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Buses and subways are crucial modes of transportation for residents, yet frequent disturbances pose serious challenges to their daily commutes. To tackle these disruptions and boost the stability of the transportation network, it is vital to accurately measure the resilience of a bus–subway composite network under such events. Therefore, this study utilizes the generalized cost between stations as weights with which to construct a bus–subway weighted composite network. Subsequently, three indicators, namely reachability, path importance, and weighted coreness, are proposed to evaluate the significance of the nodes, thereby combining the improved CRITIC-TOPSIS method to identify the critical nodes. Then, deliberate attacks and preferential restorations are conducted on the nodes, considering their importance and the critical nodes sequences, respectively. Finally, network resilience changes are characterized by the network connectivity coefficient and global accessibility, and the network resilience is compared under different attack and recovery strategies. The research results indicate that resilience is lowest when using reachability sequences to attack and recover the network. The network's recovery is most significant when using the critical nodes sequences. When 70% of the nodes are restored, the network's performance is essentially fully recovered. Additionally, the resilience of a bus–subway network is higher than that of a single bus network. This study applies the generalized cost to weight the transportation network, and considers the impact of multiple factors on the ease of connectivity between the nodes, which facilitates the accurate measurement of the resilience of a bus–subway network and enhances the ability to cope with disruptions. [ABSTRACT FROM AUTHOR]

City-wide near-miss data can be beneficial for traffic safety estimation. In this study, we evaluate urban traffic safety and examine spatial patterns by incorporating city-wide near-miss data (59,277 near-misses). Our methodology employs a grid-based method, the Empirical Bayes (EB) approach, and spatial analysis tools including global Moran's I and local Moran's I. The study findings reveal that near-misses have the strongest correlation with observed crash frequency among all the variables studied. Interestingly, the ratio of near-misses to crashes is roughly estimated to be 1957:1, providing a potentially useful benchmark for urban areas. For other variables, an increased number of intersections and bus stops, along with a greater road length, contribute to a higher crash frequency. Conversely, residential and open-space land use rates show a negative correlation with crash frequency. Through spatial analysis, potential risk hotspots including roads linking bridges and tunnels, and avenues bustling with pedestrian activity, are highlighted. The study also identified negative local spatial correlations in crash frequencies, suggesting significant safety risk variations within relatively short distances. By mapping the differences between observed and predicted crash frequencies, we identified specific grid areas with unexpectedly high or low crash frequencies. These findings highlight the crucial role of near-miss data in urban traffic safety policy and planning, particularly relevant with the imminent rise of autonomous and connected vehicles. By integrating near-miss data into safety estimations, we can develop a more comprehensive understanding of traffic safety and, thus, more effectively address urban traffic risks. [ABSTRACT FROM AUTHOR]

Optimizing travel routes in urban transportation networks is essential for enhancing traffic efficiency in major cities. This study focuses on optimizing the combined subway and taxi travel routes from Beijing Union University to Beijing South Railway Station, a critical route for students and staff in Beijing. To address this, we propose three genetic algorithms: the Standard Genetic Algorithm (SGA), the Multipoint Crossover and Elitist Selection Genetic Algorithm (MPEGA), and the Improved Dynamic Crossover and Mutation Rate Genetic Algorithm (IDCMGA). Experimental results indicate that MPEGA reduces the mean travel cost by 15.21%, the variance by 81.72%, and the standard deviation by 57.25% compared to SGA. Additionally, IDCMGA reduces the average by 1.76%, the variance by 63.16%, and the standard deviation by 39.30% compared to MPEGA. The improved IDCMGA algorithm demonstrates significant advantages over the other two algorithms in terms of global optimization capability, convergence speed, and stability, and is more adept at adapting to new traffic conditions to identify the optimal route. Optimizing this route not only reduces commuting time and costs but also alleviates traffic congestion, thereby enhancing the overall efficiency of the urban transportation system. [ABSTRACT FROM AUTHOR]

In the realm of Intelligent Transportation Systems (ITS), vehicular communication technologies such as Dedicated Short-Range Communications (DSRC), Cellular Vehicle-to-Everything (C-V2X), and LoRa 2.4 GHz play crucial roles in enhancing road safety, reducing traffic congestion, and improving transport efficiency. This article explores the integration of these communication protocols within smart intersections, emphasizing their capabilities and synergies. DSRC, based on IEEE 802.11p, provides reliable short-range communication with data rates up to 27 Mbps and latencies below 50 ms, ideal for real-time safety applications. C-V2X leverages LTE and 5G networks, offering broader coverage up to 10 km and supporting data rates up to 100 Mbps, with latencies as low as 20 ms in direct communication mode (PC5). LoRa 2.4 GHz, known for its long-range (up to 15 km in rural areas, 1–2 km in urban settings) and low-power characteristics, offers data rates between 0.3 and 37.5 kbps, suitable for non-critical data exchange and infrastructure monitoring. The study evaluates the performance and interoperability of these technologies in urban environments, focusing on data latency, transmission reliability, and scalability. Experimental results from simulated and real-world scenarios show that DSRC maintains reliable communication within 1 km with minimal interference. C-V2X demonstrates superior scalability and coverage, maintaining robust communication over several kilometers in high-density urban settings. LoRa 2.4 GHz exhibits excellent penetration through urban obstacles, maintaining connectivity and efficient data transmission with packet error rates below 10%. [ABSTRACT FROM AUTHOR]

In the context of contemporary urbanization, the significance of diversifying urban public facilities has attracted significant attention. This study examines the relationship between road network and the diversity of urban public facilities in Changsha City. These factors are measured through the Shannon-Weiner Index and Space Syntax method. To provide greater specificity, the study employs Random Forest and Geographically Weighted Regression models to analyze the relationship between road networks and the diversity of urban public facilities. The results identify a nonlinear relationship between these variables. In addition, high accessibility exhibits a stronger association with diversity than accessibility alone, and this correlation is reflected in varying degrees of inconsistency across different study areas. Finally, the cartographic depiction of diversity clusters is overlaid on the road network, demonstrating a significant relationship between the configuration of the road network and diversity patterns. In conclusion, this research emphasizes the robust correlation between the roadway network and the diversity of urban public facilities. It prompts local governments to focus beyond mere equality, with a greater commitment to enhancing the quality of life for residents through diversity. [ABSTRACT FROM AUTHOR]

This study examines the issue of traffic organization on sections of city streets, in which there are public transport stop zones of different configurations (with a drive-in pocket and along a traffic lane). Accordingly, the object of research is the parameters of traffic flows; the subject is the regularities in their change on such specific sections of the street-road network, taking into account the configuration of stops, the number of traffic lanes, and priority conditions for the movement of public transport. The task addressed in this work was the quantitative assessment of the efficiency of traffic management in public transport stop zones. It was also important to compare different configurations of street segments in terms of their impact on delays of private and public transport. As a result of the research, the speed and time characteristics of the traffic flow and their dependence on its composition and intensity for different number and specialization of traffic lanes, as well as the location of the public transport stop, were determined. The resulting quantitative indicators of delays of all types of transport for each modeling option made it possible to determine the best of them according to the criterion of minimal time loss. This applies to both private and public transport, as the values of delays may differ from each other by 10–50 %. An applied aspect related to using the scientific result is the possibility of justifying the number of traffic lanes, or their allocation for public transport, as well as determining the appropriate configuration of stops. This constitutes the prerequisites for the transfer of the scientific results to the relevant interested structures in the field of transport, which are responsible for the organization and safety of traffic under urban conditions. [ABSTRACT FROM AUTHOR]

