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• A general two-wheeler riding behavior questionnaire (TWRBQ) was developed. • Aberrant riding behaviors of e-bike riders were more like those of motorcyclists. • Riders with driving licenses for automobiles behaved significantly better than those without. The aberrant driving behavior of two-wheelers, primarily powered two-wheelers, is one of the complex problems in urban traffic safety management. This study aims to describe the aberrant riding behaviors of two-wheelers, including pedal cyclists, e-bike riders and motorcyclists, as a whole and to explore the similarities and differences in behavioral characteristics among different types of two-wheelers. Therefore, a general two-wheeler riding behavior questionnaire (TWRBQ) was developed for the current two-wheeler traffic conditions in China, and a survey was conducted in Xi'an to test the construct validity of the questionnaire. Through statistical tests and factor analyses, e-bike riders, who were not given enough attention in real-world safety management, were carefully studied. It was found that the characteristics of aberrant riding behaviors of e-bike riders were closer to those of motorcyclists. Both of them showed a much higher frequency of aberrant riding behaviors compared with cyclists. The result implicated that the behaviors of motorcyclists and e-bike riders need to be regulated more than those of cyclists. In addition, correlations were found between some attributes of riders and aberrant riding behavior factors named errors and violations, among which having a driving license for automobiles had a more significant effect on riders' self-reported driving errors than having a driving license regardless of vehicle types. The results suggest that safety training is necessary for both motorcyclists and e-bike riders, while the level of the training should be improved. Additionally, more attention should be paid to novice, elderly, intense, and commuting riders. [ABSTRACT FROM AUTHOR]

• Cooperative behaviors among road users, such as predicting and compensating for another road user's mistakes, were investigated. • A 12-day traffic observation with stationary mounted cameras was conducted at an urban T-intersection in Germany, where cars with priority appearing from the right encountered cars or bikes without priority coming from the left. • Cyclists were found to disregard the "priority-to-the-right" rule more often than motorists. • Road users with priority are more likely to yield when turning right and arriving at the intersection later. • Road users who disregard the "priority-to-the-right" rule are more likely to stay near the center of the road, while those who yield tend to move closer to the roadside. The integration of autonomous driving into mixed traffic environments poses unique challenges, especially at unsignalized intersections where communication and cooperation with human road users is necessary. This study addresses a critical research gap by investigating the adherence to the "priority-to-the-right" rule at an urban unsignalized intersection, comparing behavioral patterns of cyclists and motorists. Utilizing stationary mounted cameras, a 12-day traffic observation of an urban T-intersection in Braunschweig, Germany, was conducted. The class and the trajectory of road users as well as their scenarios were identified. In 202 cases, cars appearing from the right (ego, with priority) encountered cars or bikes coming from the left (foe, without priority). The study analyzed the impact of variables, ego's direction, foe's class and lateral position and their arrival time on their passing order through descriptive statistics and logistic regression. The findings reveal that cyclists disregard the "priority-to-the-right" rule more often than motorists. Additionally, road users who have the priority are more likely to yield when turning right, arriving at the intersection later, and encountering an opposing road user who is close to the center of the road. This study highlights the importance of implicit communication in traffic and provides essential benchmarks for developing more human-like autonomous driving systems, capable of interpreting and responding to nuanced road user interactions at unsignalized intersections. [ABSTRACT FROM AUTHOR]

Metal(loid) pollutants in road dust (road-deposited sediment) show obvious seasonal characteristics under the influence of sources. However, the short-term (days to weeks) variations of metal contamination in road dust in different functional areas with antecedent dry days (ADDs) and their relationships with the source factors were poorly understood, which reduced the comparability of researches and effectiveness of pollution remediation. Herein, temporal characteristics of As, Ba, Ca, Cd, Cr, Cu, Pb and Zn contamination in road dust were studied in two distinct functional areas in Nanjing, i.e. an urban park (Xuanwu, XW) and a stop light-controlled traffic intersection (Baima, BM), following a continuous 23-day sampling between two rainfall events. Mean concentrations of each metal at BM and XW were 1.6–29.5 and 1.3–10.8 times higher than those in the background soil, respectively. The enrichment factor (EF) was used to compensate for grain-size effect. EFs of metals were between 1.3 and 50.3, showing minor to extremely high contamination and different temporal trends. Combined with the EFs and positive matrix factorization (PMF) results, dry atmospheric deposition of industrial and construction sources was the main cause for comparable contamination levels of As, Ca, Cd and Zn between BM and XW and those stable temporal trends. Due to the traffic contribution, contamination levels of Cu, Ba, Pb and Cr at BM were higher (p <.05) than at XW. Different from the stable contamination levels at XW, contamination levels of Cu, Ba, Pb and Cr decreased at BM with the ADDs due to the dilution of traffic particles by less metal enriched atmospheric deposition. These results shed light on the quite different variations of metal contamination in road dust from different functional areas with ADDs under the influence sources, and have instructive significance on road dust sampling, metal contamination remediation and comparison between studies. [ABSTRACT FROM AUTHOR]

Traffic flow forecasting is crucial for improving urban traffic management and reducing resource consumption. Accurate traffic conditions prediction requires capturing the complex spatial-temporal dependencies inherent in traffic data. Traditional spatial-temporal graph modeling methods often rely on fixed road network structures, failing to account for the dynamic spatial correlations that vary over time. To address this, we propose a Transformer-Enhanced Adaptive Graph Convolutional Network (TEA-GCN) that alternately learns temporal and spatial correlations in traffic data layer-by-layer. Specifically, we design an adaptive graph convolutional module to dynamically capture implicit road dependencies at different time levels and a local-global temporal attention module to simultaneously capture long-term and short-term temporal dependencies. Experimental results on two public traffic datasets demonstrate the effectiveness of the proposed model compared to other state-of-the-art traffic flow prediction methods. [ABSTRACT FROM AUTHOR]

Extra-long underwater tunnels are important channels for realising cross-water traffic and play a crucial role in urban traffic. To study the influence of longitudinal slope, curve and combination of both on the driving safety of extra-long underwater tunnels, the physiological changes of drivers under the effect of different combinations of longitudinal slope and curve are analyzed. A driving risk evaluation model is established by introducing the coupling coordination degree, quantifying the relationship between the longitudinal slope, curve radius, and driving risk, and a reasonable combination of the longitudinal slope and curve is selected. The results indicate that there are significant differences in the physiological load of drivers between each longitudinal slope and curve. More specifically, the smaller the longitudinal slope and the larger the curve radius, the smaller the coupling coordination degree and better the driving safety. For a small longitudinal slope area (− 1.5% < longitudinal slope < 1.5%), the minimum curve radius is 400 m, and the ideal curve radius is ≥ 1700 m. For a small radius curve area (curve radius < 1000 m), the maximum longitudinal slope is − 1.5% (downhill area) and 2% (uphill area), respectively. [ABSTRACT FROM AUTHOR]

