Traffic Management Authorities Research Articles

Quantitative Estimation and Analysis of Spatiotemporal Delay Effects in Expressway Traffic Accidents

Expressway traffic accidents often result in severe congestion, with their unpredictable nature complicating timely and effective response measures. This paper presents a comprehensive method for accurately estimating and analyzing the spatiotemporal delay effects of expressway accidents through the integration of multi-source geographic data. The innovation lies in utilizing real-world vehicle trajectory data, combined with a Traffic Performance Index (TPI), to quantitatively assess delay impacts. By applying spatial clustering and hotspot detection techniques, we investigate the distribution patterns of delays and further employ a Spatial Error Model (SEM) to examine the relationships between accident characteristics and associated delay effects. Using expressway accident data and vehicle trajectory records from Hunan Province, the results demonstrate that the TPI-based approach effectively captures the duration, extent, and severity of traffic delays. Moreover, significant correlations are identified between delay impacts and specific accident characteristics, such as accident type, road type, road environment, pre-accident vehicle speed, and secondary accidents. This approach provides traffic management authorities with actionable insights into the overall roadway impact, facilitating targeted emergency response strategies and informing road usage policies tailored to the characteristics of accident impacts, thus helping to mitigate future risks.

A review of vehicle speed measurement methods for the statistical pass-by noise testing

The Statistical Pass-By method (SPB, ISO11819-1) is used in environmental impact studies to evaluate, classify, and compare different road surfaces according to their influence on traffic noise. It relates the noise level to the type of vehicles and their speed. There are different methods to measure vehicle speed and the cost plays a significant role in their practicality and widespread adoption. Radar, Lidar, and Photocells-based method systems typically involve higher initial costs due to the sophisticated technology employed, but they offer real-time and accurate data, justifying their investment. On the other hand, Video camera systems may have lower initial costs, but their accuracy can be influenced by environmental conditions and calibration requirements. This paper will assess the cost-effectiveness of these technologies, considering not only the initial setup expenses but also maintenance and operational costs over time. Cost-benefit analyses are crucial in determining the most economical yet reliable solution for specific budget constraints. As the research in this field progresses, a key focus lies in optimizing the balance between cost and performance to make these systems more accessible and practical also for a wide range of applications, ensuring that advancements benefit both traffic management authorities and users alike.

Identifying Truck Activity from Global Positioning System Data Based on Satellite Imagery

Road freight transportation plays a crucial role in fostering economic growth and enhancing logistics efficiency. Existing research primarily focuses on analyzing freight truck activity characteristics based on global positioning system (GPS) data of freight truck trajectories. Those studies proposed a variety of identification methods from different perspectives, but they mainly rely on rule-based methods and subjectively defined key threshold parameters, ignoring the complexity of truck freight activities and relying on long sequences of trajectory data. To bridge this gap, we propose the satellite image augmented truck activity recognition method, a structured framework that utilizes satellite imagery to identify truck activities from GPS data. Firstly, we categorize truck activities into three types: loading/unloading, resting, and driving. We extract geographic entities from the area of interest data to create the satellite image dataset of truck activity. Secondly, we evaluate the recognition accuracy of benchmark image classification models on our dataset, and ResNet50 achieves 98.04% accuracy on the test set. Thirdly, we apply the image classification model to identify truck activity from GPS trajectory data and use the recognition results of neighboring points to improve the identification results. The framework delineated in this paper entails the extraction of truck activities from GPS data at the satellite image level. Our approach holds promise in facilitating logistics operation fleets and traffic management authorities to maintain near real-time surveillance of the truck status, thereby fostering enhancements in both the efficiency and safety of freight transport.

Factors affecting public support for the development of urban consolidation center: The case of hanoi city, Vietnam

Urban consolidation centres (UCCs) have become an integral part of the city logistics system. This study aims to identify factors influencing public support for the implementation of UCC in Hanoi City, Vietnam. In essence, the authors assess whether UCC are perceived as essential elements contributing to the sustainable development of the city logistics system of Hanoi. To accomplish such objectives, the study employed a data sample from an extensive empirical survey, including142 representatives from freight transport companies and logistics services providers, traffic management authorities, and research institutions in transportation and logistics fields. The key findings indicate that anticipated positive effects on transport organization significantly enhance public support for the development of UCC in Hanoi. However, the study suggests that concerns regarding the impact of UCCs on urban road safety do not appear to significantly influence public support. Interestingly, the study also reveals that barriers to UCC implementation have minimal influence on public support for UCC development. In summary, this study represents an initial step towards establishing a decision support system for city logistics measures, with a specific focus on the UCC initiative in Vietnam.

