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

In fifth-generation (5G)-based vehicular networks, vehicles share information about the road's condition to make driving safer and alleviate traffic congestion. Given the public nature of the medium, addressing issues of confidentiality and security is of paramount importance. Existing systems are vulnerable to a distributed denial-of-service (DDoS) attack from an attacker or high traffic in a dense area because of the time-consuming and complex operations based on bilinear pair and elliptic curve cryptographies used. In order to protect vehicle-to-vehicle communication in 5G-based vehicular networks from DDoS attacks, a strategy based on the Chebyshev polynomial is proposed in this study. The semi-group and chaotic features of the Chebyshev polynomial are utilised in our proposal. Our solutions also meet all applicable standards for security and privacy, such as node authentication, message integrity, preserving identity privacy, traceability, unlinkability, and resistance to attacks. Finally, our innovation decreases the computational burden to produce a message signature by 66.67% and the burden to verify a signature by 33.33%. However, the size of the message-signature tuple is affected by the communication expenses of our work by 5.00%. [ABSTRACT FROM AUTHOR]

With increased vehicular traffic on roadways, traffic jams and accidents are common, leading to substantial social and financial losses. Early detection of roadway accidents is beneficial as help can be directed to the accident site quickly, saving the lives of the injured. With this goal, an intelligent accident detection system is proposed in this paper using a Vehicular Adhoc Network model. Each vehicle has an on-board unit with multiple sensors in the proposed work. Outputs of these sensors, which monitor the condition of the driver and the vehicle, are input to a fuzzy logic controller for quick detection of an accident. The proposed system is validated by designing and implementing a hardware prototype. When an accident is detected, the proposed hardware uses a low-range radio (LoRa) transceiver network to send messages to a central server. After receiving the message from a vehicle, the server can inform a nearby ambulance. It can also identify the shortest path to a nearby hospital using Dijkstra's algorithm and inform the ambulance. This would facilitate timely treatment of the accident victim(s). [ABSTRACT FROM AUTHOR]

In the fast modernised world, usage of vehicles increased day by day, leads to vehicle traffic and congestions. Traditional way of monitoring traffic is less efficient and requires man power. Besides, safety of traffic controllers is the major concern in the manual monitoring. For that reason, effective prediction of vehicular traffic and monitoring the level of congestion is significant in VANET (Vehicular Adhoc Network) for mitigating the delays and danger of accidents. In order to make clear prediction of vehicles and the collision free path in the network, numerous existing algorithms have been provided for prediction and classification. However, the conventional techniques have faced complications in satisfying the accuracy while making predictions of path images. The accuracy in prediction along with the faster computational time have been addressed as a drawback which hinders in making appropriate travel path decisions. In order to overcome such challenges, proposed system employed Yolo v5 algorithm, GGA (Greedy based Genetic Algorithm) and Deep CNN (Convolutional Neural Network) with LSTM (Long Short Term Memory) for traffic monitoring and congestion control in VANET. The Yolo v5 algorithm is utilised for the vehicle prediction mechanism, for the capability of detection with high speed and accuracy. GGA is utilised for the feature selection for the capability of handling numerous features in data and to increase the computational speed. The Deep CNN with LSTM is utilised for Classification for the capability of handling larger datasets and to enhance the accuracy. Though, DCNN is the effective algorithm for classification, it has few limitations like slow processing time. To resolve this, DCNN is utilised with LSTM for enhancing the speed in the projected system. Vehicle detection dataset is used in the proposed system. Further, the experimental evaluations and comparative analysis exhibits the efficiency of the system in terms of accuracy, precision, recall and F1 score when compared to several existing algorithms. The proposed model holds the potential in efficient vehicular congestion management with expected accuracy and it is intended to contribute in to the path identification for vehicles in VANET applications. [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]

Recently, learning classifier systems (LCS) have been used for a variety of Internet of Things (IoT) devices and multi-cloud services, including cloud-based centralized control of physical robots and actuators in continuous-time environments. Performance analysis of sensors, navigation of humanoid robots and intelligent control of rescue systems. In these systems, we can run evolutionary or intuitive algorithms on cloud servers to search the space of rules and simultaneously other learning processes to assign how to interact with the environment to the rules in the classification. Also, the problem of continuous congestion, traffic reduction, network routing and predicting traffic conditions in wireless networks is the main challenge facing these systems in real environments. Usually such systems are non-Markovian. Therefore, they need memory to save system states. This paper presents a framework for XCS-based memory-based LCS. In addition to identifying optimal rules in overlapping modes, the XCS architecture is equipped with a memory. Memory stores the most efficient classifier rules. These rules reduce the steps to reach the goal. In the first proposed method, only those rules that affect the moving motion are kept in memory. As the number of rules increases, some of them are deleted according to memory space. In the second proposed method, some features are added to the rules of this memory and the performance of the memory is optimized using evolutionary algorithms, which are used to remove the less used rules. The relative success of the proposed LCS architecture in solving well-known maze problems compared to the conventional XCS architecture confirms its efficiency in increasing the number of successes and reducing the steps to reach the goals. The results of this research are suggested as a suitable solution for reducing routing time, reducing network load in the problem of congestion and traffic in solving problems related to wireless networks. [ABSTRACT FROM AUTHOR]

Recent advancements in the industrial revolution and artificial intelligence have aided in the development of novel approaches that have considerable potential for increasing the efficiency of traffic networks. Emerging concepts of autonomous driving and machine learning can be incorporated intelligently for improving traffic operations and control. In the coming years, traffic composition can vary in terms of autonomous vehicle (AV) penetration rate, which can lead to a heterogeneous traffic environment. Traffic control methods for such complex networks need to be designed effectively for accommodating the positive effects of AV implementation without compromising the safety and level of service in the presence of regular vehicles (RVs). An intelligent-based optimization framework for traffic signal control under heterogeneous AV-based traffic is proposed in this paper. This framework utilizes state-of-the-art machine-learning approaches to represent and design different components of the optimization process of cycle length. Further, SHapley Additive exPlanations (SHAP) is used to enhance model interpretability. The proposed optimization framework improves performance under congested traffic conditions compared with that of the conventional optimization methods. Compared to pure RV-based traffic, the penetration rates of 25%, 50%, and 100% can decrease optimized cycle lengths by 26%, 39%, and 53%, respectively, which can result in delay reductions of approximately 18%, 31%, and 56%, respectively. The proposed framework when applied in an adaptive-based manner can help in the generalization of controlling existing signalized intersections with the gradual penetration of AVs without the extra infrastructure or specific operational and connectivity capabilities of the involved vehicles. [ABSTRACT FROM AUTHOR]