The intensive development of road transport has resulted in a significant increase in air pollution. This phenomenon is particularly noticeable in urban areas. This creates the need for analyses and forecasts of the scale and extent of future emissions of harmful substances into the environment. The aim of this study was to estimate the costs of the emission of toxic components of exhaust gases generated by all users of conventionally propelled vehicles travelling on a section of urban road in the next 25 years. The traffic study was carried out on an urban traffic route, playing a key role for road transport in the dimension of a given urban agglomeration. The traffic forecast for the analysed road section was based on the results of our own measurements carried out in April 2023 and external data from the General Directorate for Roads and Motorways. The results of the observations concerned six categories of vehicles for the morning and afternoon rush hours. Based on the data obtained, the generic structure of the vehicle population on the analysed section and the average daily traffic were determined. Using the methodology contained in the Blue Book of Road Infrastructure, parameters were calculated in the form of annual indicators of traffic growth on the analysed section, travel speed, and annual air pollution costs for selected vehicle categories, remembering at the same time that the Blue Book-based methodology does not distinguish between unit costs in relation to the type of emissions. The results of the study confirmed that there was an increase in the cost of toxic emissions for each vehicle category over the projected 25-year period. The largest increases were seen for trucks with trailers and passenger cars. In total, for all vehicle categories, emission costs nearly doubled from 2024 to 2046, from EUR 3,745,229 to EUR 7,443,384, due to the doubling of the number of vehicles resulting from the traffic forecast. The analyses presented here provide an answer to the question of what pollution costs may be faced by cities in which road transport will continue to be based on conventional types of propulsion. In addition, the research presented can be used to develop urban mobility transformation plans for the coming years, within the scope of the widely promoted smart city concept and the idea of electromobility, by pointing out to local authorities the direct economic benefits of these changes. [ABSTRACT FROM AUTHOR]

In urban smart city environments, traffic hazards can lead to catastrophic outcomes, including significant property losses and severe threats to public safety. Conventional traffic monitoring systems are limited in terms of accuracy and speed, presenting significant challenges for real-time traffic surveillance. To tackle these challenges, this paper introduces the GC-YOLOv9 algorithm. Specifically, we have enhanced the YOLOv9 model by incorporating Ghost Convolution, markedly improving the model's perceptual abilities and detection accuracy. Furthermore, this study designed an integrated smart city framework that includes layers for service applications, the Internet of Things, edge processing, and data centers. By deploying the enhanced YOLOv9 model within this framework, our method achieved mAP@0.5 scores of 77.15 and 74.95 on the BDD100K and Cityscapes datasets, respectively, surpassing existing technologies. Additionally, the potential applications of this method in public area fire safety management, forest fire monitoring, and intelligent security systems further underscore its significant value in improving the safety and efficiency of smart cities. [ABSTRACT FROM AUTHOR]

In this article, we investigate the edge cache deployment for high definition (HD) urban map provisioning, which is an essential building block for future autonomous driving. Given a deployment budget, we first formulate a satisfied downloading request maximization (SDRM) problem to obtain the deployment locations and customized cache sizes. The SDRM problem is unsolvable directly as the urban transportation traffic is highly dynamic and future traffic conditions are unknown in advance. Based on data analytics of the vehicular GPS trace, we propose an architecture named CoDe, built on which we transform and address the SDRM problem. The reference implementation of CoDe is respectively developed based on the insights from two urban-scale carpool GPS traces. The novelty and contributions of CoDe lie in its three-layer design. Particularly, at data feeding layer, we make use of two urban 60-day GPS traces involving respectively 37,801 and 17,517 vehicles, to extract the analytics samples. At mobility characterization layer, we conduct extensive data analytics on traffic mobility in terms of their distribution, correlation, and variation, to mine the crucial traffic mobility patterns for strategy customization. At cache deployment layer, we propose the K-order subgraph for each block to record the moving statistics within its K-order neighborhood, and transform the SDRM problem accordingly. Then, the Route weIght baSed grEedy (RISE) algorithm is devised for the problem, which can deliver the deployment decisions. Extensive data-driven experiments are carried out to demonstrate the superior performance of CoDe in terms of request hit ratio and caching resource utility. [ABSTRACT FROM AUTHOR]

Carbon emissions from urban transportation significantly contribute to overall transportation emissions and are a major cause of the continuous rise in global temperatures. Understanding the spatial distribution and influencing factors of carbon emissions from operating vehicles can aid in formulating targeted policies and promoting emission reduction. To analyze the factors influencing urban traffic carbon emissions, we calculated emissions using trajectory data from operating vehicles in Shenzhen. We then used gradient boosting regression tree methods, specifically RF, XGBoost, and LightGBM models, to analyze the impact of the built environment on vehicle emissions. We used the XGBoost model for detailed factor analysis by comparing the models. The results indicate that bus stops, intersections, housing density, metro stops, and land use mix are the top five factors influencing emissions. When road density is 0–15 km/km2, the distance from the city center is 0–6 km, and the population exceeds 2000/km2, the built environment significantly reduces vehicle emissions. [ABSTRACT FROM AUTHOR]

Rainstorms and flooding are among the most common natural disasters, which have a number of impacts on the transport system. This reality highlights the importance of understanding resilience—the ability of a system to resist disruptions and quickly recover to operational status after damage. However, current resilience assessments often overlook transport network functions and lack dynamic spatiotemporal analysis, posing challenges for comprehensive disaster impact evaluations. This study proposes an SR-PR-FR comprehensive resilience evaluation model from three dimensions: structure resilience (SR), performance resilience (PR), and function resilience (FR). Moreover, a simulation model based on Geographic Information System (GIS) and Simulation of Urban MObility (SUMO) is developed to analyze the dynamic spatial–temporal effects of a rainstorm on traffic during Xi'an's evening rush hour. The results reveal that the southwest part of Xi'an is most prone to being congested and slower to recover, while downtown flooding is the deepest, severely affecting emergency services' efficiency. In addition, the road network resilience returns to 70% of the normal values only before the morning rush the next day. These research results are presented across both temporal and spatial dimensions, which can help managers propose more targeted recommendations for strengthening urban risk management. [ABSTRACT FROM AUTHOR]

Urban greening is threatened by the concern that street trees increase traffic-related injury/death. Associations between all serious and fatal traffic crashes and street tree percentages were examined in Sydney, Australia. Associations were adjusted for confounding factors relating to driver behavior (speeding, fatigue, and use of alcohol) and road infrastructure, including alignment (e.g., straight, curved), surface condition (e.g., dry, wet, ice), type (e.g., freeway, roundabout), and speed limit. Models indicated that 10% more street trees were associated with 3% and 20% higher odds of serious or fatal injuries and 20% tree collisions on roads of any speed, respectively. However, further analysis stratified by speed limit revealed contrasting results. Along roads of 70 km/h or greater, 10% more street trees were associated with 8% higher odds of serious or fatal injury and 25% higher odds of death. Comparable associations were not found between street trees and serious or fatal injuries along roads below 70 km/h. Reducing speed limits below 70 km/h saves lives and may mitigate risks of serious or fatal traffic accidents associated with street trees, enabling greener, cooler, healthier cities. [ABSTRACT FROM AUTHOR]