Discovering the Power of External Human-Machine Interface: An EEG Study on the Driving Anger Regulation

Driving with anger can result in hazardous driving behaviors, often arising from inadequate communication among Traffic Participants (TPs). Currently, external Human-Machine Interface (eHMI) technology provides a novel channel for enhancing communication among TPs, yet its role in regulating driving emotions remains uncertain. This study investigates the impact of eHMI technology on the regulation of driving anger. We simulated a driving scenario in which the preceding vehicle abruptly applied brakes to elicit anger from the following driver. Three eHMI formats (Symbol, Text, and Symbol + Text) positioned at the rear of the preceding vehicle were implemented to explain the sudden braking event and regulate drivers’ emotions. Objective electroencephalogram (EEG) signals and subjective assessment data were analyzed to assess the effects of eHMIs on driving anger regulation. The results indicate that driving with eHMIs exhibited significant regulatory quality of drivers’ anger. Among the three formats, Symbol + Text eHMI exerted a notable regulatory impact, while both Symbol and Text eHMI contributed to emotional regulation. This study illustrates the positive impact of eHMI on emotion regulation in traffic environments, providing valuable backing for traffic management authorities in developing guidelines and policies, and contributing to the standardization of future eHMI technologies.

Mixed traffic capacity estimation of autonomous vehicles impact based on empirical data

This study refined the composite headway distribution model to estimate the mixed traffic capacity by utilizing extensive empirical mixed traffic data. The enhanced model integrated an automatic iterative algorithm into the stochastic process to capture the headway distributions of autonomous and human-driven vehicles in five types of car-following pair combinations. The proposed iterative algorithm automatically fitted the free-flow headway distribution and dynamically determined the threshold for distinguishing the car-following and free-flow states of autonomous and human-driven vehicles based on extensive trajectory data. Quantitative analysis was carried out to assess the impact of time period and vehicle type on the headway distributions of following vehicles in the five car-following pairs. The mixed traffic capacity was estimated using the overall composite headway distribution model, considering the effects of penetration rate, platoon intensity, and maximum platoon size of the autonomous vehicle. The findings indicated that autonomous vehicles showed more cautious and sensitive car-following behaviors compared to human-driven vehicles. Compared to the daytime, autonomous vehicles showed more cautious car-following behaviors at night. When following heavy vehicles, autonomous vehicles typically adapt conservative and stable car-following behaviors. In addition, the results also showed that the penetration rate, platoon intensity, and maximum platoon size significantly affected the capacity of mixed traffic flow. These results of this study can assist traffic management authorities in formulating rules or policies that take into consideration the growing prevalence of autonomous vehicles.

Road Condition Prediction Based on ARIMA Algorithm

This study investigates a road condition prediction method based on autoregressive integral moving average model (ARIMA) and its application in road traffic management and planning. With the acceleration of urbanization and the increasing demand for transportation, effective road condition prediction is crucial for traffic management and planning. This paper first introduces the challenges faced in the field of road transportation. Then, the principles and applications of the ARIMA algorithm in road condition prediction are elaborated, focusing on its advantages in capturing trends and cyclical changes in road traffic data. Subsequently, this paper verifies the effectiveness and usefulness of the ARIMA algorithm in road condition prediction through empirical analysis and case studies. The results show that the ARIMA algorithm exhibits high accuracy and stability in short- and medium-term road condition prediction, providing a simple and effective prediction tool for traffic management authorities and planners. Finally, this paper provides an outlook on the future research direction, presenting research outlooks on model optimization and improvement, combining other methods, and real-time prediction and application, in order to further improve the accuracy and practicality of road condition prediction.

Structural Framework for Managing Road Traffic Noise in Jos Metropolis, Nigeria

The management of road traffic noise largely seeks to maintain low human exposure to high noise levels, such that human health and well-being are protected. The main objective of this study was to evaluate the existing management structures for managing road traffic noise in Jos metropolis, Nigeria. A descriptive and qualitative survey was conducted in all the road traffic-related agencies in Jos, Nigeria. A purposive sample of at least 10 road traffic-related ministries and agencies in Jos voluntarily participated in the study and responded to a set of questions related to their operational codes and conducts, existing laws, statutes, policies and structures, practices and standards, accident violation, penalties fact sheets of operations among others have been put on the ground to reduce traffic volume and noise among other traffic- related challenges in Jos. A reconnaissance survey was carried out to get the agencies’ evidences of their operations on the field. The findings of this study revealed that there is no direct organogram at present for managing traffic noise in Jos metropolis, but a series of uncoordinated agencies and standards. This study therefore recommends, a structural framework for managing road traffic noise in Jos metropolis, Nigeria, and also, the establishment of a road traffic management agency to be known as the Plateau State Traffic Management Authority (PLASTMA).

