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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]

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]

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]

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]

The Internet of Things (IoT) is essential in several Internet application areas and remains a key technology for communication technologies. Shorter delays in transmission between Roadside Units (RSUs) and vehicles, road safety, and smooth traffic flow are the major difficulties of Intelligent Transportation System (ITS). Machine Learning (ML) was an advanced technique to find hidden insights into ITSs. This article introduces an Improved Arithmetic Optimization with Deep Learning Driven Traffic Congestion Control (IAOADL-TCC) for ITS in Smart Cities. The presented IAOADL-TCC model enables traffic data collection and route traffic on existing routes for avoiding traffic congestion in smart cities. The IAOADL-TCC algorithm exploits a hybrid convolution neural network attention long short-term memory (HCNN-ALSTM) method for traffic congestion control. In addition, an IAOA-based hyperparameter tuning strategy is derived to optimally modify the parameters of the HCNN-ALSTM model. The presented IAOADL-TCC model effectively enhances the flow of traffic and reduces congestion. The experimental validation was performed using the road traffic dataset from the Kaggle repository. The proposed model obtained an average accuracy of 98.03 % with an error rate of 1.97 %. The experimental analysis stated the superior performance of the IAOADL-TCC approach over other DL methods. [ABSTRACT FROM AUTHOR]

Background: In Jordan, the confluence of traffic congestion and overcrowding in public hospitals poses a significant challenge for patients to collect their medications timely. This challenge was further intensified during the COVID-19 pandemic. Recognizing this issue, the Ministry of Health (MOH) and Electronic Health Solutions (EHS) intend to establish a Medication Delivery System (MDS), designed to provide patients with home delivery of medications and ensure proper treatment. This paper outlines a comprehensive framework to guide requirements engineers in devising an effective MDS framework, with a focus on expediting the development and testing processes and mitigating the risks associated with constructing such a system. Method: The proposed methodology entails a robust, structured approach to requirements development for an MDS that integrates an electronic health record system, billing system, pharmacy application, the patient-oriented My Hakeem app, and a delivery tracking system. The requirements elicitation and analysis processes were undertaken by a multidisciplinary committee from MOH and EHS teams, ensuring a diverse understanding of stakeholder needs and expectations. The requirement specifications were meticulously documented via a data dictionary, unified modeling language (UML), and context diagrams. The quality and accuracy of the requirements were verified through an extensive validation process, involving thorough review by various EHS teams and the MOH committee. Results: The MDS was implemented across numerous MOH facilities within a timeline that was a third of the original projection, leveraging the same level of resources and expertise. Post the requirements development phase, there were no changes requested by any stakeholders, indicating a high level of requirement accuracy and satisfaction. Conclusion: The study illustrates that our proposed methodology significantly results in a comprehensive, well-documented, and validated set of requirements, which streamlines the development and testing phases of the project and effectively eliminates requirement errors at an early stage of the requirements development process. [ABSTRACT FROM AUTHOR]

Stream speed pattern of traffic plays a crucial role in urban transportation. The presence of on-street parking significantly reduces this speed by narrowing the carriageway width available for traffic movement, impeding the traffic flow. Therefore, understanding the traffic flow behavior in the context of on-street parking could be challenging. In this context, the present study explores the influence of on-street parking on the stream speed of regular traffic. To achieve this, data were collected at various ideal and parking sections. The stream speed ranges from 39.28 km/h to 51.47 km/h for ideal sections with significant percentage reduction observed at parking sections, ranging from 39.09% to 81.30%. This speed reduction was then modelled considering the most significant parking parameters conceptualized in this study. The findings of the study offer valuable insights that can be implemented for developing the on-street parking norms. [ABSTRACT FROM AUTHOR]

AbstractMining traffic congestion dynamics presents difficulties in data structure and spatiotemporal analysis. Existing studies mainly provide insights from a supply perspective, with a restricted examination of why congestion occurs and how travel demands affect congestion. This study introduces an innovative framework to mine congestion dynamics from the perspective of human mobility. In human mobility modeling, we refine the conventional origin-destination representation of activity flow by introducing "path flow" (PF) which considers space-time paths and movement patterns. In congestion scenarios, congestion-related path flow (CPF) and congested path sub-flow (CPSF) are extended to track individuals’ congestion exposure and explore the correlations between congestion and human mobility. To finely classify congestion-related travel demands, a Bayesian inference approach, incorporating destination and spatiotemporal heterogeneities, is developed to deduce trip purposes. The experiments conducted in Wuhan demonstrate the availability and importance of PF in spatiotemporal dynamics analysis of human mobility. Interestingly, we find that 1) the job-housing relationship is imbalanced, with massive residents opting for cross-district living and working; 2) individuals tend to visit tertiary hospitals on weekends and secondary medical facilities with less congestion on weekdays. Notably, path flow can promote the fine-grained modeling of human mobility and provide theoretical support for many urban issues. [ABSTRACT FROM AUTHOR]