The increasing problem of urban traffic congestion has led to the extensive use of underground tunnels. However, tunnel lining cracks pose a major threat to the integrity and safety of the structure. Although the traditional repair method is effective, it often requires higher construction technology and higher cost, and may cause damage to the concrete structure. In this study, microbial-induced calcium carbonate precipitation (MICP) was combined with basalt fiber cloth to repair and reinforce tunnel lining cracks. Bacillus pasteurii was used to optimize the microbial mineralization process, and the effectiveness of the method on cracks with different widths was evaluated using a water seepage test. In addition, the mechanical properties of the reinforced tunnel lining were tested. The microbial mineralization process effectively repaired cracks with widths of 1 mm, 2 mm, and 3 mm. The use of unidirectional basalt fiber cloth increased the bearing capacity of the strengthened member by 12.5%. The combined reinforcement method also enhances the deflection performance and alleviates the influence of water seepage on the bonding performance. This innovative and sustainable approach not only provides an effective solution for the repair of tunnel lining cracks, but also contributes to the broader field of eco-friendly building materials. This study highlights the potential of using this combination approach to improve the durability and performance of underground infrastructure. [ABSTRACT FROM AUTHOR]

This study identifies factors influencing spatial and temporal variations in magnetic susceptibility and heavy metal content in soils and airborne particulate matter within the Kyiv megapolis, Ukraine, and highlights how source apportionment differs in the long and short run. Topsoil magnetic susceptibility anomalies of > 70 × 10−8 m3kg−1 are observed around old factories. The tree bark magnetic susceptibility map provides a record of industry general low emissions for the last 2–3 decades. The patterns of both spatial distributions confirm that factory emissions dominate the composition of particulate falling on the ground in urban area, with exclusion of streets with heavy traffic. Enhanced concentrations of Cu, Ni, and Zn have been found in urban soils, showing a positive correlation with magnetic susceptibility. Re-suspended road dust dominates temporal variation of particulate matter magnetic susceptibility collected on air filters. The air at busy streets is cleaner in winter, when the street dust gets immobilized by snow cover or freezing. Industries in Kyiv pose no significant effect on air quality; the concentrations of Cr, Ni, Cu, Zn, Cd, and Pb are at normal urban level with the exception of the near vicinity to factories. Air in streets with heavy traffic is enriched with Fe and Mn. Principal component analysis reveals different pattern of air pollution for the busy streets and yard areas. Yards are less affected by road dust; thus, contribution of industrial emissions can be distinguished. The results provide context for further quantification of any alterations in ecological state of Kyiv megapolis that may have arisen from socio-economic shocks and direct threats connected to the current war. [ABSTRACT FROM AUTHOR]

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In recent urban studies, understanding the flow patterns of urban residents has become crucial for effective transportation planning and business district design. Traditional data-driven approaches have provided insights but often lead to random and uninterpretable results due to their sole reliance on data features, lacking a deeper contextual and semantic analysis of the underlying patterns. To overcome these limitations, our work introduces a novel framework that fuses holographic knowledge embedding with graph deep learning to predict urban population travel patterns. This dual-driven approach of data and knowledge uniquely integrates traffic geographic information, vehicle trajectory data, and Points of Interest (POI) into a comprehensive urban traffic knowledge graph. Our method not only captures the spatial-temporal dependencies of big data traffic but also models the relationships between geographic, semantic POI information, and urban travel behaviors. The knowledge graph is then processed through a graph deep learning model, enhancing the embedding features and enabling sophisticated link prediction. Compared with conventional data-driven methods, our approach demonstrates significant advancements in harnessing semantic information, leading to more accurate and interpretable predictions of travel patterns. Experimental validation on real-world datasets confirms the effectiveness of our method in capturing complex urban dynamics. [ABSTRACT FROM AUTHOR]

Traffic flow prediction plays a crucial role in the development of smart cities. However, existing studies face challenges in effectively capturing spatio-temporal contexts, handling hierarchical temporal features, and understanding spatial heterogeneity. To better manage the spatio-temporal correlations inherent in traffic flow, we present a novel model called Dynamic Spatio-Temporal Memory-Augmented Network (DSTMAN). Firstly, we design three spatial–temporal embeddings to capture dynamic spatial–temporal contexts and encode the unique characteristics of time units and spatial states. Secondly, these three spatial–temporal components are integrated to form a multi-scale spatial–temporal block, which effectively extracts hierarchical spatial–temporal dependencies. Finally, we introduce a meta-memory node bank to construct an adaptive neighborhood graph, implicitly representing spatial relationships and enhancing the learning of spatial heterogeneity through a secondary memory mechanism. Evaluation on four public datasets, including METR-LA and PEMS-BAY, demonstrates that the proposed model outperforms benchmark models such as MTGNN, DCRNN, and AGCRN. On the METR-LA dataset, our model reduces the MAE by 4% compared to MTGNN, 6.9% compared to DCRNN, and 5.8% compared to AGCRN, confirming its efficacy in traffic flow prediction. [ABSTRACT FROM AUTHOR]

With the help of traffic lights and street cameras, optical camera communication (OCC) can be adopted in Internet of Vehicles (IoV) applications to realize communication between vehicles and roadside units. However, the encoded light emitted by these OCC transmitters (LED infrastructures on the roadside and/or LED-based headlamps embedded in cars) will generate stripe patterns in image frames captured by existing license-plate recognition systems, which seriously degrades the accuracy of the recognition. To this end, we propose and experimentally demonstrate a method that can reduce the interference of OCC stripes in the image frames captured by the license-plate recognition system. We introduce an innovative pipeline with an end-to-end image reconstruction module. This module learns the distribution of images without OCC stripes and provides high-quality license-plate images for recognition in OCC conditions. In order to solve the problem of insufficient data, we model the OCC strips as multiplicative noise and propose a method to synthesize a pairwise dataset under OCC using the existing license-plate dataset. Moreover, we also build a prototype to simulate real scenes of the OCC-based vehicle networks and collect data in such scenes. Overall, the proposed method can achieve a recognition performance of 81.58 % and 79.35 % on the synthesized dataset and that captured from real scenes, respectively, which is improved by about 31.18 % and 24.26 %, respectively, compared with the conventional method. [ABSTRACT FROM AUTHOR]

The research presents a study of developing and verifying mathematical models for traffic control at traffic light intersections. The research's primary goal is to create accurate and reliable models that optimize the operation of traffic lights and the distribution of traffic flows, which contributes to reducing the waiting time at intersections and reducing harmful emissions into the atmosphere from idling cars. The study examines various criteria for evaluating the effectiveness of traffic management, such as total travel time, number of stops, traffic intensity, and average delays. The developed models consider the dynamics of urban traffic flows, including the characteristics of vehicles and the random nature of the flow. The results demonstrate that using mathematical models allows you to significantly improve the ecological situation in cities, increase the capacity of roads, and increase the overall speed of vehicles. The study emphasizes the importance of developing accurate mathematical models for increasing traffic management efficiency and improving city environmental safety. [ABSTRACT FROM AUTHOR]