Signalized intersections that are pretimed are commonly used in rapidly developing cities with diverse traffic patterns, including those found in Indonesia. These intersections are a common source of congestion and delays in road networks, particularly when there is non-lane based (NLB) traffic in urban areas. Accurate estimation of both the base saturation flow rate and capacity is essential for this type of facility, as an error in the prediction of the base saturation flow rate can result in significant bias in capacity evaluation and design at signalized intersections. The estimation of capacity at signalized intersections is critical for ensuring optimal signal timings, minimizing delay, and reducing congestion. Heterogeneous traffic, which refers to the presence of various types of vehicles with distinct static and dynamic characteristics, is a common phenomenon. To address this issue, this paper presents a modeling approach for the SFR that takes into account heterogeneous traffic and NLB movements. Indonesia, being an archipelago with 34 provinces, served as the focus of this study, which specifically concentrated on Banda Aceh, the capital province of Aceh province. Employing primary and observed data collected at a specific, predetermined signalized timing, this study aimed to investigate the impact of intersection geometry and heterogeneous traffic composition on the SFR. By adopting the modeling approach for NLB movements, the study formulated the BSFR model. To estimate the scale parameters of the BSFR, the multiple linear regression approach was utilized. The analysis results reveal that the existing BSFR based on the IHCM formula gives a substantially biased estimation because the PCEs from the Indonesian Highway Capacity Manual (IHCM) are underestimated. This source of error could be partially due to the heterogeneous (varied vehicle composition) traffic flow with NLB movements, unlike that observed under the prevailing conditions of IHCM 1997. The empirical results show that the existing IHCM should be improved to avoid overestimation, particularly for effective approach width (We) greater than 4.5 m. A comparison of the BSFR prediction model between IHCM's PCEs and new PCEs shows that the BSFR is predicted more accurately in the latter case. This finding demonstrates that the existing IHCM can be adjusted in two ways: adjusting PCEs or calibrating the BSFR formula. The proposed models can also enhance the accuracy of BSFR prediction, leading to better signalized intersection capacity estimation, providing support for designing traffic operation, alleviating traffic congestion, and reducing congestion delay within the city. [ABSTRACT FROM AUTHOR]

The consequences of sustainable and greener mobility have been demonstrated previous year by the pandemic situation. One significant benefit was to reduce the level of noise, dust and air pollution from the traffic in many European countries due to reduced mobility most of the population. Positive phenomena was also recorded in the Slovak Republic. Today's urban mobility offers new, more sustainable ways of transport for residents, especially app-based and shared-ride services, which have become increasingly widespread and very popular. Undoubtedly offer a level of convenience unseen before in the urban mobility systems throughout the world. Although new forms of transport are developed in most Slovak cities, their operation is not very rational and therefore individual car transport still dominates at the expense of sustainable ways of transport. In our paper, we analyze the traffic situation in the city of Nitra, as the large number of trips in the course of the peak-hours often results in severe traffic congestion. Multimodality is one of the solutions to improve the current traffic situation, as it allows urban residents to choose from a range of alternative travel options. The main aim of this paper is to determinate the possibilities of multimodal urban mobility in the city of Nitra in addition to analyze how the potential change in the variability of choice of means of transport affects the urban mobility behaviour. In order to meet stated objectives, a marketing research was conducted. The research findings point to the fact that the current urban infrastructure and overall possibilities are limited, the consequence of which is that travel behaviour of citizens does not demonstrate the features of sustainable urban mobility. Our results point to major differences in attitudes of Nitra´s residents from different urban and sub-urban areas. [ABSTRACT FROM AUTHOR]

The intelligent prediction of urban road traffic congestion is of great significance for improving traffic efficiency, reducing congestion, and optimizing resource allocation, thereby improving the overall mobility and quality of life of the city. In order to overcome the shortcomings of traditional prediction methods for urban road traffic congestion, such as poor prediction accuracy and long prediction time, an intelligent prediction method of urban road traffic congestion based on knowledge graph technology is proposed in this paper. Urban road data are collected in a static and dynamic way, and urban road traffic data are clustered by improved K-means clustering algorithm. Using stepwise regression method to repair urban road traffic data, combining knowledge graph technology to mine the influencing factors of urban road traffic congestion, and combining hidden Markov model to realize intelligent prediction of urban road traffic congestion. The experimental results show that the average absolute error of this method is 0.378, the mean root mean square error is 1.284, and the time formula of urban road traffic congestion intelligent prediction is below 0.32s. It prove that new the prediction method has the characteristics of high precision and high efficiency. [ABSTRACT FROM AUTHOR]

The conventional intelligent prediction methods for urban road traffic congestion suffer from drawbacks including limited prediction accuracy and prolonged prediction times. To address these issues, this paper introduces a novel approach for predicting urban road traffic accident losses, leveraging a variable weight TOPSIS method in conjunction with a modified entropy weight scheme. By refining the entropy weight calculation to determine the significance of factors influencing urban road traffic accident losses, and integrating this with the variable weight TOPSIS method, we achieve effective screening of these factors. Subsequently, utilizing these identified factors and the XGBoost algorithm, we conduct predictions of urban road traffic accident losses. Experimental results demonstrate that our method attains a maximum precision of 98.67% in selecting influential factors, and a peak prediction accuracy of 98.26% for urban road traffic accident losses. Furthermore, the prediction time ranges efficiently between 0.29s and 0.71s, underlining the reliability and practicality of our proposed method. [ABSTRACT FROM AUTHOR]

Accurate analysis of the influencing factors of traffic accidents helps identify safety hazards and bottlenecks in the transportation system, and proposes corresponding improvement measures and suggestions based on the analysis results. To address the challenges of limited mining and analysis accuracy, coupled with prolonged analysis durations, inherent in conventional approaches to dissecting the factors influencing urban road traffic accidents, this study introduces a novel analysis methodology grounded in weighted association rules. This innovative approach not only enhances our understanding of accident causation but also contributes to safer road networks and improved traffic management strategies. By leveraging weighted association rules to mine urban road traffic accident data, we initially extract valuable insights. Subsequently, principal component analysis is employed to streamline the data's dimensionality, facilitating a more focused and efficient analysis. Within the framework of Grey correlation analysis, we meticulously establish comparison and reference sequences, calculating both the weighted Grey correlation degree and weight coefficient of these sequences to rigorously screen factors. This meticulous process culminates in a comprehensive analysis of the factors influencing urban road traffic accidents. Experimental results underscore the efficacy of this new method with a peak data mining accuracy of 97.4%, an unparalleled analysis accuracy of 97.8%, and a streamlined analysis time of merely 1.36 seconds. [ABSTRACT FROM AUTHOR]

In this paper, a discrete model of delays at oversaturated intersections was established by using the graphic analysis method and a reward function. The problem of optimizing signal control at an oversaturated intersection was transformed into a Markov decision process by utilizing queue length as the state variable and choice of green ratio as the action. Following this, a self-learning method for signal optimization (SLSO) at oversaturated intersections based on deep learning (dueling Q-leaming, Dueling DQN) with priority experience replay (PER) was proposed. Moreover, dynamic learning rate and dynamic e-greedy strategy were used to accelerate convergence and achieve better model optimization. Constraints and penalties on queue length were set to avoid traffic overflow. The results of comparative simulations showed that the traditional method yielded the longest delay while the two-stage bang-bang control method led to the shortest delay, with a reduction in the delay of about 13.3%. The proposed SLSO could reduce the delay by about 12.3% and the maximum queue length by 1.7% without constraints on queuing, and by 5.1% and 13%, respectively, with constraints on queuing in place. Finally, a real-world 4-phase intersection was selected to prove that the proposed SLSO method is widely applicable. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the word recognition effects of the use of all-uppercase (e.g., VALENCIA) or titled-case (e.g., Valencia) for city names in traffic signs, controlling for word size, and comparing stationary and dynamic viewing situations. Background: Prior studies provide mixed evidence regarding the effects of word case on the recognition of city names in traffic signs. Moreover, the evidence on the potential impact of visual motion on these effects is scarce. Method: We carried out an experimental study using simulated traffic signs. The task was to indicate, for each sign, whether it contained a given city name or not (word search task, 50% positive trials). Visual motion of signs was manipulated as a between-participants factor: stationary (the sign was still) versus dynamic (the sign expanded as if the participant was approaching to it). Word case was manipulated as a within-participants factor: all-uppercase versus two titled-case conditions varying in font size: width-matched titled-case and point size-matched titled-case. Results: In both the stationary and dynamic conditions, all-uppercase resulted in more incorrect responses and slower latencies than width-matched titled-case. When compared to point size-matched titled-case, all-uppercase produced slower correct responses in the stationary condition, whereas faster in the dynamic condition. Conclusion: Other factors being equal, all-uppercase city names will be recognized worse than their titled-case versions in traffic signs, both in stationary and dynamic situations. Application: Results in the current experimental study would be of interest in the design of traffic signs and other circumstances in which text is presented in motion. [ABSTRACT FROM AUTHOR]

