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Population growth in urban cities has created transport problems such as traffic congestion, pollution, accidents, and infrastructure failures. These problems often lead to disruption of mobility, especially in emerging economies. The goal of any city is to have a resilient transport system that is free from frequent and severe disruptions. Resilience can be incorporated into smart mobility systems to create a robust, integrated and intelligent transport system. Therefore, this study sought to determine resilience factors required for a resilient smart mobility system in emerging economies. The study used interpretive structural modelling (ISM) to establish the causal interrelationships among the factors drawing from data from experts in smart cities, city transport and academia. It was found that the most critical factor to drive resilience is foresight – the ability to anticipate disruptions. Three other resilience factor categories identified were dynamism, certainty and optimisation, which all point to the importance of building dynamic capabilities. The key implication for practice highlights the role that human and artificial intelligence can play in anticipating natural, social and infrastructure disruptions. ISM offered a mechanism to determine the critical causal resilience factors to be prioritised amongst the competing demands on the limited resources of emerging economies. Apart from foresight, which was found to be a driving factor, the other resilience factors are equally strong linkage factors (influence each other), thus further revealing the complexities that decision-makers in urban cities of emerging economies constantly face. A research agenda for resilience in smart mobility in emerging economies was developed. [ABSTRACT FROM AUTHOR]

Despite the promising benefits that ride-sharing offers, there has been a lack of research on the benefits of high-capacity ride-sharing services. Prior research has also overlooked the relationship between traffic volume and the degree of traffic congestion and emissions. To address these gaps, this study develops an open-source agent-based simulation platform and a heuristic algorithm to quantify the benefits of high-capacity ride-sharing with significantly lower computational costs. The simulation platform integrates a traffic emission model and a speed-density traffic flow model to characterise the interactions between traffic congestion levels and emissions. The experiment results demonstrate that ride-sharing with vehicle capacities of 2, 4, and 6 passengers can alleviate total traffic congestion by approximately 3%, 4%, and 5%, and reduce traffic emissions of a ride-sourcing system by approximately 30%, 45%, and 50%, respectively. This study can guide transportation network companies in designing and managing more efficient and environment-friendly mobility systems. [ABSTRACT FROM AUTHOR]

Ride-sharing is increasingly recognised as a potential solution to transportation issues such as traffic congestion and parking problems. The success of ride-sharing systems depends on the willingness of individuals to share rides, and effective pricing strategies are among the crucial factors. However, creating a fair pricing system for riders and providers remains challenging. This research introduces a new pricing scheme based on the concept of shareability. This concept considers all the possible sharing situations among trips. It is designed to incentivize riders and accommodate providers' criteria. This scheme calculates the trip fare for passengers, considering various ride-sharing scenarios derived from the shareability function, thus ensuring fairness and transparency for passengers. Using the macroscopic fundamental diagram, our model incorporates traffic dynamic conditions. We have applied the method to the real case of Lyon city in France to assess the effectiveness of our approach. Through a comprehensive analysis of various configurations of the proposed pricing scheme and benchmarking with the existing approaches, we demonstrate its superior performance in reducing ride rejections compared to traditional methods. This result showed the proposed method's potential for enhancing ride-sharing systems' effectiveness. [ABSTRACT FROM AUTHOR]

This study aims to understand the phase transitions, between congested and free-flow states, by estimating the congestion boundary. Since the congestion transition process occurs rapidly, the probability density distributions of traffic flow parameters, i.e. speed and density, show bimodal shapes. The congestion boundary is defined as the threshold that decomposes the bimodal distribution. The dedicated short-range communication data and toll collection system data were used to estimate congestion boundaries on the highway section from Seoul tollgate to Shingal junction. The results show that the congestion boundaries for speed, density, and flow rate were estimated to be 66.9 km/h, 22.8 veh/km, and 341.3 veh/5 min, respectively. With the flow rate at the congestion boundary, the capacity was estimated to be between 71.4% and 82.7% of traditional capacities. The congestion boundary approach provides critical thresholds for each dynamic transition phase on highways, offering crucial insights for effective congestion management. [ABSTRACT FROM AUTHOR]

To enhance the efficiency and stability of traffic flow in the vicinity of traffic signals under mixed traffic, a macroscopic model considering the characteristics of the vicinity of traffic signals is proposed from the perspective of cyber-physical systems (CPS). In this model, the effects of traffic lights and vehicle lane changing behavior are analyzed. Among them, the remaining time of the signal light, the distance between the vehicle and the signal light, and the urgency of lane change are considered to build a macro model. Finally, the simulation results show that compared with the traditional macroscopic model, the proposed model can improve the stability of traffic flow in the vicinity of traffic signals and effectively alleviate traffic congestion. [ABSTRACT FROM AUTHOR]

Traffic congestion in urban areas poses several challenges to municipal authorities including pollution, productivity loss, reckless driving, and delays in dealing with emergencies. Smart cities can use modern IoT infrastructure to solve the congestion problem and reduce pollution and delays. In this article, we focus on congestion mapping and mitigation for emergency vehicles in smart cities. We use a novel traffic light control technique to change the flow of cars on lights of interest thereby making way for emergency vehicles. We use a simulation model for a selected area of Manhattan to implement congestion mapping and to help find the fastest path for routing emergency vehicles based on the congestion metrics. The system controls traffic lights to block off the roads feeding into congestion and allows flow away from the congested path. This helps in clearing the preferred route to help emergency vehicles reach the destination faster. We show that the proposed algorithm can map congestion on city roads with accuracy thus helping to improve the response time of the emergency services and saving precious lives. [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]