Presently, online ride-hailing firms and municipal transportation systems substantially influence urban traffic management. Considering the increasing use of these services, it is essential to assess and predict travel demand to optimize service efficiency. This study analyzes Uber data from New York City, employing automated machine learning algorithms to assess and forecast ride demand inside the city. In conventional transportation planning, forecasting travelers' destination selections is a crucial stage. Traditional methodologies, such as physical models like gravity models, are constrained in their capacity to encompass the comprehensive array of elements affecting travel behavior, frequently addressing just two or three variables. This study employs machine learning methodologies for predicting passenger destination choices, illustrating that these algorithms can include a wider array of variables, hence providing enhanced forecast accuracy relative to conventional approaches. The utilization of ArcGIS Pro and Python modules for automation enhanced the efficiency of spatial analysis. A range of machine learning methodologies, such as decision trees, Light-GBM, XG-Boost, Cat-Boost, and hybrid models, were utilized to predict demand. The selected source demand forecasting model is an ensemble model, with a R2 of 0.94 and a Mean Absolute Error of 91.63. The optimal model for predicting destination demand was identified as a composite model comprising eight distinct algorithms. The model's R2 is 0.95, while the Mean Absolute Error is 67.12. Moreover, the examination of environmental factors indicated that proximity to recreational activities, median age, and population density had the most substantial influence on predicting travel demand. [ABSTRACT FROM AUTHOR]

Bus drivers have an important role in ensuring road safety, as their driving circumstances fluctuate due to the combined influence of physiological, psychological, and environmental dynamics, which can cause complex and varied driving dangers. Quantifying and assessing drivers' risk characteristics under various scenarios, as well as finding the best fit with their work schedules, is critical for enhancing bus safety. This research first uses the entropy weight method, which is based on historical warning data, to examine the risk characteristics of bus drivers in various complicated contexts. It then creates an objective function targeted at minimizing the operational risk for a specific bus route. This function uses the quasi-Vogel approach and an improved simulated annealing algorithm to optimize and restructure the scheduling table, taking individual driver risk characteristics into account. Finally, the analysis is confirmed and examined with actual operational data from the Zhenjiang Bus Line 3. The data show that enhanced bus operations resulted in a 7.22% gain in overall safety and a 33.76% improvement in balancing levels. These insights provide valuable theoretical guidance as well as practical references for the safe operation and administration of public buses. [ABSTRACT FROM AUTHOR]

Trajectory prediction is a key component in the development of applications such as mixed urban traffic management and public safety. Traditional models have struggled with the complexity of modeling dynamic crowd interactions, the intricacies of spatiotemporal dependencies, and environmental constraints. Addressing these challenges, this paper introduces the innovative Social Attention Graph Neural Network (SA-GAT) framework. Utilizing Long Short-Term Memory (LSTM) networks, SA-GAT encodes pedestrian trajectory data to extract temporal correlations, while Graph Attention Networks (GAT) are employed to precisely capture the subtle interactions among pedestrians. The SA-GAT framework boosts its predictive accuracy with two key innovations. First, it features a Scene Potential Module that utilizes a Scene Tensor to dynamically capture the interplay between crowds and their environment. Second, it incorporates a Transition Intention Module with a Transition Tensor, which interprets latent transfer probabilities from trajectory data to reveal pedestrians' implicit intentions at specific locations. Based on AnyLogic modeling of the metro station on Line 10 of Chengdu Shuangliu Airport, China, numerical studies reveal that the SA-GAT model achieves a substantial reduction in ADE and FDE metrics by 34.22% and 38.04% compared to baseline models. [ABSTRACT FROM AUTHOR]

In China, the total energy consumption and greenhouse gas (GHG) emissions will reach considerable levels based on the current speed of urban rail transit system development. Based on the life-cycle assessment theory, this research constructs an urban rail transit system GHG emission assessment method, calculates emission outputs based on resource inputs from actual investigated data and makes a quantitative analysis of GHG emissions. The results show that in recent years, the GHG emission of urban rail transit construction and operation in China is between 2000 × 104 and 4200 × 104 tonnes of carbon dioxide equivalent (tCO2e) per year. The proportions of the construction and operational phases in this emission are 57 and 43%, respectively. In the construction phase, the GHG emission intensity per unit mileage of shield tunnels and per unit area of stations is about 1.3 × 104 tCO2e/km and 3.71 × 104 tCO2e/ha, respectively. In the operational phase, the GHG emission intensity per unit trip is 0.084 kg carbon dioxide equivalent/passenger-km. The entire life-cycle GHG emission per kilometre of urban rail transit systems is 11.69 × 104 tCO2e (with a service life of 50 years) in China. The construction phase and operation phase generated about 18.73 and 81.27% of this emission, respectively. The preliminary conclusions of this study may help shed light on the emission reduction potential of urban rail transit systems and the emission reduction targets in China. [ABSTRACT FROM AUTHOR]

Background: Motorcycle traffic accidents represent a serious social, economic, and health problem in Brazil. Parte superior do formulário. Objective: To analyze the spatial distribution and factors associated with urban traffic accidents attended by the emergency mobile care service (SAMU) in Arapiraca, Alagoas, in 2018. Method: Ecological study including victims of urban traffic accidents that occurred in Arapiraca, Alagoas, in 2018. The sociodemographic and clinical variables were obtained from the SAMU care records. Incidence rates (IT) per 10,000 inhabitants were calculated. The spatial units adopted were the neighborhoods. Global and local Moran statistics were used in the analysis. Results: 1,296 victims were registered, with a higher incidence rate in males (TI 79.62) and in the age group 20-29 years (TI 106.52). The occurrences involving motorcycles corresponded to 85.3%. The time with the highest accident rate was between 18h00min and 20h59min (RR 6.48; 95% CI: 6.48-10.36) and the day was Saturday (RR 1.62; 95% CI: 1.46-1, 78). The spatial analysis identified priority areas on the highways (n = 172; 13.27%) and concentration in five neighborhoods (n = 433; 33.41%). Conclusion: The spatial distribution was heterogeneous, and the associated factors were male gender, young age, involving motorcycles, time between 18:00 and 20:59 on Saturdays. [ABSTRACT FROM AUTHOR]

Traffic conflict is frequently utilized as a stand-in for crashes for analyzing traffic safety from a broader perspective for varying roadways and traffic conditions. In Indian heterogeneous traffic conditions, vehicles with various static and dynamic properties interact simultaneously in longitudinal and lateral directions, forming traffic conflicts. To this end, the present study develops crossing conflict-based safety performance functions (C-SPFs) for eight urban un-signalized T-intersections. The video-graphic survey approach was used to gather the necessary traffic data with different intersection and traffic flow characteristics. After that, from the recorded video, traffic conflicts were identified using the Post encroachment time (PET) for the selected eight study intersections. Based on the PET values, crossing conflicts were initially divided into critical conflicts (CC) and non-critical conflicts (NCC). Then, using the Poisson-Tweedie regression technique, crossing conflicts were modeled as a function of traffic flow and intersection-related parameters. The findings showed that the most important factors defining the number of CC and NCC are intersection geometry (with or without Central Island), time of day, traffic volume, and composition (offending and conflicting approach). Based on the study's findings, city planners and traffic engineers estimate the number of CC and NCC; as a result, they may project the necessary laws, rules, and regulations to enhance traffic safety operations. [ABSTRACT FROM AUTHOR]

