Traffic Routing Research Articles

TPTrans: Vessel Trajectory Prediction Model Based on Transformer Using AIS Data

The analysis of large amounts of vessel trajectory data can facilitate more complex traffic management and route planning, thereby reducing the risk of accidents. The application of deep learning methods in vessel trajectory prediction is becoming more and more widespread; however, due to the complexity of the marine environment, including the influence of geographical environmental factors, weather factors, and real-time traffic conditions, predicting trajectories in less constrained maritime areas is more challenging than in path network conditions. Ship trajectory prediction methods based on kinematic formulas work well in ideal conditions but struggle with real-world complexities. Machine learning methods avoid kinematic formulas but fail to fully leverage complex data due to their simple structure. Deep learning methods, which do not require preset formulas, still face challenges in achieving high-precision and long-term predictions, particularly with complex ship movements and heterogeneous data. This study introduces an innovative model based on the transformer structure to predict the trajectory of a vessel. First, by processing the raw AIS (Automatic Identification System) data, we provide the model with a more efficient input format and data that are both more representative and concise. Secondly, we combine convolutional layers with the transformer structure, using convolutional neural networks to extract local spatiotemporal features in sequences. The encoder and decoder structure of the traditional transformer structure is retained by us. The attention mechanism is used to extract the global spatiotemporal features of sequences. Finally, the model is trained and tested using publicly available AIS data. The prediction results on the field data show that the model can predict trajectories including straight lines and turns under the field data of complex terrain, and in terms of prediction accuracy, our model can reduce the mean squared error by at least 6×10−4 compared with the baseline model.

The Correlation of the Smart City Concept with the Costs of Toxic Exhaust Gas Emissions Based on the Analysis of a Selected Population of Motor Vehicles in Urban Traffic

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.

Potential of Selected Species of Spiraea L. for Phytoremediation of Heavy Metals from Soils of Urban Areas

Four taxa of Spiraea were selected for this study: S. × cinerea Zabel ‘Grefsheim’, S. nipponica Maxim. ‘Snowmound’, S. splendens É. N. Baumann ex K. Koch and S. × vanhouttei (Briot) Carrière growing for a minimum of 5 years along heavily trafficked traffic routes. This study included the genus Spiraea due to its popularity in horticultural practice (commercial availability, widespread in urban environments). In addition, the use of ornamental shrubs for phytoremediation in urban green spaces effectively combines the aesthetic needs of residents with those of caring for the urban environment. This study was conducted in Poznań (population 550,000, the fifth largest city in Poland). Soils and foliage were examined in spring and autumn. Soil pH and specific electrolytic conductivity (EC) were determined. The content of micronutrients (Cu, Fe, Mn, Ni, Zn) and toxic heavy metals (Cd, Cr, Pb) in soil dry matter and leaves was determined. The uptake capacity of bioavailable forms of heavy metals by Spiraea from the soil was analyzed by determining the bioconcentration factor (BCF). It was found that the studied taxa meet the basic requirements for plants used for soil phytoremediation processes, especially for chromium phytoextraction. The degree of salinity of the tested soils did not pose a threat to the shrubs growing there, and most of the sites, despite the alkaline reaction, are suitable for their cultivation. S. × cinerea and S. × vanhouttei have BCFs for lead <1. The remaining taxa are characterized by strong concentrations of all analyzed elements. A particularly high BCF, above 10, was recorded for chromium and high for manganese and nickel.

What Makes a Liveable Neighborhood? Role of Socio-Demographic, Dwelling, and Environmental Factors and Participation in Finnish Urban and Suburban Areas.

Quality of residential area, or neighborhood, is one determinant of overall quality of life and associated with health and health behavior. Ensuring that urban and suburban neighborhoods remain liveable and comfortable to reside in is a challenge amidst growing urbanization. Neighborhood quality has been associated with socio-demographic, dwelling-related, and subjective and objective neighborhood factors. However, only a few studies have additionally included participation in neighborhood decision-making and examined whether these associations are stronger among residents who are more settled in the neighborhood. We examined whether subjectively and objectively measured environmental factors and participation are associated with perception of neighborhood liveability and possible effect moderators. We analyzed survey data collected in 2021 among adults (aged 18-97) living in suburbs and urban centers in five Finnish cities (n = 2057; response rate 34%) with logistic regression models. Almost 80% of the respondents perceived their neighborhood as liveable or very liveable. The following factors were most strongly associated with perceiving the neighborhood as liveable: low area-level socioeconomic deprivation; central urban zone; green view from home; satisfaction with neighborhood safety, green areas, blue areas, and maintenance of traffic routes; and good or very good possibilities to influence decisions regarding the neighborhood. Only a few interactions between indicators of being more settled in the neighborhood (e.g., age group and dwelling ownership) modified these associations. In all, our study suggests that to maintain liveability, urban or community planning should consider local residents' favorable perceptions of safety, recreational areas, and engagement in decision-making.

