Traffic Conditions Research Articles

C 3 -GAN+: C omplex- C ondition- C ontrolled G enerative A dversarial N etworks with Enhanced Embedding

Given historical traffic distributions and associated urban conditions observed in a city, the conditional urban traffic estimation problem aims at estimating realistic future projections of the traffic under a set of new urban conditions, e.g. , new bus routes, rainfall intensity and travel demands. The problem is important in reducing traffic congestion, improving public transportation efficiency, and facilitating urban planning. However, solving this problem is challenging due to the strong spatial dependencies of traffic patterns and the complex relations between the traffic and urban conditions. Recently, we proposed a C omplex- C ondition- C ontrolled G enerative A dversarial N etwork ( \(C^{3}\) -GAN), which tackles both of the challenges and solves the urban traffic estimation problem under various complex conditions by adding a fixed embedding network and an inference network on top of the standard conditional GAN model. The randomly chosen embedding network transforms the complex conditions to latent vectors, and the inference network enhances the connections between the embedded vectors and the traffic data. However, a randomly chosen embedding network cannot always successfully extract features of complex urban conditions, which indicates \(C^{3}\) -GAN is unable to uniquely map different urban conditions to proper latent distributions. Thus, \(C^{3}\) -GAN would fail in certain traffic estimation tasks. Besides, \(C^{3}\) -GAN is hard to train due to vanishing gradients and mode collapse problems. To address these issues, in this paper, we extend our prior work by introducing a new deep generative model, namely, \(C^{3}\) -GAN+, which significantly improves the estimation performance and model stability. \(C^{3}\) -GAN+ has new objective, architecture and training algorithm. The new objective applies Wasserstein loss to the conditional generation case to encourage stable training. Shared convolutional layers between the discriminator and the inference network help to capture spatial dependencies of traffic more efficiently, part of the shared convolutional layers are used to update the embedding network periodically aiming to encourage good representation and avoid model divergence. Extensive experiments on real-world datasets demonstrate that our \(C^{3}\) -GAN+ produces high-quality traffic estimations and outperforms state-of-the-art baseline methods.

Carbon emission of urban vehicles based on carbon emission factor correlation analysis

The CO2 emission factor is the basis for analyzing vehicle CO2 emissions. This study establishes a correlation model between the fuel CO2 emission factor and the mileage-based CO2 emission factor using fuel consumption data, then analyzes the fuel consumption and CO2 emission situation of vehicles in Beijing with the established models. The main research conclusions are as follows: The proposed correlation models are effective for analyzing urban vehicle CO2 emissions. Cars can only meet the national standard limit when traveling at an average speed of 42.17 km/h. During rush hours, fuel consumption in most cities in China exceeds national standards, making it urgent to improve urban traffic efficiency. Due to the decline in urban traffic conditions in Beijing in 2023 (average speed decreased from 29.14 km/h to 24.26 km/h), each passenger car consumes an average of 0.282 L more gasoline per day, emitting an additional 619.87 g of CO2. This study is of great significance for energy conservation and emission reduction in road transportation.

Research on Urban Road Traffic Flow Prediction Based on Hybrid CNN-LSTM Model

Long-term traffic flow forecasting is crucial for intelligent transportation systems and also helps in the dynamic management of traffic flow. The goal of this research is to estimate future road traffic conditions using neural network models to reduce road congestion. Reducing prediction error is the most difficult task in traffic prediction. To predict traffic flow, this paper uses traffic data from Paris, France road dataset with normalized preprocessing of time series data. In this work, a novel CNN-LSTM hybrid model is built using MATLAB to predict traffic flow. The preprocessed data is used to train the CNN model, LSTM model and hybrid CNN-LSTM model. Finally, the predicted traffic flow data is evaluated using three evaluation indicators: coefficient of determination (R2), mean absolute error (MAE) and root mean square error (RMSE). The results show that the proposed hybrid CNN-LSTM model can well capture and learn the long-term temporal characteristics of traffic flow and can predict traffic flow quickly and effectively.