In preventing large-scale congestion caused by urban road traffic attacks, both random and deliberate attack methods were employed, with the robustness of the traffic system analyzed using a cascading failure model. The details of the proposed methods are, firstly, a road network model is constructed based on Geographic Information System (GIS), and the traffic flow data of a provincial city during peak traffic hours is obtained by Python. Then, the flow data of Origin-Destination (OD) pairs for the road network under normal operation is obtained using OD estimation module in TransCAD, the urban transportation network is modeled based on the actual traffic flow distribution. Subsequently, random attacks and deliberate attacks based on traffic volume were conducted on the road network. The traffic flow of the attacked segments was reallocated according to a cascading failure model, with the robustness of the urban road network assessed using topological and traffic flow indicators. The results indicate that, in the robustness analysis considering cascading failures, deliberate attacks have a more significant impact on high-traffic segments compared to random attacks with the same number of attack steps. Therefore, future urban traffic management should focus on ensuring the efficiency of high-traffic road segments, and prevent it from becoming ineffective due to excessive traffic flow. [ABSTRACT FROM AUTHOR]

Traffic anomaly detection is crucial for urban management, yet current research is often confined to small-scale endeavors. This study collected 9 months of real-time Wuhan traffic-monitoring data from Amap. We propose Traffic-ConvLSTM, a multi-scale spatial-temporal technique based on long short-term memory (LSTM) networks and convolutional neural networks (CNNs) to effectively achieve long-term anomaly detection at the city level. First, we converted traffic track points into an image representation, which enables spatial correlation between traffic flow and roads and correlations between traffic flow and roads, as well as the surrounding environment, to be captured. Second, the model utilizes convolution kernels of different sizes to extract spatial features at road-, regional-, and city-level scales while incorporating the temporal features of different time steps to capture hourly, daily, and weekly dynamics. Additionally, varying weights are assigned to the convolution kernels and temporal features of varying spatio-temporal scales to capture the heterogeneous strengths of spatio-temporal correlations within patterns of traffic anomalies. The proposed Traffic-ConvLSTM model exhibits improved performance over existing techniques in the task of identifying long-term and large-scale traffic anomaly occurrences. Furthermore, the analysis reveals significant traffic anomalies during holidays and urban sporting events. The diverse travel patterns observed in response to various activities offer insights for large-scale urban traffic anomaly management, providing recommendations for city-level traffic-control strategies. [ABSTRACT FROM AUTHOR]

To address the challenge of constructing spatial and temporal dependencies in existing traffic flow prediction methods, this paper proposed a new method called spatial temporal position attention graph neural network (ST-PAGNN), which utilized spatiotemporal location attention. Firstly, the graph neural network contained a location attention mechanism, which could better capture the spatial dependence of traffic nodes in the urban road network. Then, it used a gated recurrent neural network with trend adaptive transformer (Trendformer) to capture the local and global information of the traffic flow sequence in the time dimension. Finally, it used the improved grid search optimization method to optimize the introduced para-meters of the model, obtaining the global optimal solution with high time efficiency. The experimental results show that in the dataset PEMS-BAY, the evaluation indexes RMSE, MAE and MAPE of the ST-PAGNN method are 1.37, 2.57, 2.67%, 1.55, 3.64, 3.37%, 1.97, 4. 37 and 4.43%, respectively, when the prediction step size is 15 min, 30 min and 60 min, respectively. In the dataset METR-LA, when the prediction step size is 15 min, 30 min and 60 min, the evaluation indexes RMSE, MAE and MAPE of the ST-PAGNN method are 2. 73, 5. 16, 7.13%, 2.99, 5.97, 7.86%, 3.53, 7. 16 and 9.96%, respectively. The results show that the proposed ST-PAGNN method is higher than the existing models in the evaluation indexes under different granularities, which illustrates the effectiveness and superiority of ST-PAGNN in solving traffic prediction problems. [ABSTRACT FROM AUTHOR]

As the economy advances and rail transit technology progresses, rail transit plays an important role in alleviating urban traffic pressure and promoting regional economic development. However, railway safety faces potential hazards such as foreign object intrusion in the trackside environment, seriously threatening the safety of trains and passengers. Therefore, to achieve high-performance recognition of obstacle target images in rail transit, a small sample object detection algorithm YOLOv5-RTO, which uses the EVC (Explicit Visual Center) attention mechanism and lightweight up-sampling operator CARAFE (Content-Aware ReAssembly of FEatures) to improve YOLOv5, has been proposed. Based on the self-collected image data of rail transit obstacles, a dense and time-varying dataset was created within the rail transit system, and data augmentation methods were used to reduce the impact of small sample datasets on the detection performance of the subsequent algorithm. Using this dataset, target detection tests were conducted on rail transit obstacle images to analyse the actual performance of the training model. The experiment results indicate that the enhanced YOLOv5-RTO algorithm integrates the advantages of EVC and CARAFE while preserving the lightweight and accurate characteristics of YOLOv5. The improved algorithm scheme has an average inference time of 1.3 ms per frame in the field of rail transit images, achieving an overall mAP of 96.5%, an increase of 1.8% compared to the original algorithm. It has a certain engineering application value and provides reference for further optimizing the safety system of urban rail transit. [ABSTRACT FROM AUTHOR]

In the context of transportation development, the simultaneous emergence of automated vehicles (AVs) and human-driven vehicles (HDVs) can lead to varied traffic system performance. For the purpose of improving traffic systems, this paper proposes a traffic restriction scheme only for HDVs. We develop a variational inequality (VI) model to describe travel mode and route choices under this restriction scheme and design an algorithm to solve the model. The proposed model and algorithm are applied to a Sioux Falls network example to evaluate the effects of the traffic restriction scheme. Our findings indicate that the scheme improves overall social welfare, with a higher proportion of restricted travelers leading to greater social welfare as well as increased travel demand due to changes in capacity. However, some lower exogenous monetary factors lead to negative social welfare, as the presence of AVs may exacerbate road congestion. Additionally, advancements in technology are needed to adjust the weightings of travel time and congestion level estimates to further enhance social welfare. These results offer valuable insights for traffic demand management in traffic systems with a mix of AVs and HDVs. [ABSTRACT FROM AUTHOR]

Shared autonomous electric vehicles (SAEVs) have recently attracted significant public interest. The dynamic ride-sharing using SAEVs appears to have the advantages of reducing travel costs and relieving urban traffic congestion. It is meant to improve the practical application value of the dynamic ride-sharing mode of SAEVs. In this paper, to reduce the solution time complexity, a pre-matching algorithm considering the driverless and charging characteristics of SAEVs is developed, and then a two-stage, graph-based many-to-one ride-matching (GMOM) algorithm is proposed for the dynamic ride-sharing problem in the Autonomous Mobility-on-Demand system (AMOD). The dataset from DiDi during the peak travel time and the real-time traffic flow from the AutoNavi map were used to verify the effects of the method. The results demonstrate that the GMOM approach can effectively reduce computational complexity and improve user satisfaction. The dynamic ride-sharing mode based on the GMOM algorithm has a 5.67% higher service rate and 45.56% more vehicle calls than the non-ride-sharing mode under the same conditions. It is found that the cost-effectiveness of using ride-sharing services is relatively high for 10–20 km trips during peak travel time and the dynamic ride-sharing may extend total travel time but will reduce passengers' waiting time. [ABSTRACT FROM AUTHOR]