Retraction Note: An adaptive traffic routing approach toward load balancing and congestion control in Cloud–MANET ad hoc networks

Retraction Note: An adaptive traffic routing approach toward load balancing and congestion control in Cloud–MANET ad hoc networks

Airspace Constrained Free-Flight Analysis: Implications for Uncrewed Air Traffic Management

This paper provides a study of free-flight air traffic behaviour in increasingly constrained airspace environments. Traffic assumes three different free-flight operational constructs with airspace constraints considered as restricted (no-fly) regions. Simulations combine path planning and Monte Carlo techniques to qualitatively analyse emergent traffic behaviour and quantitatively assess spatial–temporal airspace conflict as the airspace constraints vary. Findings indicate that airspace constraints have a much stronger influence on aircraft behaviour than the free-flight operational construct, with any benefits of free flight rapidly diminishing as the airspace becomes more constrained. We conclude that structured traffic route (or network) designs and associated risk modelling approaches should be considered for safe and efficient traffic management of highly constrained and congested (or dense) airspace. This work therefore provides evidence to inform new airspace design and management initiatives, including low-altitude uncrewed traffic.

The Spatial Geographies of Women Urban Runners

ABSTRACT Taking the northern suburbs of Melbourne, Australia, as a case study, we researched the running experiences of women through a feminist geography lens. Interviews with runners informed the design and interpretation of a spatial analysis of women’s running patterns in Strava Metro data. Our study showed how women runners are limited in where they run, and illuminated features of urban environments that can contribute to gendered limitations running women experience. Our approach took us beyond critiques of park management (e.g. lighting, toilets, trail surface), to eventually form findings about how urban form influences the spatial geographies of women runners. HIGHLIGHTS Women restrict where they run alone more than men do because of fear of being attacked by a man, avoiding isolated places in particular. Running is an activity in which women can feel at their most vulnerable in urban public space, as they are often alone, outside beyond daylight hours, and are seeking soft surfaced paths away from motor traffic, places where real-life passive surveillance is usually lower. Gendered differences in the use of urban space by runners can be observed in Strava Metro data. A key feature of women’s running route decision-making is that women will avoid and seek certain places based on how many other people they expect to be present there. Women runners benefit from urban forms which encourage high pedestrian activity, including medium-high urban density and pedestrian prioritised links to greenspaces that are separated from key traffic routes.

Validation of Noise Barrier Insertion Loss Simulation in Reducing Road Traffic Noise by Site Measurement Data

A noise barrier serves as a method to control noise pollution resulting from road traffic, particularly prevalent in rural areas near heavy traffic routes such as highways. This study focuses on simulating the sound Insertion Loss (IL) of existing noise barriers designed to mitigate road traffic noise around school areas. The primary goal is to assess the precision of the Finite Element Modeling (FEM) program. Utilizing the finite element analysis software, COMSOL, the study models the noise barrier and its surroundings in two-dimensional acoustic radiation problem based on on-site dimensions and parameters. By examining the often-neglected aspect of noise leakage and its impact on noise reduction, the research aims to provide specific guidance for urban planners and architects to optimize noise mitigation in various urban settings. The study reveals a strong agreement between the simulated IL and on-site measurements, with a minimal difference ranging from 0.4 dB to 1.1 dBA on average. Additionally, the research suggests potential improvements to simulation accuracy through adjustments in aperture sizes. The findings demonstrate that finite element simulation can be a valuable tool for designers to assess the effectiveness of noise barriers before construction begins.

