Road Traffic Information Research Articles

Expressway Network Model Construction and Road Section Impedance Estimation Based on Mixed Data

The acquisition of high-speed road network information and traffic environment information is of great significance for traffic management and planning, and at the same time, an accurate and efficient road network model is also the basis for the acquisition and application of traffic parameters. In order to quickly obtain high-speed road network information and road section traffic state information, this paper takes Chongqing Highway as an example, analyzes the traffic condition and characteristics of the highway network, and establishes the topology of the high-speed road network using network data and actual high-speed toll data from the graph theory and complex network theory. At the same time, based on the topology theory, fully exploiting the high-speed toll data, a more accurate estimate of different time periods, vehicles in the high-speed road section of the travel time, for highway traffic analysis to provide data reference, to improve the traffic planning program to enhance the efficiency of traffic management, to promote traffic planning and decision-making, as well as to enrich the theoretical basis of transportation research is of great significance.

Versatile Lamellar Wrap-Structured PVDF/PZT/CNTs Piezoelectric Sensor for Road Traffic Information Sensing, Monitoring, and Energy Harvesting

Active monitoring and accurate sensing of road traffic information provide crucial data for road managers and traffic law enforcement. However, current sensors and monitoring systems struggle to capture comprehensive vehicle information simultaneously. In the study, a novel piezoelectric sensor using PVDF/PZT/CNTs composites was developed for actively monitoring road traffic information and explored for energy harvesting suitability. Piezoelectric composites with a lamellar wrapped structure and excellent localized conductive networks were prepared through solution blending, non-solvent-induced phase separation, and layered hot pressing. The results revealed that this structural design improves the proportions, crystallinity, and content of the β-phase crystals in the composites, while significantly enhancing their piezoelectric properties, dielectric properties, and electronegativity. When the content of CNTs was 3.0 wt%, the proportion and content of β-phase crystals in the composites reached the highest values (91.36 % and 62.34 %), respectively. Additionally, the open-circuit voltage (Voc) and the short-circuit current (Isc) were 3.36 and 6.16 times higher, respectively, than those of the undoped CNTs composites. The developed piezoelectric sensor shows a wide pressure monitoring range (0.1 MPa–2.1 MPa), excellent sensitivity (6.94 MPa−1 at (<0.6 MPa) and 12.35 MPa−1 at (0.6–2.1 MPa)). As a vehicle non-stop weighing sensor, it presents extremely fast response (15 ms) and recovery (16 ms) speeds with long-term stability of 500,000 cycles. The most important feature of the developed piezoelectric sensor is its dual function as both a sensor and an energy harvester. Furthermore, the developed piezoelectric sensor can actively monitor vehicle axle weight, speed, and wheelbase while capturing energy from vehicle movement. This work opens up innovative solutions for road traffic monitoring and vehicle movement energy harvesting, with promising applications in intelligent transportation.

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.

Studying the Structure of the Road Pavement and Specifying the Repair Method

Studying the structural condition of road pavement is essential for making effective decisions about the type of maintenance or rehabilitation that will be carried out on a pavement section. Reliable quantitative assessment of the volume of necessary repairs will ensure the performance characteristics and durability throughout the entire service life at minimal cost. This article presents detailed results from Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR) studies. In addition, an analysis of these results and recommendations for strengthening the road pavement for the Sh-176 “Sighnaghi-Saint Nino Monastery” road to develop rehabilitation and maintenance scenarios. FWD results combined with road pavement layer thickness and traffic load information to determine residual service life and necessary treatment type. Thus, using the nonlinear theory of layered halfspace elasticity, pavement structure analyzed in the same way as other civil engineering structures.

Using Deep Neural Networks and Big Data to Predict Microscopic Travel Time in Work Zones

Traffic management in work zones is a challenging task that requires a balance between creating a safe environment for workers and minimizing traffic delays. Accurately predicting travel times through work zones is essential for dynamic traffic management and reducing congestion. However, complex interrupted traffic patterns in work zones (also known as construction zones) make this challenging compared with regular traffic congestion and free-flow conditions. To address this complexity, the paper develops a data-driven deep feed-forward artificial neural network to forecast under-construction travel times in work zones using an integrated data set of almost half a million observations. The variables considered in the neural network include the work zone characteristics, road design features, weather information, and traffic flow information. The raw data set comprised approximately 15 million travel time observations collected at 674 work zones. After cleaning and preprocessing the data and applying feature engineering techniques, the raw data set was reduced to 81 work zones spread along a 700 km corridor between the western borders of Alberta and Vancouver, British Columbia. The data were split into training and test data using a 90:10 ratio. Training data were used to develop a 27-input neural network with four hidden layers. After validating the test data, the neural network achieved a root mean squared error of 0.150 min and an R-squared score of 0.945, indicating a high accuracy level in estimating the under-construction travel time.

