Traffic Management Center Research Articles

Особенности применения обеспечительных мер административного принуждения в отношении несовершеннолетних, совершивших правонарушения в области дорожного движения

This article is devoted to the specifics of the application of interim measures of administrative coercion in relation to minors who have committed offenses in the field of traffic. The author examines the legislation and practice of applying measures to ensure proceedings in cases of administrative offenses. Without taking into account the specifics of work in this area, it is impossible to carry out effective activities. This is especially true for traffic police officers, transport security services, and employees of traffic management centers. Proceedings in cases of administrative offenses in the field of traffic against minors have many features that are discussed in this study.

Ensemble Random Forest-based Gradient Optimization based Energy Efficient Video Processing System for Smart Traffic Surveillance System

Deep learning solutions in big data applications can benefit cloud centres and can also lead to network communication overhead. Typically, data collected from traffic are sent to the traffic management centre for analysis. However, this process can worsen the network route to the traffic management centre. A two-tier mechanism has been developed to address this issue, which performs vehicle speed estimation and traffic congestion detection for efficient traffic management. The real-time traffic video data are captured and the video frames are initially processed through a foreground extraction process, which extracts the temporarily stopped vehicles on the road by removing background pixels from the frames. The video frames are then wrapped in an up-down view to remove the influence of the observation angle. The traffic congestion is then detected accurately based on the traffic characteristics using the proposed Ensemble Random Forest-based Gradient Optimization (ERF-GO) algorithm. The generalization error occurs when learning complex features on frames is minimized using a gradient-based optimization (GO) algorithm. Finally, the learned information on traffic conditions is forwarded to the cloud and edge computing environments based on network connection speed. The efficiency of the proposed ERF-GO is investigated in terms of performance metrics, namely root mean square error, speed detection error, execution time, computational cost, accuracy, latency, workload balance, precision, recall, f-measure, and congestion detection error rate. The analytic result displays that the proposed ERF-GO algorithm attains a greater accuracy rate of about 98.65% in detecting traffic congestion which is comparably higher than state-of-the-art methods.

Integrating AI-Technology with Unmanned Aerial Vehicles for Traffic Congestion Recognition

This article explores the integration of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) technology to address traffic congestion. By leveraging UAVs equipped with route-planning capabilities, aerial images of traffic scenes are captured and analyzed using convolutional neural networks (CNNs). The processed data is then transmitted to a traffic management center, enabling timely recognition and response to congestion. This framework offers a practical approach to alleviate traffic delays, reduce fuel consumption, mitigate pollution, and enhance overall road safety

Lateral Track Buckling in Sweden: Insights from Operators and Infrastructure Managers

Rail transport is expected to become a key component in the development of a long-term sustainable transport system. The planning, construction, operation, and maintenance of railway infrastructure are crucial in this effort. Hence, it is essential to ascertain that the railway infrastructure withstands and is adapted to extreme weather conditions and climate change. This study focuses on evaluating climate adaptation measures for lateral track buckling in Sweden. Through a literature review and interview with an expert at Swedish Transport Administration, it is highlighted that the maintenance status of railway infrastructure plays a significant role in the occurrence of lateral track buckling. According to the expert, inadequate track maintenance is the primary cause of lateral track buckling rather than weather variables like air temperature. The interview also clarifies that the chain of events related to the handling of track buckling is mainly initiated by the observation of a discrete lateral irregularity by a train driver, whereupon the train dispatcher at the traffic management center stops traffic until the location in the track has been inspected by a track entrepreneur. During the inspection, up to half of the observed cases of track buckling turn out to be false.