Ethiopia's economy has made significant strides forward since the beginning of the 21st century. However, along with this, there is a high rate of urbanization, and internal migration, which lead to an increase in the number of vehicle owners in Addis Ababa city, causing traffic congestion problems. This is adversely impacting the socioeconomic and well-being of the residents. Therefore, this study attempted to identify the perception of drivers and passengers on the socio-economic impact (SEI) of traffic congestion in Addis Ababa city by taking the study corridors in the Kolfe keraniyo sub-city. Nine segments were sampled. Data collection included questionnaires distributed among 3240 respondents. A generalized ordered logit model was used. Accordingly, age, income, household size, gender, education, and respondent type (driver or passenger) were significant variables. The descriptive statistical results also indicated that the first, second, and third significant SEI perceived by the respondents were late arrival to the office, extra time added to the car or bus journey, and the increased risk of being in an accident. The outcomes of this study, including the responses to the research inquiries and the acquisition of extensive scientific knowledge, will contribute to the formulation of comprehensive mitigation strategies and management plans. [ABSTRACT FROM AUTHOR]

In densely populated cities, parking space scarcity results in issues like traffic congestion and difficulty finding parking spots. Recent advancements in computer vision have introduced methods to address parking lot management challenges. The availability of public image datasets and rapid growth in deep learning technology has led to vision-based parking management studies, offering advantages over sensor-based systems in comprehensive area coverage, cost reduction, and additional functionalities. This study presents an innovative fusion algorithm that integrates object detection with occupancy state algorithms to accurately identify vacant parking spaces. The employment of the YOLOv7 framework for vehicle instance segmentation, combined with three occupancy algorithms Euclidean distance (ED), intersection over reference (IoR), and intersection over union (IoU) are compared to determine the occupancy state of observed areas. The proposed method is evaluated using the CNRPark-EXT dataset, and its performance is compared with state-of-the-art methods. As a result, the proposed approach demonstrates robustness under varying conditions. It outperforms existing methods in terms of system evaluation performance, achieving accuracies of 98.88%, 97.99%, and 90.04% for ED, IoR, and IoU, respectively. This fusion detection method enhances adaptability and addresses occlusions, emphasizing YOLOv7's advantages and accurate shape approximation for slot annotation. This study contributes valuable insights for effective parking management systems and has potential usage in the real-world implementation of intelligent transportation systems. [ABSTRACT FROM AUTHOR]

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Car dependence has been threatening transportation sustainability as it contributes to congestion and associated externalities. In response, various transport policies that restrict the use of private vehicles have been implemented. However, empirical evaluations of such policies' effects on traffic congestion have been limited. To this end, we compile a fine spatiotemporal resolution data set of the floating vehicle-based traffic performance index to examine the effects of a recent nonlocal vehicle driving restriction policy in Shanghai, one of most populous cities in the world. Specifically, we explore whether and how the policy impacted traffic speeds in the short term by employing a quasi-experimental difference-in-differences modeling approach. We find that: (1) In the first month, the policy led to an increase of network-level traffic speeds by 1.47% (0.352 km/h) during evening peak hours (17:00–19:00) but had no significant effect during morning peak hours (7:00–9:00). (2) The policy also helped improve network-level traffic speeds during some unrestricted hours (6:00, 12:00, 14:00, and 20:00), although the impact was marginal. (3) The short-term effects of the policy exhibited heterogeneity across traffic analysis zones. The lower the density of metro stations, the greater the effects. We conclude that driving restrictions for non-local vehicles alone may not significantly reduce congestion, and their effects can differ both temporally and spatially. However, they can have potential positive side effects such as increased purchase and usage of new energy vehicles, owners of which can obtain a local license plate of Shanghai for free. [ABSTRACT FROM AUTHOR]

The article offers a solution to traffic congestion caused by temporary disruptions, such as accidents or road construction, through a method called Congestion Management based on Inflow and Outflow Volumes (COMIO). Topics include the limitations of traditional route control methods, the introduction of COMIO to stabilize traffic flow, and how COMIO manages the balance between traffic inflow and outflow to prevent the recurrence of congestion.

Road traffic management requires the ability to foresee geographical congestion conditions in an urban road traffic network. The proposed investigation is aimed to envisage the presence of blockage in a specific region of geographical location using gated recurrent unit (GRU) combined with the convolutional neural network (CNN) and long short-term memory (LSTM). The data related to road traffic is extracted from the time—frequency domain of a specific urban road. The collected data is then compressed into a RGB coded image which is the input to the GRU which determines the parameters of traffic blockage in a specific region of the road. The road traffic network features are extracted from those parameters and identified using a CNN's feature extraction technology. We used LSTM model to assess the time series temporal images of the road traffic blockage and to utilize the time division to pool the flow of traffic. For model validation, CTT (Chicago Traffic Tracker) dataset network is chosen. The parameters assessed had ensured highest prediction accuracy of 98.87%, F1-score of 0.98, 0.971 of specificity, 0.987 of recall and 0.978 of precision to forecast the presence road traffic. The proposed model demonstrates a significant advancement in road traffic detection with an accuracy of 98.87%, surpassing existing models that achieve the highest accuracy of 94.3%. This represents a notable improvement of approximately 4.77% over the highest accuracy achieved by the previous models. [ABSTRACT FROM AUTHOR]