Increasing urban traffic density in today's living conditions causes noise pollution. Noise is a serious source of pollution for the entire ecosystem, especially for humans. Within the scope of this study, the noise level caused by traffic in Iskenderun district center was determined and its distribution over area uses was determined. For this purpose, noise measurements were made at 36 points on urban road routes in Iskenderun district center on weekdays and weekends in the summer and winter months of 2022. The measurement results were transferred to the ArcMap 10.0 program and noise pollution distribution maps were created with the kriging interpolation method. According to the noise values classified according to the Regulation on the Evaluation and Management of Environmental Noise, the pollution levels were determined in residential areas, industrial and commercial areas, agricultural areas, parks and green areas, Iskenderun Technical University center. It was calculated to distribute it to a total of eight usage areas: campus, port, roads and water surface. As a result of the study, the period when noise pollution is highest is summer weekdays and the least is summer weekend evening hours. [ABSTRACT FROM AUTHOR]

The management of transportation infrastructure, particularly urban road networks, faces significant challenges due to limited financial resources. To overcome this hurdle, many have turned to public-private partnerships (PPPs) to enhance the economic value of transportation infrastructure outputs. However, monetizing the value of urban road networks and their surrounding areas remains a complex issue. Urban road networks are complex and vast systems, requiring a comprehensive approach to assess their true value accurately. This study proposes a novel model aimed at monetizing the assets of urban road networks. Unlike previous approaches that solely focus on physical values, our methodology also considers social values in relation to the performance, and spatial importance of urban road networks. The model employs the asset replacement cost as the foundational component and incorporates unquantifiable factors that contribute to economic externalities. Additionally, it takes into account the physical condition of the roads, allowing for the depreciation of road assets. Furthermore, we investigate the impact of social values on roads, a crucial aspect often overlooked in existing studies. This comprehensive approach was demonstrated through a case study in a city in China, where a section of the urban road network was selected for evaluation. The results showcased the model’s effectiveness in evaluating the assets of each link in the urban road network. Moreover, it revealed the evolution pattern based on the spatial distribution of various assets and their influence on the overall asset valuation of the road network. The proposed methodology also provides a theoretical foundation to transition from road infrastructure management to asset management. Beyond its academic contributions, our model offers valuable insights for decision-makers, enabling investment analyses and trade-offs in PPPs aimed at enhancing road network assets. By utilizing this approach, stakeholders can make informed decisions to optimize the economic benefits of urban road infrastructure and ensure sustainable transportation systems for the future. [ABSTRACT FROM AUTHOR]

Objective: Media centralism and the lack of attention to the preferences and demands of local audiences is an obvious reality that must be faced by those who live outside large cities and capitals, understanding that there are audiences that demand local content, but that must settle for a programming produced from and for the capital, even more so when it comes to the Chilean television system. This article characterizes the tensions of local audiences in northern Chile in relation to their local television, also called proximity. Materials and methods: The study carried out a qualitative methodology, since it seeks to understand human behavior in a given territory and historical moment. The data collection was carried out using the technique of focus groups in order to explain and deepen the situations of people as local audiences and their feelings and thoughts on various problems. The focus groups discussed their local television and their link with national television considering the informational centralism and political centralism within the Coquimbo Region. The sample corresponds to the audience of cities belonging to urban and rural communes of the Coquimbo Region, in Chile, where there are local television media and that have declared their intention to migrate to digital television. The cities that are part of the study are: Andacollo, La Serena, Los Vilos, Illapel and Salamanca. For recruitment, the snowball method was used, with a total of 52 participants. All the participants were over 18 years of age with a residence of more than 10 years in the city. There was gender balance. Six focus groups were held in neighborhood offices in each city. The sessions were recorded and later transcribed, being systematized using the Atlas.ti software, version 9. Resulted: The results are described through three categories of analysis, regarding the opinions of the audiences: 1) Local information 2) National television: positive and negative aspects and 3) Information centralism and media demands. 1) Local information as a center of interest: Audiences place local news among their preferences. In each focus group, the informative genre of these media was highly valued and required. It represents a fundamental demand when evaluating local television positively. 2) Link with national television coverage: Along with the preference for local news, there is also an unfavorable opinion towards national news and towards the journalistic routines of the capital television stations whose coverage privileges the events that occurred in the Metropolitan Region in the information guidelines, often falling into sensationalism, especially when it comes to police court facts. 3) Information centralism and media demands: in the opinion of the participants, the tensions between the local and the capital are not only appreciated at the country level, but also between regional capitals, provinces and communes, following the political-administrative division of Chile. Conclusions: Regarding the characteristics of local television consumption, a first conclusion was the confirmation of the interest and consumption of local audiences in content that addresses issues related to their environment, an issue that is in line with previous studies on communication and local television. Similar to national television consumption, local news represented the preferred content. If there is a fire or a traffic accident in the city, audiences want to see it on their local television and, hopefully, live or as instantly as possible. This is informative content that does not compete in any case with national television, as it deals with local news events. Unless something out of the ordinary happens, whose connotation is national and in this case -the fewest times-the city, province or region makes news and appears nationally. A very important link was detected between local audiences with their own territories. The link was verified through two factors: a) the expectations of seeing local content not only on themes about their cities, but also from geographically broader places, such as communes, provinces or the Coquimbo Region itself; b) a large part of the social demands that underlie the opinions of the audiences surround feelings of belonging, desire for decentralization and local development. The need for representation of local audiences is verified, that is, seeing their territories on the screen, feeling reflected on national TV through their cultural, territorial, geographical, touristic, or other particularities is the cause that is associated Local (non-capital) audiences are part of a marginalized sector from a political and economic point of view. In this sense, there is a glimpse of a disconnection between the political/public world that promotes actions that should benefit the community, but that fail to reach potential users in a good way. In the same line of discussion, social demands for access to public information that benefits them and brings them closer to local political actors are detected. Know and understand the territorial public policies that result in a better quality of life. These social/political demands are intermingled with the media needs that could be channeled into more spaces to debate, talk and express their opinions to participate in the local public debate. [ABSTRACT FROM AUTHOR]

INTRODUCTION: This manuscript aims to provide a synopsis of the current situation surrounding noise exposure at work and the risk of noise-induced hearing loss (NIHL) in Malaysia. NIHL is an occupational illness that affects most many people and can be prevented in many countries in the world. The production and agriculture businesses and the extraction of natural resources and urban traffic all contribute to ambient noise levels. Working in a noisy environment is the main occupational risk factor for adult-onset hearing loss. In this study, we investigate the prevalence of NIHL across different industries, as well as research publication trends on NIHL. MATERIAL AND METHODS: Conducted thorough several online databases, like Science Direct, Web of Science, and Google Scholar, for related research that had already been published. The review phases were carried out according to the recommendations laid out by the preferred reporting items for systematic reviews. A set of criteria was developed for selecting relevant studies. RESULTS: The prevalence of NIHL among workers in Malaysia, varies across different industries and has been reported to range from 5.0% to 82.6%. NIHL has multiple contributing factors, including noise intensity, long-term exposure to daily noise, hearing protection devices and age. A review of these studies found varying percentages of individuals affected by each contributing factor. CONCLUSION: Occupational NIHL is a preventable health risk for workers across industries. Employers must adopt engineering and administrative controls (education and training) and provide personal hearing protectors to protect workers from noise. [ABSTRACT FROM AUTHOR]