Traffic congestion has caused great concern among policymakers in large cities around the world. In contrast with constantly increasing transport supply, individual-based active travel demand management (ATDM) has been proposed as a more efficient policy alternative for combating congestion. However, how individuals make routing choices during commuting in response to ATDM incentives is still largely unknown, given the lack of individual travel data. Using a desensitized one-week travel trajectory data set involving 5641 personal electric vehicles, we examine the major influencing factors of commuting route stability during working days and make suggestions on targeting the most responsive commuters. We first filter family-used vehicles by clustering vehicle usage patterns through employing the Gaussian mixture model and interpret drivers' route choice behaviors during morning peak hours. To look for factors affecting route stability, we develop a generalized additive model and find that route stability is significantly associated with road network density of origins and destinations, departure time, travel duration, commuting distance, reliability of the expressway, and volatility of the congested sections by which the routes passed. The empirical results may contribute to the understanding of individual route choices, and help urban managers develop more refined ATDM policies to alleviate traffic congestion in the future. [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 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]

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]

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]

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]

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]

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

Traffic congestion has several adverse effects on urban traffic networks. Increased travel times of vehicles, with the addition of excessive greenhouse emissions, can be listed as harmful effects. To address these issues, transportation engineers aim to reduce private car usage, reduce travel times through different control strategies, and mitigate harmful effects on urban networks. In this study, we introduce an innovative approach to optimizing traffic signal control settings. This methodology takes into account both pedestrian delays and vehicular emissions. Non-dominated sorting genetic algorithm-II and Multi-objective Artificial Bee Colony algorithms are adopted to solve the multi-objective optimization problem. The vehicular emissions are modeled through the MOVES3 emission model and integrated into the utilized microsimulation environment. Initially, the proposed framework is tested on a hypothetical test network, followed by a real-world case study. Results indicate a significant improvement in pedestrian delays and lower emissions. [ABSTRACT FROM AUTHOR]

The spatial mismatch between jobs and housing in cities creates long daily travels that exacerbate climate change, air pollution, and traffic congestion. Yet, not enough research on occupational differences has been done. This study first applies the Hidden Markov Mixture Model (H3M) to model travel patterns for different occupation groups in Hong Kong. Then, the Variational Bayesian Hierarchical EM algorithm is used to identify common lifestyle clusters. Next, a binary logistic regression is developed to examine whether the lifestyle clusters can be explained by jobs-housing balance. This study is among the first to consider travel patterns as a Markov process and apply H3M to examine jobs-housing balance by fine-grain occupation group. The method is transferable and universally applicable; and the results provide occupation-specific insights on jobs-housing balance in an Asian context. The research findings suggest that different occupation groups have different travel patterns in Hong Kong. Two lifestyle clusters, "balanced and compact activity space" and "work-oriented and extensive travels", are unveiled. Notably, the latter is associated a lower level of jobs-housing balance. Some occupations in the quaternary industry ("information and communications", "profession, science and technology", "real estate", and "finance and insurance") are having more serious jobs-housing imbalance. The paper concludes with a discussion on improving the occupation-specific jobs-housing balance in accordance with Hong Kong's future development goals. [ABSTRACT FROM AUTHOR]

In today's world, creating safe, comfortable, and beautiful slow communities has become an urgent research topic for addressing energy crises, environmental pollution, and traffic congestion. This study explored the relationship between the built environment and residents' leisure-time physical activities in slow communities in Shanghai. This study uses the analytic hierarchy process and entropy weight method to construct a new evaluation model to explore the sustainability mechanism. The results of the study are as follows. First, women participated in walking, jogging, and bicycling activities at a higher rate than men. Second, various leisure-time physical activities have different requirements for the built environment. Third, the built environment of slow communities in Shanghai shows a "pyramid" type of spatial stratification phenomenon. This study contributes to a new evaluation system and optimization model for promoting leisure-time physical activities, providing theoretical and methodological guidance for constructing livable slow communities in developing countries and promoting slow living. [ABSTRACT FROM AUTHOR]

Coastal cities in the Amazon lack basic infrastructure to treat and finally discharge urban domestic sewage. Implementing sub-river outfalls and waste-treatment pre-conditioning that significantly reduce pollution impact would be a sustainable alternative to cope with such an issue. The aim of the present research is to analyze the technical, economic and environmental feasibility of alternative projects applied to domestic waste's proper destination in the coastal zone of Amapá State (ZCEA), Brazil. Infrastructure basic sizing and the current treatment provided to domestic waste discharged in Amazonas River, in natura, were taken into consideration for study conduction. Research methodology was applied as follows: (a) pre-sizing the main infrastructure elements of sub-river outfalls (EM); (b) assessing the reduction of impacts on quality of water in the metropolitan seashore of Macapá and Santana cities; (c) economic assessment based on the alternative implementation of underwater outfalls in Macapá's Northern and Central zones, where one finds Amazonas River's high self-depuration ability and regular bathymetry. Technical feasibility, mainly in Fazendinha and Santana cities, was not satisfactory due to topobathymetric unevenness and traffic jam in Santana Channel's waterway. We have concluded that the self-depuration and sanitary features in Amazonas River's assessed stretches are a sustainable, effective and competitive technology extremely favorable for the implementation of sub-river outfalls, given its simplicity and, most of all, adjustment to the regional socioeconomic reality. [ABSTRACT FROM AUTHOR]