Predicting pedestrian-involved crash severity using inception-v3 deep learning model

This research leverages a novel deep learning model, Inception-v3, to predict pedestrian crash severity using data collected over five years (2016–2021) from Louisiana. The final dataset incorporates forty different variables related to pedestrian attributes, environmental conditions, and vehicular specifics. Crash severity was classified into three categories: fatal, injury, and no injury. The Boruta algorithm was applied to determine the importance of variables and investigate contributing factors to pedestrian crash severity, revealing several associated aspects, including pedestrian gender, pedestrian and driver impairment, posted speed limits, alcohol involvement, pedestrian age, visibility obstruction, roadway lighting conditions, and both pedestrian and driver conditions, including distraction and inattentiveness. To address data imbalance, the study employed Random Under Sampling (RUS) and the Synthetic Minority Oversampling Technique (SMOTE). The DeepInsight technique transformed numeric data into images. Subsequently, five crash severity prediction models were developed with Inception-v3, considering various scenarios, including original, under-sampled, over-sampled, a combination of under and over-sampled data, and the top twenty-five important variables. Results indicated that the model applying both over and under sampling outperforms models based on other data balancing techniques in terms of several performance metrics, including accuracy, sensitivity, precision, specificity, false negative ratio (FNR), false positive ratio (FPR), and F1-score. This model achieved prediction accuracies of 93.5%, 77.5%, and 85.9% for fatal, injury, and no injury categories, respectively. Additionally, comparative analysis based on several performance metrics and McNemar’s tests demonstrated that the predictive performance of the Inception-v3 deep learning model is statistically superior compared to traditional machine learning and statistical models. The insights from this research can be effectively harnessed by safety professionals, emergency service providers, traffic management centers, and vehicle manufacturers to enhance their safety measures and applications.

The Influence of the Use National Traffic Management Center (NTMC) Technology at the National Police Traffic Corps Contact Center in Handling Traffic Accidents

This research aims to analyze the influence of the use of National Traffic Management Center (NTMC) technology at the National Police Traffic Corps contact center in handling traffic accidents. The government, through Korlantas Polri, established the National Traffic Management Center (NTMC) to realize safe, secure, orderly, and integrated traffic and road transportation services. The service provided is in the form of the NTMC Contact Center application which was developed in 2017 to complement the call center and SMS center services that have been available since 2014. The ease of use of technology and the provision of quality services will have an impact on community satisfaction. The purpose of this research is to analyze the influence of acceptance of the use of technology, the influence of service quality on the NTMC Contact Center application for the National Police Traffic Corps, and the simultaneous influence of acceptance of the use of technology and quality of service on the NTMC Contact Center application for the National Police Traffic Corps on the level of public satisfaction in handling traffic violations and accidents. Theoretical concepts in the Technology Acceptance Model, User Acceptance, and Community Satisfaction are used to analyze the acceptance of technology use and service quality on community satisfaction in the NTMC Contact Center application for the National Police Traffic Corps. The research method used in this research is quantitative explanatory research with a population of 10,000 people who downloaded the NTMC Contact Center Polri application on Google Playstore. From the total population, a sample of 100 people can be obtained using the Slovin formula. The sampling technique used was the snowball sampling technique. The results of the research show that there is a positive and significant influence of acceptance of the use of technology, influence of service quality, and a simultaneous influence of acceptance of the use of technology and service quality in the NTMC Contact Center application on the level of public satisfaction in handling traffic violations and accidents.

Vulnerability analysis of an electric vehicle fleet for car-sharing service under cyber attacks

The booming development of the car-sharing industry and the continuous increase in the market share of electric vehicles demand effective dispatching strategies that consider collaborative optimization of route selection and charging management. Furthermore, in recent years, network attacks have posed significant threats to traffic management centers. Therefore, this paper presents a pioneering work aimed at investigating the vulnerability of electric vehicle sharing services in traffic management systems under network attacks. Firstly, we develop a bilevel optimization model to capture the game between network attackers and electric vehicle fleet operators. The upper-level problem represents the behavior of network attackers, who aim to maximize the total cost by manipulating traffic control signals and sensor data. The lower-level problem determines the system operator's decisions on optimizing route selection and charging scheduling to minimize costs. Secondly, a nested column generation algorithm is employed to solve the bilevel optimization model. Finally, several sensitivity studies are conducted using real-world traffic networks to examine the effectiveness of the proposed model and the influence of various parameters on system performance.