Accurately predicting the next track point of vehicle travel is crucial for various Intelligent Transportation System (ITS) applications, such as travel behavior studies, traffic control, and traffic congestion monitoring. Recent works on trajectory prediction follow a paradigm that first represents the raw trajectory and subsequently makes predictions based on that representation. Currently, trajectory representation methods tend to project trajectory points to road networks by map matching and represent trajectories based on the representation of matched roads. However, precisely matching trajectories to roads is a challenge in ITS, as the matching precision is greatly affected by the quality of the trajectory. Meanwhile, since it is difficult to discern whether trajectory matching results are accurate or confounded, how to effectively utilize this type of uncertain geographic context information is also a challenge, which is defined as the Uncertain Geographic Context Problem (UGCoP) in geographic information science. Therefore, we propose a flexible strategy of subgraph learning, referred to as SLM, for predicting the next track point of vehicles. Specifically, a subgraph generation module is first proposed to extract topology contextual information of the roads around historical trajectory points. Secondly, a subgraph learning module is designed to learn rich spatial and temporal features from generated subgraphs. Finally, the extracted spatiotemporal features will be fed into a prediction module to predict the next track points of vehicles on road networks. Our model enables the effective utilization of uncertain geographic context information of trajectories on road networks while avoiding the error brought by map matching. Extensive experiments based on trajectory datasets in two different cities confirm the effectiveness of our approach. [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]

Constructing new roads and upgrading existing roads are two common approaches to address congestion caused by increasing traffic volumes. But they can also impact local communities if roads are built through neighborhoods or existing roads and intersections are widened and placed closer to nearby homes. In this paper, we estimate how local communities are impacted by these changes using data on housing prices near roads and roadway construction projects. We find that homes near major roads sell for less than homes further away, but that congestion-reducing road improvements only lead to small average increases in nearby home prices. Our results suggest that major roads in close proximity negatively impact housing prices by affecting neighborhood aesthetics, and this relationship is not contingent upon the level of traffic on the roads. Our results can help policymakers consider the external costs borne by local communities when conducting cost–benefit analyses of roadway expansions or improvements. • Homes near roadways face price penalty, regardless of traffic on the roadway. • Roadway construction increases home prices only slightly. • Co-located amenities likely mute effect of traffic on home prices. [ABSTRACT FROM AUTHOR]

Forecasting traffic patterns is an essential component of intelligent transport systems. It helps forecast traffic congestion, improve traffic management, and make roads safer. However, most existing studies focus on the direct traffic data dependencies between road segments but lack sufficient attention to the implied higher-order dependencies within the road network. Furthermore, although existing models attempt to capture spatio-temporal relationships by integrating different components, they are still inadequate in effectively modeling the interaction between spatio-temporal relationships. We propose a higher-order graph convolutional network model (STK-HGCN) to address the above issues. Firstly, the model extracts and fuses temporal low-order and high-order information using temporal convolutional layers; in terms of spatial aspects, we capture low-order spatial information by constructing geographic and functional graphs and introduce hypergraphs to create the implied high-order information between road networks, which are then convolved to obtain the high-order spatio-temporal dependencies. Secondly, we construct a spatial-temporal interaction module with a "sandwich" structure to capture spatial-temporal interaction features effectively. Finally, we performed comparative experiments on the METR-LA and PEMS-BAY datasets, and the results indicate that, compared to the optimal baseline model, the STK-HGCN model improves MAE and RMSE by 0.08, 0.13, 0.15, and 0.37, respectively, while reducing MAPE by 0.25% and 0.18%, respectively. [ABSTRACT FROM AUTHOR]

Urban traffic congestion represents a complex challenge influenced by many dynamic factors. Peak periods typically exacerbate congestion, while bad weather can slow vehicle movements and increase travel times. Accidents and road closures cause sudden and unexpected disruptions, making traffic management a constant challenge. Using a dataset of over 66,000 GTFS records with machine learning classifiers like Random Forest, XGBoost, CatBoost, and Decision Tree models, the study seeks to forecast traffic conditions. SMOTE is used to ensure greater representation of minority classes in order to solve the dataset's intrinsic imbalance, and feature scaling enhances model convergence. With an accuracy of 98.8%, Random Forest was the most accurate model for this challenge. The outcomes demonstrate that the system is able to precisely forecast traffic in real-time, which aids in route planning, traffic control, and enhancing urban mobility. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) have inspired tremendous interest in solving existing transportation sector difficulties, especially with recent breakthroughs in sustainable ways of transportation. However, theoretical frameworks or models to efficiently analyze the threats, challenges, opportunities, and sustainability associated with AVs' usage remain limited in application. This study introduces the Problem Cause Solution Sustainability Advantage Disadvantage Threat Opportunities (PC-SSADTO) conceptual framework, a modified form of the Strength Weaknesses Opportunities Threats (SWOT) analysis to analyze AVs as a sustainable solution. This framework examines the difficulties, risks, and possibilities of using AVs in the transportation sector as well as how their worldwide implementation and integration might help lower carbon dioxide (CO2) emissions, enhance road safety, and improve mobility and accessibility, thus accelerating the accomplishment of Sustainable Development Goals (SDGs). The requirements of the PC-SSADTO framework are confirmed by a survey that gathers over 200 public answers on the present issues or challenges that AVs may address, such as lowering traffic congestion, greenhouse gas emissions, and climate change for an improved livelihood. Despite some uncertainty in human opinions about AVs systems, the application of totally autonomous operations appears to be essential for the effective integration of AVs. Therefore, high degrees of integration with public transportation and cooperative decision-making structures would favor practical AVs implementations and usage positively. [ABSTRACT FROM AUTHOR]