Road surface damages allocation with RTI-IMS software based on YOLO V5 model

ABSTRACT Research on applying artificial intelligence to autonomously detect road surface damages contributes positively to road traffic maintenance and management. This study introduces the Road Traffic Infrastructure Intelligent Management System (RTI IMS) software, utilizing the Yolo V5 programming platform. This software is designed to automatically identify road surface damages during operation. Based on machine learning image recognition technology, the model is trained with a diverse database of road surface damage images from Japan, India, and Vietnam. Additionally, to improve the inference efficiency of the RTI IMS software, the study uses the Mosaic method, tripling the variation to diversify input data during training, thereby minimizing prediction errors. The research results in the successful development of the RTI IMS software, capable of autonomously detecting road surface damages and providing corresponding images to traffic management authorities. This study significantly contributes to the modernization of current road traffic management practices.

Dylematy interpretacyjne stosowania sygnałów drogowych ze szczególnym uwzględnieniem sygnalizatora S-2 w „bezpiecznym” zarządzaniu ruchem drogowym. Część I/III

Interpretative dilemmas of the use of road signals with particular emphasis on the S-2 signal in “safe” traffic management The paper is devoted to the issue of signal traffic control in the context of proper design, opinion, and approval of traffic organization, focusing on the safety principles and practices of road users. Formal and legal aspects derived from the basic provisions of the Act on Road Traffic and its executive acts are explained in depth. Both designers and traffic management authorities have a major dilemma about the interpretation of many signal phase systems. This is confirmed by the results of a study - as many as 71% of the analyzed intersections contained errors in the application of the signal allowing turning in the direction indicated by the “green arrow”. In the paper the possibilities of using individual traffic signals on roads and intersections, and the problems associated with the so-called “green arrow” in particular are explained. Interpretative shortcomings regarding the traffic control of public transport means, traffic control at pedestrian crossings and in the “intersection areas”, including the entrance of a non-public road are thoroughly characterized. The paper is divided into three parts. The first one is devoted to the legal basis of signal traffic control in Poland, and description of the rules of traffic participants’ movement in the conditions of traffic controlled by traffic lights. The three-part paper, as a whole, will be crowned by the authors' conclusions in the final part, along with recommendations for amendments to the Act on Road Traffic and executive acts.

Impacts of Red-Light Enforcement Cameras on Start-Up Lost Time at Urban Signalized Intersections

Red-light cameras (RLCs) are used extensively in numerous countries as a tool to regulate traffic and enhance safety at signalized urban intersections. In this regard, a case study conducted in the Dammam metropolitan area has uncovered a potential unintended consequence of RLC usage. The presence of RLCs can lead to an increase in first-vehicle reaction time (FVRT), resulting in a higher start-up lost time (SLT) at these intersections. To investigate this phenomenon, data were collected from two distinct types of locations wherein three intersections were equipped with RLCs and three were without RLCs. A total of 311 observations or cycles were recorded, with 72 belonging to the first type and 239 belonging to the second type. The analysis revealed that the average FVRT for the intersections with RLCs was 4.27 s. For those without RLCs, it was slightly lower, at 3.68 s. Similarly, the average SLT for the RLC-equipped intersections was 3.63 s, while it measured 2.73 s at the non-RLC intersections. These findings highlighted a noticeable disparity in SLT between the two types of intersections. Further statistical analysis confirmed the existence of significant differences in the means of SLT and FVRT for the two categories of intersections. Specifically, for every 1-second delay in FVRT per location, the total SLT at intersections with RLCs increased by 7.5 s. These results underscored the potential impact of RLCs on traffic efficiency and supported the recommendation for implementing a red-light countdown system at intersections with RLCs. Such a system has the potential to reduce the FVRT and enhance traffic flow. These findings emphasize a careful consideration of the implications of RLC implementation and provide valuable insights for traffic management authorities.

