Traffic Management Departments Research Articles

The effect of guardrail color on driver behavior based on driving style along mountain curves

Objective Mountain highways are linearly complex, with extensive curves and high accident injury rates, how to improve driving safety is the key to traffic safety management on mountain highways, and it also meets the need for harmonious and sustainable development of the society. Therefore, this study investigates the effects of different guardrail color configurations on the driving behavior of different styles of drivers when driving on mountainous curves from the perspective of improving road aids – guardrails. Methods A virtual reality experiment was designed using a driving simulator and VR technology, and 64 subjects were recruited to participate and complete the experiment. Results Drivers with non-adaptive driving styles (Reckless, Angry, Anxious) traveled at significantly higher speeds than subjects with adaptive driving styles (Cautious) on mountainous roads; drivers with Cautious styles had better lane-keeping ability when passing through different radii of curves as compared to non-adaptive drivers; and the red and yellow guardrails were more effective in decreasing the speeds at which drivers passed and in increasing the stability of lane-keeping. Conclusions The results of the study show that the effectiveness of red and yellow guardrails is better, which provides a reference for the traffic management department to propose a standardized color setting of guardrails in mountainous areas, which is conducive to the development of more precise traffic management measures to reduce the occurrence of traffic accidents.

INVESTIGATION OF TRAFFIC EFFICIENCY ON KARBALA ROADS

In this article, we analyze the operation of the road network of the city of Karbala in Iraq, which is characterized by its religious and tourist character, and evaluate the effectiveness of the urban street network in it in order to identify the main steps that need to be taken to take measures to improve traffic efficiency and reduce delays. The scientific novelty of this work lies in the fact that it is the first of its kind, as far as we know, which addresses the problem of traffic efficiency in the city of Karbala as a whole, since the traffic density on the main transport axes in this city varies greatly during normal times and peak hours (morning and evening). The results of the analysis showed that some sectors operate in conditions of congested (stable) traffic (LOS F) in the morning and evening hours, and such sectors require immediate traffic management strategies. While the rest of the sectors are operating at or close to the same capacity (LOSE). Purpose - Analyzed the operation of the Karbala city road network in Iraq and evaluate its effectiveness to identify the main steps that need to be taken to improve traffic efficiency and reduce delays. Methodology. In this work, an experimental analytical methodology was used, in which data were collected, analyzed and compared with relevant information sources. Results. The categories of urban roads were determined based on the speed of free movement (FFS), and the level of service (LOS) is calculated for selected urban streets taking into account the average speed of traffic (ATS). Measurements of speed and travel time showed that traffic jams occurred on Fatima Al-Zahra Street, the second part of Ramadan Street and some sections of Al-Abbas Street in the morning. These areas require immediate traffic management strategies. In the evening, traffic jams were also observed on sections of Fatima Al-Zahra Street, part 2 of Al-Iskan Street and the first section of Ramadan Street. The decrease in traffic speed led to traffic jams on Al-Abbas, Al-Iskan Streets and some sections of Fatima Al-Zahra Street. Low FPS values indicate that the average speed of movement in these areas is low even with a small flow. Practical implications. It is recommended to apply the results obtained to the Traffic Management Department of the city of Karbala.

Exploring the associations of demographics and scale measures with cognitive driving behavior among older drivers in China