The development of alternative environmentally friendly modes of transportation is becoming an increasingly promising solution in traffic-congested and polluted urban areas. E-bikes, as one of them, are recognized as an ecologically sustainable means of transportation that has significant potential to replace motorized modes of transportation that can improve urban mobility. Relying on artificial intelligence and considering an ecological approach when considering the acceptability of e-bikes by setting a direct question for users influences the development of an innovative way of understanding and evaluating the use of more sustainable modes of transportation. In this regard, this study aims to elucidate the main variables influencing the acceptability of e-bike use using artificial neural network (ANN) models—multilayer perceptron (MLP) and radial basis function (RBF). For training and testing the models, data from a random sample obtained through an online questionnaire, which was answered by 626 residents of Belgrade (Serbia), were used. A multilayer perceptron with nine and seven neurons in two hidden layers, respectively, hyperbolic tangent activation function in the hidden layer and identity function in the output layer, gave better results than the radial basis function model. With an accuracy of 89%, a precision of 83%, a recall of 79%, and an area under the receiver operating characteristic (ROC) curve of 0.927, the multilayer perceptron model recognized the influential variables in predicting acceptability. The results of the model indicate that the mileage traveled, the frequency of motorcycle use, the respondents' awareness of the pollution in Belgrade, and the age of the respondents have the greatest influence on the acceptability of using e-bikes. In addition to majority acceptability (69.8%), the results obtained by the model can represent a useful basis for decision-makers when defining strategies for the development and application of e-bikes while reducing traffic congestion and environmental pollution in Belgrade. [ABSTRACT FROM AUTHOR]

Many countries encourage their populations to use public urban transport to decrease pollution and traffic congestion. However, this can generate overcrowded routes at certain times and low economic efficiency for public urban transport companies when buses carry few passengers. This article proposes a Public Urban Transport Scheduling System (PUTSS) algorithm for allocating a public urban transport fleet based on the number of passengers waiting for a bus and considering the efficiency of public urban transport companies. The PUTSS algorithm integrates artificial intelligence (AI) methods to identify the number of people waiting at each station through real-time image acquisition. The technique presented is Azure Computer Vision. In a case study, the accuracy of correctly identifying the number of persons in an image was computed using the Microsoft Azure Computer Vision service. The proposed PUTSS algorithm also uses Google Maps Service for congestion-level identification. Employing these modern tools in the algorithm makes improving public urban transport services possible. The algorithm is integrated into a software application developed in C#, simulating a real-world scenario involving two public urban transport vehicles. The global accuracy rate of 89.81% demonstrates the practical applicability of the software product. [ABSTRACT FROM AUTHOR]

