Traffic Management Information Research Articles

DETECTION OF LPG VEHICLES IN RISKY AREAS WITH COMPUTER VISION TECHNIQUES

Vehicle use is becoming more widespread day by day due to the world population growth. Within the scope of intelligent transportation systems, the information technologies sector and the transportation sector work in an integrated manner to solve the problems caused by the increasing number of vehicles. Data obtained with sensors and cameras are analyzed with artificial intelligence-based information technologies and used in autonomous vehicles, security, traffic management, navigation and passenger information systems. Computer vision enables machines to extract meaningful patterns and relationships from images by combining image processing and deep learning technologies. Computer vision techniques are applied in many fields such as tourism, health, industry, defense, transportation, service, e-commerce, etc. The applications developed provide solutions to various challenges in the transportation sector. For vehicles using Liquified Petroleum Gas (LPG) fuel, the gases in LPG tanks are flammable and pose a potential explosion hazard, especially in certain areas in cities. Entry of LPG vehicles is prohibited in institutions and organizations such as hospitals, shopping malls, hotels that have indoor parking services. The control method of the ban is carried out by assigning a personnel and checking the vehicle trunks. In this study, LPG fueled vehicles were automatically detected using computer vision techniques. Vehicle image data captured by mobile cameras in different provinces in Turkey were trained and compared with four different deep learning models. As a result of training and performance tests on the models, the YOLOv8 model was more effective than the other models with an accuracy of 0.994 mAP and a speed of 11.6 ms. It has been shown to be a stable model in terms of real-time monitoring in real life. It is envisaged that the developed system can contribute to the applications of computer vision techniques as well as benefit the national economy, public life safety and environmental protection.

Broadcast signcryption scheme with equality test in smart transportation system

With the generation of massive traffic information in the smart transportation system, the traffic control center efficiently utilizes broadcast communication to send multiple messages to multiple vehicles. Besides, diversified privacy disclosure and security attack issues also emerged spontaneously. To achieve secure communication between the traffic control center and vehicles in the smart transportation system, we design a broadcast signcryption scheme with equality test in the smart transportation system based on the certificateless cryptosystem and equality test. The scheme realizes message confidentiality and vehicle privacy by using the Lagrange interpolation theorem to encrypt messages and vehicle identities, while also achieving classify ciphertext by using the equality test and facilitate road traffic information management. Through numerical experiment analysis, the proposed work has higher operation efficiency and is more suitable for application in smart transportation systems.

The Development of Air Traffic Management Information Exchange Interoperability Framework for Civil Aviation Authority of Malaysia

The Development of Air Traffic Management Information Exchange Interoperability Framework for Civil Aviation Authority of Malaysia

Towards better traffic volume estimation: Jointly addressing the underdetermination and nonequilibrium problems with correlation-adaptive GNNs

Traffic volume is an indispensable ingredient to provide fine-grained information for traffic management and control. However, due to the limited deployment of traffic sensors, obtaining full-scale volume information is far from easy. Existing work on this topic focuses primarily on improving the overall estimation accuracy of a particular method and ignores the underlying challenges of volume estimation, thereby having inferior performance in some critical tasks. This paper studies two key problems with regard to traffic volume estimation: (1) underdetermined traffic flows caused by fully undetected paths that can allow arbitrary volume values without violating the conservation law, where using local side information is insufficient to tackle, and (2) nonequilibrium traffic flows arise when traffic flows vary in density over space and time due to congestion propagation delay, which produce time-shifted volume readings to varying degrees. Here we demonstrate a graph-based deep learning method that offers a data-driven, equation-free, and correlation-adaptive approach to address the above issues and perform accurate network-wide traffic volume estimation. Particularly, in order to quantify the dynamic and nonlinear speed-volume relationships for the estimation of underdetermined flows, a speed pattern-adaptive adjacency matrix based on graph attention is developed and integrated into the graph convolution process, to capture nonlocal correlations between volumes. To measure the impacts of nonequilibrium flows, a temporal masked and clipped attention combined with a gated temporal convolution layer is customized to capture time-asynchronous correlations between upstream and downstream sensors under varying impacts of congestion delay. We then evaluate our model on a real-world highway traffic volume dataset and compare it with several benchmark models. It is demonstrated that the proposed model achieves high estimation accuracy even under 20% sensor coverage rate and outperforms other baselines significantly, especially on underdetermined and nonequilibrium flow locations. Furthermore, comprehensive quantitative model analysis is also carried out to justify the model designs. The source code and datasets are publicly available at: .

