Actual Traffic Situation Research Articles

The Game Theory Application in the Transport Flows Control Algorithm Development in the Intelligent Transport System

Traffic management in modern intelligent transport systems includes monitoring the actual traffic situation in real time (volumes, speeds, incidents, etc.) and management or controlling transport traffic using this information. At the same time, cars as intelligent transport systems components must be equipped with communication capabilities for exchanging information with other vehicles (V2V) and with road infrastructure (V2I). All this is connected with the presence of special equipment connected to the on-board network for local data collection, which can be exchanged between cars and with a central communication station using wireless Internet. At the same time, the issue of developing the traffic organization algorithms themselves is still open. This problem can be solved with the help of game theory, a fairly new but rapidly developing part of modern mathematics. Unlike optimization theory, which studies the possibilities of constructing an optimal solution for the entire system as a whole, game theory studies ways to optimize individual benefits in competition with other persons (events) that rationally seek to satisfy their own benefits. The problem of “smart” regulation of intersections is quite difficult to solve, it combines the efforts of scientists from different fields of knowledge. However, the rapid development of ICT technologies and their rapid application to transport tasks makes it possible to develop some approaches to optimize the current situation at the intersection.

Algorithm for Control of Traffic Flow in an Intelligent Transport System

Traffic management in modern Intelligent Transport Systems (ITS) includes monitoring the actual traffic situation in real time and management transport traffic using this information. At the same time, cars as ITS components must be equipped with communication capabilities for exchanging information with other vehicles (V2V) and road infrastructure (V2I). Such approach is connected with usage of special equipment connected to on-board network for local data collection, which can be exchanged between cars and with a central communication station using wireless Internet. At the same time, the issue of developing the traffic organization algorithms themselves is still open. This problem can be solved with the help of game theory, a fairly new but rapidly developing part of modern mathematics. Unlike optimization theory, which studies the possibilities of constructing an optimal solution for the entire system as a whole, game theory studies ways to optimize individual benefits in competition with other persons (events) that rationally seek to satisfy their own benefits. The problem of "smart" regulation of intersections is quite difficult to solve a problem, nevertheless it is possible due to rapid development of ICT technologies. Keywords: ITS, transport crossroads, game theory

Mobile Phone Data Feature Denoising for Expressway Traffic State Estimation

Due to their wide coverage, low acquisition cost and large data quantity, the mobile phone signaling data are suitable for fine-grained and large-scale estimation of traffic conditions. However, the relatively high level of data noise makes it difficult for the estimation to achieve sufficient accuracy. According to the characteristics of mobile phone data noise, this paper proposed an improved density peak clustering algorithm (DPCA) to filter data noise. In addition, on the basis of the long short-term memory model (LSTM), a traffic state estimation model based on mobile phone feature data was established with the use of denoising data to realize the estimation of the expressway traffic state with high precision, fine granules, and wide coverage. The Shanghai–Nanjing Expressway was used as a case study area for method and model verification, the results of which showed that the denoising method proposed in this paper can effectively filter data noise, reduce the impact of extreme noise data, significantly improve the estimation accuracy of the traffic state, and reflect the actual traffic situation in a fairly satisfactory manner.

Traffic flow simulation of modified cellular automata model based on producer-consumer algorithm.

With the rise of new technologies such as the Internet of Vehicles and the Internet of Things, research on the intelligent connected vehicle has become a hot topic in contemporary times. The modeling and simulation of traffic flow are mainly used to analyze the characteristics of traffic flow and study the formation and dissipation mechanism of traffic congestion to better guide the real traffic. Cellular automata are suitable for the simulation of complex giant systems. Because of the randomness and discreteness of vehicle driving, cellular automata are often used to model and analyze traffic flow. This article mainly studies the traffic flow formed by intelligent connected vehicles. Based on the traditional NaSch model, the producer-consumer algorithm is introduced to form a multi-buffer vehicle information access mode, and an improved cellular automata model with random updates is constructed. The simulation results show that the improved cellular automata model improves the traffic congestion significantly compared with the original NaSch model in the intelligent network environment, which is consistent with the actual traffic situation. Therefore, the algorithm proposed in this article can effectively simulate the traffic flow characteristics of intelligent connected vehicles, and provide a theoretical basis for solving traffic problems.

A Real-Time Recognition System of Driving Propensity Based on AutoNavi Navigation Data.