Internet of Vehicles (IoV) aka V2X is a growing area of research that aims at information exchange between vehicles and all other related objects to develop intelligent transportation systems. IoVs are characterized by high mobility, high-speed internet, varying node density, and dynamic topology and aim to minimize and communicate situations like traffic congestion, accidents, etc. Discovering a routing path in a highly unstable network environment to ensure the successful delivery of packets with minimal overheads. Finding reliable routing links as against shortest path routing is a necessity in IoV networks. In this paper, a routing protocol coined as the dynamic opportunistic routing protocol for IoV (DORP-IoV) is presented. DORP-IoV is an on-demand position-based protocol that seeks the advantage of wireless broadcast advantage to select a hop close to the virtual line of sight between source and destination for communicating the information to the destination. Vehicle movement direction and vehicle density around the ideal hop location are also considered while selecting the next hop for information forwarding. Communicating the information through an optimal number of intermediate nodes is the novelty of the work which ensures better packet delivery with minimized delay and routing overheads. The performance of DORP-IoV is evaluated and results are compared with the performance of Weighted- Greedy Perimeter Coordinator Routing (W-GPCR), Greedy Perimeter Coordinator Routing, and Greedy Perimeter Stateless Routing for varying node density and network connections for various metrics. DORP-IoV shows an improved performance in the range of 8–12% for packet delivery with similar performance for average end-to-end delay compared to W-GPCR. The optimal hop selection mechanism in DORP-IoV reduces the number of hops by 10–30% compared to W-GPCR. [ABSTRACT FROM AUTHOR]

In recent years, the problem of traffic congestion has become increasingly serious, and research on traffic system control has become a new hotspot. Studying the bifurcation characteristics of traffic flow systems and designing control schemes for unstable support points can alleviate traffic congestion from a new perspective. This article improves the full speed differential model considering strong wind models from the perspective of bifurcation control to adjust traffic flow. This article theoretically proves the existence conditions of Hopf bifurcation and saddle node bifurcation in the model and finds the stability mutation point of the transportation system stability. A nonlinear system feedback controller was designed for unstable bifurcation points using Chebyshev polynomial approximation and random feedback control methods. Without changing the system equilibrium point, the advance, delay, and elimination of Hopf bifurcation were achieved, and the abrupt behavior of the transportation system was controlled, thereby alleviating traffic congestion. This article explains the changes in the stability of complex transportation systems from the perspective of bifurcation analysis, which can better capture the characteristics of traffic flow. By adjusting the control parameters in the feedback controller, the influence of boundary conditions on the stability of the transportation system is fully described, and the influence of unstable focal points and saddle points on the system is suppressed, thereby slowing down the traffic flow. In addition, unstable bifurcation points can be eliminated, and the Hopf bifurcation can be controlled to advance, delay, and disappear, thereby achieving control over the stable behavior of the transportation system. This helps alleviate traffic congestion and also helps describe actual traffic phenomena. [ABSTRACT FROM AUTHOR]

Smart Autonomous Vehicles (AVSs) are networks of Cyber-Physical Systems (CPSs) in which they wirelessly communicate with other CPSs sub-systems (e.g., smart -vehicles and smart-devices) to efficiently and securely plan safe travel. Due to unreliable wireless communication among them, such vehicles are an easy target of malware attacks that may compromise vehicles' autonomy, increase inter-vehicle communication latency, and drain vehicles' power. Such compromises may result in traffic congestion, threaten the safety of passengers, and can result in financial loss. Therefore, real-time detection of such attacks is key to the safe smart transportation and Intelligent Transport Systems (ITSs). Current approaches either employ static analysis or dynamic analysis techniques to detect such attacks. However, these approaches may not detect malware in real-time because of zero-day attacks and huge computational resources. Therefore, we introduce a hybrid approach that combines the strength of both analyses to efficiently detect malware for the privacy of smart-cities. [ABSTRACT FROM AUTHOR]

Rapid urbanisation has led to a remarkable increase in travel demand as well as the number of vehicles globally. Inadequate road networks, especially in developing countries fail to serve this increasing vehicle population resulting in frequent traffic congestion. Proportional vehicular emissions are found to be responsible for serious consequences on human health and the environment. The nature and impact of these emissions depend on the traffic, environmental and meteorological factors. The present study focuses on vehicular emissions in a typical identified urban road stretch. The dispersion of vehicular pollutants in the selected urban stretch was studied using Fluidyn PANACHE version 5.2.6 software. The analysis of dispersion revealed that the highest concentration of pollutants is observed along the road sections, with a gradual decrease in concentration as you move away from the streets in both directions. From the vertical profile plotted, the maximum concentration is found to be on the ground and decreases as the height increases, which reveals the influence of meteorological parameters on urban air quality. These results indicate the significant influence of vehicular emissions and meteorological characteristics on ambient air quality. [ABSTRACT FROM AUTHOR]

In this study, the effect of median guardrails in no-passing zone sections of a two-lane two-way highway was investigated. In this regard, the Firuzkuh-Qaemshahr highway in Mazandaran province of Iran which has 5.5 km median guardrails was selected. All types of crashes studied in two periods, before-and after-the implementation of median guardrails, were considered, and using crash predictive models for the two-way two-lane highway, the effect of implementation was explored. To evaluate the economic effects of this treatment, various crash cost factors were converted to monetary equivalents to compare the cost of crashes before-and after-applying the treatment. This study revealed that using median guardrails caused the monthly average of fatal and injury crashes to decrease by 84% and 29%, respectively. Moreover, the proportion of fatal and injury crashes to total crashes had a 55% drop, indicating that the severity of crashes was reduced, leading to fewer deaths or injuries. Also, this treatment had an impressive reduction in the average monthly cost of crashes, so that the largest reduction among all cost factors belonged to the cost of fatalities in traffic crashes with an 85% reduction, followed by the cost of lost time due to the traffic jams caused by fatal crashes with an 84% decrease. Furthermore, the economic analysis of the implementation of median guardrails revealed that this treatment reduced the average monthly traffic crash cost by 65%. Therefore, the median guardrails in no-passing zone sections of the two-way two-lane Firuzkuh-Qaemshahr highway had a remarkable economic benefit. [ABSTRACT FROM AUTHOR]