Redesign of the communications network based on high availability of traffic management technologies to improve the communication

Rapid urbanisation and rising population mobility have resulted in an increase in the number of vehicles on the road, providing considerable challenges for road traffic management authorities in the form of increased congestion, accidents, and pollution. Researchers in both industry and academia have been attempting to improve the performance of present road Traffic Management Systems in light of the issues that future "smart cities" would face by leveraging advances in sensing, communication, and dynamic adaptive technologies. The interaction between the development of society and the development of technologies does not escape these two factors, which impact each other more profoundly each time. This research is being conducted in response to challenges in Baghdad, Iraq, such as infrastructure, supply, and equipment, which have an impact on the proper performance of work by employees and employees who operate in the business. Fifty individuals from the Iraqi city of Baghdad were hired for the study project. Following that, the communications device research was carried out. Based on the study's findings, a strategy for upgrading Baghdad's communications network using high-availability technology is given. Its execution would help the organisation. We believe that the redesign work provided in this work can also help to improve organisational communicative performance by providing subsidies for network communications and contemplation on the most appropriate internal communication forms for public profile and organisational culture.

Self-Organized Neural Network Method to Identify Crash Hotspots

Crash hotspot identification (HSID) is an essential component of traffic management authorities’ efforts to improve safety and allocate limited resources. This paper presents a method for identifying hotspots using self-organizing maps (SOM). The SOM method was used to identify high-risk areas based on five commonly used HSID methods: crash frequency, equivalent property damage only, crash rate, empirical Bayes, and the societal risk-based method. Crashes on a major road in Iran were examined using the proposed method. Based on these criteria, high-risk locations were grouped into six clusters, which provided appropriate criteria for each location depending on the importance of the cluster. The findings show that the identification of crash hotspots tends to focus on areas with more crashes and deaths, demonstrating that the research methodology was appropriate.

Urban Taxi Slogans: The People's Arts

Urban Taxi Slogans: The People's Arts

Exploring spatiotemporal patterns and influencing factors of ridesourcing and traditional taxi usage using geographically and temporally weighted regression method

ABSTRACT The rivalry between ridesourcing and the traditional taxi has posed great challenges to traffic management authorities. Understanding the spatial patterns and influencing factors of their usage can help traffic authorities develop insightful policies and strategies to coordinate the operations of the two services better. This study develops a novel geographically and temporally weighted regression model (GTWR) to unravel the spatiotemporal patterns and influencing factors of the two services based on a high-resolution GPS dataset. The developed GTWR model achieves greater performance than other traditional methods. The results reveal that the spatiotemporal impacts of influencing factors on the usage of ridesourcing are quite different from that of traditional taxi. The spatiotemporal distribution and evolution of the coefficients are further discussed. The findings of the study could help traffic management authorities develop efficient regulatory policies to enhance the operations of the two services in specific areas and periods.

Automatic Recognition of Road Damage Based on Lightweight Attentional Convolutional Neural Network.

An efficient road damage detection system can reduce the risk of road defects to motorists and road maintenance costs to traffic management authorities, for which a lightweight end-to-end road damage detection network is proposed in this paper, aiming at fast and automatic accurate identification and classification of multiple types of road damage. The proposed technique consists of a backbone network based on a combination of lightweight feature detection modules constituted with a multi-scale feature fusion network, which is more beneficial for target identification and classification at different distances and angles than other studies. An embedded lightweight attention module was also developed that can enhance feature information by assigning weights to multi-scale convolutional kernels to improve detection accuracy with fewer parameters. The proposed model generally has higher performance and fewer parameters than other representative models. According to our practice tests, it can identify many types of road damage based on the images captured by vehicle cameras and meet the real-time detection required when piggybacking on mobile systems.

Analysis of the Accident Propensity of Chinese Bus Drivers: The Influence of Poor Driving Records and Demographic Factors

Previous studies have shown that bus drivers are a major contributing factor to bus accidents. The aim of this study is to explore the factors that contribute to the presence of accident propensity among bus drivers, as well as the relative importance of each influencing factor and the mechanism of influence. To this end, a C5.0 decision tree model was developed to determine the relative importance as well as rank the importance of the impact of poor driving records and demographic factors on accident propensity, and a binary logistic regression model was developed to analyze the relationship between accident propensity and the different values of each essential influencing factor. Based on our results, we found that: (1) the number of violations had the most significant effect on bus drivers’ accident propensity, followed by age, driving age, and number of alarms; (2) violations and alarms are positively related to bus driver accident propensity; age and driving age are inversely related to bus driver accident propensity; and (3) men have a higher accident risk probability than women. This study’s findings will help bus companies and traffic management authorities to implement more targeted improvements to their bus driver management programs.