Exploring the Relationship between Data Sample Size and Traffic Flow Prediction Accuracy

Efficiently extracting and analyzing large urban traffic data, accurately predicting traffic conditions, and improving urban traffic management require careful selection of an appropriate data sample size. The suitable size of data sample assumes paramount importance in fostering sustainable transportation development. This paper investigates the relationship between traffic flow prediction performance and data sample size, considering data sample missing rates, duration, and road segment coverage. Real traffic flow data from 13 road sections in Changsha, China, are analyzed using the Decision Tree, Support Vector Machine, Gaussian Process Regression and Artificial Neural Network models. Some key findings include: Lower data sample loss rates improve prediction accuracy by capturing traffic flow patterns effectively, while higher loss rates decrease accuracy; an optimal data sample duration of around 7 days balances prediction accuracy and data stability, with longer durations providing more historical data but risking complexity; Broader road segment coverage gives a more comprehensive traffic flow information, but excessive coverage introduces noise and impacts the improvement of prediction accuracy. The results highlight the significant impact of data sample size on prediction performance. Enhancing reliability can be achieved by reducing data loss, selecting suitable durations, and considering appropriate road segment coverage, supporting improved traffic management and route planning.

Deep Learning based Road Traffic Assessment for Vehicle Rerouting: An Extensive Experimental Study of RetinaNet and YOLO Models

Currently, there is a notable prevalence of substantial traffic congestion and frequent vehicular accidents on roadways in contemporary times. Amalgamation of latest front-line technologies involving Internet of Things (IoT) and image classification has immense potential to advance the progress of a proficient traffic regulation system. To mitigate the occurrence of vehicular accidents, our research endeavors revolve around the comprehensive analysis of the prevailing road conditions. This meticulous examination allows us to effectively automate traffic routes orchestration, ensuring smooth vehicular movement across all lanes of the road network. The analysis of traffic patterns is conducted through the utilization of visual data images. The real time captured traffic images undergo processing using various object detection models named RetinaNet and the YOLO (You Only Look Once) models. A series of comparative evaluations suggests an improved traffic object identification capacity for the RetinaNet model as compared to all YOLO models.

Dominance of Artificial Intelligence and Machine Learning Algorithms in Real-Time Traffic Flow prediction and Route Optimization in Autonomous Vehicles

The evolution of autonomous vehicles has elicited significant interest in understanding how real-time data may be used to provide enhanced driving experiences. This research project explored AI and Machine Learning methodologies applied for traffic forecasting and route optimization, and their implications for autonomous vehicles and urban mobility. For this project, the road traffic flow Dataset was utilized from Kaggle, containing 48,000 records of the flow of traffic, each including the following key features. In our work, we deployed some credible and well-established machine learning models: linear regression and random forest. These algorithms were separately trained by using a part of preprocessed data. The MSE for the Random Forest model was significantly lower, which means that the Random Forest Regressor had much smaller errors in estimating the volume of traffic compared to the Linear Regression model. The Random Forest Model had a high R² score, proving that this model explains a great deal of variance in the volume of traffic. This means that the 'model of random forest regressors' excellently fitted the data, snatching most of the important patterns and relationships between input features and target variables.

Enhanced building energy harvesting through integrated piezoelectric materials and smart road traffic routing

Enhanced building energy harvesting through integrated piezoelectric materials and smart road traffic routing

Model and Methods of Traffic Routing in a Communication Network Using UAVs

Relevance. The development of 5G networks and subsequent generations is accompanied by the development of new services, in particular, virtual, augmented reality services, as well as telepresence, as well as radio access networks. In particular, there is an increase in operating frequencies, which poses additional challenges for organizing a network that can meet the requirements for the quality of traffic service from new services and ensure the availability of communication to users. These problems can be solved by various methods of placing access points, including using UAVs. This approach ensures the efficiency of construction and flexibility of the access network structure, but also requires the use of methods for placing access points in relation to users and other elements of the communication network. Problem statement: development of methods for placing routers in a UAV swarm and selecting traffic routes when organizing an access network, in order to improve the efficiency of the communication network. Purpose of the work: improving the efficiency of building an access network using UAVs through the development of clustering methods and distributing routers in a UAV swarm. Methods. The studies were carried out using the provisions of information theory, mathematical optimization methods, graph theory methods and clustering methods. The numerical results were obtained using the numerical simulation method in Python. Result. The developed model and methods allow for the distribution of network routers (access points) located on UAVs taking into account the quality of service and ensuring the construction of a connected mesh network and its connection with the mobile network, which can be used in both modern and future communication networks. Novelty: a modeling and methodological apparatus has been developed that allows for increasing the efficiency of building wireless access networks using UAVs, in particular, allowing for selecting the placement positions of routers in a UAV swarm and the logical structure of the network. The developed modeling and methodological apparatus solves the problem of traffic routing taking into account the quality of its service. Practical significance: the proposed model and methods can be used to organize service in 5G networks and subsequent generations. In particular, they allow for ensuring the availability of communication and the efficiency of network organization in cases of insufficient coverage, as well as in cases of failure of individual network elements. The ability to unload traffic to a local network allows for improving the quality of traffic service in the operator's network.

