scenarios. The results show that the proposed algorithm achieves an average of 9% performance gain compared to the AQoS algorithm. [ABSTRACT FROM AUTHOR]

Rapid urbanization has led to an extreme environment, especially in cities like Lahore that is overpopulated and overuse of land resulting in accelerating temperature. The research aims to investigate the commercialization impact on the microclimate of Karim Block, Lahore, which is being over-commercialized, and to propose a strategy to mitigate rising temperature from land use conversion. Theoretical review, policy analysis, ENVI Erdas Imagine, and GIS satellite imagery analysis are conducted to assess Land Surface Temperature from 2016 to 2023. It was revealed that the temperature has increased significantly due to land use change policies, elimination of green infrastructure, and traffic congestion. It highlights the importance of reclassifying land use, incorporating green infrastructure, and mitigating climate change impact. It is recommended to incorporate green roofing to reduce temperature significantly, reclassify Karim Block's land use by prioritizing green infrastructure, and develop future commercialization policies that cater to environmental susceptibility and promote pedestrianization. [ABSTRACT FROM AUTHOR]

As urbanization continues to surge, the demand for efficient parking management systems becomes imperative to mitigate congestion and enhance overall urban mobility. This mini project proposes a Smart Parking Management System (SPMS) designed to address the challenges associated with traditional parking systems through the integration of advanced technologies. Upon entering the parking area, drivers can access real-time information regarding available parking spaces through the mobile application. Sensors installed in each parking slot detect the presence of vehicles, transmitting data to the central server. Utilizing cloud-based algorithms, the SPMS dynamically allocates parking spaces to incoming vehicles, minimizing the time spent searching for parking spots and reducing traffic congestion. [ABSTRACT FROM AUTHOR]

Dynamic toll pricing based on demand can increase transportation revenue while also incentivizing travelers to avoid peak traffic periods. However, given the unpredictable nature of traffic, travelers lack the information necessary to accurately predict congestion, so dynamic pricing has minimal effect on demand. Dynamic toll pricing also poses equity concerns for those who lack other travel options. This research explores a potential remedy to these concerns by using a simple “futures market” pricing mechanism in which travelers can lock in a toll price for expected trips by prepaying for future tolls, with the future price increasing as more travelers book an overlapping time slot. This approach encourages travelers to avoid driving during the peak periods when pricing increases toward capacity or to purchase trips in advance when the price remains low or discounted, thus using infrastructure capacity more efficiently. Travelers that do not prepurchase their trip are subject to the real-time market price, which is determined by dynamic congestion pricing. This futures-market mechanism can augment existing toll collection technologies and provide travelers with sufficient pricing information and purchasing options to preplan their travel and avoid excessive prices.

Currently, one of the serious problems in the world is transport-related air pollution. Air pollution from vehicles was not considered properly in the plan, design, and management system of the roads in Addis Ababa city, to solve the problem of traffic congestion. It is influenced by different factors such as road geometry, road surface type, traffic congestion, traffic characteristics, fuel type, and meteorological parameters. This study was aimed to investigate the contribution of congested traffic flow conditions on air pollution at intersection points in Addis Ababa city. To carry out the study, seven intersection points were selected in the city randomly. For the measurement technique of carbon monoxide (CO) and sulfur dioxide (SO2) gases, a portable Aeroqual series 500 instrument was exercised, and for the particular matter (PM2.5 and PM10), Light Amplification by Stimulated Emission of Radiation (LASER) was adapted. To count the number of traffic on the site, a video camera method was utilized. For the data analysis techniques, a multiple linear regression method was practiced, which is very powerful for multiple independent variables with a single dependent one. The study revealed that the level of traffic congestion at each selected intersection is at a severe level. During the congested time, as a base of the steady condition, the pollutants concentration of CO, SO2, PM2.5, and PM10 were increased by an average of 19.10, 51.61, 33.83, and 29.07 % respectively. Based on the analyzed results, when the concentration of pollutants increases, the traffic volume, percentage of heavy vehicles, green time, and approach grade are also increased. On the other hand, the concentration of pollutants decreased when the lane width and wind speed increased. Therefore, any concerned bodies in and around Addis Ababa city should take remedial measures to decrease the emission of the concentration of the pollutants. This investigation is very important for policymakers, municipalities, and planners to do any activities in the city. Researchers can also use it as a reference, for further investigations and remedial measurements.