Age-related changes and frailty are reasons for the high proportion of older drivers in certain types of crashes, such as giving right of way at intersections and turning left. The identified crash causes include the driver’s demographics, driving style, cognitive function, and mental workload. This study aimed to explore the associations of demographics and scale measures with cognitive driving behavior. Thirty-nine drivers, consisting of twenty younger drivers (18–60 years old) and nineteen older drivers (above 60 years old), participated in driving simulation experiments after completing scale tests. The selected scale measures included the demographic questionnaire, Multidimensional Driving Style Inventory (MDSI-C), Mini-Mental State Examination (MMSE), Trail Making Test Part A (TMT-A) and Part B (TMT-B), and the National Aeronautics and Space Administration Task Load Index (NASA-TLX) for obtaining subjective information from drivers. Driving scenarios were developed based on the driving characteristics of older adults to investigate age-related driving ability. The driving behavior parameters included reaction time, lateral stability, and driving speed, corresponding to reaction, perception, and execution. Three stepwise regression models showed that NASA-TLX, the interaction between age and driving experience, and the interaction between age and TMT-A significantly explained 53.3 % of reaction time variance; TMT-A, risk driving style, anxiety driving style, and gender significantly explained 53.5 % of lateral stability variance; TMT-A, NASA-TLX, and MMSE significantly explained 60.6 % of driving speed variance. Subsequently, the impact of four age-related predictor variables on driving behavior was further discussed. It is worth noting that a rich driving experience may compensate for driving performance. However cognitive impairment impairs this compensation. Driving behavior is influenced by a combination of various factors. Age, as a physiological indicator, is not sufficient to be a strong predictive factor for lateral stability and driving speed. The results provide a reference for traffic safety management departments to streamline driving suitability test procedures and propose targeted training methods for older drivers.

Can the development of electric buses bring economic and environmental benefits

The transition of urban buses to e-buses can be a significant contribution to the ecological construction of cities. Understanding the ecological and economic consequences of the transition is challenging. In this paper, Copenhagen is chosen as a case study, and specific parameters for the city are established through the model to assess the impact of the electric bus transformation program. Traditional diesel buses (Yutong C12) and electric buses (BYD-K19) are examined using the Eco-efficiency Model. Additionally, harmful gas emissions, such as sulfide, are quantitatively analyzed, and the daily production of harmful gases by traditional diesel buses and electric buses is calculated. In the second part, Economic model is grounded in the actual circumstances of the bus fleet, considering various factors such as initial costs, maintenance costs, energy costs, and external investments. This comprehensive evaluation assesses the feasibility of the transformation plan and is applied in a selected city. By simulating and comparing the cost consumption of traditional fuel buses and electric buses over the past 10 years, it was found that electric buses have high long-term economic benefits, providing a solid foundation for informed decision-making by urban traffic management departments. In the third part, through the sensitivity analysis of the influence of external investment, we find that when external investment is approximately $15,000 per E-bus, the economic benefits of electric buses and fuel buses are comparable over time. Finally, the calculation results of the model are clearly shown in the form of charts, including the change trend of maintenance cost and external investment in 10 years. These results provide comprehensive data support for urban transportation authorities to help them better develop and adjust electric bus transformation plans to promote urban public transportation towards a more sustainable and environmentally friendly direction.

An Urban Built Environment Analysis Approach for Street View Images Based on Graph Convolutional Neural Networks

Traditionally, research in the field of traffic safety has predominantly focused on two key areas—the identification of traffic black spots and the analysis of accident causation. However, such research heavily relies on historical accident records obtained from the traffic management department, which often suffer from missing or incomplete information. Moreover, these records typically offer limited insight into the various attributes associated with accidents, thereby posing challenges to comprehensive analyses. Furthermore, the collection and management of such data incur substantial costs. Consequently, there is a pressing need to explore how the features of the urban built environment can effectively facilitate the accurate identification and analysis of traffic black spots, enabling the formulation of effective management strategies to support urban development. In this study, we research the Kowloon Peninsula in Hong Kong, with a specific focus on road intersections as the fundamental unit of our analysis. We propose leveraging street view images as a valuable source of data, enabling us to depict the urban built environment comprehensively. Through the utilization of models such as random forest approaches, we conduct research on traffic black spot identification, attaining an impressive accuracy rate of 87%. To account for the impact of the built environment surrounding adjacent road intersections on traffic black spot identification outcomes, we adopt a node-based approach, treating road intersections as nodes and establishing spatial relationships between them as edges. The features characterizing the built environment at these road intersections serve as node attributes, facilitating the construction of a graph structure representation. By employing a graph-based convolutional neural network, we enhance the traffic black spot identification methodology, resulting in an improved accuracy rate of 90%. Furthermore, based on the distinctive attributes of the urban built environment, we analyze the underlying causes of traffic black spots. Our findings highlight the significant influence of buildings, sky conditions, green spaces, and billboards on the formation of traffic black spots. Remarkably, we observe a clear negative correlation between buildings, sky conditions, and green spaces, while billboards and human presence exhibit a distinct positive correlation.