Holistic life cycle cost analysis of road bridges with non-metallic reinforcement

Corrosion-related damage due to exposure to environmental conditions is the main cause of costs in the maintenance of transport infrastructure. Because of its high corrosion resistance and the associated higher durability, non-metallic reinforcement offers great potential for preventing such damage, thus reducing maintenance costs. In this article, potential savings for an integral road bridge are studied through a holistic life cycle cost (LCC) analysis considering four different reinforcement materials (steel, glass, basalt, carbon). A fair comparison is enabled by a material-specific design, the calculation of maintenance and user costs and the consideration of the respective disposal scenario. Moreover, environmental costs are recognised by carbon pricing based on life cycle analysis (LCA). The influence of individual parameters is quantified by means of a sensitivity analysis and the probability of savings is studied by Monte Carlo simulation. It is shown that higher investment costs for non-metallic reinforcement can be compensated by lower user costs. This is mainly due to shorter maintenance periods, as less time is required for repair action, whereby potential savings in user costs are particularly evident if the traffic route below the bridge is not disrupted. It is concluded that even from today's perspective, the use of glass and basalt fibre-reinforced polymer (FRP) reinforcement in highway bridges with average traffic volumes very likely offers an economic advantage over corrosion-prone reinforcing steel.

Urban air quality comparison on bike and driving routes: a case study for UK

Air quality in cities is very important in both tackling climate change and promoting healthy sustainable urban living. Understanding the air quality around cities has been a challenge faced by local authorities around the world. With the new net zero targets in place, government and charitable organisations across the UK have been trying hard to both monitor and control air pollutants to influence citizen behavior towards environmentally friendly living. This study looks at urban air quality in two UK cities with the idea of understanding air quality and its relation to the mode of transport, health and well-being. This case study compares the urban air quality as Particulate Matter (PM 10 and PM 2.5) and gaseous pollutants (NO 2) on the bike and motor driving routes in Cambridge and Colchester. The study designed experiments and data collection campaigns to understand the factors leading to air quality fluctuations in urban traffic routes. The resulting measurements show that NO 2 concentrations are higher in motor driving routes and in certain locations on the bike routes that are part of (or very close to) the motor route. The PM concentrations are generally higher near parks and open greenspaces but can be argued to be within the recommended limits. Finally, this paper proposes steps to improve the urban infrastructure to tackle emissions and spread awareness among the citizens.

고구려의 압록강 하류 진출과 방어체계 운용

The downstream of the Yalu River(鴨綠江) was a geographical location halfway between Liaodong(遼東) and the Korean Peninsula(韓半島). However, in the early days of Goguryeo(⾼句 麗)'s founding, the downstream area of the Yalu River and its center, West Anpyeong(⻄安平), were within the influence of the Han(漢). Therefore, it was necessary for Goguryeo, located in the middle and upper reaches of the Yalu River, to secure control over the downstream of the Yalu River in order to advance into the Yalu River and the Korean Peninsula. Accordingly, Goguryeo fought with the Han to claim sovereignty over this area. Meanwhile, when Goguryeo occupied West Anpyeong in 312, it was able to advance into this area in earnest, and the defense system for this area began to be established around the late 4th century. At this time, the main base in the downstream of the Yalu River was Daehaengseong(⼤⾏城). It is located in Dandong City(丹東) as a long flatland castle. It is presumed that the Daehangseong Fortress was a castle where a Rucho(婁肖) under the Bakjakseong (泊灼城) was dispatched to which Cheoryeogeunji(處閭近⽀) was dispatched. The defense system centered on Ogolseong(烏骨城), where Yoksal(褥薩) were dispatched from the downstream of the Yalu River, played a key role, and the defense system centered on Bakjakseong seems to have supplemented this. In addition, although a defense system centered on Dabeorakseong(多伐嶽城) existed in the downstream of the Yalu River, it is believed that it was not widely used compared to the defense system centered on Ogolseong and Bakjakseong. Like these Goguryeo built a defense system for the downstream of the Yalu River, centering on the land traffic route leading from Liaodong to the Yalu River. Meanwhile, in the case of the North Pyongan(平北) area connected to the Yaluchaek(鴨綠柵), a defense system was established centering on Yogiseong(辱夷城) located near the south of the Cheongcheon River(淸川江).