Blockchain-Assisted Privacy-Preserving Traffic Route Management Scheme for Fog-Based Vehicular Ad-Hoc Networks

Traffic route management is essential for reducing traffic jams and enhancing driving safety becauese of the growing number of vehicles and frequent occurrence of traffic accidents. However, in vehicular ad-hoc networks (VANETs), real-time messages are transmitted via wireless channels, which can result in security and privacy concerns. Existing proposals for traffic route management exist security vulnerabilities, as well as high calculation and communication costs. Encouraged by this fact, we design a lightweight traffic route management scheme for fog-based VANETs. In this scheme, vehicles utilize homomorphic encryption to encrypt their driving routes and then send the encrypted information to a fog node. The traffic management center (TMC) decrypts the received ciphertexts that are aggregated by the fog node and performs traffic management according to the decrypted data, without knowing individual route of each vehicle. Furthermore, blockchain is used in the scheme to conduct public keys management of vehicles. Our detailed security proof and analysis indicate that our proposal can meet the security objectives of VANETs. Further, to demonstrate the feasibility of the scheme, we also implement it in the Ethereum test network (i.e.,Rinkeby). Significantly, the performance analysis demonstrates that our proposal achieves a better performance than other relevant representative schemes.

Modeling an ITS Management Solution for Mixed Highway Traffic With Eclipse MOSAIC

Mixed traffic of conventional and automated vehicles entails many challenges concerning the safe coexistence and the improvement of traffic efficiency. As a solution, a Traffic Management Center (TMC) combines information from physical sensors and communicated data to enable advanced traffic controllers. Since there is no sufficient penetration rate of automated vehicles nowadays, such a solution could be developed and tested with synthetically generated data using simulation, which models the future Intelligent Transportation System (ITS). This paper addresses the challenges for evaluation of such a system by the means of simulation. For this purpose, we present the Eclipse MOSAIC Multi-Domain Simulation Framework, which provides the possibility to couple best-in-class simulators of the domains traffic, application, and communication, as well as to include further models and external native code libraries. Moreover, we discuss modeling extensions for traffic control algorithms and our method to create a highway scenario based on real traffic data. As primary result, many research questions for mixed traffic and ITS could be investigated using the MOSAIC framework. In an exemplary study, we analyze the deployment effort for infrastructure and communication technologies in order to facilitate a Mainstream Traffic Flow Controller in a realistic scenario with mixed traffic. Simulations performed with MOSAIC confirmed that traffic efficiency improvement is feasible even with fewer infrastructure components, thus with less cost.

Real-Time Framework to Predict Crash Likelihood and Cluster Crash Severity

This study proposes a three-stage framework for real-time crash likelihood and severity prediction. Firstly, a real-time crash likelihood prediction model was developed. Secondly, a real-time crash severity clustering model was proposed to cluster the crashes into different severity levels. Thirdly, a severity clustering validation model was developed to assess the performance of the proposed severity clustering model. Extensive data processing techniques were employed to collect real-time features from State Road 408 in Orlando, Florida, and a total of 6,750,072 events (625 crash events and 6,749,447 non-crash events) along with 24 real-time features were used. To develop the crash likelihood prediction model, nine machine-learning techniques were attempted, and the convolutional neural network model was found to provide the best result with respect to the sensitivity (0.916), false alarm rate (0.111), and area under the receiver operating characteristic curve (0.967). Davies–Bouldin index criteria were used to find the detector location that generated the most accurate traffic information to cluster the crashes into severity levels, and based on this traffic information, k-means clustering was applied to develop the severity clustering model. Finally, a severity clustering validation model was developed after investigating nine machine-learning techniques to validate the developed severity clustering model, and the decision tree model provided the best results based on three levels of sensitivity and specificity values. The developed framework has the potential to help traffic management centers to warn road users or develop transportation systems management and operations strategies in real time to avoid crashes or minimize the severity and, thus, can significantly contribute to improving road safety.