Modern urban centers have one of the most critical challenges of congestion. Traditional electronic toll collection systems attempt to mitigate this issue through pre-defined static congestion pricing methods; however, they are inadequate in addressing the dynamic fluctuations in traffic demand. Dynamic congestion pricing has been identified as a promising approach, yet its implementation is hindered by the computational complexity involved in optimizing long-term objectives and the necessity for coordination across the traffic network. To address these challenges, we propose a novel dynamic traffic congestion pricing model utilizing multi-agent reinforcement learning with a transformer architecture. This architecture capitalizes on its encoder-decoder structure to transform the multi-agent reinforcement learning problem into a sequence modeling task. Drawing on insights from research on graph transformers, our model incorporates agent structures and positional encoding to enhance adaptability to traffic flow dynamics and network coordination. We have developed a microsimulation-based environment to implement a discrete toll-rate congestion pricing scheme on actual urban roads. Our extensive experimental results across diverse traffic demand scenarios demonstrate substantial improvements in congestion metrics and reductions in travel time, thereby effectively alleviating traffic congestion. [ABSTRACT FROM AUTHOR]

Jam-absorption driving (JAD) can effectively prevent the generation and propagation of traffic oscillation. To alleviate the traffic congestion in the signalized intersection with mixed traffic flow, including human driving vehicles (HDVs) and connected and automated vehicles (CAVs), this study provides a jam-absorption driving strategy based on the traffic delay prediction of the mixed platoon under traffic congestion. An online traffic congestion prediction method with the objective of JAD is proposed and focuses on the leaving state of the trajectory to achieve fast capture of congestion features. Then, with real-time status and prediction information, we develop a Jam-absorption driving strategy based on a deep reinforcement learning (DRL) model to improve adaptability to the mixed traffic environment. The results show that this strategy can suppress more than 70% of traffic oscillations with excellent execution efficiency, improving traffic safety and efficiency. [ABSTRACT FROM AUTHOR]

We investigate the formation of dust traffic jams in polar-aligning circumbinary discs. In our first paper, we found as the circumbinary disc evolves towards a polar configuration perpendicular to the binary orbital plane, the differential precession between the gas and dust components leads to multiple dust traffic jams. These dust traffic jams evolve to form a coherent dust ring. In part two, we use 3D smoothed particle hydrodynamical simulations of gas and dust to model an initially highly misaligned circumbinary disc around the 99 Herculis (99 Her) binary system. Our results reveal that the formation of these dust rings is observed across various disc parameters, including the disc aspect ratio, viscosity, surface density power-law index, and temperature power-law index. The dust traffic jams are long-lived and persist even when the disc is fully aligned polar. The midplane dust-to-gas ratio within the rings can surpass unity, which may be a favourable environment for planetesimal formation. Using 2D inviscid shearing box calculations with parameters from our 3D simulations, we find streaming instability modes with significant growth rates. The streaming instability growth time-scale is less than the tilt oscillation time-scale during the alignment process. Therefore, the dust ring will survive once the gas disc aligns polar, suggesting that the streaming instability may aid in forming polar planets around 99 Her. [ABSTRACT FROM AUTHOR]

The rapid growth of cities and the increasing traffic congestion have made vehicle emissions worse, especially in developing countries. Governments worldwide are now relying on regulations and policies to manage and reduce these emissions effectively. This change in approach towards emission control is also happening in developing nations. In this study, the effects of these policy measures are quantified using a calibrated integrated traffic and emission model (AIMSUN), and two hypothetical scenarios were analyzed: one scenario is where electric vehicles (EVs) replace traditional internal combustion EVs (ICEVs) in the study area and another scenario is assuming strict implementation of Euro 4/IV emission standards. The results showed that shifting towards a higher proportion of EVs leads to significant reductions in emissions but requires increased battery consumption, highlighting the trade‐off between reducing emissions and higher energy demand. Implementing Euro 4/IV standards could considerably reduce emissions, especially from motorcycles and trucks. It suggests that focusing on these categories with a phased implementation approach could bring significant environmental benefits. Policymakers in developing countries should adopt a rounded approach instead of implementing strict policies. It is crucial for them to carefully weigh the pros and cons of policy instruments before making any decisions. This study shows how traffic micro‐simulation modeling coupled with emission models can be used in evidence‐based decision‐making. [ABSTRACT FROM AUTHOR]