Transit crashes are a serious social problem for any country, with a significant loss of human lives and economic consequences that are difficult to quantify. This article proposes a characterization of the transit crash rate for urban road safety using time series. A quantitative descriptive study was conducted, characterizing the variables of each crash extracted from the National Traffic Agency of Ecuador (NTA); the data were processed at a descriptive and predictive level for the city of Guayaquil. The first step was an exploration of the scientific interest of the topic with the processing of bibliographic data taken from Scopus and Web of Science articles. Among the results obtained, there is a growing trend of research related to the evaluation of traffic crash through applied statistics. Every day, approximately 155 people die as a result of a traffic crash. In addition, traffic crashes are analyzed based on three indicators: number of crashes, injuries and onsite fatalities. Finally, an adequate performance is found, with very few differences in the forecast of incidents using three times series models, autoregressive integrated moving average (ARIMA). It is expected that this study will be valuable for data analysts and decision makers at the security level to reduce human losses related to these events in urban cities with similar characteristics to the analyzed cases. [ABSTRACT FROM AUTHOR]

This paper bridges the gap between the literature on the pandemic's effects on mobility and the literature on the impact of low emission zones (LEZ). Using data for large European cities in the period 2018–2021, we examine whether LEZs may explain differences in the recovery patterns of traffic in European cities after the shock of Covid. Controlling for several city attributes, we examine whether LEZ cities are less congested before and after the pandemic in comparison to non-LEZ cities. LEZs may have been more effective in reducing congestion after the pandemic because the fleet renewal process has slowed down or, alternatively, LEZs may be a proxy of unobservable factors related with attitudes of governments and citizens toward a sustainable mobility. Our results validate the traffic-mitigating role of the LEZ after the Covid-19 pandemic, although such result only holds for the pioneering LEZ cities. Hence, the traffic-mitigating role of the LEZ after the Covid-19 pandemic seems to be related to unobservable attributes that influenced the early decision to implement a LEZ. In this regard, we also find that LEZs may have induced a change in local attributes related to sustainable mobility given that we do not find differences between LEZs decided at the local or regional level. [ABSTRACT FROM AUTHOR]

• Traffic climate affect safety driving. • Psychological factors play mediating roles. In the research field of safe driving behavior, the emergence of prosocial driving research complements the more traditional dangerous driving research. In recent years, studies on the relationship between the urban traffic climate and prosocial driving have even been taken as a new direction for further improving urban traffic safety based on the construction of traffic safety facilities and the development of traffic safety regulations. The mechanism by which urban traffic climate affects prosocial driving is explored in this study. From a dual research perspective, i.e., a variable-centered and person-centered perspective, a theoretical model of prosocial driving is constructed based on the following three levels: environmental, psychological and behavioral. According to the results, First, rational, affective and moral factors at the individual psychological level play mediating roles between urban traffic climate at the environmental level and prosocial driving behavior at the behavioral level. As indicated by the results of a mediating pathway analysis, rational, affective and moral factors are intricately linked by five complex mediating paths at the psychological level. Second, there is a differentiated environmental-psychological activation mechanism between individuals' prosocial driving and aggressive driving. Third, urban traffic climate consists of a variety of typical safety climate types, and individuals have diverse psychological tendencies regarding safe driving in traffic climates of varying safety levels. The study concludes with a systematic discussion of its theoretical contributions and practical value. [ABSTRACT FROM AUTHOR]

• Attitudes of different VRU Groups towards AVs in urban traffic were studied. • Interaction with AVs and AVs in mixed traffic with CVs was explored. • Some VRUs are concerned that mixed traffic could lead to more aggression from human drivers and make traffic participation more complex. • VRUs share similar evaluations, intentions, and AV requirements in urban traffic. • VRUs want AV identification, explicit communication, and strict traffic rules. In today's urban traffic, vulnerable road users (VRUs) have a somewhat ambivalent relationship with human drivers. The introduction of automated vehicles (AVs) that are no longer controlled by such human drivers could impact this relationship, both in positive and negative ways. Five focus groups (pedestrians, cyclists, e-scooter riders, individuals with walking disabilities, and older adults) were conducted to investigate how different VRUs evaluate the participation of AVs in urban traffic and whether they hold specific requirements or concerns. The discussions focused on exploring potential interactions with AVs and the effects of mixed traffic scenarios (encompassing AVs and conventional vehicles (CVs)) on the well-being of VRUs in urban traffic. The results revealed that the concerns and expectations about AVs in urban traffic were quite similar among the various VRU groups. Regarding their interaction with AVs, participants expressed advantages (e.g., eliminating human errors) and disadvantages (e.g., lack of communication possibilities). In the context of mixed traffic, especially cyclists and individuals with walking disabilities feared aggressive behavior from CVs taking advantage of rule-abiding AVs. They also saw the risk of increasing complexity in mixed urban traffic, as AVs are likely to behave differently from CVs. In addition to the increased caution described by most VRUs, a few participants could imagine taking advantage of the defensive driving style of AVs. The cyclist group discussed the possibility of sabotaging AVs to make a general statement against cars in urban traffic. Furthermore, essential requirements were gathered for vehicle manufacturers (e.g., external Human-Machine Interfaces) and policies (e.g., stringent regulation enforcement for car traffic in the city) that could enable successful coexistence between VRUs and AVs in urban traffic. [ABSTRACT FROM AUTHOR]

The First Egongyan Bridge in Chongqing, China was opened to traffic on December 27, 2000. It carries six-lanes of city traffic and two pedestrian paths, one on each side. It is a 600 m span suspension bridge. It was planned that the two pedestrian paths might eventually be converted to transit tracks in the future. However, with the rapid increase in urban traffic, the deck was transformed into an 8-lane bridge instead. Therefore, a new bridge parallel and adjacent to the old bridge was built for the rail transit. For aesthetic reasons, the city decided that the new bridge should also be a suspension bridge, with the same span length and tower height as the first bridge. The First Egongyan Bridge was a true suspension bridge with main cables anchored to ground anchors. However, the new bridge bridge's proximity to the first bridge necessitated the construction of a self-anchored suspension bridge that did away with a second set of ground anchors. This 600 m span happened to be the world's longest span for a self-anchored suspension bridge. [ABSTRACT FROM AUTHOR]

This study explores the significant role of three-wheelers (3W) on urban road traffic, focusing on the reduction in average vehicle speeds and its consequential impact on the Level of Service (LOS) of the road. Data collected from various traffic volumes reveal significant speed reductions ranging from 4 % to 35 % due to the movement of 3Ws on the road. To model this phenomenon, Artificial Neural Network (ANN) is employed. The resulting reductions in speed, induced by the 3W, trigger LOS degradation, leading to congestion and delays. A noteworthy finding is that the traffic flow naturally gravitates towards the LOS C, underscoring the substantial role played by the 3W in diminishing the overall LOS of the road. This research offers critical insights into the dynamics of urban traffic influenced by the 3W, providing a valuable foundation for traffic management and urban planning efforts. [ABSTRACT FROM AUTHOR]