A vehicle network is a complicated, dense network with a range of network and node-level complexity. The communication issues in a vehicular network are made more difficult by the high mobility, heterogeneity, energy restriction, high density, and scenario circumstances. Additionally, scenarios including traffic jams, accidents, and high communication loads make communication more challenging. Such difficult circumstances are beyond the capabilities of the current routing methods. A zone-based, load- and position-aware routing strategy for vehicular networks is presented in this paper. The infrastructure devices are set up statically and given zonal control in this protocol. In order to determine the load, energy, and fault-safe intermediate nodes, zone-based greedy weighted parameters are analyzed. The neighbor count, load, energy, and distance metrics are among the weighted parameters. The current GPSR protocol incorporates the greedy rule-based optimum neighbor identification method and weighted evaluation. The V2V and V2I communication is optimized using the proposed Load, Fault, Energy adaptive GPSR (LFE_GPSR) protocol. This LFE_GPSR protocol is simulated in a densely populated, heterogeneous environment. The SUMO setup NS2 environment is used for the simulation. The ZRP, GPSR, AMGRP, GSR, E-GyTAR, TFOR, EE-FMDRP, FBAODV, AFMDR, RMRPTS, FLAR-C, and D-CALAR protocols are the subjects of the comparative analysis. To evaluate the effectiveness of long- and short-distance communication, the analytical observations are carried out in a variety of scenarios. Multiple scenarios are generated with different vehicle densities, vehicle speed, and RSUs. In various circumstances, the suggested protocol's performance and efficacy are measured against the transmission delay, PDR ratio, and LFR characteristics. The outcomes show that, in comparison to existing protocols, the suggested protocol significantly reduced communication failure and latency. [ABSTRACT FROM AUTHOR]

Traveling demand increased rapidly on the roads of big cities. Traffic congestion is observed on a regular basis and causes serious problems for citizens. It is a vital need in larger cities to deal with the problem of overcrowded roads because Traffic congestion has negative consequences on daily routines and activities. All of the factors that cause traffic-jam affect our lives physically, mentally, and economically. It has been observed that one of the factors of the traffic jam is traffic diversion. Traffic diversion reroutes all the traffic towards alternative tracks that causes traffic jams to the diverted road. It has been observed in the past that the researchers less considered traffic congestion due to traffic diversion. There is a need for analysis to determine the risk factor related to traffic diversion that impacts the causes of traffic jams. This paper focuses on proposing an architecture based on the Internet Cloud of Vehicles for traffic congestion control through the mobile crowdsensing technique. [ABSTRACT FROM AUTHOR]

Road traffic which results in significant time lags, increased fuel consumption, and financial losses, remains a noteworthy challenge in developed and developing countries. As a result, the Ethiopian Government and the City Administration of Addis Ababa have built extensive road networks and imposed restrictions on driving, vehicle acquisition and parking. However, despite all these efforts, drivers and passengers waste significant time on long traffic queues, resulting in unpredictable and delayed travel. The current study investigated the cost of travel time delay, vehicle operating costs, time reliability, and the factors influencing these variables. The study used questionnaires, measurements, and traffic counting techniques to collect data from nine road segments. The sample comprised 3240 participants. The cost functions of both drivers and passengers were examined using a multiple linear regression model, with estimation performed using ordinary least squares. According to the findings, the economic costs of congestion depend on the number of lanes, the length of the road segment, the volume of traffic, and the respondents' income level. The study also revealed that travel, vehicle operation, and unreliability costs account for 74%, 6%, and 20%, respectively, of the total congestion costs. [ABSTRACT FROM AUTHOR]

Now-a-days since number of vehicles are growing day by day on the roads in urban and metropolitan area, hence traffic jam is becoming very common problem which everyone is facing time to time. So it becomes very crucial to forecast traffic to avoid the regular traffic congestion situation in daily life. In today's intelligent traffic system, reliable and precise traffic forecasting in metropolitan road networks is critical. Many models based on classical methods have been presented to address this; however, they lag in certain circumstances, failing to incorporate spatial and temporal dependency in the environment. As a result, this research aims to solve the geographical and temporal dependency problem that plagues most traffic forecasting models. Graph Convolution Network (GCN) and Gated Recurrent Unit (GRU) are employed in conjunction with an attention-based model for more accurate traffic forecasts in cities. A combined model called Spatio-Temporal Attention-based Model with GCN and GRU (ST AGG) is employed to anticipate traffic. ST-AGG model mainly focuses on extracting the relevant information from all the input given to the attention-based model. The spatial dependency is determined by using GCN and the temporal dependency is determined by using GRU, used in the ST-AGG model. It assists in short-term traffic forecasting as well as long-term traffic forecasting. The results demonstrate that the suggested ST AGG has properly predicted the volume of traffic on city roadways in real time with greater accuracy. The model depicts how traffic is influenced by geography and time. The Shen Zhen (SZ) car dataset from China and the Los Angeles (LAS) dataset from California are used to test the suggested model. And the result shows that Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) have reduced by up to 2.7% less, and accuracy has increased by 2.7% more compared to the previous model ST GCN for the SZ vehicle dataset. Similarly, although the accuracy has not changed significantly for the Loss Angeles data set, RMSE and MAE have reduced by 3.71% less as compared to the previous ST GCN model. The proposed model is more effective than the prior models like GCN, GRU, and ST GCN. [ABSTRACT FROM AUTHOR]