Road Traffic Safety in Developing Countries: Taking China as an Example.

Road traffic accidents are still serious, especially in developing countries. This paper takes China as a typical example of a developing country with rich characteristics related to road traffic safety for analysis. Temporal, spatial, road traffic accidents and economic information were gathered from the China Statistical Yearbook. Principal components analysis (PCA) was employed to establish a comprehensive indicator to represent road traffic safety based on different types of road traffic accidents information. Pearson correlation analysis and Eta coefficient test were performed to analyze whether time and space characteristics would affect the established indicator. Then the established indicator was introduced as dependent variable while year and regions as independent variables in the mixed linear model (MLM). At last, single-element regression model was built to study the impact of GDP per capita on road traffic safety. In PCA, the variance explained by the established indicator was 93.993%. The results of Pearson correlation analysis and Eta coefficient test suggested that time and region were both related to the established indicator. MLM showed that the year, the regions and the interaction between them influenced road traffic safety in China significantly. The single-variable regression analysis indicated that, with the increase in GDP per capita, road traffic safety initially decreased and then increased. Road traffic safety in China was grim and changed greatly between different regions and years. This might be attributed to the yearly economic development and disparities among regions. .

Traffic Image Analysis Based on Stacked Denoising Autoencoder Neural Network

This study aims to explore major neural network models - Stacked Denoising Autoencoder (SDAE), Deep Belief Network (DBN), Backpropagation - that have recently garnered attention and propose the most suitable and reliable artificial neural network model for real-time road traffic information collection. In this study, to enhance the reliability of experimental results, numerous experiments were conducted under identical conditions (such as parameter values and network configuration) by setting different initial values for the weight vector. The results of the experiments were statistically validated to draw conclusions. The research results showed that the SDAE model exhibited the most superior performance, while the accuracy of the DBN was somewhat lower compared to the SDAE model. On the other hand, the Backpropagation model demonstrated a relatively low predictive accuracy compared to both models, particularly showing a significant influence of the initial values

A novel fuzzy inference method for urban incomplete road weight assignment

ABSTRACT One of the keys in time-dependent routing is determining the weight of each road network link based on traffic information. To facilitate the estimation of the road’s weight, Global Position System (GPS) data are commonly used in obtaining real-time traffic information. However, the information obtained by taxi-GPS does not cover the entire road network. Aiming at incomplete traffic information on urban roads, this paper proposes a novel fuzzy inference method. It considers the combined effect of road grade, traffic information, and other spatial factors. Taking the third law of geography as the basic premise, that is, the more similar the geographical environment, the more similar the characteristics of the geographical target will be. This method uses a Typical Link Pattern (TLP) model to describe the geographical environment. The TLP represents typical road sections with complete information. Then, it determines the relationship between roads lacking traffic information and the TLPs according to their related factors. After obtaining the TLPs, this method ascertains the weight of road links by calculating their similarities with TLPs based on the theory of fuzzy inference. Aiming at road links at different places, the dividing – conquering strategy and globe algorithm are also introduced to calculate the weight. These two strategies are used to address the excessively fragmented or lengthy links. The experimental results with the case of Newcastle show robustness in that the average Root Mean Square Error (RMSE) is 1.430 mph, and the bias is 0.2%; the overall RMSE is 11.067 mph, and the bias is 0.6%. This article is the first to combine the third law of geography with fuzzy inference, which significantly improves the estimation accuracy of road weights with incomplete information. Empirical application and validation show that the method can accurately predict vehicle speed under incomplete information.

Studies Advanced in Traffic Sign Detection in Autonomous Driving Scenarios

Traffic sign detection and recognition is a hot research topic in the environmental awareness module of autonomous driving, which aims to build a model of road traffic information and provide a decision basis for driving scheme design. Early traffic sign recognition methods were mostly based on color features, shape features, or multi feature fusion. Thanks to the rapid development of convolutional neural networks, traffic sign recognition methods based on deep learning have continuously made breakthroughs in both accuracy and speed. In this paper, focusing on the four major types of mainstream frameworks mentioned above, we will introduce the latest research progress in traffic sign detection and recognition technology, including the principles, steps, advantages and disadvantages of representative algorithms. In addition, we quantitatively compared the performance of different recognition methods on common data sets. Finally, we discussed the existing problems in traffic sign recognition and its future development direction.