With the continuous progress of society, people's living consumption needs are also constantly changing. The traditional vehicle positioning technology can no longer meet the new positioning needs of travelers, so a more accurate positioning system is urgently needed. This paper aims to study a more accurate positioning system and proposes a vehicle positioning system based on radio frequency identification (RFID). After comparing several common positioning methods, the use of LANDMARC positioning algorithm makes the vehicle positioning more accurate. This paper has effectively integrated advanced computer and artificial intelligence technology and applies it to control and management and finally established a comprehensive traffic positioning system that can effectively alleviate traffic congestion, reduce traffic accidents and quickly deal with traffic emergencies. The experimental results have shown that when the vehicle runs at a speed of 40 km/h and the communication rate is 860 kpbs, the average positioning accuracy of the improved algorithm is doubled compared with the traditional algorithm, and the error is reduced by nearly one. Therefore, compared with the traditional positioning system and artificial intelligence, the RFID vehicle positioning system under cloud computing has more accurate positioning information and more timely and convenient services. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: One of the most challenging aspects of city living is traffic congestion. The multitude of vehicular modes and their sheer volume coupled with inadequate road infrastructure unable to keep up with the escalated travel demand of Kolkata is a major cause of concern. Recognizing the significant toll on commuters' time and health, the research aims to identify the root causes of congestion across fifteen selected Traffic Intersection Points, explore its multifaceted impacts on the environment, economy, and society, and propose solutions to alleviate this pressing issue effectively. METHODS: This study employed a mixed-method approach. An on-site survey with 375 regular commuters in Kolkata was conducted, utilizing questionnaires and focus group discussions. The survey gathered data on travel patterns, socio-demographic information, and perceptions of traffic congestion. The Garrett Ranking method and Relative Importance Index (RII) were employed to evaluate the significance of various contributing factors, their impact on commuters, and potential solutions. Statistical analysis using Microsoft Excel and SPSS 26 complemented the data analysis, with cartographic visualizations providing spatial insights. Additionally, the Wilcoxon Signed-Rank Test validated the differences in travel times during congested and free-flowing traffic conditions. FINDINGS: The results revealed that the commuters' average daily travel delay owing to congestion is approximately 17 minutes. Utilizing Garrett score ratings, the most significant obstacles to smooth traffic flow were identified as intersectional conflicts (66.19) and curbside parking (64.75). Following the same methodology, increased reliance on personalized vehicles (69.87) and encroached road space (64.3) were attributed to rush hour saturation, whereas political rallies (71.36) and demonstrations (59.74) contributed to unprecedented incidents. Work schedule disruptions and hearing anomalies were the most common offshoots of this hazard. Relative Importance Index (RII) scores highlighted the consensus among commuters emphasizing the economic, environmental, and social impact of congestion, with particular emphasis on enhanced fuel consumption (RII=1), decreased economic opportunities (RII=0.96), worsened pollution levels (RII=0.91), and reduced family time (RII=0.93). Congestion pricing (RII=0.88) and ride-sharing (RII=0.87) emerged as themost viable strategies to mitigate congestion. Furthermore, Garrett ratings indicated training of drivers (63.74) and road users (61.03) along with parking management (63.51) to be the most desired areas of improvisation suggested. CONCLUSION: Implementing the 'Avoid, Shift, Improve' framework in conjunction with a 'people-first' mentality would encourage sustainable urban living by placing a premium on public transport, land use planning, and technological improvements to reduce traffic congestion and enhance commuter well-being in Kolkata. [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]

This study contributes to the growing body of literature on traffic congestion prediction using machine learning (ML) techniques. By evaluating multiple classifiers and selecting the most appropriate one for predicting traffic congestion, this research provides valuable insights for urban planners and policymakers seeking to optimize traffic flow and reduce jamming and. Traffic jamming is a global issue that wastes time, pollutes the environment, and increases fuel usage. The purpose of this project is to forecast traffic congestion at One of the most congested areas in Amman city using multiple ML classifiers. The Naïve Bayes (NB), stochastic gradient descent (SGD) fuzzy unordered rule induction algorithm (FURIA), logistic regression (LR), decision tree (DT), random forest (RF), and multi-layer perceptron (MLP) classifiers have been chosen to predict traffic congestion at each street linked with our study area. These will be assessed by accuracy, F-measure, sensitivity, and precision evaluation metrics. The results obtained from all experiments show that FURIA is the classifier that presents the highest predictions of traffic congestion where By 100% achieved Accuracy, Precision, Sensitivity and F-measure. In the future further studies can be used more datasets and variables such as weather conditions; and drivers behavior that could integrated to predict traffic congestion accurately. [ABSTRACT FROM AUTHOR]

Asthma is one of the most prevalent chronic conditions in the United States and is particularly sensitive to environmental changes in urban areas. While it is known that traffic congestion contributes to increased vehicle emissions and poorer air quality, its direct association with asthma incidence has not been thoroughly explored. This study aimed to address this void by analyzing 148 city-level observations from 2016 to 2020 in Texas, using data from the Texas A&M Transportation Institute and Definitive Healthcare. We investigated the association between traffic congestion, measured by the travel time index, and annual city-level asthma hospital discharges, while adjusting for refinery productivity, minority groups, and education levels through multivariate regression. Our findings revealed a significant positive correlation between the travel time index and asthma visits, indicating that higher traffic congestion is associated with increased hospital visits for asthma. This finding remains consistent across different models, regardless of whether control variables are included. For the control variables, we found that higher refinery productivity was linked to elevated risks of asthma-related hospitalizations, aligning with previous research findings. Although correlations with Black or African American and Hispanic or Latino populations, as well as those with less than a high school education, were not statistically significant, a positive trend was observed. These results emphasize the impact of traffic congestion on asthma prevalence and the necessity for targeted public health interventions and urban planning strategies. [ABSTRACT FROM AUTHOR]

Traffic management poses pervasive challenges in urban areas, resulting in substantial time and monetary losses, alongside critical safety risks. This paper presents a novel approach that merges graph theory with fuzzy logic to optimize the placement of traffic signals, signs and personalized traffic route selection. Emphasizing the enhancement of road infrastructure as a pivotal solution, the study integrates fuzzy graph theory to normalize parameters with varying limits. The innovative framework explores the connectivity of Intuitionistic Anti-Fuzzy–Fuzzy Combination Graphs (IAFFCG), unveiling an optimization strategy tailored to specified parameter preferences. This holistic approach offers a comprehensive solution to the complex challenges of traffic management and road infrastructure enhancement. Furthermore, the study introduces a method for normalizing traffic congestion using IAFFCG theory which enables the examination of the strength and frailty of network connectivity simultaneously, utilizing anti-fuzzy–fuzzy membership and non-membership functions that capture the uncertainty of traffic conditions across categories ranging from easy to difficult. Finally, the incorporation of fuzzy automata into this IAFFCG presents a groundbreaking approach in the field of decision-making, marking a significant advancement in this domain. [ABSTRACT FROM AUTHOR]