Distributed optimization for multi-commodity urban traffic control

A distributed method for concurrent traffic signal and routing control of traffic networks is proposed. The method is based on the multi-commodity store-and-forward model, in which the destinations are the commodities. The system benefits from the communication between vehicles and infrastructure, providing optimal signal timings to intersections and routes to vehicles on a link-by-link basis. Using the augmented Lagrangian to model the constraints into the objective, the baseline centralized problem is decomposed into a set of objective-coupled subproblems, one for each intersection, enabling the solution to be computed by a distributed-gradient projection algorithm. The intersection agents only need to communicate and coordinate with neighboring intersections to ensure convergence to the optimal solution while tolerating suboptimal iterations that offer more flexibility, unlike other distributed approaches. Through microsimulation, we demonstrate the effectiveness of the proposed algorithm in traffic networks with time-varying demand. Computational analysis shows that the distributed problem is suitable for real-time applications. A robustness analysis show that the distributed formulation enables a graceful degradation of the system in case of failure.

Carbon emissions from urban takeaway delivery in China

Online food delivery has become a popular mode of urban food consumption in China as its underlying business mechanism, Online To Offline (O2O), gaining popularity. However, the environmental impacts of a rapidly expanding online food delivery industry and its potential to mitigate environmental burdens remained unexplored in China. Our research found that Chinese cities generated 1.67 MtCO2-equivalent (CO2e) from 13.07 billion times of deliveries in 2019, including transport and packaging. The transportation-related GHG emissions were 745 KtCO2e in 2019, with an average of 0.057 kg CO2e per order and an average of 0.011 kg CO2e per capita. These emissions have surged from 0.31 MtCO2e in 2014 to 2.74 MtCO2e in 2021. We predict that this figure will increase further to 5.94 MtCO2e by 2035. However, with a range of policies such as replacing motorcycles with electric bikes and optimizing traffic routes, it is possible to mitigate such GHG emissions by 4.39–10.97 MtCO2e between 2023 and 2035. These findings highlight the need for further research into the environmental impact of online food delivery and the potential for mitigating it.

Disentangling drivers of vertebrate roadkill in a protected area in the Amazon rainforest

AbstractVertebrates' roadkill impacts many species in natural areas. However, data on this topic in the Amazon domain are still scarce, especially within protected areas. In this study, we analysed data from 2 years of monitoring amphibians, reptiles, mammals and birds' roadkill on five types of terrestrial transport infrastructure in a protected area in the Brazilian Amazon. We tested which vertebrate classes and feeding guilds were most recorded, the influence of seasonality and the spatial pattern of roadkill. In the studied area, 2795 roadkills were recorded, with amphibians suffering the most casualties. Most of the roadkills occurred during the rainy season (57%). Correcting the roadkill data using observer efficiency and carcass persistence time data, we concluded that mortality rates might be underestimated by up to 40 times when compared to raw data. There was no significant difference between feeding guilds concerning the number of fatalities, and rainfall and temperature significantly affected the roadkill pattern of all vertebrate classes. The spatial pattern of roadkill varied between traffic routes and vertebrate class types. The results indicate that amphibians are the most common victims of roadkill, although they are neglected in many roadkill monitoring. Consequently, mitigation strategies should prioritize periods with the highest mortality rates, as rainfall and temperature influence the frequency of these accidents. In addition, different classes of vertebrates have different patterns of impact concentration along traffic routes, which makes mitigation planning more complex. Therefore, planning to reduce vertebrate deaths from roadkill must consider each taxon's particularity and seasonality.

The Development of a Prototype Solution for Detecting Wear and Tear in Pedestrian Crossings

Crosswalks play a fundamental role in road safety. However, over time, many suffer wear and tear that makes them difficult to see. This project presents a solution based on the use of computer vision techniques for identifying and classifying the level of wear on crosswalks. The proposed system uses a convolutional neural network (CNN) to analyze images of crosswalks, determining their wear status. The design includes a prototype system mounted on a vehicle, equipped with cameras and processing units to collect and analyze data in real time as the vehicle traverses traffic routes. The collected data are then transmitted to a web application for further analysis and reporting. The prototype was validated through extensive tests in a real urban environment, comparing its assessments with manual inspections conducted by experts. Results from these tests showed that the system could accurately classify crosswalk wear with a high degree of accuracy, demonstrating its potential for aiding maintenance authorities in efficiently prioritizing interventions.