Traffic Speed Prediction Based on Time Classification in Combination With Spatial Graph Convolutional Network

With the advancement of automatic driving and smart city, it is critical to predict traffic information for traffic management, traffic planning, and traffic safety. When predicting traffic information, the spatial structure of the roads will also affect the traffic flow information, such as speed, occupancy rate, etc. The common method either merely focusing on the temporal feature without the considering the spatial structure, or the method of spatial feature extraction is only applicable to Euclidean structure, which does not apply to Non-Euclidean structure. This paper proposes a traffic speed prediction method based on time classification in combination with spatial Graph Convolutional Network. This method employs Gated Recurrent Unit to extract the temporal correlation and Graph Convolutional Network to extract the traffic network’s spatial structure. In consideration of the varying features of traffic speed on weekdays and weekends in the time dimension, time is divided into two types: weekdays and weekends. Since the structure of the road network will not change in the short term in actual process, the same network structure of spatial graph convolution can reasonably be shared in the spatial dimension after which the two sections are fused for training and prediction. Finally, this proposed method is compared to some baseline models to prove the performance. Generally speaking, this strategy produces more accurate prediction results on the PEMS_BAY and METR_LA data sets than the baseline models.

Resource Management for Collaborative 5G-NR-V2X RSUs to Enhance V2I/N Link Reliability.

In the development of autonomous driving technology, 5G-NR vehicle-to-everything (V2X) technology is a key technology that enhances safety and enables effective management of traffic information. Road-side units (RSUs) in 5G-NR V2X provide nearby vehicles with information and exchange traffic, and safety information with future autonomous vehicles, enhancing traffic safety and efficiency. This paper proposes a communication system for vehicle networks based on a 5G cellular network with RSUs consisting of the base station (BS) and user equipment (UE), and validates the system performance when providing services from different RSUs. The proposed approach maximizes the utilization of the entire network and ensures the reliability of V2I/V2N links between vehicles and each RSU. It also minimizes the shadowing area in the 5G-NR V2X environment, and maximizes the average throughput of vehicles through collaborative access between BS- and UE-type RSUs. The paper applies various resource management techniques, such as dynamic inter-cell interference coordination (ICIC), coordinated scheduling coordinated multi-point (CS-CoMP), cell range extension (CRE), and 3D beamforming, to achieve high reliability requirements. Simulation results demonstrate improved performance in outage probability, reduced shadowing area, and increased reliability through decreased interference and increased average throughput when collaborating with BS- and UE-type RSUs simultaneously.

An empirical investigation of driver car-following risk evolution using naturistic driving data and random parameters multinomial logit model with heterogeneity in means and variances

This study aims to address the questions of how driving risk evolves during car-following processes and what factors contribute to the underlying evolution patterns. An empirical study is conducted using real world car-following data collected in the Shanghai Naturalistic Driving Study (SH-NDS). The evolution of the driving risk induced by the dynamic coupling between the leading and following vehicles during the car-following process is characterized by how an instantaneous crash-risk measure - rear crash risk index (RCRI) - changes by time. A spectral clustering analysis is first conducted to classify the driving risk evolution of the observed car-following maneuvers, showing the existence of five distinctive risk evolution patterns in the car-following processes. In order to investigate the relationship between the identified driving risk evolution clusters and their contributing factors, a regression analysis employing a random parameter multinomial logit model with heterogeneity in means and variances is followed, revealing several significant contributing factors to the car-following risk evolution patterns, such as congestion level, driver’s ability to maintain stable headways, and vehicle deceleration. This study has provided important insights into driving risk from the new perspective of risk evolution patterns, which is expected to have significant implications for the future development of advanced traffic management and traveler information systems (ATMS/ATIS) strategies, advanced driver assistance systems (ADAS), and connected and autonomous vehicles (CAV).

Dynamic Traffic Flow Prediction Based on Long-Short Term Memory Framework With Feature Organization

Traffic flow is an important piece of information for traffic management and control. In particular, the dynamic prediction of traffic flow provides the basis for efficient control measures. The existing studies focus on improving the prediction accuracy by integrating the long short-term memory (LSTM) into various complex frameworks without paying attention to the feature engineering, which has a significant impact on the performance of machine learning methods. In this article, we propose a dynamic traffic flow prediction approach based on the LSTM framework with different feature organizations: feature division modes and feature selection. The feature division modes consider the periodicity of traffic flow by intervals (e.g., 5 min) and periods (e.g., daily). The feature selection determines different types of features as inputs to the prediction model. The impact of different feature organization strategies on the prediction accuracy is investigated using field data collected by the Caltrans Performance Measurement System. Two types of LSTM frameworks, the fully connected LSTM and the sequence-to-sequence LSTM (seq2seq-LSTM), are used to evaluate the performance of the proposed prediction approach. The results show that the seq2seq-LSTM model with optimized feature organization can significantly improve the prediction performance.