Applying AVWEWM to ethical decision-making during autonomous vehicle crashes

At present, a few scholars studied influencing factors, rules and mechanisms of decision-making in ethical dilemmas. Many factors have been identified, and a few rules and mechanisms have been proposed. However, due to the inability to evaluate the weight and role of each factor in decision-making, it is difficult to establish a computational decision-making model to solve ethical dilemmas. Therefore, entropy weighted method (EWM) and Attribute Value Weighted EWM (AVWEWM) are used to process 84 dilemmas respectively to evaluate the weight and role of each factor in decision-making, then decision-making models based on EWM and AVWEWM are constructed to make decisions during autonomous vehicle (AV) crashes respectively. Lastly, 40 dilemmas are designed to test both decision-making models. The test results indicate that both can make clear decision-making in 40 dilemmas. However, the decision-making by AVWEWM is more consistent with public opinion than EWM. In addition, according to the weight and role of each factor in decision-making, it can also provide a few references for traffic management and legal departments to formulate traffic laws and regulations for AV in the future.

Probabilistic En Route Sector Traffic Demand Prediction Based on Quantile Regression Neural Network and Kernel Density Estimation

With the development of civil aviation in China, airspace congestion has become more and more serious and has gradually spread from airport terminal areas to en route networks. Traditionally, most prediction methods that obtain traffic flow data are based on the number of aircraft passing through an en route sector and require flight data to meet strict assumptions and conditions. While these methods are normally used in the actual operation of air traffic flow management departments in China, the results are not satisfactory due to the nonlinearity of traffic demand along en route sectors and the change in high-frequency noise. In order to refine aircraft control in airspace, it is necessary to predict traffic flow accurately. Thus, this paper proposes the quantile regression neural network and kernel density estimation method to obtain some quantiles of continuous traffic demand data in the future, which combines the strong nonlinear adaptive ability of neural networks with the ability of quantile regression to describe explanatory variables. By using these continuous conditional quantiles, we obtain the probability density function and probability density curve of the continuous traffic demand in the future using the kernel density estimation method. In this way, we can obtain not only a specific point prediction value and its change interval but also the probability of each value in the prediction change interval of traffic demand in the en route sector as well as a more accurate point prediction value for a specific day.

Study on Spatio-Temporal Patterns of Commuting under Adverse Weather Events: Case Study of Typhoon In-Fa

This study focuses on the main urban area of Yangzhou City and conducts a quantitative comparative analysis of traffic accessibility during normal weather and extreme precipitation conditions (typhoon) based on GPS trajectories of buses. From both temporal and spatial dimensions, it comprehensively examines the impact of extreme precipitation on bus travel speed, travel time, and the commuting range of residents in the main urban area of Yangzhou City. (1) Through the mining and analysis of multi-source heterogeneous big data (bus GPS trajectory data, bus network data, rainfall remote sensing data, and road network data), it is found that the rainstorm weather greatly affects the average speed and travel time of buses. In addition, when the intensity of heavy rainfall increases (decreases), the average bus speed and travel time exhibit varying degrees of spatio-temporal change. During the morning and evening rush hour commuting period of rainstorm weather, there are obvious differences in the accessibility change in each typical traffic community in the main urban area of Yangzhou city. In total, 90% of the overall accessibility change value is concentrated around −5 min~5 min, and the change range is concentrated around −25~10%. (2) To extract the four primary traffic districts (Lotus Pond, Slender West Lake, Jinghua City, and Wanda Plaza), we collected Points of Interest (POI) data from Amap and Baidu heat map, and a combination analysis of the employment–residence ratio model and proximity methods was employed. The result show that the rainstorm weather superimposed on the morning peak hour has different degrees of impact on the average speed of the above-mentioned traffic zones, with the most obvious impact on the Lotus Pond and the smallest impact on Wanda Plaza. Under the rainstorm weather, the traffic commute in the main urban area of Yangzhou in the morning and evening peak hour is basically normal. The results of this paper can help to quantify the impact of typhoon-rainstorm weather events on traffic commuting in order to provide a scientific basis for the traffic management department to effectively prevent traffic jams, ensure the reliability of the road network, and allow the traffic management department to more effectively manage urban traffic.