Purpose: The paper aims to examine the state of infrastructure facilities and its impact on the prosperity of Tricity. Design/methodology/approach: A literature review was carried out, monitoring and analysis of statistical data on the condition of infrastructure facilities in the Tricity agglomeration, as well as some other regions of Poland, was carried out, which also made it possible to compare the situation in Tricity with that places. Findings: The analysis of statistical data shows the problems of traffic congestion and the need to develop the road network in the Tricity agglomeration, as well as the need to develop the cycleway network. An analysis of the characteristics of the water and wastewater infrastructure of the Tricity agglomeration shows its good condition and revealed the presence of a reserve of capacity to unlock the economic potential of the region. Practical/social implications: Identifying the needs for the development of road networks in the region is of practical importance for both local authorities and local society. The presence of a problem should pose a task to find its optimal solutions, which requires both physical capabilities and financial resources. Originality/value: This study emphasizes the importance of analyzing the state of the infrastructure of a specific region, in this case, the Tricity agglomeration. The ratio of the population to the quantitative indicators of certain types of infrastructure facilities, as well as other methods of analyzing available statistical data, made it possible to identify both problems and development prospects. [ABSTRACT FROM AUTHOR]

Due to urban overcrowding, available land is limited and traffic congestion has increased. Underground roads are being built to mitigate traffic congestion as an alternative. Studies associated with underground roads are needed because these roads are dark and closed and have a high risk of accidents compared to surface roads. In particular, there is limited study on junctions that connect two or more underground roads. In this study, an underground road network including junctions was constructed to analyze the factors behind rear-end collisions at underground road connections. To reflect the driving behavior on underground roads, the scenario analysis was conducted by applying the speed distribution of underground roads in Korea. The results of the analysis showed that variables such as acceleration standard deviation and lateral position standard deviation are crucial for accidents on underground roads. Thus, this study can be used as a basis for traffic management and safety improvement in the operation of underground road junctions in the future. [ABSTRACT FROM AUTHOR]

Road congestion in Amman City has been increasing yearly, due to the increase in private car ownership and traffic volumes. This study aims to (a) evaluate the toll road's effects on society and the economy in Amman, Jordan, through a survey questionnaire using statistical software (SPSS), (b) assess the impact of the toll road on reducing congestion and delays using micro-simulation (VISUM), (c) identify the optimal toll price for a selected road using VISUM and (d) validate the simulated models with the optimal revenue. Traffic, geometric, and cost data about the toll technique of two sections on the Airport Highway (from the Ministry of Foreign Affairs to the Madaba Interchange; and from the Madaba Interchange to the Queen Alia International Airport (QAIA) Interchange) were used for simulation purposes. The toll road (across seven different scenarios at different prices) was evaluated for optimal revenue. The survey questionnaire was made based on all scenarios, including the AM peak hour. The operation cost for the toll road was determined based on the Greater Amman Municipality (GAM). The best scenario was determined based on the value of revenue (JOD). The results indicate that higher acceptance is achieved when applying road pricing during the AM peak hour and that users prefer the charging method based on travelled distance (54.02%). Additionally, the total cost of the manual toll collection (MTC) method is 126,935 JOD. Road pricing can reduce traffic delay (or speed up traffic flow) by 4.61 min in the southbound direction and by 9.52 min in the northbound direction. The optimal toll value is 0.25 JOD (34.08%), with revenues of 1089.6 JOD for 2024 and 1122.6 JOD for 2025. Eventually, applying road pricing on the airport road is shown to be effective and economically feasible only when using the manual method. [ABSTRACT FROM AUTHOR]

The fog computing environment expands the capabilities of cloud computing by moving computing, storage, and networking services closer to IoT devices. These resource‐constrained IoT devices often face challenges like high task failure rates and extended execution latency due to data traffic congestion. Distributing IoT services through task offloading across different layers of computing paradigms enhances QoS (Quality of Service) parameters. This endeavor aims to allocate custom workflow‐based real‐time tasks or jobs for processing across various cloud/fog/edge layers, optimizing QoS factors like makespan, energy consumption, and cost. In the fog computing environment, challenges arise due to uncertainties related to job execution locations and the ability to predict future user requirements. Fuzzy logic offers low‐complexity solutions for handling unpredictable and rapidly changing conditions. This paper proposes a hybrid fog‐cloud‐based computing architecture and an intelligent fuzzy logic‐based computation offloading approach. This approach effectively allocates workloads among edge, fog, and cloud layers, resulting in improvements in makespan time (7.51%), energy consumption (4.63%), and cost (13.60%). The proposed method selects suitable processing units or compute nodes for job execution, utilizing heterogeneous resources. Simulation results demonstrate that the proposed methodology outperforms current state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