Driving propensity is the driver’s attitude towards the actual traffic situation and the corresponding decision-making or behavior during the driving process. It is of great significance to improve the accuracy of safety early warning and reduce traffic accidents. In this paper, a real-time identification system of driving propensity based on AutoNavi navigation data is proposed. The main work includes: (1) A dynamic data acquisition method of AutoNavi navigation is proposed to obtain the time, speed and acceleration of the driver during the navigation process. (2) The dynamic data collection method of AutoNavi navigation is analyzed and verified through the dynamic data obtained in the real vehicle experiment. The principal component analysis method is used to process the experimental data to extract the driving propensity characteristics variables. (3) The fruit fly optimization algorithm combined with GRNN (generalized neural network) and the feature variable set are used to build a FOA-GRNN-based model. The results show that the overall accuracy of the model can reach 94.17%. (4) A driving propensity identification system is constructed. The system has been verified through real vehicle test experiments. This paper provides a novel and convenient method for building personalized intelligent driver assistance systems in practical applications.

Study on Risk of Long-Steep Downgrade Sections of Expressways Based on a Fuzzy Hierarchy Comprehensive Evaluation

The long-steep downgrade sections of expressways are characterized by a large elevation difference, poor horizontal and vertical alignment, and the easy failure of brakes on large trucks. They are sections with a high overall operation safety risk. It is necessary to strengthen the research on traffic risk evaluation. In order to study the traffic safety risks of long-steep downgrade parts of expressways, the fuzzy hierarchical comprehensive evaluation method is used to establish the calculation model. First, an evaluation index system including the target level, rule level, first-level index level and second-level index level is established. The qualitative and quantitative indicators are processed by the set value statistical method and the linear standard method, respectively, so that all indicators can be quantitatively evaluated together. Then, each indicator is assigned a score and divided into five risk levels, and a ridge-shaped fuzzy distribution is used to constitute a membership function for each level. A hierarchical structure model is established with the analytic hierarchy process to determine the affiliation between the upper and lower levels, and the relative weight of each level to the upper level also can be obtained. Finally, according to the hierarchical relevance of each evaluation indicator, a three-level fuzzy comprehensive evaluation model is constructed. The traffic risk evaluation level for long-steep downgrade sections can be obtained, and the probability of the corresponding risk evaluation level can be calculated. Through the risk evaluation of the long-steep downgrade sections of the Fuzhou Yinchuan Expressway in China, this shows that the risk evaluation conclusion obtained by using this evaluation method is consistent with the actual traffic safety situation, which shows that the traffic safety risk evaluation model based on a fuzzy hierarchy comprehensive evaluation is operable.

Interdisciplinary Urban Tunnel Control within Smart Cities

Nowadays, urban road tunnels are considered to be independent entities within a city. Their interactions with the rest of the city and vice versa are usually not considered and, if they are, are only considered in a limited way (for example, through the nearest traffic controller). Typically, only the traffic parameters and not the environmental impacts are considered. This paper has two major objectives. First, we provide a systemic view on a road urban tunnel. The major focus is on the interfaces between the tunnel and the rest of the city and the way they will be managed. We are providing a tool to take into consideration a sustainable development of a tunnel (i.e., not only traffic flow parameters such as average speed, but also environmental and societal characteristics). This model expresses the actual traffic situation in a monetary form (i.e., cost of congestions). The second objective is to provide a new road urban tunnel control approach that follows the original methodology and systemic view described in the paper. If the tunnel is controlled autonomously, which corresponds to the current state-of-the-art in many cities, the algorithm decides to close it based on only local parameters. However, the proposed new algorithm takes into consideration not only the traffic situation in the tunnel (expressed by the parameter traffic density), but also the actual traffic situation within the city (expressed by its level of service (LOS)). This allows more environmentally, socially and economically sustainable oriented road urban tunnel management. The described algorithm is demonstrated on a specific example of the tunnel complex Blanka in Prague.

The Research on Traffic Congestion Problem in Beijing Based on Random Effect Model

With the advancement of urbanization and the rapid development of the economy, the number of motor vehicles has also increased sharply, which has led to the increasingly heavy traffic congestion problem, especially in the international metropolis like Beijing. It is more serious, bringing economic loss and seriously affecting its further development. Therefore, it is of great importance to solve the problem in Beijing. In response to the above issue, despite the introduction and implementation of a series of measures in Beijing, it is still unable to properly solve the traffic congestion problem. Today, traffic congestion in Beijing still exists. This paper will mainly start from the causes of traffic congestion, and analyze the problems and phenomena based on the random effects model to obtain the reasons. According to the goal of relieving the traffic congestion problem, we will use the traffic supply and demand theory combined with the actual traffic situation, establish the traffic light model, the travel optimization model and the automobile demand curve model. With the hypothesis, simulation and analysis, we drew the corresponding conclusions. At the same time, the corresponding policy recommendations were put forward to relieve the traffic congestion problem in Beijing.