Ensemble learning based approach for traffic incident detection and multi-category classification

Traffic incident is one of the important causes of road congestion. Traffic incident detection plays a crucial role in the safety application of intelligent transportation systems, which provides timely information for traffic management departments and reducing losses. Despite many researches on incident detection approach, the identification of different incident categories is not enough. In addition, traffic incident detection is still a challenging task due to the problem of data imbalance and feature selection. In this study, we propose a two-stage traffic incident detection framework based on ensemble learning. In the first stage, a binary classification algorithm based on XGBoost (eXtreme Gradient Boosting) is established to detect whether there is a traffic incident, and 24 feature variables are determined by model feature selection. In the second stage, three resampling algorithms are utilized to reconstruct and balance the dataset. Through comparative analysis, SMOTE (Synthetic Minority Over-sampling Technique)-XGBoost is the best method for incident multi-category classification with precision of 87.27%, 78.52% and 92.54%, respectively. Moreover, the baseline comparison experiments are conducted to evaluate our model performance with real-word datasets. The proposed model achieves the highest average accuracy of 93.45% in the first stage and the macro-precision of 86.11% in the second stage. The results indicate that the proposed method outperforms baselines and the two-stage framework can accurately realize the incident detection and multi-category classification.

Influence of Snowy and Icy Weather on Vehicle Sideslip and Rollover: A Simulation Approach

Many northern hemisphere countries have experienced exceptionally heavy snow, blizzards, and cold snaps in recent years, causing considerable public concern about the high crash rate and safety issues in road traffic. This study used the CarSim dynamics simulation to recreate several vehicle driving scenarios in snow and ice conditions. To explore the influence of speed, curve radius, and road adhesion coefficient on vehicle sideslip and rollover, four lateral stability evaluation indicators, namely lateral offset, lateral acceleration, yaw rate, and roll angle, are chosen. Unfavorable combinations of these factors result in vehicle deviation from their intended trajectory and dramatically increase the likelihood of sideslip and rollover incidents. In particular, road adhesion coefficients ranging from 0.10 to 0.20 lead first to sideslip, while coefficients of 0.21 to 0.35 lead straight to rollover. Additionally, in the initial segment of the curve, cars are more susceptible to lateral instability. Curve radius has the greatest influence on sideslip when the three influencing factors are combined, while speed is the key component affecting rollover incidents. Smaller curve radii and higher speeds are major factors in such incidents. The results are helpful for proper road alignment parameter selection and dynamic speed-limit measures. This can provide a theoretical basis for traffic management departments to take targeted measures, which is of great significance to improving road traffic safety in snowy and icy weather.

Data mining and spatio-temporal characteristics of urban road traffic emissions: A case study in Shijiazhuang, China