Understanding the impact of the built environment on traffic congestion can provide reliable references for alleviating traffic congestion. Previous research has explored traffic congestion evolution but often overlooks two key aspects: the frequency of congestion state updates and the spatial-temporal impact of built environment features. Based on data from Hefei, China, we propose a novel geographically and temporally weighted regression (GTWR) model that integrates temporal variables and spatial grids into the traditional GWR model. The empirical results show that public facilities most significantly impact congestion during morning peak hours, scenic spots during afternoon peaks, and motorcycle services in the evening. The study also reveals the rules for the spatiotemporal impact of the built environment on traffic congestion. Finally, the comparison of models shows that the GTWR model outperforms the OLS and GWR models. The findings can guide traffic managers in creating targeted strategies to enhance transportation system efficiency. [ABSTRACT FROM AUTHOR]

The United States has emerged as a prominent actor in the international fight against transnational terrorism in the Sahel region of Africa since 2002, exerting significant political influence on regional affairs. Under the leadership of the United States, a coalition of Sahel countries has collaboratively established security programs, notably including the Trans-Saharan Counterterrorism Partnership (TSCTP). However, the U.S. counterterrorism strategy in the Sahel region has not only proven ineffective in containing the deterioration of regional security, but also has exacerbated the vulnerability to coups in recipient countries. The observed deterioration can be attributed to various driving factors, such as deviations in the willingness and ability to involve and the security traffic jam. We contend that the U.S.-Sahel counterterrorism partnership is fundamentally misaligned and plagued by a securitization problem. Firstly, a significant impediment to international cooperation lies in the United States' pursuit of consolidating its dominant role in Sahel security affairs, which usually overlooks the pragmatic principle of 'African solutions to African problems'. In the counterterrorism process, there is a misalignment between U.S. national interests and those of Sahel states. Furthermore, the United States' extensive reliance on military strategies indicates a significant securitization of non-violent civil affairs in certain Sahel states, inadvertently creating an opportunity for genuine terrorist organizations to recruit members. In addition, U.S. interference in the internal affairs of Sahel states heightens the risk of coups, thereby undermining the legitimacy of U.S. leadership in the region. Currently, with both the withdrawal of the United Nations Stabilization Mission in Mali (MINUSMA) and France withdrawing their security forces from the Sahel region, there has a significant weakening effect on regional security capabilities. Therefore, it is imperative for the international community to collaborate with African nations and adopt more proactive measures to safeguard regional security and maintain order. [ABSTRACT FROM AUTHOR]

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]

Deep learning solutions in big data applications can benefit cloud centres and can also lead to network communication overhead. Typically, data collected from traffic are sent to the traffic management centre for analysis. However, this process can worsen the network route to the traffic management centre. A two-tier mechanism has been developed to address this issue, which performs vehicle speed estimation and traffic congestion detection for efficient traffic management. The real-time traffic video data are captured and the video frames are initially processed through a foreground extraction process, which extracts the temporarily stopped vehicles on the road by removing background pixels from the frames. The video frames are then wrapped in an up-down view to remove the influence of the observation angle. The traffic congestion is then detected accurately based on the traffic characteristics using the proposed Ensemble Random Forest-based Gradient Optimization (ERF-GO) algorithm. The generalization error occurs when learning complex features on frames is minimized using a gradient-based optimization (GO) algorithm. Finally, the learned information on traffic conditions is forwarded to the cloud and edge computing environments based on network connection speed. The efficiency of the proposed ERF-GO is investigated in terms of performance metrics, namely root mean square error, speed detection error, execution time, computational cost, accuracy, latency, workload balance, precision, recall, f-measure, and congestion detection error rate. The analytic result displays that the proposed ERF-GO algorithm attains a greater accuracy rate of about 98.65% in detecting traffic congestion which is comparably higher than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization construction, the contradiction between supply and demand of urban public transportation resources is becoming increasingly prominent, resulting in increasingly serious problems such as traffic congestion and environmental pollution, which has a great impact on the sustainable development of cities. The experience of mature markets both domestically and internationally indicates that priority must be given to the development of public transportation networks with rail transit as the backbone. Due to the fact that the operation of the rail transit system should not only focus on market benefits but also meet social benefits as much as possible, evaluating efficiency has become a prerequisite for managing rail transit and an important topic in the performance evaluation of urban public transportation. The urban public transport operations evaluation is a multiple-attribute group decision-making (MAGDM). Then, the TODIM and EDAS method has been constructed to deal with MAGDM issues. The interval neutrosophic sets (INSs) are constructed as an effective tool for representing uncertain information during the urban public transport operations evaluation. In this manuscript, the interval neutrosophic number TODIM-EDAS (INN-TODIM-EDAS) method is constructed to solve the MAGDM under INSs. Finally, a numerical example study for urban public transport operations evaluation is constructed to validate the INN-TODIM-EDAS method. The main research contribution of this paper is constructed: (1) the INN-TODIM-EDAS method is put up for MAGDM with INSs; (2) the INN-TODIM-EDAS method is put up for urban public transport operations evaluation and were compared with existing methods; (3) Through the detailed comparison, it is evident that INN-TODIM-EDAS method for urban public transport operations evaluation proposed in this paper are effective. [ABSTRACT FROM AUTHOR]