The development of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs), and autonomous driving (AD) has progressed rapidly in recent years, driven by artificial intelligence (AI), the internet of things (IoT), and their integration with dedicated short-range communications (DSRC) systems and fifth-generation (5G) networks. This has led to improved mobility conditions in different road propagation environments: urban, suburban, rural, and highway. The use of these communication technologies has enabled drivers and pedestrians to be more aware of the need to improve their behavior and decision making in adverse traffic conditions by sharing information from cameras, radars, and sensors widely deployed in vehicles and road infrastructure. However, wireless data transmission in VANETs is affected by the specific conditions of the propagation environment, weather, terrain, traffic density, and frequency bands used. In this paper, we characterize the path loss based on the extensive measurement campaign carrier out in vehicular environments at 700 MHz and 5.9 GHz under realistic road traffic conditions. From a linear dual-slope path loss propagation model, the results of the path loss exponents and the standard deviations of the shadowing are reported. This study focused on three different environments, i.e., urban with high traffic density (U-HD), urban with moderate/low traffic density (U-LD), and suburban (SU). The results presented here can be easily incorporated into VANET simulators to develop, evaluate, and validate new protocols and system architecture configurations under more realistic propagation conditions. [ABSTRACT FROM AUTHOR]

With the continuous advancement of the economy and technology, the number of cars continues to increase, and the traffic congestion problem on some key roads is becoming increasingly serious. This paper proposes a new vehicle information feature map (VIFM) method and a multi-branch convolutional neural network (MBCNN) model and applies it to the problem of traffic congestion detection based on camera image data. The aim of this study is to build a deep learning model with traffic images as input and congestion detection results as output. It aims to provide a new method for automatic detection of traffic congestion. The deep learning-based method in this article can effectively utilize the existing massive camera network in the transportation system without requiring too much investment in hardware. This study first uses an object detection model to identify vehicles in images. Then, a method for extracting a VIFM is proposed. Finally, a traffic congestion detection model based on MBCNN is constructed. This paper verifies the application effect of this method in the Chinese City Traffic Image Database (CCTRIB). Compared to other convolutional neural networks, other deep learning models, and baseline models, the method proposed in this paper yields superior results. The method in this article obtained an F1 score of 98.61% and an accuracy of 98.62%. Experimental results show that this method effectively solves the problem of traffic congestion detection and provides a powerful tool for traffic management. [ABSTRACT FROM AUTHOR]

This paper addresses the diversified needs of urban cold chain distribution and proposes innovative solutions based on storage type multi-temperature co-distribution and mechanical type multitemperature co-distribution modes. We present an electric vehicle path optimization model aimed at minimizing total costs, taking into account time-varying speed in accordance with urban traffic patterns. Additionally, a genetic algorithm is designed to solve the multi-temperature co-matching optimization path. The study's results reveal that the storage type multi-temperature co-distribution transport mode offers superior economic efficiency, product security, safety, and resource utilization. By comparing and analyzing the results of model solving under different battery capacities, loads, and distributions speeds, the total cost of distribution is optimal when the battery capacity is 120 kWh, the maximum load is 100 kg, and the normal driving speed is 60 km/h. The mechanical multi-temperature co-distribution mode is optimal for the total cost of distribution at a battery capacity of 100 kWh, a maximum load of 100 kg, and a normal driving speed of 50 km/h. The study aims to provide reference significance for logistics companies when making route selection. [ABSTRACT FROM AUTHOR]

Regarding alleviating urban traffic pressure and conserving environmental energy, it is essential to utilize the redundant capacity of urban rail transit during non-peak hours for passenger-freight collaborative transportation. In order to achieve the "dual carbon" goals in urban rail transit, a study has been conducted on the coordinated transportation of passengers and goods, which can adapt to the evolving carbon emission policies. A method for calculating the waiting time that allows passengers to queue for a second time has been proposed by studying the queue of passengers. A multiobjective optimization model for urban rail transit passenger-freight collaborative transportation has been constructed, with the objectives of minimizing carbon emissions and passenger waiting time, considering constraints such as carbon emission policy, train operation safety, passenger and freight loading balance, and train formation scheme. An improved nondominated sorting genetic algorithm (NSGA-II) has been designed to deal with the complexity of solving the multiobjective model. The algorithm utilizes the filtering function of the constraint conditions during the initial population generation process, effectively balancing the relationship between the filtering and time consumption of the NSGA-II algorithm. Taking the Wuxi Metro Line 3 as an example for analysis, the results show that: ① Although the average passenger waiting time increases by about 0.53 minutes in this method, the carbon emissions of the trains decrease by approximately 21.3%. Meanwhile, the average seating rate of the optimized solution is 5%~15% higher than the traditional transportation scheme used by Wuxi Metro Line 3. The superiority of urban rail transportation for passenger-freight collaborative transportation has been verified. ② With the mandatory control of carbon emission control, the passengerfreight collaborative optimization scheme is not affected by different carbon emission limits. Under the carbon tax policy, transportation schemes are less affected by different carbon tax levels. Transportation schemes remain consistent under the different limits of carbon trading policies. The proposed model and algorithm provide decision-making support for urban rail transit operators to optimize transportation plans under different carbon emission policies. [ABSTRACT FROM AUTHOR]

Urban traffic congestion, obstacle avoidance, and driving efficiency are the challenges faced by autonomous-vehicle path-planning technology in cities. The traditional artificial potential field (APF) algorithm is insufficient to meet the requirements of efficiency and safety in path planning, as it easily gets trapped in local optima when dealing with complex environments. Therefore, this paper proposes a novel AS-IAPF path-planning algorithm to more efficiently enhance the target reachability of autonomous vehicles in complex traffic environments. Firstly, this paper analyzes and elucidates the macroscopic traffic model, achieving effective modeling of dynamic traffic flow stability based on Lyapunov stability theorem and a classical 1D flow model. Thus, the threshold discriminant formula for traffic element stability is obtained. Secondly, based on the aforementioned threshold discriminant formula, a new AS-IAPF algorithm is proposed. The algorithm mainly includes two aspects: firstly, by pre-generating initial paths and introducing a Gaussian oscillation coefficient of force fields, it avoids the algorithm falling into local optima; secondly, by using the aforementioned driving stability threshold discriminant formula as a dimensional adjustment for adaptively improving and adjusting the strength coefficient of the AS-APF repulsive field, the algorithm further improves the efficiency of path planning. Finally, the algorithm is subjected to joint simulations of 2D and 3D scenarios of different types. The research results show that the AS-IAPF algorithm outperforms other algorithms of the same type with respect to comprehensive performance based on multiple 2D scenario simulation experiments. In the 3D simulation experiments of three different typical traffic scenarios, the proposed algorithm can drive autonomous vehicles to effectively perform corresponding obstacle avoidance actions based on the actual traffic scenarios ahead, ultimately achieving safe obstacle avoidance. The path-planning method proposed in this paper can enhance driving efficiency while considering the safety and stability of vehicles, providing a promising approach and reference for the path planning of autonomous vehicles. [ABSTRACT FROM AUTHOR]

Copyright of Revista Luciérnaga - Comunicación is the property of Politechnico Colombian Jaime Isaza Cadavid and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Electric tilt-wing flying cars are an efficient, economical, and environmentally friendly solution to urban traffic congestion and travel efficiency issues. This article addresses the high energy consumption and obstacle interference during the takeoff phase of the tilt-wing eVTOL (electric Vertical Takeoff and Landing), proposing a trajectory planning method based on energy minimization and obstacle avoidance. Firstly, based on the dynamics analysis, the relationship between energy consumption, spatial trajectory, and obstacles is sorted out and the decision variables for the trajectory planning problem with obstacle avoidance are determined. Secondly, based on the power discretization during the takeoff phase, the energy minimization objective function is established and the constraints of performance limitations and spatial obstacles are derived. Thirdly, by integrating the optimization model with the SLSQP (Sequential Least Squares Quadratic Programming algorithm), the second-order sequential quadratic programming model and decision variable update equations are derived, establishing the solution process for the trajectory planning problem of the tilt-wing eVTOL takeoff with obstacle avoidance. Finally, the Airbus Vahana A3 is taken as an example to verify and validate the effectiveness, stability, and robustness of the model and optimization algorithm proposed. The validation results show that the OTPEM (obstacle avoidance trajectory planning methodology based on energy minimization) can effectively handle changes in the takeoff end state and exhibits good stability and robustness in different obstacle environments. It can provide a certain reference for the three-dimensional obstacle avoidance trajectory planning of Airbus Vahana A3 and other tilt-wing eVTOL trajectory planning problems. [ABSTRACT FROM AUTHOR]