Accurate traffic prediction is indispensable for relieving traffic congestion and people's daily trips. Nevertheless, accurate traffic flow prediction is still challenging due to the traffic network's complex and dynamic spatial and temporal dependencies. Most existing methods usually ignore the dynamicity of spatial dependencies or have limitations, as using the self-attention mechanism for capturing dynamic spatial dependencies is computation forbidden in large networks. In addition, there are both short- and long-range dynamic temporal dependencies, which are not well captured. To overcome these limitations, we propose an intelligent dynamic spatial and temporal graph convolutional system for traffic flow prediction. First, we propose a dynamic spatial block to capture the complex and dynamic spatial dependencies, which is computation-friendly. Next, we propose a dynamic temporal block to capture the complex and dynamic temporal dependencies, which well balances the short- and long-range dynamic temporal dependencies. We validate and analyze the performance of the proposed method through extensive experiments on two traffic datasets. Analysis of results demonstrates that our proposed model has better prediction performance than the state-of-art baselines. Compared with the best contrast methods, the proposed method improves by 2.28% and 8.01% in terms of the mean absolute error on PEMS04 and PEMS08 datasets. [ABSTRACT FROM AUTHOR]

Engineering multilayer networks that efficiently connect sets of points in space is a crucial task in all practical applications that concern the transport of people or the delivery of goods. Unfortunately, our current theoretical understanding of the shape of such optimal transport networks is quite limited. Not much is known about how the topology of the optimal network changes as a function of its size, the relative efficiency of its layers, and the cost of switching between layers. Here, we show that optimal networks undergo sharp transitions from symmetric to asymmetric shapes, indicating that it is sometimes better to avoid serving a whole area to save on switching costs. Also, we analyze the real transportation networks of the cities of Atlanta, Boston, and Toronto using our theoretical framework and find that they are farther away from their optimal shapes as traffic congestion increases. Finding an optimal shape for transport networks, represented as multilayer structures, is a challenging problem. The authors propose analytical and computational frameworks to analyze sharp transitions from symmetric to asymmetric shapes in optimal networks, that can be applied for planning and development of improved multimodal transportation systems within a city. [ABSTRACT FROM AUTHOR]

Nowadays Road traffic is a major issue in developing and under-developing countries. With the rampant increase in traffic, society faces major traffic threats including life threats and environmental threats, thus traffic management is a gruesome problem to address. The consequences of poor traffic management include road accidents, jamming traffic, pollution, and many more that can be life-threatening. Living in the twenty-first century with the emergence of technology and the applicability of smart cities provides a perfect solution to curb traffic issues. Keeping in view the deadlock and congestion in traffic, this work will provides solution by indigently detecting and prioritizing vehicles and non-vehicles. The research involves the implementation and comparison of two states of art algorithms Aggregated channel feature and a Point Tracker. Further, the algorithms are enhanced by improving traffic management in terms of identifying the category of transport, prioritizing the traffic which contains vehicles and non-vehicles on basis of the size of vehicle, type of the vehicle, and emergency providing priority to resolve the deadlock. Further, the proposed enhanced point tracker algorithm includes emergency detection in case of an accident and provides an alternative route to neighboring vehicles and non-vehicles. Enhanced ACF has detected a true positive rate of 80%, 89%, has detected true positive rates of 69%, and 79% having non-vehicle detected with assigned priority. Enhanced point tracker has detected true positive rate of 88%, 94%, and 86% having vehicles, non-vehicles, and assigned priority. [ABSTRACT FROM AUTHOR]

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

Predictive analytics over mobility data is of great importance since it can assist an analyst to predict events, such as collisions, encounters, traffic jams, etc. A typical example is anticipated location prediction, where the goal is to predict the future location of a moving object, given a look-ahead time. What is even more challenging is to be able to accurately predict collective behavioural patterns of movement, such as co-movement patterns as well as their course over time. In this paper, we address the problem of Online Prediction of Co-movement Patterns. Furthermore, in order to be able to calculate the accuracy of our solution, we propose a co-movement pattern similarity measure, which facilitates the comparison between the predicted clusters and the actual ones. Finally, we calculate the clusters' evolution through time (survive, split, etc.) and compare the cluster evolution predicted by our framework with the actual one. Our experimental study uses two real-world mobility datasets from the maritime and urban domain, respectively, and demonstrates the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

To address the challenges of traffic congestion and suboptimal operational efficiency in the context of large-scale applications like production plants and warehouses that utilize multiple automatic guided vehicles (multi-AGVs), this article proposed using an Improved Q-learning (IQL) algorithm and Macroscopic Fundamental Diagram (MFD) for the purposes of load balancing and congestion discrimination on road networks. Traditional Q-learning converges slowly, which is why we have proposed the use of an updated Q value of the previous iteration step as the maximum Q value of the next state to reduce the number of Q value comparisons and improve the algorithm's convergence speed. When calculating the cost of AGV operation, the traditional Q-learning algorithm only considers the evaluation function of a single distance and introduces an improved reward and punishment mechanism to combine the operating distance of AGV and the road network load, which finally equalizes the road network load. MFD is the basic property of road networks and is based on MFD, which is combined with the Markov Chain (MC) model. Road network traffic congestion state discrimination method was proposed to classify the congestion state according to the detected number of vehicles on the road network. The MC model accurately discriminated the range near the critical point. Finally, the scale of the road network and the load factor were changed for several simulations. The findings indicated that the improved algorithm showed a notable ability to achieve equilibrium in the load distribution of the road network. This led to a substantial enhancement in AGV operational efficiency. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic led to unprecedented global challenges, with governments enforcing strict confinement measures to curb its spread. Within the European Union (EU) and United Kingdom (UK), these measures had significant socio-economic repercussions and manifested in altered environmental parameters, notably CO2 emissions, NO2 concentrations, and traffic congestion. The objective of this study is to determine the coronavirus's influence on CO2 emissions, NO2 concentrations, and traffic congestion, as well as the relationship between NO2 concentrations and CO2 emissions across the EU and UK from January 2019 until end of 2022. In agreement with earlier research, our results indicate a decrease in CO2 emissions over a more extended period of 2020 until 2022 than the annual emissions in 2019. This study shows significant reductions of 10.66%, 2.66%, and 0.18% in 2020, 2021, and 2022, respectively, compared to the same period in 2019. Furthermore, we discovered monthly NO2 concentrations decreased by 2–39% for ± 1σ in 2020, 4.16–30.19% for ± 1σ in 2021, and 3.39–32.81% for ± 1σ in 2022 compared to 2019. Average annual mobility was substantially decreased by 36% in 2020 and 24% in 2021 (until August) compared to 2019. Lastly, the results of the CO2 and NO2 ratio showed a decreasing trend from the first of January 2020 until the end of 2022 compared with the same period in 2019. This research sheds light on the influence of enforced socio-economic restrictions on NO2 levels, CO2 emissions, and traffic, offering valuable insights for policymakers in crafting effective strategies. [ABSTRACT FROM AUTHOR]