The integration of autonomous vehicles into urban traffic systems offers a significant opportunity to improve traffic efficiency and safety at signalized intersections. This study provides a comprehensive evaluation of how different autonomous vehicle driving behaviors—cautious, normal, aggressive, and platooning—affect key traffic metrics, including queue lengths, travel times, vehicle delays, emissions, and fuel consumption. A four-leg signalized intersection in Balgat, Ankara, was modeled and validated using field data, with twenty-one scenarios simulated to assess the effects of various autonomous vehicle behaviors at penetration rates from 25% to 100%, alongside human-driven vehicles. The results show that while cautious autonomous vehicles promote smoother traffic flow, they also result in longer delays and higher emissions due to conservative driving patterns, especially at higher penetration levels. In contrast, aggressive and platooning autonomous vehicles significantly improve traffic flow and reduce delays and emissions. Mixed-behavior scenarios reveal that different driving styles can coexist effectively, balancing safety and efficiency. These findings emphasize the need for optimized autonomous vehicle algorithms and signal control strategies to harness the potential benefits of autonomous vehicle integration in urban traffic systems fully, particularly in terms of improving traffic performance and sustainability. [ABSTRACT FROM AUTHOR]

The increase in traffic caused by new development affects the change in traffic conditions on the surrounding roads, and shopping centres are significant traffic generators. The development of local travel generation rates and their characteristics for individual land uses from the aspect of traffic demand is a reliable way to plan traffic in order to come up with preventive solutions to traffic problems, that is, prevention of possible negative consequences on traffic conditions in the street network occurring due to the construction of shopping centres. One of the main aims of this paper is to develop a model for objective assessment of the generated traffic demand for significant changes in land use, such as the construction of shopping centres in medium-sized towns. All these would be steps in the right direction for the promotion of reliable traffic planning and adoption of TIA for every new development before a decision regarding the change in land purpose has been made. This kind of process still has not been established systematically in either Bosnia and Herzegovina and the Republic of Serbia, or in surrounding countries. This paper focuses on the formulation of a model for determining the volume of traffic generated by shopping centres in medium-sized towns in two countries of the Southeast Europe region. The survey was conducted in eight different locations (cities) where there are shopping centres with common facilities. The analysis showed that the number of visitors and vehicles attracted by the shopping centre zone can be determined by a model based on a linear regression analysis. The analysis included exploring several different factors of trip generation in shopping centres, including the relationship between trip generation and combinations of several independent variables. The verification of the model was conducted in real conditions of the traffic flow generated by a shopping centre which was not the analysis subject when forming the forecasting model. In this way, the validity of the proposed model is credibly assessed. The developed model can be applied in the procedures of planning the construction of shopping centres in medium-sized cities in the Republic of Serbia and Bosnia and Herzegovina, and wider, in the region of Southeast Europe, in order to estimate the volume of generated traffic demand, that is, its impact on the conditions of traffic on the surrounding traffic network. [ABSTRACT FROM AUTHOR]

Objective Pedestrian injury is a leading contributor to childhood deaths. This study compared the crossing behaviors of children with adults when crossing in virtual single-lane vs. two-lane traffic conditions. Method Using a fully immersive virtual reality system, children 7–10 years and adults crossed streets with traffic in one lane (near lane) or in two lanes (near and far lane) with 0 offset so cars from opposing directions arrived at the child's crossing line at the same time. On each trial, pedestrians made a decision on when to cross and then completed the crossing, with measures of attention and crossing behaviors automatically recorded by the system as they did so. Results In comparison to crossing a single lane of traffic, crossing two-lane roads increased injury risk for all pedestrians, though children experienced significantly greater risk than adults. Children predominantly crossed by stopping before entering the far lane, whereas adults showed greater synchronization of self-movement to traffic flow and more often crossed both lanes without stopping. Conclusions Children experience more high-risk outcomes than adults when crossing single-lane roads. Crossing two-lane roads elevates risk for pedestrians of both ages, though this risk is significantly greater for child than adult pedestrians. The predominant strategy used to cross two lanes of traffic shows significant developmental changes. Implications for injury prevention are discussed. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the relationship between air pollution (PM2.5) and a collection of features related to urban areas economic welfare and social‐wellbeing based on data collected from a number of 15 European capitals, for a seven years' period (2010–2017). The empirical analysis describes the correlation between the particulate pollution and welfare features in selected urban areas. Moreover, the PM2.5 pollutions may affect the economic and social development of these areas as, both entrepreneurs and population are inclined to relocate for a healthier environment. The models emphasize aspects related to the impact of PM 2.5 on different dimensions as follows: real estate rent values and land prices, restaurants food prices, community economic development, human welfare, as well as urban traffic. From the various correlations we have also discovered that for the urban areas included in the research, at early stages urbanization has the highest impact on air pollution. The findings of this study may offer hints for decision makers for their social and economic policy options. A clean urban environment will attract more skilled employees seeking for economic, social and environmental wellbeing. If the urban agglomerations authorities will fail to mitigate properly the air pollution issues the wellbeing will decrease along with the willingness of urban population to stay in such areas. [ABSTRACT FROM AUTHOR]

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Objective: The visual guiding system, as a tunnel traffic safety improvement method by using visual guiding facilities to actively guide driving safely, has been widely used in countries with many tunnels, in recent years. This paper aims to quantitatively study the comprehensive evaluation of traffic safety of the visual guiding system in tunnels, which has certain engineering application value and can provide support for the quantitative evaluation and optimal design of tunnel traffic safety. Methods: Based on the analysis of the relevant factors of urban tunnel traffic safety, a multi-factor comprehensive evaluation system with 5 upper-level indicators and 12 basic-level indicators was proposed. Considering the independent and incompatible indicators, a comprehensive evaluation method of traffic safety of the visual guiding system in urban tunnels was constructed by using the extension matter-element model. Taking the scene of 4 types of tunnel curves, such as no facilities, horizontal stripe, chevron alignment sign, and LED arch, as examples, the comprehensive evaluation of various schemes were carried out by using simulation tests. Results: The traffic safety comprehensive evaluation system of visual guiding system in urban tunnels can be analyzed from five aspects: perception reaction, guidance ability, driver factor, driving task, and facility appearance. The results demonstrated significant the comprehensive evaluation result of the target level of scene 1 was L4, scene 2 was L3, scene 3 was L2, and scene 4 was L1. That is, the final results of the comprehensive evaluation of the four scenes were poor, medium, good, and very good, respectively. Conclusions: In the scheme of visual guiding system for urban tunnel curves, the effectiveness of the three types of designs, from high to low, was the LED arch, chevron alignment sign, and horizontal stripe, and the safety of the scene without facilities was the lowest. Hence, setting the LED arch in the urban tunnel curve has a good effect in the aspects of guidance ability, sight distance, and sight zone, and is conducive to the driver's perception reaction and driving task. [ABSTRACT FROM AUTHOR]