Accurate real-time prediction of pedestrian crossing intent during the autonomous driving process is crucial for ensuring the safety of both pedestrians and passengers, as well as improving riding comfort. However, existing methods for pedestrian crossing intent detection mostly rely on extracting complete pose information of pedestrians, leading to reduced accuracy when pedestrians are occluded. To address this issue, this paper proposes FHPE-Net: a lightweight, multi-branch prediction model that utilizes only the head pose features of pedestrians. In pedestrian crossing scenarios, pedestrian behavior is highly influenced by surrounding vehicles and the environment. FHPE-Net encodes pedestrian head poses and global context semantic image sequences to comprehensively capture spatiotemporal interaction features between pedestrians, vehicles, and the environment, thereby enhancing the accuracy of pedestrian crossing intent prediction. To improve the robustness of the FHPE-Net method, this study further processes bounding box positions and vehicle velocity features, making it more stable and reliable in complex traffic scenarios. Finally, a novel U-BiGRUs module is introduced for feature fusion, and an optimal fusion strategy is employed to achieve the best predictive performance in terms of F1 score and accuracy (ACC). Extensive ablation experiments are conducted on the PIE dataset, and performance analysis demonstrates that FHPE-Net achieves an accuracy of 90%, outperforming baseline methods such as PCPA and Multi-RNN, while using only pedestrian head pose features. This research holds significant guidance in enhancing traffic safety and optimizing urban traffic management. Furthermore, it provides essential technological support for advancing the commercialization of autonomous driving. [ABSTRACT FROM AUTHOR]

Developing country megacities suffer from severe road traffic congestion, yet the level of congestion is not a direct measure of equilibrium inefficiency. I study the peak‐hour traffic congestion equilibrium in Bangalore. To measure travel preferences, I use a model of departure time choice to design a field experiment with congestion pricing policies and implement it using precise GPS data. Commuter responses in the experiment reveal moderate schedule inflexibility and a high value of time. I then show that in Bangalore, traffic density has a moderate and linear impact on travel delay. My policy simulations with endogenous congestion indicate that optimal congestion charges would lead to a small reduction in travel times, and small commuter welfare gains. This result is driven primarily by the shape of the congestion externality. Overall, these results suggest limited commuter welfare benefits from peak‐spreading traffic policies in cities like Bangalore. [ABSTRACT FROM AUTHOR]

Copyright of Acta Scientiarum Naturalium Universitatis Sunyatseni / Zhongshan Daxue Xuebao is the property of Sun-Yat-Sen University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research introduces the Urban Traffic Mobility Optimization Model (UTMOM), a data-driven methodology for analyzing two distinctive urban traffic datasets through the integration of data mining and mathematical modeling. Designed to decode the complexities of urban mobility patterns, UTMOM meticulously evaluates daily traffic dynamics with a focus on reducing discrepancies and underscoring variations in traffic intensity, particularly during peak times. Our findings unveil pivotal insights into the differences across datasets, providing a substantial contribution to the realms of traffic management and urban planning. UTMOM delves into the intricacies of traffic flow variations, emphasizing the critical importance of comprehending fluctuations in traffic volume across diverse times and locations. By incorporating detailed graphical representations and statistical validations, including ANOVA analysis, our study delivers a comprehensive evaluation of UTMOM's precision in reflecting real-world traffic scenarios. These insights affirm the value of data-informed strategies in optimizing traffic flow and alleviating congestion. Positioned as a valuable asset for traffic engineers, data scientists, and urban planners, UTMOM advocates for advanced modeling techniques to improve urban mobility. Beyond enriching academic discourse on traffic analysis, UTMOM offers actionable intelligence for enhancing the efficiency and sustainability of urban transportation systems. Through this in-depth investigation, our aim is to catalyze the development of innovative solutions to traffic challenges, steering towards smoother and more sustainable urban environments. [ABSTRACT FROM AUTHOR]

In recent years, the application prospect of urban logistics unmanned aerial vehicles has attracted extensive attention. The high-density operation of UAVs requires autonomous separation maintenance capability. To achieve autonomous separation maintenance, it is necessary to conduct autonomous track prediction and formulate the required separation accordingly. Based on the target level of safety requirements for UAV operation, aiming at the autonomous separation maintenance ability of UAVs and considering the accuracy of track prediction, a method to calculate the required separation between UAVs is proposed. This study consists of two parts. Firstly, based on historical data, the position prediction error of the flight track is investigated. Using a machine learning model, a two-stage track prediction method, which involves classification followed by prediction, is proposed for urban logistics UAV track data. Subsequently, based on the track prediction error distribution, by designing a gas model and a position error probability model, a separation-formulating model for urban logistics UAVs in free flight is proposed in which UAV maneuverability is considered. By applying this model, the required separation is formulated for UAVs. When the required separation is set to 48.5 m, the overall collision risk meets the TLS requirements. The research provides a feasible method for establishing autonomous separation for urban logistics UAVs. [ABSTRACT FROM AUTHOR]

The complexity of urban traffic environments poses unique challenges for automotive radar sensors. Limited range and Doppler resolution hinder the ability to distinguish closely spaced objects and detect vulnerable road users, particularly pedestrians. Therefore, it is important to know the limits of FMCW radars. To assess these limitations, the WinProp channel simulator is used to model and simulate traffic scenarios regarding the detection of the range, velocity, and SNR of targets. To validate WinProp as a simulation tool for traffic scenarios, a real traffic scenario is configured and the simulation results compared to the measurement results in order to demonstrate the suitability of WinProp for simulating traffic scenarios and evaluating radar detection performance. The close agreement between the simulation and measurement results provides validation for WinProp's accuracy in modeling detections and signal processing within traffic scenarios. To showcase the limitations of FMCW radars, two scenarios with challenging detection conditions are simulated: an occluded pedestrian between parked vehicles and a densely populated road. A monostatic setup with a single patch antenna and a 16 × 16 patch antenna array is evaluated, using chirp bandwidths of 1 GHz and 4 GHz. The simulations have shown that although it is feasible to detect an occluded pedestrian by multipath propagation, there is still a need in improving the detection performance when there is no multipath from an occluded pedestrian. Furthermore, the results indicate that higher bandwidth improves target separation but is insufficient for occluded pedestrian detection without multipath. However, with some targets, even the range resolution at 4 GHz was not sufficient, which required a separation in the Doppler dimension, emphasizing the need for overall resolution improvement of FMCW radars. Future work should focus on developing more complex road user models and simulating a broader range of scenarios to comprehensively evaluate radar performance in road traffic environments. [ABSTRACT FROM AUTHOR]

Multidimensional sensory data prediction and resource scheduling are paramount challenges in the design of smart cities. This paper delves into the utilization of multi-objective evolutionary algorithms to enhance the accuracy and efficiency of target detection through optimized YOLO_v3 network models. By integrating the YOLO_v3 model with the K-means++ algorithm for Anchor_Box generation, the novel approach exhibits superior adaptability and flexibility, particularly in handling variable-sized feature pattern mappings. This adaptability better caters to the detection of targets of diverse sizes, thus elevating the performance and precision of target detection algorithms. To further scrutinize the YOLO-v3 joint algorithm's performance in urban traffic detection, P-R curves were plotted for various loss types on the NEU-DET dataset. Comparative analysis of these curves highlights the optimized algorithms' superiority in detecting various types of losses in urban model completeness. Additionally, practical application analysis revealed that the optimized monitoring results outperform the detection time of the original YOLO-v3_means++ network model on FP_GA. Notably, post-processing with C-FENCE can reduce average single-frame image detection time to 2.01 seconds, while convolutional degree-level fusion with the BN layer cuts it down to 2.25 seconds. In summary, the FP_GA-based YOLO-v3_means++ network algorithm offers superior detection capabilities, and the multi-objective evolutionary algorithm's optimization of the YOLO-v3 model enhances target detection performance and precision. [ABSTRACT FROM AUTHOR]