Using Crowdsourced Data to Reduce Traffic Congestion by Improving Detection of and Response to Disabled or Abandoned Vehicles on Florida Limited-Access Facilities

Disabled or abandoned vehicles (DAVs) cause a significant proportion of non-recurring traffic congestion. Waze data has the potential to help traffic management center (TMC) operators detect and respond to DAVs more quickly, reducing congestion. Previous studies have examined Waze data on a small scale, but not at a statewide level. This paper analyzes over 2 years of DAV Waze data on Florida Department of Transportation (FDOT) limited-access roadways (with more than 10 million alerts) and compares them with reported DAV events (crashes and non-crash TMC reports). Over 46% of the DAV events had an associated Waze alert before the DAV event occurred, indicating significant potential for earlier detection. These Waze alerts typically happened about 16 min before the DAV event and were most common during daytime hours and in urban areas. Two roadway segments, I-4 in FDOT District 5 and State Road 91 (SR-91) in FDOT District 4, were examined in more detail. A methodology was developed to estimate the delay reduction and congestion savings achieved by the earlier detection provided by the Waze alerts for lane-blocking and shoulder-blocking disabled vehicle events. The estimated congestion savings for the second half of 2019 were $4.3 million (I-4) and $2.5 million (SR-91), with benefit-cost ratios of 61 and 27, respectively. An additional $4.3 million could potentially have been saved as a result of Waze alerts allowing responders to reach DAVs before a crash occurred. These results can help agencies understand how crowdsourced data can be effectively utilized to best improve freeway operations.

AI-Enabled Cryptographic Key Management Model for Secure Communications in the Internet of Vehicles

Recent advancements in the Internet of Vehicles (IoV) technology have pathed the way for the use of various smart services for the management of urban traffic, including authentication and key management. Key management protocols are an important means of addressing security and privacy concerns. However, they can be resource-intensive in terms of network traffic and workload management, particularly at times of traffic congestion, which in turn can increase the ECU and RSU processing load and adversely impact network communications. This paper introduces a more efficient key management method, named AI-enabled and Layered Key Management (ALKM), which uses an Artificial Intelligence (AI) approach and a layered workflow to reduce network traffic and workload. Specifically, the ALKM distributes dynamic synchronous time-dependent keys among Road Side Units (RSUs) rather than static cryptographic keys. It provides three layers of secure communications: public, tunnel, and hierarchy. The public layer creates a flat secure layer between the Traffic Management Center (TMC) and all RSUs. Using AI-enabled features, the tunnel layer predicts short-term and long-term congestion areas by analysis of the acquired trajectory data, and then establishes a secure communication channel between the selected RSUs and the TMC. In the hierarchy layer, in multiple tiers, the TMC and higher-level RSUs assist lower RSUs in message decryption (but not vice-versa). Our extensive analysis shows that the ALKM generates overall between 48% and 99% less network traffic per generated key than the Master Key method, depending on the operational lifetime of the keys used.

A critical assessment of Athens Traffic Restrictions using multiple data sources

This paper presents a critical assessment of Athens Traffic Restrictions (ATR) by analyzing data on traffic volumes and speeds from the Traffic Management Centre of Athens, as well as ticket validations from Athens public transport operator (OASA S.A.). ATR were reinstated on 24/10/2021 as a response to increasing congestion. The analysis showed an increase in average speeds and reduction in congestion inside ATR. Outside ATR, no conclusive evidence was found supporting a persistent impact of the measure on traffic. The reinstatement of ATR contributed to the increase in metro ridership. Transport and local authorities should set clear goals and KPIs to monitor the success of the measure in the long term. Enhancing the implementation of ATR with micromobility options and MaaS solutions inside the restricted area can further boost compliance and strengthen the uptake of public transport.

The Role of Digital Twins in Connected and Automated Vehicles

Digital twins found their genesis in the halls of NASA and the methods of product lifecycle management. Rapidly evolving trends around the proliferation of sensors, the Internet of Things, Industry 4.0, and cyber-physical systems have spurred the growth of digital twins. This paper reviews digital twins and their use in connected and automated vehicles (CAVs). Strictly speaking, digital twins must have communication between a physical system and its model, as opposed to similar methodologies that achieve indirect communication through iteration, or that substitute different parts of a system simulation with bits of hardware or software for testing. In practice, new methodologies for testing CAVs are sufficiently complex and difficult to apply simple labels. This is seen in our review of vehicular digital twins. Several gaps and challenges are apparent for the continued advancement of digital twin applications. We note some developing areas as traffic management centers, digital maps, onboard diagnostics, and logistics. Digital twins foster model reuse and encourage the use of multiple models at different scales of resolution. The role of digital twins will continue to grow as models become more tightly integrated to the physical systems they represent. This will drive such models towards uniqueness (matching a particular vehicle or road), adaptability (evolving with changing conditions and subject to wear and tear), and interpretability (conveying useful information to a human user). A maturing connected infrastructure and the development of smart cities will cause the number of new digital twin services to explode in a myriad of unforeseen ways.