Smart cities and the Internet of Things have enabled the integration of communicating devices for efficient decision-making. Notably, traffic congestion is one major problem faced by daily commuters in urban cities. In developed countries, specialized sensors are deployed to gather traffic information to predict traffic patterns. Any traffic updates are shared with the commuters via the Internet. Such solutions become impracticable when physical infrastructure and Internet connectivity are either non-existent or very limited. In case of developing countries, no roadside units are available and Internet connectivity is still an issue in remote areas. Internet traffic analysis is a thriving field of study due to the myriad ways in which it may be put to practical use. In the intelligent Internet-of-Vehicles (IOVs), traffic congestion can be predicted and identified using cutting-edge technologies. Using tree-based decision-tree, random-forest, extra-tree, and XGBoost machine learning (ML) strategies, this research proposes an intelligent-transport-system for the IOVs-based vehicular network traffic in a smart city set-up. The suggested system uses ensemble learning and averages the selection of crucial features to give high detection accuracy at minimal computational costs, as demonstrated by the simulation results. For IOV-based vehicular network traffic, the tree-based ML approaches with feature-selection (FS) outperformed those without FS. When contrasted to the lowest KNN accuracy of 96.6% and the highest SVM accuracy of 98.01%, the Stacking approach demonstrates superior accuracy as 99.05%. [ABSTRACT FROM AUTHOR]

ABSTRACT In vehicular ad hoc networks (VANETs), the challenge of broadcast storms during data transmission arises due to an exponential increase in message rebroadcasts. This problem is exacerbated by high‐speed node movements, frequent topology changes, and repetitive discontinuities within these networks, hindering the development of efficient broadcasting protocols. Addressing this gap, our study introduces a pioneering approach utilizing a novel fuzzy method based on multicriteria decision‐making (MCDM) to prioritize vehicles in selecting optimal neighbors for data broadcast. The aim of this work is to propose for VANETs a fuzzy MCDM‐based re‐broadcasting scheme (FMRBS). This method seeks to eliminate broadcast storms and raise data distribution efficiency. We choose the best vehicles for data transportation by using fuzzy logic. The FMRBS system excelled in many respects over UMB and 802.11‐Distance. It decreased end‐to‐end latency and overhead while increasing packet delivery ratio (PDR) and network performance. By efficiently optimizing data distribution inside VANETs, FMRBS lowers broadcasting traffic and network congestion. [ABSTRACT FROM AUTHOR]

The recent rise of telework brings new debates on the impacts on travel behavior and transportation systems. One key issue concerns the weekly number and distribution of remote workdays, which affects transportation networks and their congestion levels. Using data from an online survey conducted in spring 2022 in France, Bayesian regressions and a multilevel model are used to analyze employee's current and ideal weekly number and distribution of full-day home-based remote workdays. Firstly, the findings highlight a latent demand for more remote workdays, especially among people working remotely 1 day a week. Secondly, data show a concentration of current and ideal remote workdays on Fridays, followed by Mondays and Wednesdays. Thirdly, and finally, our results show that employer's strategies and the nature of the sector (public or private) both contribute to explain the current and ideal weekly distribution of remote workdays. These results indicate that remote work should continue to develop and be distributed unevenly over the days of the week. This uneven distribution should impact travel behavior and traffic congestion. [ABSTRACT FROM AUTHOR]

Autonomous vehicles are essential to future transportation systems, potentially reducing traffic congestion. This study examines the impact of different vehicle control strategies on traffic flow through simulations. We propose a novel stochastic cellular automaton model, the controlled stochastic optimal velocity (CSOV) model, which incorporates vehicle control effects. Within the CSOV model, two control strategies are implemented: gap-based control (GC), which adjusts vehicle velocity to balance the gaps between adjacent vehicles, and flow-based control (FC), which aims to maintain a consistent local flow between the front and rear vehicles. Results show that both control strategies improve traffic flow. However, under weaker control, the GC sometimes resulted in lower flow compared to no control. In contrast, the FC consistently enhanced flow across control strengths, yielding more robust outcomes. Furthermore, when both strategies achieved comparable flow rates, the FC provided a more stable velocity distribution under varying traffic densities than the GC. [ABSTRACT FROM AUTHOR]

Recently, the increasing rapid number of cars on the roads and the great demand for traffic prediction, because of the increase in traffic congestion, has posed a great challenge to governments' economic development and social stability of most countries in the world. While many challenges are facing governments to solve traffic congestion and reduce car accidents and pollution, the objective of this study is to apply a dynamic approach for the Jordanian community to lessen traffic congestion on the roads in Amman by utilising social media, artificial intelligence efficiency methods, and decision support to lessen traffic issues in the capital city of Jordan. It also aims to communicate directly with social media users to cut down on the time and effort needed to make traffic predictions, which will help to relieve the congestion of areas and roads in Amman. To obtain the research aims, we handled a TRF2021JOR dataset related to traffic records in Amman city, collected from Amman Municipality for several years. Then, we applied the Netnography model, which was built using the data science concepts, to create an efficient model in Amman city for traffic prediction and time series based on selective features of previous congestion in city roads. The experiment results showed the accuracy preference for the proposed method in Amman city. Furthermore, the experiment tested the accuracy of results based on the machine learning methods using the KNIME Analytics Platform tool, one of the Artificial Intelligence (AI) methods used; the experiment results showed that the classifier SVM gives a low accuracy reached (89.9%). However, the accuracy using the decision tree classifier reached (90.59%), while the classifier random forest gives a high accuracy reached (93.16%). [ABSTRACT FROM AUTHOR]