A Pilot Study on Impact of Mood State on Emergency Response Capacity for Young Novice Drivers

As young novice drivers are inclined to getting involved in traffic accidents due to their improper emergency response under sorts of gender affective state, namely, mood, widely generated in fast-paced urban life, it is of great necessary to study the impact of mood state on responsive capacity for young novice drivers. Fourteen college students were recruited to take part in complex reaction experiments for this pilot study. Each subject’s mood was collected through a simplified POMS scale, while their complex reaction time (CRT) and response error rate (RER) were acquired during the experiments. The study results showed that young novice drivers’ RER was significantly positively correlated (pc=0.323 ∗ ∗ , “pc” omitted next) with their score of total mood disturbance (TMD), and a logarithmic regression model was feasible to describe the correlations with a good fitting effect. Further, their RER was also significantly positively correlated with score of negative mood state components such as nervousness (0.290 ∗ ∗ ), anger (0.300 ∗ ∗ ), fatigue (0.278 ∗ ∗ ), depression (0.287 ∗ ∗ ), and fluster (0.261 ∗ ), and a quadratic or cubic regression model was suitable to describe the correlations. Additionally, the young novice drivers’ CRT was significantly positively correlated with score of nervousness (0.222 ∗ ∗ ), vigorousness (0.227 ∗ ), and fluster (0.273 ∗ ), and a quadratic or exponential regression model was suitable to describe the correlations. The results can provide theoretical support for developing targeted intervention to improve young novice drivers’ emergency response capacity for driving training or traffic management authorities.

Feasibility of Economic Forecasting Model Based on Intelligent Algorithm of Smart City

Smart cities make better use of space and have less traffic, cleaner air, and more efficient municipal services, improving people’s quality of life. The vast number of vehicles continually seeking to reach crowded spots in smart cities complicates acquiring a public parking space. It presents challenges for both traffic and residents. With such vast populations, road congestion is a serious challenge. It wastes vital resources such as fuel, money, and, most importantly, time. Finding a good location to park is one of the reasons for traffic congestion on the highway. This paper proposes a deep learning-based economic forecasting model (DL-EFM) for long-term economic growth in smart cities. Traffic management is vital for cities to guarantee that people and products can move freely across the city. Many automobiles attempting to reach crowded areas in smart cities make getting a public parking place difficult. It is inconvenient for both drivers and residents. Different traffic management authorities have implemented an artificial neural network (ANN) to resolve the issue, and modern vehicle systems have been coupled with intelligent parking solutions. The experimental outcome of the deep learning-based economic forecasting model improves traffic estimation, accuracy prediction in traffic flow, traffic management, and smart parking when compared to existing methods.

Fast and Precise: Parallel Processing of Vehicle Traffic Videos Using Big Data Analytics

Cities worldwide use camera systems that collect and store large amounts of images, which are used to study vehicle traffic conditions, facilitating traffic management authorities’ decision-making. Typically, the inspection of those images is performed manually, which prevents extracting relevant information in a timely manner. There is a lack of platforms to collect and analyze key data from traffic videos in an automatic and speedy way. Computer vision can be used in combination with parallel distributed systems to provide city authorities tools for automatic and fast processing of stored videos to determine the most significant driving patterns that cause traffic accidents while allowing to measure the traffic density. We use a Convolutional Neural Network (CNN) to detect vehicles captured by traffic cameras, which are then tracked using an algorithm that we designed, based on multi-tracking Kalman filters. To speed up analysis, we propose a low-cost distributed infrastructure based on Hadoop and Spark frameworks for data processing: videos are equally divided and distributed to multicore CPU nodes for analysis. However, splitting up videos could generate inaccuracies in vehicle counting, which were avoided through the use of an algorithm that we present in this work. We found that it is possible to rapidly determine traffic densities, identify dangerous driving maneuvers, and detect accidents with high accuracy by using low-cost commodity cluster computing. There is a lack of computing platforms to collect and analyze key data from traffic videos in an automatic and speedy way. Computer vision can be used in combination with parallel distributed systems to provide city authorities tools for automatic and fast processing of stored videos to determine the most significant driving patterns that cause traffic accidents while allowing to measure the traffic density. This study explores the integration of different tools such as parallel data processing, deep learning, and probabilistic models. We present an approach based on Convolutional Neural Network (CNN) and Kalman filters to detect and track vehicles captured by traffic cameras. To speed up analysis, we propose and evaluate a low-cost distributed infrastructure based on Hadoop and Spark frameworks and comprised of multicore CPU nodes for data processing. Finally, we present an algorithm to allow vehicle counting while avoiding inaccuracies generated when videos are split to be distributed for analysis. We found that it is possible to rapidly determine traffic densities, identify dangerous driving maneuvers, and detect accidents with high accuracy by using low-cost commodity cluster computing.