Geometric Design and Safety Outcomes of Roundabouts in the Canadian Context: A Case Study in the Region of Waterloo

Road safety remains a critical concern in Canada and worldwide. In response, roundabouts have emerged as a safer intersection design under certain road, traffic and environmental conditions, with extensive research highlighting the critical role of geometric design in their safety effect. However, such geometric effects remain relatively unexplored in Canada, where roundabouts are a recent addition. Moreover, it is unknown whether the underlying relationship varies over time due to the evolution in driver behaviour. This study attempts to address these questions by utilizing data from the Region of Waterloo and constructing negative binomial models. Results indicate that geometric design significantly impacts collision frequency, with effects generally consistent but showing some temporal variability. The study also highlights unique relationships between geometries and safety in Canada, advocating for tailored roundabout designs and cautioning against direct application of findings from other regions.

Analysis and Research on the Intelligent Control System of Comprehensive Traffic Signals

The existing urban traffic signal control system exhibits certain inefficiencies, including prolonged waiting times and frequent traffic congestion. Research indicates that the integration of neural networks can enhance the effectiveness of intelligent traffic control systems, potentially improving overall traffic flow and reducing delays. Neural network is a highly parallel, nonlinear and highly redundant system. Meanwhile, the self-learning and self-organizing capabilities of neural network have unique advantages for those systems that cannot establish accurate mathematical models. According to the characteristics of complex, variable and random traffic conditions at urban intersections, this paper analyzes the traffic signal control by using neural network control method. This paper introduces a design method of intelligent traffic light based on dynamic time adjustment of traffic flow. By detecting the traffic flow on site, and then allocating the green time of each lane according to the neural network algorithm, the traffic flow dynamic adjustment can be realized, and the traffic capacity of the intersection can be effectively improved. This kind of intelligent control traffic lights can effectively control traffic, and has obvious effects on dreading traffic flow, improving road capacity and reducing traffic accidents.

Multi-objective optimization of urban logistics land: a gradient-based method approach with Wuhan city as an example

ABSTRACT Effective planning of logistics land is crucial for mitigating urban freight congestion, fostering economic activities, and achieving environmental equilibrium. However, the dual challenge of mismatched logistics supply and demand, along with conflicts in land use functions, can lead to inefficiencies in resource allocation and urban freight system performance. To tackle this issue, our study integrates truck GPS trajectory data with urban land use datasets to formulate a multi-objective optimization model. By utilizing the gradient descent algorithm, which effectively handles large-scale datasets, we can navigate the complexities of logistics land planning with precision. The application of this model in the Wuhan Urban Development Area reveals that: (1) across various scenarios, the model balances the utilization of multiple optimization objectives and demonstrates high solution efficiency; (2) in both the equal weight scenario and the economic preference scenario, the areas of logistics land change are characterized by high economic output, relatively good traffic conditions, greater distance from residential zones, and comparatively low land prices; and (3) based on urban development goals, the model can determine the upper bounds of the optimization objectives through manual supervision of the selection of ideal points.

THIN OVERLAYS OF STEEL FIBER REINFORCED CONCRETE AND CONTINUOUSLY REINFORCED CONCRETE STATE OF THE ART IN BELGIUM

In Belgium, resurfacing of existing roads with cement concrete has been a common practice for over a decade, with approximately 500 km of roads overlaid with continuously reinforced concrete pavement (CRCP). This interest expanded to include thin resurfacings (<15 cm) of steel fibrous reinforced concrete (SFRC) and thin continuously reinforced concrete (TCRCP) due to the advantages offered by steel fibrous reinforced concrete in terms of lower fiber content and better incorporation possibilities. Experimental sections constructed since 1982, covering nearly 25 km of roads, have explored the use of SFRC and TCRCP overlays on both old concrete and bituminous pavements. The study reveals promising outcomes for thin overlays in SFRC, particularly when applied over bituminous underlayers, showcasing remarkable and durable bond strength. The best results were achieved with new leveling courses with an overall open structure. On old concrete pavements, achieving an adequate bond is crucial for success, necessitating intensive roughening of the existing surface. Grout application showed minimal improvement in bond strength. TCRCP overlays, particularly those with a thickness of 14 cm, demonstrated satisfactory performance with no significant issues. Utilizing welded-wire fabric for reinforcement can offer cost savings and facilitate construction under traffic conditions. This research, conducted with financial assistance from the IRSIA organization and supervised by a Working Party chaired by Prof. dr. ir. F. Van Cauwelaert, aims to establish practical guidelines for thin resurfacing and explores the relationship between empirical experience and thickness design computations. The study underscores the potential of thin concrete resurfacings in the rehabilitation of the Belgian road network, emphasizing careful consideration of bond strength and overlay thickness for successful implementation. (Abstract generated by AI tool ChatGPT 4)