A Vehicle-Consensus Information Exchange Scheme for Traffic Management in Vehicular Ad-Hoc Networks

Vehicular Ad-Hoc Networks (VANETs) provide roadside communication for improving the ease of driving user information exchange. It interconnects hierarchical infrastructure units and other vehicles for real-time traffic and vehicle management applications. The growth of vehicle and information density requires concord information exchange for application responses. In this article, Vehicle-Consensus Routing Management Scheme (VCRMS) is proposed for achieving fair roadside assistance for driving users. The proposed scheme exploits the surrounding vehicle information for infrastructure selection and traffic management. The infrastructure and vehicle information are analyzed for their similarity using deep learning for extracting monotonous decisions. The current and previous decisions are used for succeeding in information selection and traffic information retrieval. This prevents unnecessary data from being congesting the traffic and vehicle management application during driver assistance. The performance shows that the proposed scheme achieves 10.7% high application response, 15.9% less information delay, and 10.7% less traffic for different vehicle velocities.

How does E-Service Affect Satisfaction of Shipping Companies through Trust?

Services are vital for transportation and logistics activities. In the context of sea transportation, services can facilitate shipping activities and determine the satisfaction of shipping stakeholders. Hence, the purpose of the study was to determine the effectiveness of the Foreign Ship Agency Notice (FSAN) Directorate of Marine Traffic and Transportation Management Information System (DMTTMIS) e-service in increasing the satisfaction of shipping companies through trust among national shipping companies in the Indonesian Territory in 2022. The study used a survey method. The sample is 130 shipping companies taken by random sampling from 250 companies. Data analysis used descriptive statistics and path analysis inferential statistics. The results show E-service of FSAN DMTTMIS has an effect on shipping company satisfaction, trust has an effect on shipping company satisfaction, FSAN DMTTMIS e- service affects trust, and FSAN DMTTMIS e-service has an indirect effect on shipping company satisfaction with trust mediation. The conclusion of the study is that the e-service FSAN DMTTMIS has an indirect effect on the satisfaction of shipping companies with trust mediation so that the satisfaction of shipping companies can be increased through improvements to FSAN DMTTMIS e-services and trusts.

GOLIATH: A Decentralized Framework for Data Collection in Intelligent Transportation Systems

Intelligent Transportation Systems (ITSs) technology has advanced during the past years, and it is now used for several applications that require vehicles to exchange real-time data, such as in traffic information management. Traditionally, road traffic information has been collected using on-site sensors. However, crowd-sourcing traffic information from onboard sensors or smartphones has become a viable alternative. State-of-the-art solutions currently follow a centralized model where only the service provider has complete access to the collected traffic data and represent a single point of failure and trust. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GOLIATH</i> , a blockchain-based decentralized framework that runs on the In-Vehicle Infotainment (IVI) system to collect real-time information exchanged between the network’s participants. Our approach mitigates the limitations of existing crowd-sourcing centralized solutions by guaranteeing trusted information collection and exchange, fully exploiting the intrinsic distributed nature of vehicles. We demonstrate its feasibility in the context of vehicle positioning and traffic information management. Each vehicle participating in the decentralized network shares its position and neighbors’ ones in the form of a transaction recorded on the ledger, which uses a novel consensus mechanism to validate it. We design the consensus mechanism resilient against a realistic set of adversaries that aim to tamper or disable the communication. We evaluate the proposed framework in a simulated (but realistic) environment, which considers different threats and allows showing its robustness and safety properties.