Impact of Data Loss on Multi-Step Forecast of Traffic Flow in Urban Roads Using K-Nearest Neighbors

Data-driven models have recently proved to be a very powerful tool to extract relevant information from different kinds of datasets. However, datasets are often subject to multiple anomalies, including the loss of important parts of entries. In the context of intelligent transportation, we examine in this paper the impact of data loss on the behavior of one of the frequently used approaches to address this kind of problems in the literature, namely, the k-nearest neighbors model. The method designed herein is set to perform multi-step traffic flow forecasts in urban roads. In our study, we deploy non-prepossessed real data recorded by seven inductive loop detectors and delivered by the Traffic Management Center (VMZ) of Bremen (Germany). Firstly, we measure the performance of the model on a complete dataset of 11 weeks. The same dataset is then used to artificially create 50 incomplete datasets with different gap sizes and completeness levels. Afterwards, in order to reconstruct these datasets, we propose three computationally-low techniques, which proved through empirical testing to be efficient in reproducing missing entries. Thereafter, the performance of the E-KNN model is assessed under the original dataset, incomplete and filled-in datasets. Although the accuracy of E-KNN under incomplete and reconstructed datasets depends on gap lengths and completeness levels, under original dataset, the model proves to deliver six-step forecasts with an accuracy of 83% on average over 3 weeks of the test set, which also translates to a less than one car per minute error.

Smart Traffic Monitoring System Using Computer Vision and Edge Computing

Traffic management systems capture tremendous video data and leverage advances in video processing to detect and monitor traffic incidents. The collected data are traditionally forwarded to the traffic management center (TMC) for in-depth analysis and may thus exacerbate the network paths to the TMC. To alleviate such bottlenecks, we propose to utilize edge computing by equipping edge nodes that are close to cameras with computing resources (e.g., cloudlets). A cloudlet, with limited computing resources as compared to TMC, provides limited video processing capabilities. In this paper, we focus on two common traffic monitoring tasks, congestion detection, and speed detection, and propose a two-tier edge computing based model that takes into account of both the limited computing capability in cloudlets and the unstable network condition to the TMC. Our solution utilizes two algorithms for each task, one implemented at the edge and the other one at the TMC, which are designed with the consideration of different computing resources. While the TMC provides strong computation power, the video quality it receives depends on the underlying network conditions. On the other hand, the edge processes very high-quality video but with limited computing resources. Our model captures this trade-off. We evaluate the performance of the proposed two-tier model as well as the traffic monitoring algorithms via test-bed experiments under different weather as well as network conditions and show that our proposed hybrid edge-cloud solution outperforms both the cloud-only and edge-only solutions.

Change Point Models for Real-Time Cyber Attack Detection in Connected Vehicle Environment

Connected vehicle (CV) systems are subject to potential cyber attacks because of increasing connectivity between its different components, such as vehicles, roadside infrastructure, and traffic management centers. However, it is a challenge to detect security threats in real-time and develop appropriate or effective countermeasures for a CV system because of the dynamic behavior of such attacks, high computational power requirement, and a historical data requirement for training detection models. To address these challenges, statistical models, especially change point models, have potentials for real-time anomaly detection. Thus, the objective of this study is to investigate the efficacy of two change point models; Expectation Maximization (EM) and two forms of Cumulative Summation (CUSUM) algorithms (i.e., typical and adaptive), for real-time vehicle-to-infrastructure (V2I) cyber attack detection in a CV Environment. To prove the efficacy of these models, we evaluated these two models for three different types of cyber attack, denial of service (DOS), impersonation, and false information, using basic safety messages (BSMs) generated from CVs through simulation. Results from numerical analysis revealed that EM, CUSUM, and adaptive CUSUM (aCUSUM) could detect these cyberattacks, such as DOS, impersonation, and false information with low false positives.