Connected and Autonomous Vehicles (CAVs) hold great potential to improve traffic efficiency, emissions and safety in freeway on-ramp bottlenecks through coordination between mainstream and on-ramp vehicles. This study proposes a bi-level coordination strategy for freeway on-ramp merging of mixed traffic consisting of CAVs and human-driven vehicles (HDVs) to optimize the overall traffic efficiency and safety in congested traffic scenarios at the traffic flow level instead of platoon levels. The macro level employs an optimization model based on fundamental diagrams and shock wave theories to make optimal coordination decisions, including optimal minimum merging platoon size to trigger merging coordination and optimal coordination speed, based on macroscopic traffic state in mainline and ramp (i.e., traffic volume and penetration rates of CAVs). Furthermore, the micro level determines the real platoon size in each merging cycle as per random arrival patterns and designs the coordinated trajectories of the mainline facilitating vehicle and ramp platoon. A receding horizon scheme is implemented to accommodate human drivers' stochastics as well. The developed bi-level strategy is tested in terms of improving efficiency and safety in a simulation-based case study under various traffic volumes and CAV penetration rates. The results show the proposed coordination addresses the uncertainties in mixed traffic as expected and substantially improves ramp merging operation in terms of merging efficiency and traffic robustness, and reducing collision risk and emissions, especially under high traffic volume conditions. [ABSTRACT FROM AUTHOR]

This paper studies state observation for a heterogeneous quasilinear traffic flow system with disturbances at the inlet of a considered road section. Based on the backstepping method, an observer is designed for the quasilinear traffic flow system with only the boundary measurements at the inlet of the considered road section. The observer is constructed by duplicating the quasilinear system and adding the output injection terms to the partial differential equations and boundary conditions. Making use of the backstepping transformation, the injection gains of the observer system are derived by the computation of kernel equations, which are obtained by mapping the error system into an integral input-to-state stable target system. The applicability of the observer for the design of an output feedback controller stabilizing the quasilinear system is discussed. Finally the assumptions of the design of the observer are numerically checked on a realistic congested traffic scenario. [ABSTRACT FROM AUTHOR]

With urbanization and increased vehicle usage, understanding the exposure to air pollutants inside the vehicles is vital for developing strategies to mitigate associated health risks. In-vehicle air quality influences the comfort of the driver during long commutes and has gained significant interest. This study focuses on studying in-vehicle air quality in the San Francisco Bay Area in California, an urban setting with significant traffic congestion and varied emission sources and road conditions. Each trip is about 80.5 km (50 miles) in length, with commute times of approximately one hour. Two low-cost portable sensors were employed to simultaneously measure in-vehicle pollutants (PM2.5, PM10, and CO2) during morning and evening rush hours from May 2023 to December 2023. Seasonally averaged PM2.5 varied from 5.07 µg/m3 to 6.55 µg/m3 during morning rush hours and from 4.38 µg/m3 to 4.47 µg/m3 during evening rush hours. In addition, the impacts of local PM2.5, vehicle ventilation settings, and speed of the vehicle on in-vehicle PM concentrations were also analyzed. CO2 buildup in vehicles was studied for two scenarios: one with inside recirculation enabled (RC on) and the other with circulation from outside (RC off). With RC off, CO2 concentrations are largely within the 1100 ppm range recommended by many organizations, while the average CO2 concentrations can be three times high under recirculation mode. This research suggests that low-cost sensors can provide valuable insights into the dynamics of air pollution in the in-vehicle microenvironment, which can better help commuters reduce health risks. [ABSTRACT FROM AUTHOR]