Towards a novel magnetic asphalt mixture containing ceramic ferrites for intelligently encoding road traffic sign information

With the development of smart road transport, the use of magnetic coding units (MCU) to store and transmit road traffic sign information is a novel technical solution that emerged in recent years for advancing the V2I communication. In this work, MCU was prepared by partly replacing the aggregates and mineral powder in the AC-13 asphalt mixture (AM) with ceramic ferrites (CF). It was found that the MCU was able to produce a stable and detectable magnetic field after magnetization, while its spatial distribution pattern is programmable. In order to provide technical guidance for MCU design, environmental impacts including erosion and thermal cycling on the magnetic performance of MCU were explored over in further. The following conclusions were obtained: (1) The experimental measurements show that CF has a remanence of more than 20 emu/g and a coercivity of more than 1 k Oe, and as a result, the addition of CF effectively improves the magnetic flux density and detectable range of MCU. (2) Satisfactory corrosion resistance of MUC is observed, which is mainly attributed to the stable hexagonal magnetoplumbite-type crystal structure and large binding energy of Fe3+ within the mixture. (3) Little irreversible flux density loss of the MCU is observed in a large number of thermal cycles in a range of 20–60 °C, which is attributed to the high curie temperature of the CF. Increasing the CF doping can enhance the Fe-O exchange interaction, thus reducing the reduction ratio of the MCU magnetic field at 60 ℃. (4) MCU material has typical viscoelastic response characteristics under mechanical loading, while its dynamic modulus is close to that of ordinary AC-13 AM. (5) The economic cost of using MCU to encode traffic sign information is merely 20.25% of that of conventional traffic signs, which supports further its application potential.

A feature extraction and deep learning approach for network traffic volume prediction considering detector reliability

AbstractAccurate traffic volume prediction plays a crucial role in urban traffic control by relieving congestion through improved regulation of traffic volume. Network‐level traffic volume prediction and detector failure have rarely been considered in the literature. This paper proposes a framework based on long short‐term memory and the multilayer perceptron that can predict network‐level traffic volumes even with detector failure. A profile model learns the profile of the detector's signature (traffic pattern). Detectors with similar profiles are considered to have similar traffic patterns and are grouped into a cluster. Failed detectors can obtain reference information from similar detectors in the same cluster without additional information. A predictive model is developed for each cluster. The proposed method is validated using Japan Road Traffic Information Center data for three cities. The computational results indicate that the proposed method performs well both on typical days and atypical days (the COVID‐19 lockdown period and the 2021 Tokyo Olympics). Further, it considers detector reliability: the increase in mean absolute error is less than 1 veh/5 min when the probability of detector failure increases to 20%.

Speed Limiting and Control of Vehicle in Restricted Zone Automatically

Abstract: Automatic vehicle monitoring has turned outto be a very crucial scenario in the current years. It may develop into possibility by executing the following technologies. This project targets to propose a system, which detects speeding vehicles over a specific speed limit and immediately report to concerned authorities. At present, road accidents rates have raised so, there is a necessity for developing a system that detects an over speeding vehicle. The implementation of present Smart Vehicle over speeding Detector using Internet of Things determines all the road traffic information automatically with intelligence. When the vehicle crosses the school/college, RF receiver in the vehicle will send signalto the microcontroller. The microcontroller immediatelycontrols the driver section to control the speed of the motor.Therefore when the vehicle crosses the school/College, the speed of the vehicle will be automatically decreased. This will prevent unnecessary accidents.

Magnitude and determinants of road traffic accidents in North Gondar Zone; Amhara Region, Ethiopia