The study is focused on environmental contamination with platinum metals and compares soil samples from four European cities: Jihlava (Czechia), Brno (Czechia), Vienna (Austria) and Moscow (Russian Federation). The research was aimed at determining the amount of platinum and palladium in soils that were sampled in different urban areas of the mentioned cities. The selection of sampling points was focused on crossroads and roads with a high intensity of car traffic, especially places which are frequent traffic jams in populated agglomeration. The results of this study show that the highest concentrations of platinum and palladium in the soil are close to these roads. The greatest release occurs when cars start on in places with low air circulation, such as tunnels, crossroads or highway entrances. Based on this fact, these localizations are the most interesting for research. Concentrations above the limit are also at places with poor dispersion conditions. This work includes monitoring the amount of platinum and palladium released into the environment due to automobile traffic. The measurement showed a connection between the increased traffic situation in individual cities and the concentration of platinum and palladium in the soil matrix. Platinum and palladium values ranged in the following ranges. For Jihlava, the concentration of platinum was determined from 16.93 to 38.72 ng g−1 and palladium from 2.705 to 8.452 ng g−1. For Brno, the concentration of platinum was determined from 34.53 to49.71 ng g−1 and palladium 8.450–12.78 ng g−1. For Vienna, the concentration of platinum was determined from 39.22 to 159.2 ng g−1 and palladium 18.45–98.21 ng g−1. For the Moscow center, the concentration of platinum was determined from 5.897 (background value) to 352.9 ng g−1 and palladium 2.598 (background value) to 86.11 ng g−1. For the Moscow circle, the concentration of platinum was determined from 249.7 to 520.9 ng g−1 and palladium 91.87–180.6 ng g−1. This study was created in 2021–2022. [ABSTRACT FROM AUTHOR]

In metropolitan regions, the quick increase in vehicles has caused gridlock, pollutants, and problems in the transportation of products. IoT is a breakthrough advancing the development of intelligent management systems and automated processes throughout the universe. For automatic and intelligent cultures, this is a crucial contribution. Effective management and prevention of traffic congestion are reliable strategies that contribute to conserving numerous valuable resources. Autonomous vehicles and smart devices are equipped with an IoT-based ITM system of sensors that can understand, collect, and transfer information. Another method to enhance the transportation system is deep learning (DL). Traffic jams, delays, and a high death rate result from the numerous problems of conventional transportation management systems. An adaptive traffic management system (ATM) built on DL and IoT is designed and implemented in this study's efforts. The suggested system's architecture is built on the automobile, the framework, and the events. To account for all potential problems with the transportation system, the design uses a variety of situations. The proposed methodology uses a Quantum Optical Communication (QOC) system for Traffic monitoring and enhanced data communication. The suggested adaptive traffic management-based QOC model continuously modifies the timing of traffic signals based on traffic volume and anticipated movements from neighbouring junctions. By progressively allowing cars to cross green lights, it considerably reduces the duration of travel. It also reduces congestion in traffic by creating a smoother transition within 38 s, which is less than all other techniques. [ABSTRACT FROM AUTHOR]

In this study, a traffic queue model is employed to simulate traffic parameters for optimization purposes. The primary objective is to enhance the average queue length, cumulative delay, and cumulative reward for varying numbers of vehicles generated using an innovative approach. The proposed methodology integrates deep Reinforcement Q-Learning techniques implemented through a realistic simulator, Simulation of Urban MObility, based on OpenStreetMap. This simulator is used to balance the flow of traffic by monitoring traffic conditions and adjusting signals (Red/Yellow/Green) accordingly. The research extensively evaluates simulation outcomes across different scenarios involving 50, 100, 500, and 1000 vehicles, with episodes 10, 50 and 100. The results provide valuable insights into training times, simulation durations, and total rewards obtained, indicating improvements in mitigating traffic congestion. The introduction of a traffic control strategy enhances model effectiveness through the utilization of four-layer deep neural network architectures with 400 neurons in hidden layers. However, the study also highlights persistent challenges related to real-time implementation and complexities in state-action-agent relationships. The progress highlights improvements in reducing congestion and enhancing efficiency at junctions by dynamically adjusting signal phases while leveraging historical data. The results highlight the potential of dynamic traffic management strategies in effectively addressing urban traffic challenges. [ABSTRACT FROM AUTHOR]

Traffic light optimization is known to be a cost-effective method for reducing congestion and energy consumption in urban areas without changing physical road infrastructure. However, due to the high installation and maintenance costs of vehicle detectors, most intersections are controlled by fixed-time traffic signals that are not regularly optimized. To alleviate traffic congestion at intersections, we present a large-scale traffic signal re-timing system that uses a small percentage of vehicle trajectories as the only input without reliance on any detectors. We develop the probabilistic time-space diagram, which establishes the connection between a stochastic point-queue model and vehicle trajectories under the proposed Newellian coordinates. This model enables us to reconstruct the recurrent spatial-temporal traffic state by aggregating sufficient historical data. Optimization algorithms are then developed to update traffic signal parameters for intersections with optimality gaps. A real-world citywide test of the system was conducted in Birmingham, Michigan, and demonstrated that it decreased the delay and number of stops at signalized intersections by up to 20% and 30%, respectively. This system provides a scalable, sustainable, and efficient solution to traffic light optimization and can potentially be applied to every fixed-time signalized intersection in the world. Without relying on any infrastructure-based vehicle detectors, the authors present a scalable traffic signal re-timing system that uses a small percentage of connected vehicle trajectories as the only input. Real-world tests demonstrate that the system decreases both delays and number of stops. [ABSTRACT FROM AUTHOR]