The automatic control of vehicle traffic in large urban networks constitutes one of the most serious challenges to modern societies, with an impact on improving the quality of human life and saving energy and time. Intersections are a special traffic structure of pivotal importance as they accumulate a large number of vehicles that should be served in an optimal manner. Constructing intelligent models that manage to automatically coordinate and steer vehicles through intersections is a key point in the fragmentation of traffic control, offering active solutions through the flexibility of automatically adapting to a variety of traffic conditions. Responding to this call, this work aims to propose an integrated active solution of automatic traffic management. We introduce a multi-agent reinforcement learning framework that effectively models traffic flow at individual unsignalized intersections. It relies on a compact agent definition, a rich information state space, and a learning process characterized not only by depth and quality, but also by substantial degrees of freedom and variability. The resulting driving profiles are further transferred to larger road networks to integrate their individual elements and compose an effective automatic traffic control platform. Experiments are conducted on simulated road networks of variable complexity, demonstrating the potential of the proposed method. [ABSTRACT FROM AUTHOR]

Origin-destination (OD) flow contains population mobility information between every two regions in the city, which is of great value in urban planning and transportation management. Nevertheless, the collection of OD flow data is extremely difficult due to the hindrance of privacy issues and collection costs. Significant efforts have been made to generate OD flow based on urban regional features, e.g., demographics, land use, and so on, since spatial heterogeneity of urban function is the primary cause that drives people to move from one place to another. On the other hand, people travel through various routes between OD, which will have effects on urban traffic, e.g., road travel speed and time. These effects of OD flows reveal the fine-grained spatiotemporal patterns of population mobility. Few works have explored the effectiveness of incorporating urban traffic information into OD generation. To bridge this gap, we propose to generate real-world daily temporal OD flows enhanced by urban traffic information in this paper. Our model consists of two modules: Urban2OD and OD2Traffic. In the Urban2OD module, we devise a spatiotemporal graph neural network to model the complex dependencies between daily temporal OD flows and regional features. In the OD2Traffic module, we introduce an attention-based neural network to predict urban traffic based on OD flow from the Urban2OD module. Then, by utilizing gradient backpropagation, these two modules are able to enhance each other to generate high-quality OD flow data. Extensive experiments conducted on real-world datasets demonstrate the superiority of our proposed model over the state of the art. [ABSTRACT FROM AUTHOR]

The burgeoning demand for efficient urban traffic management necessitates accurate prediction of traffic congestion, spotlighting the essence of time series data analysis. This paper delves into the utilization of sophisticated deep learning methodologies, particularly long short-term memory (LSTM) networks, convolutional neural networks (CNN), and their amalgamations like Conv-LSTM and bidirectional-LSTM (Bi-LSTM), to elevate the precision of traffic pattern forecasting. These techniques showcase promise in encapsulating the intricate dynamics of traffic flow, yet their efficacy hinges upon the quality of input data, emphasizing the pivotal role of data preprocessing. This study meticulously investigates diverse preprocessing techniques encompassing normalization, transformation, outlier detection, and feature engineering. Its discerning implementation significantly heightens the performance of deep learning models. By synthesizing advanced deep learning architectures with varied preprocessing methodologies, this research presents invaluable insights fostering enhanced accuracy and reliability in traffic prediction. The innovative RD-LSTM approach introduced herein harnesses the hybridization of a reverse AutoEncoder and LSTM models, marking a novel contribution to the field. The implementation of these progressive strategies within urban traffic management portends substantial enhancements in efficiency and congestion mitigation. Ultimately, these advancements pave the way for a superior urban experience, enriching the quality of life within cities through optimized traffic management systems. [ABSTRACT FROM AUTHOR]

One of the focal points in the field of intelligent transportation is the intelligent control of traffic signals (TS), aimed at enhancing the efficiency of urban road networks through specific algorithms. Deep Reinforcement Learning (DRL) algorithms have become mainstream, yet they suffer from inefficient training sample selection, leading to slow convergence. Additionally, enhancing model robustness is crucial for adapting to diverse traffic conditions. Hence, this paper proposes an enhanced method for traffic signal control (TSC) based on DRL. This approach utilizes dueling network and double q-learning to alleviate the overestimation issue of DRL. Additionally, it introduces a priority sampling mechanism to enhance the utilization efficiency of samples in memory. Moreover, noise parameters are integrated into the neural network model during training to bolster its robustness. By representing high-dimensional real-time traffic information as matrices, and employing a phase-cycled action space to guide the decision-making of intelligent agents. Additionally, utilizing a reward function that closely mirrors real-world scenarios to guide model training. Experimental results demonstrate faster convergence and optimal performance in metrics such as queue length and waiting time. Testing experiments further validate the method's robustness across different traffic flow scenarios. [ABSTRACT FROM AUTHOR]

The enhancement of flexibility, energy efficiency, and environmental friendliness constitutes a widely acknowledged trend in the development of urban infrastructure. The proliferation of various types of transportation vehicles exacerbates the complexity of traffic regulation. Intelligent transportation systems, leveraging real-time traffic status prediction technologies, such as velocity estimation, emerge as viable solutions for the efficacious management and control of urban road networks. The objective of this project is to address the complex task of increasing accuracy in predicting traffic conditions on a big scale using deep learning techniques. To accomplish the objective of the study, the historical traffic data of Paris and Istanbul within a certain timeframe were used, considering the impact of variables such as speed, traffic volume, and direction. Specifically, traffic movie clips based on 2 years of real-world data for the two cities were utilized. The movies were generated with HERE data derived from over 100 billion GPS (Global Positioning System) probe points collected from a substantial fleet of automobiles. The model presented by us, unlike the majority of previous ones, takes into account the cumulative impact of speed, flow, and direction. The developed model showed better results compared to the well-known models, in particular, in comparison with the SR-ResNet model. The pixel-wise MAE (mean absolute error) values for Paris and Istanbul were 4.299 and 3.884 respectively, compared to 4.551 and 3.993 for SR-ResNET. Thus, the created model demonstrated the possibilities for further enhancing the accuracy and efficacy of intelligent transportation systems, particularly in large urban centres, thereby facilitating heightened safety, energy efficiency, and convenience for road users. The obtained results will be useful for local policymakers responsible for infrastructure development planning, as well as for specialists and researchers in the field. Future research should investigate how to incorporate more sources of information, in particular previous information from physical traffic flow models, information about weather conditions, etc. into the deep learning framework, as well as further increasing of the throughput capacity and reducing processing time. [ABSTRACT FROM AUTHOR]

This study proposes a novel spatiotemporal crowdsensing and caching (SCAC) framework to address the surging demands of urban wireless network traffic. In the context of rampant urbanization and ubiquitous digitization in cities, effective data traffic management is crucial for maintaining a dynamic urban ecosystem. Leveraging user mobility patterns and content preferences, this study formulates an offloading policy to alleviate congestion across urban areas. Our approach uses an AI-based method at the cell level, providing a practical and scalable solution that can be readily adapted to bustling metropolitan areas. The implementation of our model demonstrated its effectiveness in reflecting real-world urban dynamics, resulting in significant reductions in peak-hour traffic and robust performance across diverse urban settings. The deployment strategy initiates from densely populated transportation hubs, gradually expanding to broader urban areas. This systematic expansion adheres to a policy framework that emphasizes data privacy and sustainable urban development, ensuring alignment with societal needs and regulatory frameworks. By addressing technological efficacy and societal impact, this study enhances the understanding of urban wireless traffic management. It offers mobile network operators, policymakers, and urban planners a comprehensive strategy to harness the potential of spatiotemporal technology, thereby ensuring that cities remain dynamic, efficient, and well-prepared for the future of digital connectivity. [ABSTRACT FROM AUTHOR]