The article discusses Carl Linnaeus's Systema Naturae and its classification of humanity into two distinct species: diurnal humans and nocturnal humans. The author explores the concept of esotericism and its definition, disagreeing with the approach taken by Steven Engler and Mark Gardiner. They argue that meaning can include reference, social kinds are dynamic clusters of powers, and scholarly inferences and theories are important for advancing knowledge. The author suggests that "esotericism" may encompass multiple social kinds and emphasizes the need to consider the social world when making generalizations. [Extracted from the article]

The rise of technology-enabled ride-hailing services has affected individuals’ transportation-related decisions. The impact of these ride-hailing services likely varies across traveler segments that differ in their usage of various modes of transportation. In this paper, we develop and leverage a framework that allows us to examine the impact of ride-hailing services on the transportation mode choice for three traveler segments: drivers (who primarily use a personal automobile to travel), riders (who primarily use public transit to travel), and walkers (who primarily use non-motorized modes of transport). We first develop a framework outlining how the behavior of different traveler segments would be impacted by the introduction of ride-hailing services and show how this affects traffic congestion and public transportation ridership. To test the framework, we compiled a rich dataset, combining data on public transportation ridership, traffic congestion, and individual transportation mode choice. Employing a difference-in-differences methodology, we show that the Uber entry in a market enabled those who were walkers and riders prior to the entry of Uber to travel more conveniently, leading to an increase in traffic congestion, and induced those who were drivers to substitute their use of private automobiles with a combination of Uber and public transit. We introduced urban compactness to assess the heterogeneous impact of ride-hailing services for cities that differ in their distribution of traveler segments. We found that Uber entry increases traffic congestion and reduces public transit demand more in cities with higher levels of urban compactness, i.e., where the proportion of riders and walkers is higher than that of drivers. This work provides a holistic framework to understand the mechanism underlying the impact of ride-hailing services on public transit and traffic congestion. Urban planners and policy makers can leverage our framework, methodology, and empirical results to guide city planning decisions that have implications for sustainability. [ABSTRACT FROM AUTHOR]

The average speed and density of road transport are used as indicators of the congestion situation, but do not allow tracking the evolution (stages of formatting, progressing, and vanishing) of traffic congestion. The authors proposed and justified a quantitative indicator of traffic congestion, which allows in an automated mode to identify the congestion situation on the urban road network using hardware and software video recording systems. The purpose of this study is due to the need to study the quantitative characteristics of the proposed indicator at characteristic intersections of urban roads, which will allow us to develop scientifically based recommendations for predicting congestion situations, substantiating, and making optimal decisions on measures to promptly eliminate traffic congestion. The object of study is the traffic flow at three types of intersections of the Perm city road network, equipped with a photo and video recording software and hardware complex. The subject of the study is the regularities of the evolution of the listed deterministic indicators of traffic flows, which can be used for operational forecasting of the formation, development, and elimination of traffic congestion. The theoretical and methodological approach is based on the methods of mathematical statistics used to process the results of observations of traffic flows at different types of intersections using a «sliding window», calculating the average daily value and standard deviation. The initial data were obtained with the help of hardware and software complexes for fixing violations of traffic rules installed on the street and road network of the Perm city. As a result of the study, the rational parameters of the «sliding window» were determined, ensuring the structuring of the traffic congestion indicator; the facts of the congestion situations formation were revealed; the features of the congestion evolution and the presence of problematic traffic directions for which it is advisable to change the traffic light regulation mode were determined. The theoretical and practical significance of the work consists in checking the operability of the proposed indicator and criterion of traffic congestion, which is of practical interest from the point of view of predicting anomalies in the movement of vehicles on the road network, adjusting the operating modes of traffic lights, etc. It is also possible to use the proposed traffic congestion indicator to assess the effectiveness of traffic light regulation on the Perm city road network. The direction of further research is to study the patterns of traffic congestion at intersections of the urban road network, of various types that are not included in this study.

Significant social, economic, and environmental costs are associated with traffic congestion. The effectiveness of intersections makes a substantial contribution to the effectiveness of entire road networks. The three-leg at-grade Al-Husainea intersection, located 20 km from the center of holy Karbala city in Iraq, is subjected to serious congestion, resulting in an increase in delay time, reduction in capacity, and bad level of service (LOS). Therefore, it is essential to use advanced software tools to ensure that the current intersection can be controlled, evaluated, and improved. So, the major goal of this study is to use SIDRA, an acronym for signalized and unsignalized intersection design and research aid, software to evaluate the LOS of the Al-Husainea intersection, in which the traffic is assessed using the current LOS. LOS, delay time, and degree of saturation were the criteria utilized to evaluate the traffic flow performance. SIDRA is also used to assess benefits as a result of suggested changes in the design of particular junctions. The first stage is to gather field data regarding traffic volumes utilizing a method of traffic volume gathering. From 7 am to 6 pm, the SIDRA program gathered data for a full 7 days. The results showed that the LOS for the Al-Husainea intersection in the Al-Husainea arm is F, with an average delay of 52 s per vehicle and a saturation level of 0.86 v/c. Finally, it was determined that the Al-Husainea intersection needs additional improvements based on the study and findings from the SIDRA program, and some remedies are suggested in this study to improve the intersection traffic flow.