The State of the Art of Cooperative and Connected Autonomous Vehicles from the Future Mobility Management Perspective: A Systematic Review

Cooperative and connected autonomous vehicles (CCAVs) are considered to be a promising solution for addressing congestion and other operational deficiencies, as part of a holistic future mobility management framework. As a result, a significant number of studies have recently been published on this topic. From the perspective of future mobility management, this review paper discusses three themes, which are traffic management, network performance, and mobility management, including congestion, and incident detection using the PRISMA methodology. Three databases were considered for this study, and peer-reviewed primary studies were selected that were published within the last 10 years in the English language, focusing on CCAV in the context of the future transportation and mobility management perspective. For synthesis and interpretation, like-for-like comparisons were made among studies; it was found that extensive research-supported information is required to ensure a smooth transition from conventional vehicles to the CCAVs regime, to achieve the projected traffic and environmental benefits. Research investigations are ongoing to optimize these benefits and associated goals via the setting of different models and simulations. The tools and technologies for the testing and simulation of CCAV were found to have limited capacity. Following the review of the current state-of-the-art, recommendations for future research have been discussed. The most notable is the need for large-scale simulations to understand the impact of CCAVs beyond corridor-based and small-scale networks, the need for understanding the interactions between the drivers of CCAVs and traffic management centers, and the need to assess the technological transition, as far as infrastructure systems are concerned, that is necessary for the progressive penetration of CCAVs into traffic streams.

Toward Formalization and Monitoring of Microscopic Traffic Parameters Using Temporal Logic

Smart mobility is transforming the way the current transportation network is envisioned. It holds the promise of a more sustainable, safer, and efficient future for commuters. Nevertheless, traffic management centers are constantly facing the challenges of ensuring that transportation system components are operating as expected and in a safe manner. As a result, research efforts on improving traffic monitoring aim to design and implement novel approaches for safety applications. In this paper, we adopt formal methods to specify and apply reason to the traffic network’s complex properties. Formal methods provide a framework to rigorously define the safe operation of the traffic network by capturing non-conforming travel behavior, exploring various possible states of vehicular traffic, and detecting any irregularities that may arise. In this work, a new approach to traffic monitoring is proposed, which uses specification-based monitoring. We develop monitors that define traffic parameters, such as conforming to speed limits and maintaining appropriate headway. A formal language known as Signal Temporal Logic is used to specify and analyze these traffic rules. The proposed framework is then applied to a calibrated micro-simulated highway network to identify whether individual vehicle trajectories violate or satisfy the proposed specifications. Statistical analysis of the outputs shows that our proposed approach is effective in differentiating between violating and conforming vehicles. This approach can be used by traffic management centers that are seeking to accommodate emerging mobility technologies that are autonomous and connected. In particular, the presented work can be valuable in studying traffic stream properties, identifying possible hazards, and providing valuable feedback for automating traffic monitoring systems.

Intelligent Traffic Control System Using Deep Learning

Traffic congestion and regulating traffic in traffic signals are major issues in cities. Nowadays, in most of the cities, traffic management centers installed numerous cameras all over the roads and traffic signals. Such cameras can be effectively used for the automation of traffic signals. The objective is to develop a real time system that can automatically monitor real time traffic and make the system intelligent using artificial intelligence techniques. Specifically, Deep Convolutional Neural Networks are employed to perform the task. From statistical traffic data, it determines count, type of vehicle, average speed, distance between vehicles, etc. Based on traffic, the algorithm instructs to stop vehicle or queue or move. It can also record a wrong-way driver. Using license plate recognition, security applications such as unauthorized vehicles are identified. If there is violation of traffic rules, they are recorded with registration number. It can detect ambulances and give first preference. The proposed algorithm identifies VIP vehicles and clear traffics in automated ways. Ambulances are given priority to pass the road. The entire system have been developed using a standalone-Graphical User Interface (GUI). We have implemented successfully and the proposed framework performs satisfactorily.