Decline in the use of public transit by commuters have increased the use of private vehicles, causing higher levels of traffic congestion, accidents, etc. The present study aims to identify major latent attributes influencing the behaviour of government employees working in the study area by using an integrated mode choice model. The unobservable attributes that influence mode selection decisions were analysed using the semantic differential technique and a five-point bipolar adjective scale. Conventional mode choice models and latent variable integrated mode choice models were developed for four different modes. Sensitivity analysis was carried out to assess the impact of significant variables which has revealed that 15% decrease in travel time on public transport could lead to a 17% increase in ridership. This study also identified significant variables that influence mode selection decisions and formulated policies to increase the use of public transport in medium-sized cities. [ABSTRACT FROM AUTHOR]

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

Traffic congestion is one of the most common problems in the transportation system. In urban planning and construction, traffic congestion increases the difficulty of control and scheduling, hindering the pace of urban modernization development. The development of intelligent transportation systems has to some extent solved these problems, among which traffic flow prediction is particularly crucial. A period processing layer containing concatenation, dimension amplification, and diffusion convolution was studied and designed. On the grounds of this, a graph neural network prediction model combining spatiotemporal period characteristics was designed. And it further introduced a traffic flow prediction method on the grounds of dynamic topology maps. This model combined adaptive topology generation and parameter learning mechanisms, synchronously processing the complex correlation of traffic data and the heterogeneity between nodes through gated recurrent neural networks. The outcomes indicated that the research model is more excellent than other graph learning methods in three evaluation indicators: mean square error, mean absolute percentage error, and root mean square error. Their values were 15.63%, 9.87%, and 25.08%, respectively. The evaluation on all four datasets showed excellent performance of the research model, confirming the effectiveness of its adaptive topology and parameter learning strategy. This study had important practical significance for improving the traffic management system, alleviating traffic pressure, and enhancing the travel experience of urban residents. [ABSTRACT FROM AUTHOR]

In order to solve the problem of autonomous vehicles driving safely on the road while searching for optimal paths to avoid traffic congestion and obstruction, this paper establishes an optimal path planning model based on image feature extraction. The model consists of four parts: the selection of key turning points, interpolation between turning points, definition of the evaluation function, and global optimal path search. First, based on the map information provided by the network, an image feature detection algorithm was proposed, and the key turning points of the map route were selected and stored in an open table. Then, based on mechanical analysis theory, the cubic interpolation between two key turning points of the optimal path was defined. This allows the car to drive along a continuous curve with a certain arc radius, enabling it to drive smoothly, effectively avoid obstacles, and ensure safety. Then, an evaluation function is defined as needed, and different evaluation functions are specified for different road sections to avoid excessive local deviations in planning and to reduce the impact of known obstacles on the path. Finally, the cost of the search path is calculated based on the evaluation function. According to the principle of minimum cost, a globally optimal heuristic search algorithm is proposed to find the globally optimal path. Experimental results show that the proposed global optimal path planning algorithm has excellent performance, with an average accuracy of 96.71% and a search speed of 2.78 ms. The model not only provides accurate path planning and fast path selection capabilities but also has strong robustness against interference. [ABSTRACT FROM AUTHOR]

For smart cities, predicting traffic flow is crucial to lower traffic jams and enhancing transportation efficiency. The smart city needs effective models, highly dependable networks, and data privacy for traffic flow prediction (Traff-FP). The majority of current research uses a central training mode and ignores privacy issue conveyed by distributed traffic data. In this paper, an effective traffic flow prediction (ETraff-FP) is proposed to forecast traffic flow using actual historical traffic data. Initially, pre-processing is carried out using data normalization and handling missing value. The three major components of Traff-FP framework for each local Traff-FP model are recurrent long short-term capture network (RLSCN), federated gated graph attentive network (FGAN) and semantic connection relationship capture network (SCRCN). The long-term spatio and temporal information in each location has been captured by RLSCN, which encompasses constituents like fully connected (FC) layers, convolution, and bidirectional long short term memory (BiLSTM) to collect short-term information. FGAN, which incorporates bi-directional gated recurrent unit (Bi-GRU), exchanges short-term spatio-temporal hidden information while it trains local Traff-FP model using elliptic curve diffie-hellman (ECDiff-H) algorithm. Accordingly, the hyper parameters of ETraff-FP are tuned using extended remora optimization algorithm (EReOA). The ETraff-FP framework is trained and tested with TaxiNYC and TaxiBJ datasets. For simulation, python platform is utilized and various evaluation metrics are analysed. Accordingly, the ETraff-FP framework has reached better improvements with MSE of 8.98% and 10.57%, RMSE of 8.62% and 18.65%, MAE of 2.11% and 10.57%, R2-score of 0.959% and 0.913%, and MAPE of 21.12% and 24.89% against the existing methods using TaxiNYC and TaxiBJ datasets. Overall, the proposed work not only advances the state-of-the-art in traffic flow prediction but also proves the value of enabling effective and efficient traffic management systems in urban and smart city environments. [ABSTRACT FROM AUTHOR]

Traffic congestion is a problem that burdens urban cities daily. Despite the development of numerous research methodologies and technological solutions, the problem still persists. This article presents a system that combines traffic and demand management mechanisms to avoid the emergence of congestion by sustaining the occupancy of each road in the network up to a threshold, using a reservation scheme and keeping track of the future states of the network. Traffic management is responsible for navigating vehicles through congestion-free paths, while demand management is responsible for identifying the time when each vehicle will enter the network. Considering the size of actual urban networks and the number of vehicles utilizing the infrastructure, this is not an easy task. This work designs and validates a responsive and scalable real-life system for online traffic and demand management, while addressing the challenge of managing and processing large numbers of requests and data. The complexity and feasibility of the system are evaluated through microsimulations of real and artificial road networks. Its traffic efficiency is also evaluated by running extensive simulations of a real city, emulating realistic conditions in different scenarios. [ABSTRACT FROM AUTHOR]