Highways serve as vital connectors between cities, yet they often suffer from traffic congestion as the population continues to grow. Various intelligent frameworks or models for traffic status prediction have been employed in the Intelligent Transport System (ITS) to provide services for convenient and safe traveling, effective traffic management, and smart signal control. Most of these frameworks typically involve learning processes that utilize learning algorithms and requires training data. For highway traffic, the Greenshields model offers a practical relationship among vehicle speeds, traffic flows, and traffic density, which can serve as fundamental knowledge for developing intelligent traffic management systems. This paper proposes a fuzzy logic system based on the Greenshields model as the knowledge base for quickly predicting highway traffic congestion without extensive preparing data. Our system operates in two modes: jam and non-jam modes. In each model, the two inputs of vehicle speed and traffic flow are processed respectively with specified membership functions for effective fuzzification. The set of rules and conditions guided by the Greenshields theory is governed by the inference mechanism, which makes decisions according to the input field. Subsequently, the defuzzification process converts the fuzzy sets obtained by the inference engine into a congestion level as the output. To validate the accuracy of our system, a polynomial regression model utilizing realistic data from roadside equipment on the Sun Yat-Sen Highway in Taiwan is established for comparison. Comparing the observed data points from the polynomial regression model with the outputs obtained from our system using the same inputs, both predicting outputs are found to be consistent, affirming the practical feasibility of the proposed system. Moreover, our proposed scheme is adaptable to suit diverse road conditions without extensive training data and possesses a short memory to perform tasks. Integrating several systems by cascading them across different segments of the highway enables rapid congestion prediction for long-distance traffic. These advantages make the proposed system much more convenient for performing congestion prediction for traffic management and control in ITS.

This paper studies the time-dependent cold chain logistics vehicle routing problem considering both traffic congestion and carbon emissions. A cold chain logistics model with time-dependent green vehicle paths with time windows (TDGVRPTW) was developed to fulfil the demands of green logistics and to take comprehensive account of consideration should be given to factors such as road congestion and carbon emissions. The objective of the model is to minimise total costs, which include carbon emission costs, penalty costs, fuel consumption costs, fixed costs, damage costs and refrigeration costs. Two-phase hybrid search algorithm was developed to solve this model. During the initial stage of the algorithm, a dual-population ant colony optimization (DACO) algorithm sharing the optimal individual is employed to optimize the distribution route of the vehicle. During the second phase, an adaptive golden section search (AGSS) algorithm is used to optimise the departure time of the vehicle from the distribution centre to avoid traffic congestion time periods. To validate the effectiveness of the suggested two-phase hybrid search algorithm, it is applied to the improved Solomon benchmark test set. The experimental findings demonstrate that the two-phase hybrid search algorithm can reasonably plan the driving routes and departure times for each vehicle, effectively avoiding peak traffic congestion periods in the city, and reducing the overall delivery cost.

Increasing motorization represents a severe problem worldwide, also affecting the emission levels of the road network. Accordingly, congestion management has obtained growing importance because of its strong economic, social, and environmental implications. Macroscopic Fundamental Diagram (MFD) based traffic control is a popular and efficient approach in this scientific field. In our research work the urban network has been divided into homogeneous regions, each of them characterized by its own MFD, and they are regulated using a network-level control scheme. The proposed Multiobjective Model Predictive Control (M-MPC) takes into account the congestion and $CO_{2}$ emission levels of the urban network, modelled by the emerging Emission Macroscopic Fundamental Diagram (e-MFD). The applied strategy has been demonstrated in a realistic traffic scenario (Luxembourg City) using validated microscopic traffic simulation. According to the introduced multiobjective approach, the control method can better exploit the road network capacity while efficiently reducing traffic-induced emissions.

The continuous urbanisation and increase in vehicle ownership have increasingly exacerbated traffic congestion problems. In this paper, we advocate the use of epidemic theory to model the spreading of traffic congestion in urban cities. Specifically, we use the Susceptible-Infected-Recovered (SIR) model but propose to explicitly consider the road network structure in the model to understand the contagion process of road congestion. This departs from the classical SIR model where homogeneous mixing based on the law of mass action is assumed. For this purpose, we adopt the N-intertwined modeling framework for the SIR model based on continuous-time Markov chain analysis. In our evaluation, we used two real-world traffic datasets collected in California and Los Angeles. We compare our results against both classical and average-degree-based SIR models. Our results show better agreement between the model and actual congestion conditions and shed light on how congestion propagates across a road network. We see the potential application of insights gained from this work on the development of traffic congestion mitigation strategies.

Traffic Congestion (TC) is increasing due to urban growth and vehicle numbers, rendering the development of cities and people's well-being difficult. Traffic Prediction (TP) and control systems have been required to improve Traffic Flow (TF) and reduce TC because standard methods are unsuitable. The paper proposes an innovative method for traffic control using the Dynamic Zone Segmentation Algorithm (DZSA) to solve this significant issue. The algorithm uses real-time data and road conditions to partition city traffic into manageable units, enhancing the adaptability and accuracy of Traffic Prediction (TP) performance. Applying DZSA, the recommended Long Short-Term Memory + Bayesian Structural Time Series (LSTM + BSTS) learning model optimizes TP by integrating the best features of conventional and Machine Learning (ML) methods. The model optimized quality performance when experimentally tested against other benchmark models using metrics like Mean Absolute Error, Mean Absolute Scaled Error, Accuracy Percent, Root Mean Squared Error, and Mean Absolute Percent Error. The recommended model, LSTM + BSTS, shows a minimal error rate of 6.68%, indicating its success.