Background: Despite efforts that the government has made to minimize the number of casualties or injury levels in Ethiopia, road traffic accidents remain a severe health concern throughout the country and in the study area as well. This study aims to examine the primary contributing variables of road traffic accidents in the North Gondar zone, under 24 administrative. Methods: The functional class of roads were directly examined on the site to evaluate the existing conditions of the roads which includes the geometric design constraints, functional class, daily traffic, and surfacing materials. Secondary source of data types, previously recorded road traffic accident information was collected from North Gondar zone traffic police departments. Road inventory information was collected from Gondar branch Ethiopian road authority under the specified horizon lines. Results: The magnitude and frequency of traffic accidents caused by driver-related factors( age, sex, level of education, driving experience, ownership responsibility of a vehicle and driver responsibility, time and day-wise distribution of traffic accidents), vehicle-related factors (failures of mechanical parts and its service age), road related factors( geometric design and construction, inventory information, road surfacing materials), pedestrians, and environmental factors were critically analysed. The result shows that a higher proportion (67%) of traffic accidents are mainly due to driver behaviour related factors than other causative factors. Conclusions: According to the results of the study, bad driving behaviour’s (exceeding the design speed, using a phone while driving, dangerous overtaking, excessive braking, loading above capacities, illegally parking out of public stations should be controlled to reduce the road traffic accident in the study area. In addition, successive up-to-date advanced computerized traffic accident safety training and recording systems should be given at least once a year through regional-based or at a central zone administrative zones for the traffic officers.

Assessing the design of road traffic death information systems in Iran: A participatory systems approach

AimTo describe and analyze the information architecture and information pathways of the road traffic death recording, registration and reporting system in Guilan Province, northernIran. MethodsWe used Business Process Mapping, a qualitative approach. This participatory and iterative approach consists of a document review, key informant interviews, development of a process map and a participatory workshop with key stakeholders to illuminate and validate the findings. We classified the tasks performed in the system into three phases: (1) Identification and recording; (2) Notification and registration, and (3) Production of statistics. ResultsWe identified 13 stakeholders, with operating and influencing roles in the process of identification, registration and production of statistics about road traffic deaths in Guilan province. The three main sources of road traffic death statistics are the Ministry of Health and Medical Education, the National Organization for Civil Registration and the Forensic Medicine Organization. Our results reveal a highly fragmented system with minimal cross-sectoral data exchange. Each stakeholder operates in a silo resulting in delays and redundancies in the operating system. In the absence of an effective communication among stakeholders, the information exchange was dependent on the family of the deceased. These fragmented information silos alter the compilation of cause of death statistics and result in under-reporting and discrepancies in road traffic deaths figures. ConclusionsDesigning a comprehensive road traffic information system that provides accurate and timely information requires an understanding of the information flow and the entangled web of different stakeholders operating in the system. Participatory systems approaches such as process mapping can assist in capturing the complexity of the system and the integration process by facilitating stakeholders’ engagement and ownership in improving the design of the system.

A Novel Framework for Analysis of Road Traffic Information for Decision Support by Using Data Mining Techniques

As we know we want traffic data analysis, data mining measures are recommended to help manage and traffic information decision making. Such as this limit, the road type is added intelligence analysis and decision support are another gave. This model has a data collection system and the data warehouse at its heart, and works well mathematical intelligence techniques and the invention of decision support system. As the main part of this report, the main parts of the model, the main process and an implementation plan is developed. An intelligent urban traffic decision analysis system is a system designed to solve open road problems, improve road safety and quality, and improve road quality work. The application of big data mining technology can improve various capabilities types of analysis and processing of traffic information, spread rapidly and provide a decision reference for the application of intelligent transport systems, which will be carried out to ensure smooth and safe roads. This topic will introduce the concept of large data mining technology and intelligent transport systems, and review the architecture of smart city road systems based on big data technology and design implementation of the mining process.

Integrated macro-micro modelling for individual vehicle trajectory reconstruction using fixed and mobile sensor data

Vehicle trajectories can provide a clear picture of the traffic flow that plays a pivotal role in traffic management and control. Two types of traffic sensors, i.e., fixed and mobile sensors, are widely adopted to collect road traffic information. However, fully-sampled vehicle trajectories are far from obtainable through those sensors due to their fixed-location measurements or low penetration rates. To overcome these issues, this study proposes an integrated macro–micro framework to reconstruct individual vehicle trajectories on freeways under a multi-source data environment, in which fixed-location sensor data that collect positions and velocities of vehicles in a certain place and a small fraction of probe vehicle (PV) data that yield continuous trajectories are available. The framework consists of three modules. First, at the macro level, we apply shockwave theory and fundamental diagram to expand the partially detected information to the whole space–time diagram, and then establish a velocity baseline for evaluation of the trajectory fusion thereafter. Second, at the micro level, we develop two trajectory estimation algorithms based on extended car-following models to generate individual trajectories between any two consecutive PVs, in which two candidate trajectories will be generated for each non-probe vehicle. Third, we tailor a trajectory fusion algorithm to integrate the macro- and micro-level models, and apply dynamic programming to solve the optimal trajectories as per the established velocity baseline. The proposed framework is finally tested with NGSIM and HighD datasets. The results show that our integrated method attains a notable improvement in accuracies and smoothness of reconstructed trajectories compared to the non-integrated and the classical Variational Theory-based methods respectively.