ANALYSIS OF FULL-DEPTH PCC PATCHES FOR JOINTED CONCRETE PAVEMENTS

This study investigates the effectiveness of full-depth Portland Cement Concrete (PCC) patches for rehabilitating jointed concrete pavements, focusing on load transfer efficiency, stress distribution, and deflection characteristics under heavy axle loads. The deterioration of jointed PCC pavements, primarily at transverse contraction joints due to heavy traffic and environmental conditions, necessitates maintenance strategies like Concrete Pavement Restoration (CPR) which includes full-depth patching among other techniques. The research emphasizes the importance of dowel bar size, spacing, and concrete thickness in the design of full-depth patches to ensure effective load transfer and minimize pavement deflections and stresses. Key findings reveal that deflections along transverse joints increase with fewer dowel bars, while joint efficiency improves with thicker slabs and more dowel bars. Tensile stresses were found to be less influenced by the number and size of dowel bars, not exceeding rupture limits, indicating stresses alone are insufficient for patch design. The study highlighted that bearing stresses are critical, with six and seven dowel bars configurations exceeding allowable stresses, suggesting that a minimum of eight dowel bars of at least 1 1/4-inch diameter is necessary for effective design. The optimum design proposed involves a 10-inch thick patch with eight dowel bars positioned strategically across the transverse joint for improved load transfer and reduced bearing stress, thus enhancing pavement longevity and performance. (Abstract generated by AI tool ChatGPT 4)

A 15 YEAR PERFORMANCE AND EVALUATION OF CRCP EXPERIMENTAL ROAD

This study presents a comprehensive 15-year evaluation of the Continuously Reinforced Concrete Pavement (CRCP) implemented in the Nakayama Bypass on National Highway Route 112. Spearheaded by a collaborative effort from The Nippon Road Co., Ltd., the Tohoku Regional Construction Bureau, Road Management Technology Center, Tohoku Branch, and Rainbow Consultants Co., Ltd., the research aims to assess the long-term performance, durability, and maintenance requirements of CRCP under various traffic and environmental conditions. The methodology encompasses detailed inspections and performance analyses, including pavement condition surveys, crack mapping, deflection measurements, and structural integrity assessments. This longitudinal study also integrates advanced evaluation techniques to understand the CRCP's behavior over its service life, focusing on crack propagation, load transfer efficiency, and overall pavement stability. Results indicate that CRCP, with its design and construction innovations, exhibits superior durability and performance compared to traditional pavement types. Notably, the CRCP section demonstrated remarkable resistance to traffic-induced stresses and environmental impacts, showcasing minimal maintenance needs over the 15-year period. The study also highlights the importance of initial quality control and precise construction practices as key factors contributing to the pavement's longevity and reduced lifecycle costs. Conclusively, the findings advocate for the broader adoption of CRCP in road infrastructure, emphasizing its cost-effectiveness and sustainability benefits. This research contributes valuable insights into pavement engineering, offering a robust evidence base for future CRCP projects and innovations in pavement technology. (Abstract generated by AI tool ChatGPT 4)