Accurate estimation of traffic emissions and analysis of spatio-temporal distribution on urban roads play a crucial role in the development of low-carbon transportation system. Traditionally, a region’s emission characteristics have been studied using numerous emission models with GPS-based spatio-temporal data. Due to the heavy data processing needs of GPS-based data, emission characteristics for a large region have been studied by dividing the region into a limited number of smaller areas or units. Additionally, GPS data are based on a few vehicles in the traffic which does not fully reflect road conditions. This paper proposed an approach that can be used to study and calculate the spatio-temporal emission pattern of a region at a roadway section level by using Baidu’s online traffic data and COPERT model. The proposed method can be used to estimate road-level emission patterns while avoiding the impact of redundant data in large datasets, making the dataset more reliable, applicable, and scalable. The proposed approach has been demonstrated through a study of spatio-temporal emission patterns in the Qiaoxi district within city of Shijiazhuang, China. Online data crawling technology was used to obtain data on urban road traffic speed and driving distance. The linear reference technology was used to construct a two-layer road network model to conduct the coupling and matching of traffic data with the road network data. The COPERT model was implemented to calculate the average traffic emissions on each road in the road network, and a traffic emission intensity index was proposed to quantify the CO, VOC, NOx and CO2 emissions on urban roads in the study area. The analysis results show that the traffic emission intensity of the expressway, trunk road, secondary road, and branch road is high during the morning peak (7 AM-9 AM) and evening peak (5 PM—7 PM). The sections with higher traffic emission intensity are mainly concentrated on the main roads and secondary roads such as Jiefang South Street, Shitong Road and Xinhua Road. Nearly one-third of 2nd Ring and 3rd Ring roads also have relatively high emission intensity. The research results provide new ideas for estimating traffic emissions in urban road networks and analyzing the spatio-temporal distribution of traffic emissions. The research results can also provide a decision-making basis for traffic management departments to formulate energy-saving and emission-reduction measures and promote the development of urban green and low-carbon transportation.

L-TCM: A Lightweight Privacy-Preserving Traffic Condition Monitoring Scheme With Source Authentication in Cloud-Assisted VANETs

In vehicular ad hoc networks (VANETs), the vehicle constantly collects traffic condition messages and uploads them to the traffic management department (TMD) for traffic analysis. Traffic condition messages usually consist of vehicle-related information as well as safety-related messages. Vehicle-related information usually contains vehicles' privacy, which cannot be broadcasted. At the same time, the safety-related message does not contain the privacy of vehicles and can be broadcasted in VANETs to ease traffic congestion. However, transmitting two types of messages separately consumes numerous computation and communication costs, which is not realistic for resource-limited vehicles. In this article, we propose a lightweight privacy-preserving traffic condition monitoring (L-TCM) scheme, where the sanitizable signature is used to enable the vehicle to broadcast safety-related messages and upload traffic condition messages by sending one message. In addition, searchable encryption is used to help the TMD retrieve traffic condition messages stored in the cloud server. Security analysis demonstrates that L-TCM satisfies security goals in VANETs. Besides, the performance of L-TCM concerning computation and communication costs makes it appropriate for traffic condition monitoring.

Analysis of Traffic Characteristics in Beijing --Based on the Traffic Performance Index

This study aims to deal with the increasingly serious urban traffic congestion problem, taking Beijing as a case study. With the increase in population, traffic congestion has become a serious problem affecting peoples life quality and social and economic development. This study uses the time series analysis and uses the ARIMA (1,0,0) (0,1,1)12 model to forecast the traffic performance index (TPI) data of the past ten years, and deeply analyzes the traffic congestion situation of the next twelve months. The results show that this model can capture the seasonal trend of traffic congestion well, and provide strong decision support for traffic management departments. Through the interpretation and analysis of the forecast, this study gives a series of traffic management suggestions for different months to achieve the alleviation and optimization of urban traffic congestion. In conclusion, this study provides a useful reference for urban traffic management and planning and contributes to the construction of a more efficient, convenient, and sustainable urban transportation system.

Review on the Development of Left Turn Crossing Theory of Non-Motor Vehicles

Non-motor vehicles are an important part of urban road traffic system, which plays an important role in the daily travel of urban residents. In recent years, the congestion of urban road traffic system has become more and more serious. In order to alleviate the congestion situation, on the one hand, the traffic management department reduces the road congestion by limiting the number of motor vehicles; on the other hand, the government vigorously advocates public transportation and green and low-carbon travel to adjust the travel mode structure. This paper systematically combs, summarizes and summarizes the views of domestic and foreign scholars from two aspects of the mode and theoretical development of non-motor vehicles turning left across the street. It is found that the left-turn crossing behavior of non-motor vehicles is from simple to comprehensive, from immature to gradually mature. Finally, the research on the development of non-motor vehicle holiday behavior is summarized and prospected.