The low-carbon development of new energy vehicles (NEVs) is critical to achieving the goals of carbon peaking and carbon neutrality. As such, combining gray model theory with system dynamics (SD-GM) and based on the bidirectional-cycle prediction theory, we propose a NEV annual average mileage algorithm considering the impact of the epidemic in China, taking private cars as an example. Then, combining a voluntary advocacy strategy (VA) with the SD-GM theory (VA-SD-GM integration), we establish an energy-saving and carbon-reduction management model. To evaluate the proposed algorithm, we performed a dynamic simulation. The results indicate that the enhanced green scenario enabled significant energy-saving and CO2 reduction performance but would cause side effects in the long term. Compared with the enhanced green scenario, the linkage mode reduced the impact of parking space tension, the number of NEV trips, and the intensification of traffic congestion by approximately 33%, 50%, and 34%, respectively. It effectively suppressed the continuous increase in side effects and had a synergistic effect of carbon reduction, energy conservation, congestion alleviation, and side-effect reduction. The study provides a theoretical basis for optimizing the energy-saving and CO2 reduction path of NEVs. [ABSTRACT FROM AUTHOR]

The alleviation of traffic congestion relies on efficient traffic control and traffic guidance, which are based on real-time short-term traffic flow prediction. In this paper, the stacked autoencoder (SAE) deep learning model with powerful feature learning capability is selected to predict the traffic flow on road sections. The process of training SAE includes the pre-training phase and the fine-tuning phase, which mainly apply the BP algorithm. However, the process of training SAE is time-consuming and cannot meet the real-time performance of modern application systems. This paper proposes a parallel training strategy for the SAE prediction model based on data parallel mode. The gradient solution process in our algorithm satisfies the conditions of parallel computing, so the training process can be designed in a parallel manner. The original dataset is distributed to some computing nodes, which are work nodes. The work node is responsible for gradient calculation using the local data. The task of the sole master node is to synthesize the gradient calculation results and then broadcast the updated gradient to each work node. The simulation results show that the SAE-based prediction model achieves better results than the traditional model, and the parallel algorithm reduces the running time of training processes. [ABSTRACT FROM AUTHOR]

This paper explores the impact of population agglomeration on urban economic development quality in various cities of China. The results show that population agglomeration significantly contributes to the improvement of urban green total factor productivity by increasing population diversification, promoting knowledge spillovers, and reducing pollution emission intensity. Moreover, we find that population agglomeration in type II big cities and type I large cities significantly improves green total factor productivity, while the impact of population agglomeration in metropolises and mega-cities on green total factor productivity is not significant. On the one hand, type II big cities and type I large cities are in the period of rising economic development, the population has not yet reached saturation, and there is still a large demographic dividend space. On the other hand, excessive population agglomeration also brings about "urban diseases" such as population congestion and traffic congestion, especially in the metropolises and mega-cities. Finally, using data on producer services and its sub-sectors, we identify a more significant driving effect of high-end talent agglomeration on green total factor productivity. [ABSTRACT FROM AUTHOR]

Bike-sharing is a powerful solution to urban challenges (e.g., expanding bike communities, lowering transportation costs, alleviating traffic congestion, reducing emissions, and enhancing health). Accurately predicting bike-sharing demand not only ensures the system meets community needs but also optimizes resource allocation, reduces operational costs, and enhances the user experience, thereby increasing the system's sustainability and city-wide benefits. However, prediction is complicated in low-computing environments with insufficient data due to privacy regulations or policy constraints. This study proposes an online learning-based two-stage forecasting model based on a low-computing environment with insufficient data for robust, fast multistep-ahead prediction for bike-sharing demand in Seoul. The model was applied with exploratory data analysis (EDA) to the Seoul Bike-sharing Demand dataset, split into insufficient training and sufficient testing sets. First, we generated prediction values for the random forest, extreme gradient boosting, and Cubist methods in training and testing. Second, we used the Ranger package trained with external factors and prediction values using time-series cross-validation for multistep-ahead prediction 1 h to 1 day later. We compared the model performance with that of 23 machine and deep learning models to verify its superiority. Using interpretability methods and EDA, we reported the relationships between external factors and bike-sharing demand. [ABSTRACT FROM AUTHOR]

The parking system is a vital element within intelligent transport systems, playing a pivotal role in alleviating traffic congestion and mitigating associated stress and anxiety. In response to evolving operational requirements, specialized parking systems such as autonomous valet parking (AVP) and smart parking systems (SPS) are employed based on the specific characteristics of vehicles. This research endeavor begins by introducing a loosely connected middleware designed to accommodate a variety of vehicles within a unified parking area. While the automatic parking system is designed for automated vehicles, and the manual parking system caters to manually operated vehicles, both systems lack the ability to effectively manage both automatic and manual vehicles. To address this challenge, a middleware solution is introduced, which acts as an intermediary layer between vehicles and the parking system. This middleware consists of various modules, enabling the seamless entry of all vehicle types into parking areas, regardless of classification or mode of operation. Second, it uses the Markov Chain model to determine the enhancement in the usability of the parking system after the incorporation of the middleware. Lastly, the suggested loosely coupled middleware approach is compared with tightly coupled smart parking approaches like automatic and manual systems. The matrices of assessment evaluated showed that the recommended scheme outperform its predecessors. In addition, it demonstrated remarkable achievement in terms of cost function, space availability (70%), and traffic jam reduction (56%). The advocated middleware, apart from keeping smooth traffic flow, assists in fulfilling the dire need for parking areas. [ABSTRACT FROM AUTHOR]