Methodology for Providing Priority to Urban Passenger Transport Traffic

The article considers the efficiency of providing priority conditions for urban passenger transport by allocating separate lanes for public transport on the roadway. The results of modelling the traffic flow on the considered segments of the city street network are complemented by a conclusion about the need to improve road traffic management on these segments, considering the intensity and composition of the traffic flow.Simulation allows creating mathematical or computer models for analysing and assessing the traffic conditions of urban passenger transport. The models are based on various parameters of traffic flows, the schedule of urban passenger transport, the location of public transport stops, etc. Modelling allows conduct of different experiments to assess the effectiveness of various measures and changes in the transport system.The objective of the study is to develop a methodology for analysing traffic conditions that will identify and assign priority to urban passenger transport traffic to achieve efficiency and comfort of its operation, improve transport accessibility and reduce traffic delays.Optimisation of the traffic provides for various management scenarios to find optimal solutions that reduce delays, congestion, traffic intensity, etc.Route planning is necessary for developing an optimal strategy for efficient passenger service. Demand forecasts make it possible to predict not only passenger traffic, but also the need for transportation in different periods, which is necessary for route planning and selection of optimal rolling stock features. All these areas of modelling the urban passenger transport traffic are developed to achieve the goals of increasing efficiency, reducing the risk of road traffic accidents, improving the quality of transport services and the safety of passenger transportation, which should create a sustainable system aimed at satisfying the basic needs of urban transport passengers.

AI-Enhanced Traffic Prediction and Congestion Control: A Framework for CNF and VNF Networks

This article comprehensively analyzes artificial intelligence-driven approaches to traffic prediction and congestion control in Cloud-Native Functions (CNFs) and Virtual Network Functions (VNFs) based networks. This article examines recent advancements in predictive analytics and dynamic resource allocation, focusing on implementing deep learning frameworks such as Long Short-Term Memory (LSTM) networks for traffic pattern forecasting. This article demonstrates how continuous learning models and real-time telemetry data integration enable adaptive network responses to fluctuating traffic conditions. This article indicates that AI-enhanced load balancing and traffic shaping techniques significantly improve network performance, achieving a more efficient distribution of resources across network nodes while maintaining consistent Quality of Service (QoS). This article highlights the transformative potential of these innovations in meeting the demanding requirements of 5G networks and beyond, offering insights into cost-effective resource management strategies and scalable network solutions. Experimental results show substantial improvements in latency reduction and resource utilization efficiency, presenting a promising direction for next-generation telecom service optimization.

Cross-layer latency analysis for 5G NR in V2X communications.

The 5G network was developed to push the capabilities of wireless networks to previously unseen performance limits, e.g., transmission rates of several gigabits per second, latency of less than a millisecond, and millions of devices connected at the same time. To meet these requirements, it is necessary to access new spectrum (the so-called millimeter waves) and use techniques such as Massive MIMO (Multiple-Input Multiple-Output) and beamforming. This required the design of a new radio interface, known as 5G NR, that includes improvements to its physical components and new protocols. The performance of the 5G network will depend heavily on the behavior of these new protocols under certain configuration parameters, traffic conditions, device density, and network architecture. This paper introduces an analytical model for the performance evaluation of 5G NR. The developed model describes the behavior of the different layer 1 and 2 protocols involved in 5G radio communication. Using the model, it is possible to evaluate the performance of 5G NR in terms of throughput and latency, two key performance metrics used to describe QoS (Quality of Service) thresholds of different applications. The protocol layer approach gives the model sufficient granularity to identify critical behaviors that significantly impact performance. This can help focus efforts on improving these key points or propose improvements/modifications to the operation of network protocols or devices. The use of this model for performance evaluation is exemplified by studying a Remote Driving scenario operated over 5G. This scenario has very stringent delay requirements, which, according to the model's results, can be satisfied if the network conditions are adequate. This model and its results can be used as a starting point for performance evaluations of application involving end-to-end (E2E) communications.