Predicting and factor analysis of rider injury severity in two-wheeled motorcycle and vehicle crash accidents based on an interpretable machine learning framework

Objective As one of the vulnerable road users in accidents, how to improve the two-wheeled motorcyclist’s driving safety and reduce accident injury is a public health issue. Accurate identification of the factors influencing the severity of accidents is an important prerequisite for mitigating injury from crashes. Methods Based on a vehicle and a two-wheeled motorcycle crash accident data from the China in-depth accident study database (CIDAS), this study uses the performance evaluation indicators of accuracy, precision, recall, F1-score, AUC, and the ROC curve. The classification and prediction performances of the six machine learning methods on the dataset are compared, and the LightGBM algorithm with the best performance is selected to model the accident injury severity of the motorcyclists. The SHAP method is used to extend the interpretability of the LightGBM model results. Based on the SHAP method, the importance, main effect, and the interaction effect of factors under each accident injury severity are quantitatively analyzed. Results The model prediction accuracy is 92.6%, the F1-Score is 92.8%, and the AUC value is 0.986. The importance of factors varies with the accident injury severity of motorcyclists. The kilometers traveled per year by the driver, the throwing distance of the motorcyclist, and the road speed limit are the three most important factors. The motorcyclist is more likely to suffer fatal injuries when the throwing distance is >1,000 cm. Conclusions The prediction model of driver injury severity based on LightGBM algorithm has a good prediction performance. It can be used to analyze the influence factors of injury severity in two-wheeled motorcyclist accident by combining the model with SHAP method. These results could help the traffic management department to take measures to reduce accident injury of motorcyclists.

Development analysis and future of computer vision-based automobile license plate recognition technology and perception technology

With the increasing number of automobiles, there is a growing demand for the recognition and perception of license plates. As a unique identifier for each vehicle, license plates can help traffic management departments with vehicle tracking, real-time monitoring, and other tasks. License plate recognition and perception methods based on these technologies have been widely used. This paper aims to analyze and summarize the development of computer vision-based license plate recognition technology. The paper mainly discusses the two key steps in the license plate recognition process: license plate localization and detection, and character segmentation and recognition. These steps are classified into traditional methods and deep learning-based methods, and several methods are introduced for each category. Finally, by summarizing their advanced features and limitations, the paper compares them and predicts possible ways to improve them in the future.

Adaptive Graph Fusion Convolutional Recurrent Network for Traffic Forecasting

Traffic flow prediction is the foundation of urban traffic guidance and control, as well as the main function of an intelligent transportation system (ITS). Accurate traffic flow prediction is important for road users, traffic management departments and private enterprises. However, traffic flows usually show a high degree of variability, correlation and complex patterns in both temporal and spatial domain, which makes accurate traffic flow prediction a challenging task. How to capture the potential and dynamic spatial-temporal relationships of traffic data has been the bottleneck issue for intelligent transportation researchers. To solve the above problems, this paper proposes an Adaptive Graph Fusion Convolutional Recurrent Network (AGFCRN) to model the temporal and spatial characteristics of traffic flow data dynamically and adaptively. An adaptive graph fusion convolution is proposed to discover the changing relationships between traffic volumes without a priori knowledge. It uses a self-learned node embedding to generate static graphs and combines current and historical states to generate dynamic graphs at each time step. A gated recurrent layer with residual structure is designed to mitigate the decay of prediction effects in long-term modeling. In addition, an attention layer incorporating self-learned node embedding is introduced in the AGFCRN to efficiently adjust the prediction pattern of each node. Experiments on several public datasets demonstrate that AGFCRN can achieve competitive performance compared to other typical and state-of-the-art methods.

STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction

As an important component of intelligent transportation-management systems, accurate traffic-parameter prediction can help traffic-management departments to conduct effective traffic management. Due to the nonlinearity, complexity, and dynamism of highway-traffic data, traffic-flow prediction is still a challenging issue. Currently, most spatial–temporal traffic-flow-prediction models adopt fixed-structure time convolutional and graph convolutional models, which lack the ability to capture the dynamic characteristics of traffic flow. To address this issue, this paper proposes a spatial–temporal prediction model that can capture the dynamic spatial–temporal characteristics of traffic flow, named the spatial–temporal self-attention graph convolutional network (STA-GCN). In terms of feature engineering, we used the time cosine decomposition and one-hot encoding methods to capture the periodicity and heterogeneity of traffic-flow changes. Additionally, in order to build the model, self-attention mechanisms were incorporated into the spatial–temporal convolution to capture the spatial–temporal dynamic characteristics of traffic flow. The experimental results indicate that the performance of the proposed model on two traffic-volume datasets is superior to those of several baseline models. In particular, in long-term prediction, the prediction error can be reduced by over 5%. Further, the interpretability and robustness of the prediction model are addressed by considering the spatial dynamic changes.

YOLOv5s-M: A deep learning network model for road pavement damage detection from urban street-view imagery

Road pavement damage affects driving comfort markedly, threatens driving safety, and may even cause traffic accidents. The traffic management department conventionally captures pavement damage information mainly using manual and vehicle-mounted equipment, which is not conducive to the detection of large-scale road pavement distress. Street-view images can provide full-view images of urban roads where the data is updated regularly by navigation map service companies, making it possible to rapidly detect pavement damage in urban areas. This paper presents a new pavement damage detection approach that is built upon an improved YOLOv5 network and street-view images. The proposed model can deal with a large-scale detection layer to improve the detection precision of large distress targets, achieving thus both cross-layer and cross-scale feature fusion by using the Generalized Feature Pyramid Network (Generalized-FPN) structure. The improved network also involves a diagonal Intersection over Union loss for regression calculation of the boundary box and builds the decoupled Head structure to achieve the decoupling detection of prediction and regression. As a result, the fusion of the weak feature information in feature layers is enhanced at different spatial scales, a more suitable method is achieved for pavement damage detection in the complex context of multi-scale street-view images, and the accuracy of the modified network is much improved in the detection of pavement distress from street-view imagery. Furthermore, We created a large image sample set for model training and testing, and a total of 156,304 street-view images, obtained from Fengtai District, Beijing, China was used for demonstrating the usefulness of the proposed network. The findings indicated that the proposed approach could effectively achieve pavement damage detection of urban roads from street-view images, with a precision average of 79.8% on the test samples. Moreover, the developed model was applied for pavement damage detection for all the roads in Fengtai District, Beijing, indicating that our method can offer viable damage data for road maintenance planning.

CSFPre: Expressway key sections based on CEEMDAN-STSGCN-FCM during the holidays for traffic flow prediction.

The implementation of the toll free during holidays makes a large number of traffic jams on the expressway. Real-time and accurate holiday traffic flow forecasts can assist the traffic management department to guide the diversion and reduce the expressway's congestion. However, most of the current prediction methods focus on predicting traffic flow on ordinary working days or weekends. There are fewer studies for festivals and holidays traffic flow prediction, it is challenging to predict holiday traffic flow accurately because of its sudden and irregular characteristics. Therefore, we put forward a data-driven expressway traffic flow prediction model based on holidays. Firstly, Electronic Toll Collection (ETC) gantry data and toll data are preprocessed to realize data integrity and accuracy. Secondly, after Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) processing, the preprocessed traffic flow is sorted into trend terms and random terms, and the spatial-temporal correlation and heterogeneity of each component are captured simultaneously using the Spatial-Temporal Synchronous Graph Convolutional Networks (STSGCN) model. Finally, the fluctuating traffic flow of holidays is predicted using Fluctuation Coefficient Method (FCM). Through experiments of real ETC gantry data and toll data in Fujian Province, this method is superior to all baseline methods and has achieved good results. It can provide reference for future public travel choices and further road network operation.