In traffic flow, flyovers play a vital role in reducing traffic congestion, advancing commute timing, and helping to prevent collision scenarios. Inspired by the real-life applications of flyover, we model a coupled two-lane transport system where the lanes are divided into three segments in which the particle lane switching mechanism executes only in the middle segment of the lane, and particle inclusion and expulsion kinetics are performed in first and last segment of the lane to embody the infrastructure of flyover. The nature of the system characteristics is analyzed for diverse values of particle inclusion, expulsion, and coupling rates through graphical diagrams of phase planes, density profiles, shock dynamics, and phase transitions. The time-invariant behavior of the system is inspected numerically by utilizing the well-known method of mean-field theory, and it is discovered that the biased dynamics of particle inclusion, expulsion, and fully asymmetric coupling condition yield the phase diagram to disclose more unanticipated mixed stationary phases. Besides, the system experiences the fruitful novel incident of the double shock phase when the possibility of particle inclusion occurs relatively higher than the expulsion event. Also, it is noticed that the system experiences the fascinating phenomenon of shock propagation by traveling in either vertical/horizontal direction in the shock region of the phase diagram under the symmetric coupling circumstance, such as the position of the shock suddenly transits from the right to left segment without crossing the middle segment. The calculated numerical results are in good agreement with Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

Public highways are, in reality, the cornerstone of the country's transportation system. Accidents are unavoidable with this mode of transportation. Collisions involving resting livestock on national highways occur in most countries around the world. It endangers both the drivers and the animals. This paper proposes a method for mitigating the risk of accidents caused by deceased animals, notably cattle that are generating traffic and congestion on national highways and may constitute a safety risk. We have proposed an Internet of Things (IoT) fog-based framework for reclining livestock identification techniques for roadways, data are collected using the IoT-enabled video recording surveillance cameras. We use feature extraction, characteristic expression, assessment criteria, and an unrestricted approach for detecting deceased livestock (such as cows or buffalos), as well as recommendations on whether their placement is harmful to highway traffic. In this study, you only look once (YOLO) image recognition algorithm is implemented for reclining cattle on roadways using the fog layer for training and evaluating datasets. The performance parameters of the proposed framework, such as accuracy, recall, precision, mean average precision (mAP), and interference time, have been measured, and a comparison with existing state-of-the-art techniques has been presented. The obtained findings indicate that the suggested framework surpasses the present approaches, with a higher accuracy of 98% and an interference time of 4.68 ms. Artificially intelligent surveillance system can spot reclined livestock utilizing surveillance videos on roadways. This will ensure passenger safety as well as the safety of roadside cattle. [ABSTRACT FROM AUTHOR]

In recent years, real-time vehicle congestion detection has become a hot research topic in the field of transportation due to the frequent occurrence of highway traffic jams. Vehicle congestion detection generally adopts a vehicle counting algorithm based on object detection, but it is not effective in scenarios with large changes in vehicle scale, dense vehicles, background clutter, and severe occlusion. A vehicle object counting network based on a feature pyramid split attention mechanism is proposed for accurate vehicle counting and the generation of high-quality vehicle density maps in highly congested scenarios. The network extracts rich contextual features by using blocks at different scales, and then obtains a multi-scale feature mapping in the channel direction using kernel convolution of different sizes, and uses the channel attention module at different scales separately to allow the network to focus on features at different scales to obtain an attention vector in the channel direction to reduce mis-estimation of background information. Experiments on the vehicle datasets TRANCOS, CARPK, and HS-Vehicle show that the proposed method outperforms most existing counting methods based on detection or density estimation. The relative improvement in MAE metrics is 90.5% for the CARPK dataset compared to Fast R-CNN and 73.0% for the HS-Vehicle dataset compared to CSRNet. In addition, the method is also extended to count other objects, such as pedestrians in the ShanghaiTech dataset, and the proposed method effectively reduces the misrecognition rate and achieves higher counting performance compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Centrality is a numerical measure that characterizes the structural properties of a graph. In the presented work, centrality is used to analyze the load of the graph of urban roads in the city of Petrozavodsk. In the paper, we describe the method used to construct the road graph, present a modified centrality measure that takes into account the features of the transport network and the distribution of passenger traffic, and demonstrate the results of numerical simulations. For the transport graph, betweenness centralities were calculated with and without regard to the distribution of passenger traffic; a connectivity analysis was performed to identify critical, overloaded and reserve roads, and the routes that make the greatest contribution to the centrality of the most loaded roads. The results show that centrality can be used for the analysis of the structural features of the graph of urban roads, modeling sustainability and planning the development of the transport network. [ABSTRACT FROM AUTHOR]

Understanding people's travel behavior is necessary for achieving goals such as increased bicycling and walking, decreased traffic congestion, and adoption of clean-fuel vehicles. To understand underlying motivations, researchers increasingly are adding subjective variables to models of travel behavior. This article presents a systematic review of 158 such studies. Nearly every reviewed article finds subjective variables to be predictive of transport outcomes. However, the 158 reviewed studies include 2864 distinct subjective survey questions. This heterogeneity makes it difficult to reach definitive conclusions about which subjective variables are most important for which transport outcomes. In addition to heterogeneity, challenges of this literature also include an unclear direction of causality and tautological relationships between some subjective variables and behavior. Within the constraints imposed by these challenges, we attempt to evaluate the explanatory power of subjective variables, which subjective variables matter most for which transport choices, and whether the answers to these questions vary between continents. To reduce heterogeneity in future studies, we introduce the Standardized Transport Attitude Measurement Protocol, which identifies a curated set of subjective questions. We have also developed an open-access database of the reviewed studies, including all subjective survey questions and models, with an interactive, searchable interface. [ABSTRACT FROM AUTHOR]