The challenges in citywide traffic flow are intricate, encompassing various factors like temporal and spatial dependencies, holidays, and weather. Despite the complexity, there are still research gaps in effectively incorporating these spatio‐temporal relations through deep learning. Addressing these gaps is crucial for tackling issues such as traffic congestion, public safety, and efficient traffic management within cities. This paper underscores notable research gaps, including the development of models capable of handling both local and global traffic flow patterns, integrating multi‐modal data sources, and effectively managing spatio‐temporal dependencies. In this paper, we proposed a novel model named 3D spatial–temporal‐based adaptive modeling graph convolutional network (3D(STAMGCN)) that addresses for traffic flow data in better periodicity modeling. In contrast to earlier studies, 3D(STAMGCN) approaches the task of traffic flow prediction as a periodic residual learning problem. This is achieved by capturing the input variation between historical time segments and the anticipated output for future time segments. Forecasting traffic flow, as opposed to a direct approach, is significantly simpler when focusing on learning more stationary deviations. This, in turn, aids in the training of the model. Nevertheless, the networks enable residual generation at each time interval through learned variations between future conditions and their corresponding weekly observations. Consequently, this significantly contributes to achieving more accurate forecasts for multiple steps ahead. We executed extensive experiments on two real‐world datasets and compared the performance of our model to state‐of‐the‐art (SOTA) techniques. [ABSTRACT FROM AUTHOR]

Traffic congestion is a serious problem faced by many cities worldwide today. Congestion warning information is one of the important influencing factors of urban road congestion; To this end, based on the dynamics of infectious diseases, a congestion warning information dissemination model considering the attitudes of travelers and the network structure was constructed. The existence and stability of the equilibrium points of non congestion warning information and congestion warning information in the model were analyzed, and the optimal control strategy of the model was proposed. Numerical simulation was conducted to verify the results of theoretical analysis, simulate and analyze the impact of changes in various parameters in the model on the dissemination of congestion warning information, and perform sensitivity analysis on several parameters. The results indicate that travelers are more inclined towards "fast" modes of transportation and have a stronger willingness to share congestion warning information. The dissemination range of warning information is wider, which can play a positive role in reducing traffic congestion pressure. [ABSTRACT FROM AUTHOR]

Air taxis, a core service within urban air mobility (UAM), have the potential to enhance user satisfaction and address societal challenges such as traffic congestion and environmental pollution. However, the success of this service is often hindered by various concerns. To ensure successful implementation, we investigate the factors influencing public acceptance of air taxis. This study distinguishes itself from previous research in three key aspects. First, it introduces a novel classification of the factors into individual and societal dimensions. Second, it is among the first to apply a value‐based adoption model to understand the intention to adopt air taxis, including UAM. Third, it uniquely considers the Korean perspective, unlike most existing studies that focus on Western cultural contexts. To identify the consumers' perceptions, we conducted interviews with experts and surveyed a sample of 1,000 members of the general public in Korea. Our findings suggest that perceived value for society, as well as perceived value for individual users, significantly influences adoption intention. We discuss both academic insights and practical implications for policy and industry, supporting the commercialization of Korean UAM (K‐UAM) promoted by the Korean government. [ABSTRACT FROM AUTHOR]

Providing seamless connectivity during transit in vehicular ad hoc networks (VANETs) is the most promising and challenging task. In VANET, the vehicles are able to communicate with other vehicles and also with the infrastructure units such as road side unit (RSU). The coverage range of RSU is not sufficient to cover the whole road. The vehicle can drive to an area which is not covered by any of the RSUs, and it is termed as congested zone (CZ). This will extremely lead to traffic congestion and maximize blocking probability. In order to provide connectivity between the road segments and to avoid long delay in message transmission, unmanned aerial vehicles (UAVs) are placed in CZs, which act as relay nodes. To enhance the connectivity of the vehicles, a fuzzy-based UAV-assisted handoff scheme (F-UAV-HO) is proposed in this work. By adopting fuzzy, the unwanted participation of UAV is limited in non-congested traffic, thereby saving the battery resource. To validate the proposed work, the simulations are executed in Network Simulator 2.35. The proposed system outperforms other conventional UAV-assisted schemes in congested traffic in terms of average end-to-end delay, PDR and throughput. [ABSTRACT FROM AUTHOR]