Delay-Aware Content Delivery With Deep Reinforcement Learning in Internet of Vehicles

The rapid development of the Internet of Vehicles (IoV) enables various vehicular applications, such as image-aided navigation and traffic information management. It is important to provide efficient content delivery services for these vehicular applications. Caching popular content at roadside units (RSUs) is a promising way to improve content delivery efficiency. However, due to RSUs with limited cache space, it is very challenging to develop an effective content delivery policy that satisfies the high quality of service (QoS) requirements for vehicular applications. In this paper, we investigate the user-centric content delivery problem with service delay constraints in the IoV, where the objective is to minimize the vehicle’s cost under usage-based pricing. The problem of finding an optimal content delivery policy is modeled as a finite-horizon Markov decision process (MDP). Since the cache state of each RSU, and the wireless channel qualities between the vehicle and RSUs, are usually unknown to the vehicle a priori, the vehicle must learn the optimal delivery policy by interacting with the environment. To solve this problem and optimize the vehicle’s cost, we propose a double deep Q network (DDQN)-based algorithm, which implements dynamic content delivery decisions. Furthermore, the double deep Q network can overcome the large-scale state space and reduce Q value over-estimation. Numerical results show that our policy achieves a near-optimal performance when compared to the optimal policy that knows precisely cache state and wireless channel state. We also compare the effects of different caching strategies and vehicle mobility on the performance of the algorithm.

On the Analytical Probabilistic Modeling of Flow Transmission Across Nodes in Transportation Networks

This paper focuses on the analytical probabilistic modeling of vehicular traffic. It formulates a stochastic node model. It then formulates a network model by coupling the node model with the link model of Lu and Osorio (2018), which is a stochastic formulation of the traffic-theoretic link transmission model. The proposed network model is scalable and computationally efficient, making it suitable for urban network optimization. For a network with [Formula: see text] links, each with a space capacity of one, the model has a complexity of [Formula: see text]. The network model yields the marginal distribution of link states. The model is validated versus a simulation-based network implementation of the stochastic link transmission model. The validation experiments consider a set of small networks with intricate traffic dynamics. For all scenarios, the proposed model accurately captures the traffic dynamics. The network model is used to address a signal control problem. Compared with the probabilistic link model of Lu and Osorio (2018) with an exogenous node model and a benchmark deterministic network loading model, the proposed network model derives signal plans with better performance. The case study highlights the added value of using between-link (i.e., across-node) interaction information for traffic management and accounting for stochasticity in the network.

Research on Access Control Scheme of System Wide Information Management Based on Attribute Association

System wide information management (SWIM) involves civil aviation system control, intelligence, alarm, traffic, and other data. These data are transmitted in various forms, making SWIM system vulnerable to sensitive information leakage, data tampering, denial, and other security threats. In this article, an attribute-based air traffic management (ATM) information access control scheme is proposed to solve the security threat of SWIM. An improved extensible access control markup language (XACML) authorization model is established, combining linear secret sharing scheme (LSSS) matrix structure and monotone span program (MSP); an attribute association algorithm is designed to establish the attribute association relationship between services and users. Experimental results show that the attribute association algorithm improves the time complexity, but the algorithm can support richer policy representation capability, and the proposed ATM information access control scheme is more efficient and can effectively reduce the space cost. This scheme can achieve more fine-grained and flexible access control.

Multistage Impacts of the Heavy Rain Process on the Travel Speeds of Urban Roads

Heavy rain causes the highest drop in travel speeds compared with light and moderate rain because it can easily induce flooding on road surfaces, which can continue to hinder urban transportation even after the rainfall is over. However, very few studies have specialized in researching the multistage impacts of the heavy rain process on urban roads, and the cumulative effects of heavy rain in road networks are often overlooked. In this study, the heavy rain process is divided into three consecutive stages, i.e., prepeak, peak, and postpeak. The impact of heavy rain on a road is represented by a three-dimensional traffic speed change ratio vector. Then, the k-means clustering method is implemented to reveal the distinct patterns of speed change ratio vectors. Finally, the characteristics of the links in each cluster are analyzed. An empirical study of Shenzhen, China suggests that there are three major impact patterns in links. The differences among links associated with the three impact patterns are related to the road category, travel speeds in no rain days, and the number of transportation facilities. The findings in this research can contribute to a more in-depth understanding of the relationship between the heavy rain process and the travel speeds of urban roads and provide valuable information for traffic management and personal travel in heavy rain weather.