The growing urban population and traffic congestion underline the importance of building pedestrian-friendly environments to encourage walking as a preferred mode of transportation. However, a major challenge remains, which is the absence of such pedestrian-friendly walking environments. Identifying locations and routes with high pedestrian concentration is critical for improving pedestrian-friendly walking environments. This paper presents a quantitative method to map pedestrian walking behavior by utilizing real-time data from mobile phone sensors, focusing on the University of Moratuwa, Sri Lanka, as a case study. This holistic method integrates new urban data, such as location-based service (LBS) positioning data, and data clustering with unsupervised machine learning techniques. This study focused on the following three criteria for quantifying walking behavior: walking speed, walking time, and walking direction inside the experimental research context. A novel signal processing method has been used to evaluate speed signals, resulting in the identification of 622 speed clusters using K-means clustering techniques during specific morning and evening hours. This project uses mobile GPS signals and machine learning algorithms to track and classify pedestrian walking activity in crucial sites and routes, potentially improving urban walking through mapping. [ABSTRACT FROM AUTHOR]

The article presents an analysis of the relationship between the price of fuel and diesel oil and traffic intensity in the city. First, an analysis of fuel prices was prepared over fourteen months and the number of passenger cars and trucks per day was examined at the same time. From the results obtained, the highest, lowest, and average values were obtained. On this basis, it was noticed that when the price of fuel was the highest, there were fewer passenger cars, but when the price dropped, the number of vehicles increased. Another conclusion from the analysis is that when the price of fuel dropped even more than the average, there were no more cars. Based on the analysis, it was noticed that the number of vehicles may vary by up to 8000 passenger vehicles per day within one intersection. Then, a microsimulation model was performed in the PTV Vissim program to check the amount of pollution generated by vehicles in three variants: the highest, lowest, and average traffic intensities. The results show that the average daily CO pollution at the moment of the lowest traffic intensity is 15,000 g lower than the average, so the high price of fuel causes much less pollution for the consumer. [ABSTRACT FROM AUTHOR]

Nowadays, most people choose to commute in their own private vehicles rather than via public transport, which leads to a high volume of private vehicles on the road. Every road has traffic congestion as a result. In this kind of situation, it is impossible to restrict how much each person uses their private vehicle, but we can control traffic flow so that it doesn't ease traffic congestion. The conventional method of traffic management is only effective when there is less traffic; if there is an increase in the number of cars on one side of the road relative to the other, this method is ineffective. Therefore, our goal is to revamp the traffic signal system to use dynamic signal switching instead of static switching, which can handle and monitor signals in real time. Numerous initiatives are being developed to introduce Intelligent Transport Systems and transform the existing city transport system into a "smart system." A lot of effort is being made to create a traffic signal monitoring system that can monitor traffic signals instantly, meaning that rather than using a preset switching time, the number of vehicles on either side of the road will determine when the traffic light switches on. In regular traffic situations, the vehicle detection will determine the optimal time for the signal to switch on with high accuracy. This procedure has the potential to relieve traffic congestion more quickly and efficiently. [ABSTRACT FROM AUTHOR]

Volatile organic compounds (VOCs) significantly impact air quality and atmospheric chemistry, influencing ozone formation and secondary organic aerosol production. Despite their importance, the uncertainties associated with representing VOCs in atmospheric emission inventories are considerable. This work presents a spatiotemporal assessment and evaluation of benzene, toluene, and xylene (BTX) emissions and concentrations in Spain by combining bottom-up emissions, air quality modelling techniques, and ground-based observations. The emissions produced by High-Elective Resolution Modelling Emission System (HERMESv3) were used as input to the Multiscale Online Nonhydrostatic AtmospheRe CHemistry (MONARCH) chemical transport model to simulate surface concentrations across Spain. Comparing modelled and observed levels revealed uncertainty in the anthropogenic emissions, which were further explored through sensitivity tests. The largest levels of observed benzene and xylene were found in industrial sites near coke ovens, refineries, and car manufacturing facilities, where the modelling results show large underestimations. Official emissions reported for these facilities were replaced by alternative estimates, resulting in varied improvements in the model's performance across different stations. However, uncertainties associated with industrial emission processes persist, emphasising the need for further refinement. For toluene, consistent overestimations in background stations were mainly related to uncertainties in the spatial disaggregation of emissions from industrial-use solvent activities, mainly wood paint applications. Observed benzene levels in Barcelona's urban traffic areas were 5 times larger than the ones observed in Madrid. MONARCH failed to reproduce the observed gradient between the two cities due to uncertainties arising from estimating emissions from motorcycles and mopeds, as well as from different measurement methods and the model's capacity to accurately simulate meteorological conditions. Our results are constrained by the spatial and temporal coverage of available BTX observations, posing a key challenge in evaluating the spatial distribution of modelled levels and associated emissions. [ABSTRACT FROM AUTHOR]

The spatial heterogeneity and temporal variability of traffic in urban environments make traffic emissions inference challenging. To address this challenge, this study introduces a novel geographical context-based approach utilizing high-resolution taxi GPS data, incorporating multidimensional contextual factors such as road data, points of interest (POI), weather data, and population density. The proposed method can enhance the precision of traffic emissions inference compared to conventional macroscopic estimation techniques. To overcome the issue of missing data in traffic emissions inference from taxi data, three ensemble machine learning algorithms—Random Forest, Gradient Boosting Decision Trees (GBDT), and eXtreme Gradient Boosting (XGBoost)—are employed. These algorithms efficiently handle a substantial volume of taxi GPS data, achieving reduced computational time and model complexity. The proposed framework establishes localized models for each road segment, taking into consideration both geographical and external features that characterize the urban environment. This localized modeling contributes significantly to a more profound understanding of traffic dynamics. A thorough comparative analysis is conducted to assess the performance of the proposed method. Results indicate that incorporating multidimensional urban features is advantageous for traffic speed inference. Among the ensemble learning models, Random Forest outperforms others when dealing with a small missing rate or limited sample size, while XGBoost exhibits superior performance for larger missing rates or substantial sample sizes. Additionally, an analysis of the feature importance in traffic speed highlights that road network features are the most significant factors, followed by temporal characteristics, spatial attributes, POI data, and weather information. Finally, leveraging inferred traffic speed and volume information, emissions from large-scale urban road traffic are inferred based on the COPERT model. In contrast to methods relying on complex, multi-source data for emission estimation, our approach utilizes simple and easily accessible data, enabling precise estimation of emissions on a large-scale spatiotemporal basis. [ABSTRACT FROM AUTHOR]

This article presents an innovative IoT-enabled single-camera speed sensor designed for smart city applications. The research encompasses the development of both hardware and software components, focusing on a computer vision and artificial neural network-based system. A novel aspect of this study is the implementation of a distance-measuring algorithm that eliminates the need for expensive LIDAR sensors traditionally used in speed cameras. Instead, the system relies on convolutional neural networks (CNN) and computer vision algorithms to estimate vehicle speed accurately. Field testing was conducted in real conditions over several months, generating sufficient data to assess the device's ability to function under various adverse conditions. The results demonstrate the system's capability to perform vehicle speed detection consistently across diverse conditions, showcasing its potential as a scalable solution for urban traffic management. This makes the proposed system not only more affordable but also simpler to deploy and maintain, thereby enhancing its suitability for widespread use in smart city environments. [ABSTRACT FROM AUTHOR]