The existing Intelligent Transportation System (ITS) achieves high success. As an essential component of ITS, Traffic Flow Prediction (TFP) has attracted tremendous attention. It is a critical but challenging task to improve the robust convergence and high accuracy of TFP in real-world scenarios. This study presents an optimized Federated Learning (FL)-based ChebNet model, FedproxChebNet, to realize the highly effective and accurate TFP. By selecting the best penalty constant in the proximal term to optimize the objective function, this model can achieve fast and stable convergence. Using the ChebNet model to aggregate neighbor nodes’ characteristics, more hidden information underlying the spatio-temporal traffic data can be taken into consideration for training the global and local models. All the superiority of the FedproxChebNet model makes it outperform other FL models with the Graph Convolutional Network (GCN), Graph Attention Network (GAT) and Spatio-Temporal Graph Convolutional Network (STGCN). We designed a series of experiments on various FL-based GNN model comparisons, parameter sensitivity tests, and on verifying the performance of FedproxChebNet with different heterogeneous systems with 0%, 50% and 90%. Based on four real-world data sets from the cognitive network, the experimental results demonstrate that the presented FedproxChebNet provides the best convergence and the highest accuracy in TFP, and achieves the best performance in a highly heterogeneous system (90%). Specifically, the accuracy of FedproxChebNet is at least improved by 3.76% on PeMS07 than other FL-based GNN models. This proposed FedproxChebNet model may be preferable for different scenarios in ITS such as route planning, traffic congestion control and reversible lanes. [ABSTRACT FROM AUTHOR]

Egypt faces extreme traffic congestion in its cities, which results in long travel times, large lines of parked cars, and increased safety hazards. Our study suggests a multi-modal approach that combines critical infrastructure improvements with cutting-edge technologies to address the ubiquitous problem of traffic congestion. Assuring vehicles owners of their timely arrival, cutting down on fuel usage, and improving communication using deep learning approach and optimization algorithm within the potential of IoT enabled 5G framework are the main goals. The traffic management system incorporates detection cameras, Raspberry Pi 3 microcontroller, an Android application, cloud connectivity, and traditional traffic lights that are powered using PV modules and batteries to secure the traffic controllers operation in case of grid outage and assure service continuity. The model examines the difficulties associated with Internet of Things (IoT) communication, highlighting possible interference from device-to-device (D2D) devices and cellular user equipment. This all-encompassing strategy aims to reduce fuel consumption, increase road safety and improve traffic efficiency. The model predicts a significant increase in Egypt's urban mobility by utilizing the possibilities of IoT and 5G technologies, which would improve Egypt's towns' livability and efficiency. The goal of this paper is to modernize Egypt's traffic management system and bring it into compliance with global guidelines for intelligent transportation networks. [ABSTRACT FROM AUTHOR]

Traditional routing algorithms have several limitations that become increasingly significant in the context of Software-Defined Networking (SDN). Firstly, these algorithms often have limited support for Quality of Service (QoS) requirements, making them less suitable for handling diverse traffic types efficiently. Moreover, current SDN controllers leverage a default shortest-path routing approach, which does not allow for more dynamic and flexible traffic management based on real-time network policies. To address these issues, this paper introduces a Dynamic Fuzzy Routing algorithm based on Deep Reinforcement Learning (DRL) for SDN that provides guaranteed latency and bandwidth (DFRDRL). DFRDRL provides intelligent and efficient routing that adapts to dynamic traffic by considering path-state criteria and using DRL. Fuzzy logic mainly performs online routing between an ingress-egress pair. To adapt to dynamic traffic changes, DFRDRL can predict the traffic matrix in real time. Meanwhile, DFRDRL reduces the routing reliance on network topology by weighting the network based on critical nodes. Additionally, when the network experiences congestion based on the traffic matrix, a deferral mechanism is applied to prioritize requests with lower resource demands. This mechanism helps ensure efficient resource allocation by temporarily postponing or queuing higher-demand requests, thereby optimizing overall network performance during periods of congestion. The simulation results show that the performance of DFRDRL is better than the equivalent algorithms in terms of latency and throughput. Also, DFRDRL is about 2.5% more efficient than the best existing algorithm in terms of admission rate of routing requests. [ABSTRACT FROM AUTHOR]

In the twenty-first century, global warming has become one of the severe challenges. Around one-fourth of the total carbon footprint is contributed by transport vehicles. It is crucial to provide alternative solutions to unsustainable transportation systems. This study compares the present travel modes of students of the Capital University of Science and Technology, Islamabad, with the proposed sustainable travel mode. A campus survey (N = 565) was conducted to understand travel choices, travel times, origin and destinations, and student preferences, considering university buses and private vehicles. A comparison was made between the travel time analysis of university bus users and non-bus users. A sustainability analysis has been conducted to evaluate the effects of the present travel modes on students and the proposed university bus transport system. Five main factors were compared: fuel consumption and associated fuel costs, carbon and vehicular emissions, traffic noise pollution, traffic congestion, and accident ratio. The results show that 38% of students commute by private cars and 18% by motorbikes. Around 25% of the students avoid university buses due to long travel times, and 30% prefer because their parents ask them. On average, university buses take 56.8 min, and non-bus transports take 38.8 min for one-way travel. If all students travel by university busses, they could be 86% more sustainable in fuel costs and carbon emissions than non-bus travel modes. These results give useful policy implications for providing sustainable travel alternatives to educational institutions. [ABSTRACT FROM AUTHOR]