Detecting and analyzing unlicensed taxis: A case study of Chongqing City

Unlicensed taxis are private vehicles that are not duly licensed or permitted by the jurisdiction in which they operate. Trajectory data contain rich behavior features of mobile objects, which are valuable for unlicensed taxi detection. (Electronic Registration Identification) ERI of the motor vehicle is a way to collect trajectory data. ERI’s advantage is that it can record all kinds of vehicles traveling in the city, including taxis, private vehicles. As a pilot city of ERI in China, Chongqing has formed a massive ERI trajectory dataset of motor vehicles. This dataset provides us with an opportunity to detect and analyze unlicensed taxis from a data-driven aspect. In this paper, we complete two main works: detecting city-wide unlicensed taxis and analyzing them. Firstly, we build an unlicensed taxis detection model based on an ensemble learning approach, random forest(RF). The goal of ensemble learning is to improve prediction, generalizability, and robustness over a single classifier. We employ taxis and commuting private vehicles as training samples. The core idea is that unlicensed taxis and taxis are similar in many aspects. We also innovatively utilize the POI information as an input feature to the unlicensed taxi detection model. With the comparison of some baseline models, we have proved our model’s superiority on the ERI dataset. So, we apply the detection model on a real-world dataset and detect the city-wide potential unlicensed taxis. Secondly, we conduct some statistical analysis with the detected potential unlicensed taxis. We find that unlicensed taxis do behave very much like taxis. The hot areas of taxis and unlicensed taxis are not the same, which provides vital information for further traffic management.

Advanced methods for turning rate estimation in roundabouts

Road traffic monitoring at intersections is important in traffic engineering practice. Measured data together with traffic estimation represents base input information for traffic management or for road infrastructure development. Measuring turning rates is problematic in roundabouts due to their special geometry. This needs laborious manual traffic counts or other special methods such as automatic number-plate recognition by image processing or Bluetooth sensing, which are more costly compared to the simple automatic cross-sectional detection. As a cost-effective solution to this problem, a hybrid solution is suggested, i.e. using cross-sectional detection combined with advanced estimation. The paper investigated different methods. Traditional iteration based approach as well as estimators adopted from control theory were benchmarked and validated on real-world traffic data as well as via microscopic traffic simulation. Considering different error metrics, it is shown that constrained Kalman Filter and Moving Horizon Estimation provide reliable solution to the turning rate estimation problem in roundabouts.

A robust variance information fusion technique for real-time autonomous navigation systems

Real-time navigation systems rely on multi-object environment information for productive routing and assisted movements of vehicles. The multi-object environment includes infrastructure, neighbours, smart building, and traffic management information for providing assisted driving for the vehicles. The dynamic vehicle environment and sensor failures impact the navigation assistance for the driving users. A robust variance-based information fusion (VIF) technique is proposed in this paper for addressing this issue. The proposed technique makes use of swappable sensor information for fusion for providing reliable navigation assistance. This means the fusion process is performed based on the active sensor information in a decisive manner. For effective fusion with available information, this technique makes use of classification learning. Through this learning process, the error causing information due to sensor failures is identified and mitigated from the fusion. Multi-sensor information classification is performed for performing errorless assistance and decisions. This process is linear throughout the information sensing time intervals for reducing the errors in navigation assistance. The proposed technique's performance is verified using experiments, and the metrics processing time, error, accuracy, and swapping instances are verified.

Crash Prediction on Expressway Incorporating Traffic Flow Continuity Parameters Based on Machine Learning Approach

Real-time crash prediction helps identify and prevent the occurrence of traffic crash. For years, various real-time crash prediction models have been investigated to provide effective information for proactive traffic management. When building real-time crash prediction model, a suitable variable space together with a specific time interval for traffic data aggregation and an appropriate modelling algorithm should be applied. Regarding the intercorrelation problem with variable space, comprehensive real-time crash prediction model considering available traffic data characteristics in applicable circumstances needs to be explored. Taking Xi’an G3001 Expressway as study area, real road traffic and accident data during the period from January 2014 to January 2019 on this expressway are applied for real-time crash prediction. To better capture traffic flow characteristics on expressway and improve the practicality of real-time crash prediction model, two new variables (segment difference coefficient and lane difference coefficient) describing the smoothness and continuity of traffic flow in spatial dimension are developed and incorporated in building the crash prediction model to solve the intercorrelation problem with variable space. Random forest (RF) is then adopted to specify the quantitative relationship between specific variable and crash risk. Real-time crash prediction model based on support vector machine (SVM) using new composed variable space is built. The results show that simplified variable space could contribute to the same classification power in currently used real-time crash prediction models compared with traditional variable space. Moreover, the prediction model based on SVM reaches an accuracy level of 0.9, which performs better than other currently used prediction models.

An Ontology-based Approach for Semantic Interoperability in Air Traffic Information Management

An Ontology-based Approach for Semantic Interoperability in Air Traffic Information Management