Intelligent short term traffic forecasting using deep learning models with Bayesian contextual hyperband tuning

AbstractAn intelligent transport system (ITS) is fully valuable only if it can dynamically and aptly integrate all the latest cutting‐edge technologies. An ITS focuses on providing services like promptly offering real‐time road traffic information to interested parties, finding ways to reduce the average waiting time and offer secure and reliable services for commuters using past statistics. Short‐term traffic prediction is one such area in which the research community has focused in the past decade. Existing models developed for prediction has scope to improve in terms of accuracy and training time. There is a necessity to develop a best‐performing model that is computationally affluent to train with the optimal hyperparameter configuration as input which leads to improved performance. This article proposes a model that captures the traffic flow trend present in the past data to predict the flow for a future time interval. This model is an amalgamation of seasonal global trend (SGT) model and long short‐term memory (LSTM) model with attention mechanism. A novel hyperparameter tuning algorithm is also proposed which is based on multi‐armed bandit strategy with context, incorporating the right trade‐off between exploitation and exploration of the hyperparameter space using successive halving. Experimental results conducted proves that our forecast model in combination with the proposed hyperparameter tuning algorithm outperforms the existing models like SGT, LSTM models in terms of accuracy and time.

Coalitional graph game for area maximization of multi-hop clustering in vehicular ad hoc networks

Road traffic information can be utilized in many applications of intelligent transport systems. It can be collected from vehicles and sent over a vehicular ad hoc network (VANET) to roadside units (RSUs) acting as sink nodes. Due to rapid mobility and limited channel capacity in a VANET where vehicles must compete to access the RSUs to report their data, clustering is used to create a group of vehicles to facilitate data transfer to the RSUs. Unlike previous works that focus on cluster lifetime or throughput, we formulate a coalitional graph game for multi-hop clustering (CGG-MC) model to create a multi-hop cluster with the largest possible coverage area for a given transmission delay time constraint to economize on the number of RSUs installed. Vehicles cooperatively form a proper coalition with relation directed graphs among vehicles in a multi-hop cluster to collect, aggregate, and forward data to RSUs instead of individually competing to connect directly to RSUs. Vehicles decide to join or leave the coalition based on their individual utility, which is a weighted function of the coverage area, number of members in the cluster, relative velocities, distance to sink nodes, and transmission delay toward the sink nodes. The distributed-solution approach based on probabilistic greedy merging of coalitions is used to derive the grand coalition, and the probability of grand coalition formation is analyzed by using a discrete-time Markov chain. Our results show that the proposed solution approach yields a 95% confidence interval of the average utility between 61 and 68% relative to the maximum utility in the centralized-solutions. Additionally, our CGG-MC model outperforms the non-cooperation model by approximately 166% in terms of enlarging the coverage network area under a transmission delay time constraint.

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.

A Provably Secure Identity Based Authenticated Key Agreement Protocol with Multiple PKG Compatibility for Inter-Vehicular Ad hoc Networks

Vehicular Ad hoc Networks (VANETs) facilitate the timely dissemination of road traffic information, including early warnings related to road conditions, weather updates, traffic congestion and collisions. The information exchanged in VANETs is crucial for maintaining uninterrupted traffic flow, minimizing road mishaps, expediting emergency services and analyzing potential detours in case of congestion. In order to ensure the legitimacy of the interchanged messages and to preserve confidentiality, it is essential to establish secure authenticated channels among the communicating automotive nodes. Considering the highly dynamic nature of VANETs and the diversity of the participating vehicular nodes, Identity based Authenticated Key Agreement (ID-AKA) protocol in the multiple Private Key Generator (PKG) setting, is an ideal cryptographic technique for securing inter-vehicular communications. We cryptanalyze the existing pairing-free multiple PKG compatible ID-AKA (mPKG-ID-AKA) protocol designed for securing VANETs and prove that the protocol lacks weak Perfect Forward Secrecy (wPFS). Our investigations also reveal that previous attempts to model mPKG-ID-AKA protocols are computation-intensive and provide weaker security guarantees. To this end, we propose an efficient eCK secure mPKG-ID-AKA protocol based on the gap Diffie-Hellman assumption. Experimental analysis suggests that the proposed protocol attains the highest computation efficiency, when compared to the existing mPKG-ID-AKA schemes and is therefore highly suitable for securing inter-vehicular communications.