Mitigating stop-and-go traffic congestion with operator learning

This paper presents a novel neural operator learning framework for designing boundary control to mitigate stop-and-go congestion on freeways. The freeway traffic dynamics are described by second-order coupled hyperbolic partial differential equations (PDEs), i.e. the Aw–Rascle–Zhang (ARZ) macroscopic traffic flow model. The proposed framework learns feedback boundary control strategies from the closed-loop PDE solution using backstepping controllers, which are widely employed for boundary stabilization of PDE systems. The PDE backstepping control design is time-consuming and requires intensive depth of expertise, since it involves constructing and solving backstepping control kernels. Existing machine learning methods for solving PDE control problems, such as physics-informed neural networks (PINNs) and reinforcement learning (RL), face the challenge of retraining when PDE system parameters and initial conditions change. To address these challenges, we present neural operator (NO) learning schemes for the ARZ traffic system that not only ensure closed-loop stability robust to parameter and initial condition variations but also accelerate boundary controller computation. The first scheme embeds NO-approximated control gain kernels within a analytical state feedback backstepping controller, while the second one directly learns a boundary control law from functional mapping between model parameters to closed-loop PDE solution. The stability guarantee of the NO-approximated control laws is obtained using Lyapunov analysis. We further propose the physics-informed neural operator (PINO) to reduce the reliance on extensive training data. The performance of the NO schemes is evaluated by simulated and real traffic data, compared with the benchmark backstepping controller, a Proportional Integral (PI) controller, and a PINN-based controller. The NO-approximated methods achieve a computational speedup of approximately 300 times with only a 1% error trade-off compared to the backstepping controller, while outperforming the other two controllers in both accuracy and computational efficiency. The robustness of the NO schemes is validated using real traffic data, and tested across various initial traffic conditions and demand scenarios. The results show that neural operators can significantly expedite and simplify the process of obtaining controllers for traffic PDE systems with great potential application for traffic management.

Analysis of injury severity levels and contributory factors in traffic crashes at signalized intersections under mixed traffic conditions in a low- and middle-income country

Analysis of injury severity levels and contributory factors in traffic crashes at signalized intersections under mixed traffic conditions in a low- and middle-income country

Mobile Application Utilizing YOLOv8 for Real-Time Urban Traffic Data Collection

This paper presents a pioneering mobile application specifically designed to revolutionize the collection of urban traffic data. The application is engineered on the robust YOLOv8 platform for mobile devices, leveraging smartphone cameras to provide real-time observations of traffic conditions and vehicle counts with a notable accuracy of 93%. The development environment includes Java Android, enhancing the app with cutting-edge functionalities such as YOLOv8 for precise vehicle type detection and Deep Sort for effective vehicle counting. Despite achieving high accuracy, the application encounters difficulties in accurately detecting motorcycles, which are often hidden behind larger vehicles. This research not only demonstrates the effectiveness of a sophisticated tool for traffic data analysis but also emphasizes the essential need for continuous improvements to tackle practical challenges encountered in urban settings. The findings signify a major progression in urban mobility research and advocate for advanced traffic management and planning strategies.

Dilemma Zone Identification and Mitigation Approach at Signalized Intersections under Mixed Traffic Conditions Using UAV Data

Dilemma Zone Identification and Mitigation Approach at Signalized Intersections under Mixed Traffic Conditions Using UAV Data

Smart ETA Predictions: Leveraging AI and Neutrosophic Fuzzy Soft Sets for Real-Time Accuracy

In this paper we aims to provide a clear definition of Neutrosophic Fuzzy Soft Sets and explain its fundamental operations through relevant examples. This work examines the computation of static Expected Time of Arrival (ETA) utilizing neutrosophic fuzzy soft set values and the fundamental Expected Time of Arrival. Our research also investigates the incorporation of sophisticated artificial intelligence (AI) methods to create reliable and adaptable dynamic Expected Time of Arrival(ETA) prediction models. Through the utilization of many types of data, such as current traffic statistics, weather conditions, road conditions, vehicle status, and driver behavior, we suggest a comprehensive system that adapts to changing circumstances and consistently enhances its ability to make accurate predictions. Our methodology utilizes cutting-edge machine learning algorithms to analyze and interpret vast amounts of diverse data. In addition, we tackle the difficulties of managing uncertainty and indeterminacy in data by utilizing Neutrosophic Fuzzy Soft Sets, which improve the model’s resilience and dependability.