Traffic speed prediction is an important topic in Intelligent Transport Systems (ITS). Although traffic speed prediction for real-life applications is burgeoning, the study of explaining and interpreting AI-based speed prediction is still in its initial stage. In this paper, we applied multiple advanced regression techniques, such as XGBoost and CatBoost optimized gradient boosting, Random Forest, and LASSO to predict traffic speed more accurately in the subsequent time windows. The experiment with prediction methods was conducted using the traffic speed data of the Seoul metropolitan road network. Each road segment represented as a node in the network is associated with neighboring roads within a configurable range. We picked heavily congested nodes as prediction targets. Then, we evaluated nearby road influences to determine critical contributions to the situation of the target nodes. We interpreted the model's output and extracted the topmost influential neighboring nodes by using an ensemble of explainable artificial intelligence (XAI) techniques such as feature importance assessment using the GINI entropy function, Recursive Feature Elimination, Shapely Additive Explanation, and a method of measuring the impact of masked nodes. We validated the XAI interpretations through traffic flow simulation by tuning the topmost influential nearby roads' speed and observing the effect on the roads' traffic congestion relief correspondingly. We also proved our solution through local explanation techniques such as Local Interpretable Model-Agnostic Explanations. Our methods are applicable to any transport network and open the door to new strategies for controlling the specific nearby roads for effective congestion relief. [ABSTRACT FROM AUTHOR]

As the core of reproducing the real nonlinear phenomena of traffic congestion, the investigation of car-following models, which take into account human factors, has consistently assumed a central role within the domain of transportation science. Nevertheless, it is noteworthy that prior studies have invariably employed a fixed value for driver reaction time to stimuli, whereas it is imperative to recognize the dynamic nature of this parameter, closely associated with the instantaneous vehicle speed. To address this issue, this paper constructs a dynamic sensitivity coefficient (DSC), and further develops a nonlinear human factoring car-following model to reveal the relation between the reaction time and the current speed. Firstly, the linear critical stability condition and the mKdV equation of the model are derived by the linear stability analysis and kink–antikink solution analytic method, respectively. Then, the numerical experiments are conducted to demonstrate that the proposed model is more practical and can reproduce the real driving behavior and phenomena. Finally, calibration and validation results exhibit the actual vehicle trajectory can be simulated well. [ABSTRACT FROM AUTHOR]

Percolation theory has been widely employed in network systems as an effective tool to analyze phase transitions from functional to nonfunctional states. In this paper, we analyze the propagation of congestion on transportation networks and its influence on origin–destination (OD) pairs using the percolation process. This approach allows us to identify the most critical links within the network that, when disrupted due to congestion, significantly impact overall network performance. Understanding the role of these critical links is essential for developing strategies to mitigate congestion effects and enhance network resilience. Building on this analysis, we propose two methods to adjust the capacities of these critical links. First, we introduce a greedy method that incrementally adjusts the capacities based on their individual impact on network connectivity and traffic flow. Second, we employ a Particle Swarm Optimization (PSO) method to strategically increase the capacities of certain critical links, considering the network as a whole. These capacity adjustments are designed to enhance the network's resilience by ensuring it remains functional even under conditions of high demand and congestion. By preventing the propagation of congestion through strategic capacity enhancements, the transportation network can maintain connectivity between OD pairs, reduce travel times, and improve overall efficiency. Our approach provides a systematic method for improving the robustness of transportation networks against congestion propagation. The results demonstrate that both the greedy method and the PSO method effectively enhance network performance, with the PSO method showing superior results in optimizing capacity allocations. This research is crucial for maintaining efficient and reliable mobility in urban areas, where congestion is a persistent challenge, and offers valuable insights for transportation planners and policymakers aiming to design more resilient transportation infrastructures. [ABSTRACT FROM AUTHOR]

Atmospheric blocking is characterized by persistent anticyclones that "block" the midlatitude jet stream, causing temperature and precipitation extremes. The traffic jam theory posits that blocking events occur when the Local Wave Activity flux, a measure of storm activity, exceeds the carrying capacity of the jet stream, leading to a pile up. The theory's efficacy for prediction is tested with atmospheric reanalysis by defining "exceedance events", the time and location where wave activity first exceeds flow capacity. The theory captures the Northern Hemisphere winter blocking climatology, with strong spatial correlation between exceedance and blocking events. Both events are favored not only by low carrying capacity (narrow roads), but also a downstream reduction in capacity (lane closures causing a bottleneck). The theory fails, however, to accurately predict blocking events in time. Exceedance events are not a useful predictor of an imminent block, suggesting that confounding factors explain their shared climatological structure. Plain Language Summary: An atmospheric block is a large, high pressure weather pattern that blocks the jet stream, affecting many regions in the midlatitudes including North America and Europe. Blocks are notable for their persistence, driving extreme weather conditions for up to a week or longer. Despite their significant societal impact, we don't fully understand the mechanism(s) that generate blocks. A traffic jam theory was proposed, which suggested that the onset of a block is caused by having too much "storm activity flux", which leads to a pile up of storm activity, just as a traffic jam is precipitated by conditions where the vehicular flux exceeding the road capacity, blocking traffic. This analogy is useful for understanding the preferred locations of atmospheric blocks in the time‐mean sense, but is not predictive in terms of individual blocking events. We further propose to incorporate additional regional constraints on flux capacity, analogous to "traffic bottlenecks", to improve our understanding of preferred blocking locations. Key Points: Flow capacity exceedance events, predictors of blocking onset in the traffic jam theory, are defined and evaluated in climate reanalysesA downstream reduction in flow capacity is ubiquitous for both exceedance and blocking events: lane closures favor traffic jamsBlocks are co‐located with exceedance events in space but not in time, limiting the utility of the traffic jam theory for prediction [ABSTRACT FROM AUTHOR]