Metro systems play an important role in reducing urban traffic congestion and promoting the sustainable development of urban transport in megacities. With the expansion of a metro network, transfer stations are necessary for increasing the service connectivity of a metro network. An accurate estimation of transfer passenger flow can help improve the operations management of a metro system. This study proposes a data‐driven methodology for estimating the transfer passenger flow volume of each transfer station in a metro network by mining smart card data. The estimated transfer passenger flow data are visualized to show the spatial‐temporal distribution characteristics of metro transfer passenger flow. The case study results of the Shenzhen Metro network demonstrate that the proposed data‐driven methodological framework is very effective in estimating different types of transfer passenger flows, such as total transfer passenger flow, hourly transfer passenger flow, and inbound and outbound transfer flows at each transfer station. The spatial‐temporal distribution characteristics of transfer passenger flow can be very useful for designing effective and efficient passenger flow management measures to ensure the safe and efficient operation of a metro system. [ABSTRACT FROM AUTHOR]

Probe vehicles and loop detectors collect traffic data for empirical MFD estimation. The accuracy of MFD estimation is contingent upon the percentage of road network links covered (road coverage) and the extent of information (penetration rate) from each link. While the previous research has explored the effects of loop detector road coverage and probe vehicle penetration rates, the impact of probe vehicle road coverage remains unaddressed. Furthermore, the unequal distribution and low penetration rates of probe vehicles undermine the reliability of the data obtained from them. Therefore, this study aimed to (i) examine the impact of probe vehicle road coverage on MFD estimation, (ii) enhance the reliability of the spatial data by using speed data reconstruction, (iii) highlight the increased role of spatial data in the accuracy of MFD estimation, and (iv) develop a performance factor of speed data quality to demonstrate that the quality of spatial data is more susceptible to the penetration rate than road coverage. The results underlined the role of FCD road coverage in MFD estimation, and the proposed methodology significantly improves spatial data quality. The study concluded that spatial and temporal data affect the MFD estimation differently, and the proposed performance factor increases the role of spatial data in MFD estimation. [ABSTRACT FROM AUTHOR]

Based on small scale sample of accident data from specific scenarios, fully exploring the potential influencing factors of the severity of traffic accidents has become a key and effective research method. In order to analyze the factors mentioned above in the scenario of urban ring roads, this paper collected data records of 1250 traffic accidents involving different severity on urban ring road of a central city in northwest China in the past 3 years. Firstly, the Support Vector Machine (SVM) model of non-parametric method is utilized to analyze the data above, and three kernel functions of linear, inhomogeneous polynomial and Gaussian radial basis are constructed respectively. Considering comprehensively 16 potential influencing factors covering the driver-vehicle-road-environment integrated system, the SVM models of above three kernel functions are verified, accuracy reaches 0.771 and F1 reaches 0.841. Then, Bayesian Optimization (BO), Grids Search (GS) and Rough Set (RS) are utilized as optimizer to adjust the parameters of Gaussian radial basis function SVM model, the performance of BO-SVM is further improved and reaches the optimum, with an average accuracy of 0.875 on the test set and a F1 of 0.886, completely outperforming the benchmark models of GS-SVM, RS-SVM, Bilayer-LSTM and BP. Finally, the sensitivity analysis method is utilized to quantify the sensitivity of the potential influencing factors to the severity of road accidents, and the backward selection method is utilized to screen the core influencing factors that influence the severity of accident, concluded that core influencing factors are age, driving mileage and vehicle type. This paper will provide reference for the analysis of the significant influencing factors for road accidents severity, and to provide theoretical support for the precise formulation of accident improvement strategies. [ABSTRACT FROM AUTHOR]

In modern urban traffic, vehicles and pedestrians are fundamental elements in the study of traffic dynamics. Vehicle and pedestrian detection have significant practical value in fields like autonomous driving, traffic management, and public security. However, traditional detection methods struggle in complex environments due to challenges such as varying scales, target occlusion, and high computational costs, leading to lower detection accuracy and slower performance. To address these challenges, this paper proposes an improved vehicle and pedestrian detection algorithm based on YOLOv8, with the aim of enhancing detection in complex traffic scenes. The motivation behind our design is twofold: first, to address the limitations of traditional methods in handling targets of different scales and severe occlusions, and second, to improve the efficiency and accuracy of real-time detection. The new generation of dense pedestrian detection technology requires higher accuracy, less computing overhead, faster detection speed, and more convenient deployment. Based on the above background, this paper proposes a synchronous end-to-end vehicle pedestrian detection algorithm based on improved YOLOv8, aiming to solve the detection problem in complex scenes. First of all, we have improved YOLOv8 by designing a deformable convolutional improved backbone network and attention mechanism, optimized the network structure, and improved the detection accuracy and speed. Secondly, we introduced an end-to-end target search algorithm to make the algorithm more stable and accurate in vehicle and pedestrian detection. The experimental results show that, using the algorithm designed in this paper, our model achieves an 11.76% increase in precision and a 6.27% boost in mAP. In addition, the model maintains a real-time detection speed of 41.46 FPS, ensuring robust performance even in complex scenarios. These optimizations significantly enhance both the efficiency and robustness of vehicle and pedestrian detection, particularly in crowded urban environments. We further apply our improved YOLOv8 model for real-time detection in intelligent transportation systems and achieve exceptional performance with a mAP of 95.23%, outperforming state-of-the-art models like YOLOv5, YOLOv7, and Faster R-CNN. [ABSTRACT FROM AUTHOR]

As urban populations rise globally, cities face increasing challenges in managing urban mobility. This paper addresses the question of identifying which modifications to introduce regarding city mobility by evaluating potential solutions using city-specific, subjective multi-objective criteria. The innovative AI-based recommendation engine assists city planners and policymakers in prioritizing key urban mobility aspects for effective policy proposals. By leveraging multi-criteria decision analysis (MCDA) and ±1/2 analysis, this engine provides a structured approach to systematically and simultaneously navigate the complexities of urban mobility planning. The proposed approach aims to provide an open-source interoperable prototype for all smart cities to utilize such recommendation systems routinely, fostering efficient, sustainable, and forward-thinking urban mobility strategies. Case studies from four European cities—Helsinki (tunnel traffic), Amsterdam (bicycle traffic for a new city quarter), Messina (adding another bus line), and Bilbao (optimal timing for closing the city center)—highlight the engine's transformative potential in shaping urban mobility policies. Ultimately, this contributes to more livable and resilient urban environments, based on advanced urban mobility management. [ABSTRACT FROM AUTHOR]

Rainstorm disasters cause serious threats to people's lives and property. Enhancing emergency response and decision-making capabilities for rainstorm disasters is necessary. In this paper, 87 rainstorm disasters worldwide were first analysed, and secondary events across 15 urban lifeline systems were summarized. Based on the obtained findings and the characteristics of rainstorm disaster evolution and complex network theory, a model of rainstorm disaster chains in urban lifeline systems was constructed, and both partial and overall analyses of this model were performed. With the use of the PageRank risk matrix method, quantitative node risk levels were calculated for different parts of the model, and complex network theory was applied to assess the overall chain risk. The results showed that the highest risk disaster chain was flood → houses submerged or collapsed → road damaged → traffic congestion or paralysis. The most important node was traffic congestion or paralysis, underlining the acute need for emergency response measures in urban traffic systems during rainstorm disasters. Overall, this research provides a crucial direction for preventing rainstorm disasters in urban lifeline systems. [ABSTRACT FROM AUTHOR]