In this paper, the concept of symmetry is utilized to design the trajectory planning for parallel parking of autonomous ground vehicles—that is, the construction and the solution of the optimization-based trajectory planning approach are symmetrical. Parking is the main factor that troubles most drivers for their daily driving travel, and it can even lead to traffic congestion in severe cases. With the rise of new intelligent and autonomous vehicles, automatic parking seems to have become a trend. Traditional geometric planning methods are less adaptable to parking scenarios, while the parking paths planned by graph search methods may only achieve local optimality. Additionally, significant computational time is often required by numerical optimization methods to find a parking path when a good initial solution is not available. This paper presents a hierarchical trajectory planning approach for high-quality parallel parking of autonomous ground vehicles. The approach begins with a graph search layer to roughly generate an initial solution, which is refined by a numerical optimization layer to produce a high-quality parallel parking trajectory. Considering the high dimensionality and difficulty of finding an optimal solution for the path planning optimization problem, this paper proposes an improved safe travel corridor (I-STC) with the construction of collision constraints isolated from surrounding environmental obstacles. By constructing collision constraints of the I-STC based on the initial solution, the proposed method avoids the complexities and non-differentiability of traditional obstacle avoidance constraints, and simplifies the problem modeling the subsequent numerical optimization process. The simulation results demonstrate that the I-STC is capable of generating parallel parking trajectories with both comfort and safety. [ABSTRACT FROM AUTHOR]

Forecasting traffic congestion has become an important issue than ever before. Information provided by traffic congestion prediction methods becomes helpful to traffic management agencies to take suitable actions in due time. On the other hand, with prior information, passengers can plan their trip routes accordingly. In this paper, we tackle the problem of mixed traffic congestion prediction and develop a new Bi-State Prediction Network (BiSPNet). Two heads form BiSPNet. Each head information passed through the layers of three-dimensional convolution (Conv3D), two-dimensional convolutional long short-term memory (ConvLSTM2D), and two-dimensional convolution (Conv2D). Then the dense layer merges both head outputs. These layers capacitate the model to capture the characteristics of multiple inputs. The proposed model is validated by the ground truth data collected by the video cameras installed on the roadways in Delhi, India. Experimental results confirm that the prediction accuracy of the BiSPNet model surpasses that of several state-of-the-art benchmark models. In particular, the BiSPNet reduces mean absolute error by 53.23%, 40.95%, and 0.38%, respectively, in congestion score prediction for 5, 10, and 15-min time horizons compared to the mixed deep learning network (MDLNet). The source data and code can be accessed on GitHub at the following link: https://github.com/amanojup/. [ABSTRACT FROM AUTHOR]

Double parking has many negative effects on traffic indicators such as traffic congestion, traffic flow conditions, and traffic safety. Double parking includes parameters that affect drivers' behavioral and traffic habits. Various inspection activities and penal sanctions are implemented to prevent parking violations. Within the scope of this study, it is aimed to detect double parking with the YOLOv8 model, one of the deep learning algorithms. In this direction, a data set consisting of a total of 891 images was created, taking into account the streets with high traffic density in İzmit and Erzurum. As a result of the YOLO model, the measurement parameter F1 score value was obtained as 0.83. The mAP@0.5 values of the model for double parking, normal parking and the entire data set were obtained as 0.851, 0.922 and 0.886, respectively. When other performance parameters were examined, it was concluded that the model successfully detected the double parking situation. According to the model performance results, 89% of double and normal parking situations were detected correctly. A data set infrastructure has been created for studies on the detection of double parking. With this study, the initial work of the systems for automatic detection of parking violations and instant warning of drivers was carried out. [ABSTRACT FROM AUTHOR]

This paper introduces a two-echelon optimization model for the integrated routing of an electric vehicle (EV) and a traditional internal combustion engine vehicle (ICEV) in an urban environment. The scientific context of this study is sustainable urban logistics. The case study focuses on the distribution of pharmaceuticals in the metropolitan area of Rome. Distributing pharmaceuticals in large cities presents significant challenges, including heavy traffic congestion, the need for strict temperature control, and the maintenance of the integrity and timely delivery of sensitive medications. Furthermore, the complexity of urban logistics and adherence to regulatory requirements introduce additional layers of difficulty. Therefore, the implementation of fast and sustainable distribution mechanisms is crucial in this context. Specifically, the model seeks to minimize both total CO2 emissions and transportation costs while optimizing the use of an EV and an ICEV, all while ensuring that service level requirements are met. Computational results demonstrate the effectiveness of the proposed method in improving the sustainability of pharmaceutical distribution. [ABSTRACT FROM AUTHOR]

The aim of this study was to compare the head displacement of the KPSIT C50 dummy, representing a 50th percentile male, with the KPSIT C5 dummy, representing a 5th percentile female, during low-speed collisions. Low-speed collisions, such as those occurring in urban traffic jams, are increasingly common. The research was conducted on a dedicated educational workstation designed to measure forces in seat belts. This study is part of a comprehensive research project on crash tests involving both volunteers and physical KPSIT dummies. The tests were conducted at a speed of 20 km/h to simulate real-world low-speed collision scenarios. The findings demonstrate that using a sports bucket seat with four-point or five-point harnesses significantly reduces head displacement compared with standard car seats. Such seating configurations enhance safety by minimizing the risk of head injuries, which can occur when airbags do not deploy during low-speed collisions. Moreover, the study highlights that standard three-point seat belts allow for greater forward head movement, increasing the risk of head contact with the vehicle's interior during collisions at speeds too low to trigger airbag deployment. [ABSTRACT FROM AUTHOR]