자율차-일반차 혼재상황에서 돌발상황 대응 교통운영전략 USE CASE 시나리오 효과분석

This study aims to analyze the effects of providing traffic operation strategies to autonomous vehicles in response to incidents in mixed traffic situations involving autonomous vehicles and manual vehicles. The use case scenario of providing detour information for incidents in a living lab where autonomous vehicles are expected to operate is intended to derive the effects of traffic operation strategies through comparison of average travel speeds. In order to analyze the effectiveness of traffic operation strategy in traffic situations where autonomous vehicles and manual vehicles coexist, three scenarios were designed for peak and non-peak traffic conditions, and the average travel time of autonomous vehicles for each autonomous driving service was calculated and analyzed as an indicator for judging the effectiveness of the information provision scenario for detours. The simulation analysis results for scenario 2, where an incident occurs, showed that the average travel speed increased compared to scenario 1, and this was analyzed to reflect the bottleneck caused by the incident (reducing operation from 3 lanes to 1 lane). In scenario 3, which simulates the case where detour information is provided as part of the incident response traffic operation strategy, the average travel time of autonomous vehicles decreases compared to scenario 2 for the occurrence of an incident. Therefore, it is judged that the traffic conditions improves as autonomous vehicles select the detour as a strategy for providing detour information in response to an incident, thereby reducing the demand for vehicles passing through the incident location. This study is significant in that it analyzed the effectiveness of traffic operation strategy by implementing an actual traffic environment as a simulation program for the use case of traffic operation strategy that can be provided to autonomous vehicles when an incident occurs in a mixed situation of autonomous vehicles and manual vehicles, and as an initial study in preparation for the era of autonomous driving, it is a study on mixed situations of autonomous vehicles in urban areas.

Predictive Smart Mobility Systems for Sustainable and Efficient Urban Transportation

Abstract: The rapid urbanization and growth of cities have placed increasing pressure on transportation systems, leading to traffic congestion, environmental degradation, and inefficiencies in mobility. The Smart Mobility Prediction System (SMPS) is designed to address these challenges by leveraging advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and big data analytics to optimize transportation networks. This system gathers realtime data from sensors, traffic cameras, GPS devices, and social media feeds to predict traffic patterns, identify congestion hotspots, and suggest alternative routes to users. By utilizing predictive analytics, the SMPS can forecast traffic conditions, demand for public transportation, and the availability of shared mobility options such as ride-hailing and bike-sharing services. Furthermore, the system can support urban planners and policymakers by providing insights into the performance of transportation systems, enabling them to make informed decisions regarding infrastructure development and traffic management. The implementation of the Smart Mobility Prediction System aims to enhance the efficiency, sustainability, and safety of urban transportation networks, contributing to improved quality of life for city dwellers while reducing environmental impacts.

AGBOWA-IKOSI DEVELOPMENT MASTER PLAN REPORT

This paper aims to use the establishment of the master plan to provide an overall development strategy for the emerging area of Agbowa-Ikosi in Lagos State, Nigeria. The plan is to help develop Agbowa-Ikosi into a sustainable city that will be economically viable owing to the city's strategic geographical location and the industrial and tourism opportunities inherent in the city. The plan contains comprehensive information on the territory's climate, geography, land usage, traffic conditions, and other peculiarities. Core constraining factors including infrastructural development, drainage systems, and generic necessities are recognized. These are the challenges that this plan seeks to overcome to unlock the potential of the Region. These include the proposals for a new CBD, growing areas of the industrial estates, accessibility of transport systems, and establishment of open green space/recreational grounds. According to the principle of sustainable development, the plan proposes the utilization of renewable energy sources and the promotion of the ecological approach in construction. Therefore, the usage of the aforementioned authorities can make Agbowa-Ikosi an example of creating a sustainable city in Nigeria. KEYWORDS: Agowa-Ikosi, Master Plan, Sustainable development, Infrastructure, Tourism