The unmanned aerial vehicle (UAV) city patrol is of great significance in ensuring the safety of residents' lives and properties, as well as maintaining the normal operation of the city. However, the detection of UAV images faces challenges such as numerous small-scale objects, complex backgrounds, and high requirements for detection speed. In response to these issues, we introduce a Real-time Small Object Detection network in UAV-vision (RTS-Net), tailored for UAV patrols. Initially, we introduce a multiscale feature fusion module (MFFM) designed to augment the expressiveness of features across scales, thereby enhancing the detection of smaller objects. Subsequently, leveraging attention mechanisms, we present the coordinated attention detection module (CADM), which bolsters the detection model's ability to accurately segregate objects from the background in expansive, complex scenarios. Lastly, a lightweight real-time feature extraction module (RFEM) is crafted to diminish model computational complexity and boost inference speed. On the UAV road patrol image dataset we constructed, our proposed method attains a detection accuracy of 89.9 % mAP, breaking previous records. It surpasses all prevailing detection methods, particularly for small-scale objects. Simultaneously, it achieves an inference speed of 163.9 FPS. The experimental results show that RTS-Net can satisfy the accurate and efficient detection of ground objects by various different UAV platforms in different complex scenarios. [ABSTRACT FROM AUTHOR]

Little is known about the effect of car traffic on airborne fungal communities. We investigated the environmental factors affecting the diversity and concentration of airborne fungi at high-traffic density junctions, in Tianjin, China. A total of 244 fungal strains belonging to 78 species and 45 genera of Ascomycota (78.69%) and Basidiomycota (21.31%) were isolated and identified using morphological and molecular analysis. Aspergillus was the species-richest genus, with 9 recorded species, followed by Alternaria and Cladosporium, both with 8 species. Coprinellus radians was the most abundant fungal species, with 31 isolated strains, followed by Alternaria alternata (26 strains), Cladosporium cladosporioides (21), Alternaria compacta (13), and Cladosporium tenuissimum (11). We found a higher diversity and concentration of airborne fungi in the analysed urban air environments when the road traffic was at its highest intensity. Higher level of car traffic resulted in higher concentrations of fungal particles in the air for various taxa, including Alternaria, Aspergillus, and Cladosporium, which are known to cause respiratory allergies and infections. This result suggests that reducing vehicular traffic could be an effective measure to control airborne fungal exposure and microbial pollution. [ABSTRACT FROM AUTHOR]

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

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

Transportation resilience, as a component of city sustainability, plays a crucial role in the daily management and emergency response of urban road systems. With coastal cities becoming increasingly vulnerable to typhoons, rainstorms, and other disasters, it is essential to assess the resilience of urban road transportation in a refined and differentiated approach. Existing resilience assessment methods often overlook significant biases, neglecting the dynamic response of road traffic and non-stationary characteristics of traffic systems. To address these limitations, we develop a quantitative resilience assessment method for urban road transportation during rainfall that is based on the improved Resilience Triangle. The method is applied to DiDi urban traffic speed and meteorological data of Shenzhen, China, from April to September 2018, with a focus on Typhoon Mangkhut as an extreme weather case. By analyzing transportation resilience variations across road densities, road hierarchies, and rainfall scenarios, we found that road densities and rainfall intensities explain resilience variations better than road hierarchies. Specifically, as accumulative precipitation exceeds 100 mm, a substantial surge in loss of performance is observed. Typhoon rainfalls result in a greater loss in urban road traffic compared to general rainfalls. The results offer valuable insights for urban road planning, traffic emergency management, and transportation resilience construction in the face of increasingly severe weather challenges. [ABSTRACT FROM AUTHOR]

Consumer electronics (CE) companies have the potential to significantly contribute to the advancement of autonomous vehicles and their accompanying technology by providing security, connectivity, and efficiency. The Consumer Autonomous Vehicles market is set for significant growth, driven by growing awareness and implementation of sustainable practices using computing technologies for traffic flow optimization in smart cities. Businesses are concentrating more on eco-friendly solutions, using AI, communication networks, and sensors for autonomous city navigation, giving safer and more efficient mobility solutions in response to growing environmental concerns. Object detection is a crucial element of autonomous vehicles and complex systems, which enables them to observe and react to their surroundings in real-time. Multiple autonomous vehicles employ deep learning (DL) for detection and deploy specific sensor arrays custom-made to their use case or environment. DL processes sensory data for autonomous vehicles, enabling data-driven decisions on environmental reactions and obstacle recognition. This paper projects a Galactical Swarm Fractals Optimizer with DL-Enabled Object Detection for Autonomous Vehicles (GSODL-OOAV) model in Smart Cities. The presented GSODL-OOAV model enables the object identification for autonomous vehicles properly. To accomplish this, the GSODL-OOAV model initially employs a RetinaNet object detector to detect the objects effectively. Besides, the long short-term memory ensemble (BLSTME) technique was exploited to allot proper classes to the detected objects. A hyperparameter tuning procedure utilizing the GSO model is employed to enhance the classification efficiency of the BLSTME approach. The experimentation validation of the GSODL-OOAV technique is verified using the BDD100K database. The comparative study of the GSODL-OOAV approach illustrated a superior accuracy outcome of 99.06% over present innovative approaches. [ABSTRACT FROM AUTHOR]

The major advances in intelligent transportation systems are pushing societal services toward autonomy where road management is to be more agile in order to cope with changes and continue to yield optimal performance. However, the pedestrian experience is not sufficiently considered. Particularly, signalized intersections are expected to be popular if not dominant in urban settings where pedestrian density is high. This paper presents the design of a novel environment for simulating human motion on signalized crosswalks at a fine-grained level. Such a simulation not only captures typical behavior, but also handles cases where large pedestrian groups cross from both directions. The proposed simulator is instrumental for optimized road configuration management where the pedestrians' quality of experience, for example, waiting time, is factored in. The validation results using field data show that an accuracy of 98.37% can be obtained for the estimated crossing time. Other results using synthetic data show that our simulator enables optimized traffic light scheduling that diminishes pedestrians' waiting time without sacrificing vehicular throughput. [ABSTRACT FROM AUTHOR]

This research explores the efficacy of the YOLOR (You Only Learn One Representation) algorithm integrated with the Deep Sort algorithm for real-time vehicle detection, classification, and counting in Morelia, Mexico. The study aims to enhance traffic monitoring and management by leveraging advanced deep learning techniques. The methodology involves deploying the YOLOR model at six key monitoring stations, with varying confidence levels and pre-trained weights, to evaluate its performance across diverse traffic conditions. The results demonstrate that the model is effective compared to other approaches in classifying multiple vehicle types. The combination of YOLOR and Deep Sort proves effective in tracking vehicles and distinguishing between different types, providing valuable data for optimizing traffic flow and infrastructure planning. This innovative approach offers a scalable and precise solution for intelligent traffic management, setting new methodologies for urban traffic monitoring systems. [ABSTRACT FROM AUTHOR]