Urban rail transit signal and control based on Internet of Things

At present, China’s traffic signal control machine has a low level of intelligence and a single control strategy. It cannot make corresponding control according to the actual traffic situation, and its ability to direct traffic flow in a reasonable and orderly manner is low. In order to understand the urban rail transit signal and control system, we analyzed the requirements of the train dispatching subsystem, designed the overall architecture of the system from the perspective of function realization and architecture, and constructed the wireless sensor network of the system, which is the best for other experts. In this paper, combined with the research of related technologies of the Internet of Things (IoT), an intelligent traffic signal control machine is designed, and the traffic signal control effects under different algorithms are compared, and the relevant rail transit conditions are statistically studied. Studies have proved that sensors based on IoT technology can effectively improve the intensity and control effect of urban rail transit signals. Compared with other algorithm technologies, the overall score of the sensor algorithm is higher than other algorithms, and the ratio is about 30% higher. This article realizes the maintenance function of various data on the system simulation operation terminal, and builds the overall framework of the system; realize the main functions of the train dispatching operation terminal, including the realization of train dispatching functions such as station map, manual route arrangement, automatic route triggering, station deduction and station jump settings, and log report generation. This shows that the sensor algorithm under the IoT has a great promotion effect on the urban rail transit signal and control system.

The socio-economic efficiency of computer-integrated transport modelling

In the article has been analysed the possibilities of using modern digital technologies to optimize the transport system of a big city. The Urbanization’s processes lead to high rates of urban development, which provides for the creation of assorted transport supplying. However, there are situations when the geographic, landscape, natural and climatic area features, architectural solutions (characteristics) or blunders in the design of the city do not allow to create the road infrastructure of the necessary level. This leads to congestion of the main transport networks and generate traffic jams, increasing the degree of environmental pollution. The authors consider the horizons of creating a transport model of the Odessa city as an effective method of traffic management. It has been made out a case that the digitalization of the urban highways management reduces the trip time, alleviates the level of the road network congestion, increases the reliability and safety of transport, minimizes the number of non-standard traffic situations, and increases the economic efficiency of the city’s transport system. The using of powerful content solution allows you to build a mental (virtual) model, taking into account the actual traffic situation at this time and predict its changing when you have to make a certain management decision. This article is dedicated to the economic issue of the building a city transport model, the contemplation for its use to improve the efficiency of the city economy operation.

An extended smart driver model considering electronic throttle angle changes with memory

Based on the fact that the electronic throttle angle effect performs well in the traditional car following model, this paper attempts to introduce the electronic throttle angle into the smart driver model (SDM) as an acceleration feedback control term, and establish an extended smart driver model considering electronic throttle angle changes with memory (ETSDM). In order to show the practicability of the extended model, the next generation simulation (NGSIM) data was used to calibrate and evaluate the extended model and the smart driver model. The calibration results show that, compared with SDM, the simulation value based on the ETSDM is better fitted with the measured data, that is, the extended model can describe the actual traffic situation more accurately. Then, the linear stability analysis of ETSDM was carried out theoretically, and the stability condition was derived. In addition, numerical simulations were explored to show the influence of the electronic throttle angle changes with memory and the driver sensitivity on the stability of traffic flow. The numerical results show that the feedback control term of electronic throttle angle changes with memory can enhance the stability of traffic flow, which shows the feasibility and superiority of the proposed model to a certain extent.

Incorporating Deblurring Techniques in Multiple Recognition of License Plates from Video Sequences

Recognition of license plate is the process of wherein photographic video or images of license plates are being captured and then processed using an application that implements series of algorithms that will provide the alpha numeric conversion of the captured data. In this study, the researchers developed a license plate recognition that incorporates image deblurring to accommodate multiple recognition from video sequences. The approach uses Background Subtraction and Connected Component Analysis for the detection of license plates, Image deblurring to enhance the image and reduce the difficulties in recognition, and LBP Cascade Classifier was implemented for recognition of characters. Since multiple detection for license plate produces different difficulties such as motion blur and camera angle view the approach attempts to minimize the effects of these problems while still being applicable to multiple detection. 30 videos with 3 minutes length each of actual traffic situation were gathered and recorded at the footbridge of UP Ayala Technohub, Commowealth Ave. Quezon City, Philippines and 10 of these videos were used as input for the testing and experiment of the system. The accuracy for plate detection were computed using F-measure which yields to 87.32% for both system with image deblurring and none, while the accuracy for character recognition is 62.66% for system with image deblurring and 48.25% for the system without image deblurring. The result shows that there is a significant difference in the accuracy of license plate recognition between the system with image deblurring and without image deblurring

Deep learning detection of anomalous patterns from bus trajectories for traffic insight analysis

Existing data-driven methods for traffic anomaly detection are modeled on taxi trajectory datasets. The concern is that the data may contain much inaccuracy about the actual traffic situations, because taxi drivers often choose optimal routes to evade from the congestions caused by traffic anomalies. We use bus trajectory data in this work. Bus trajectories can capture real traffic conditions in the road networks without drivers’ preference, which are more objective and appropriate for accurately detecting anomalous patterns for a broad range of insight analyses on traffics. We proposed a deep learning-based feature visualization method to map 3-dimensional features into a red–green–blue (RGB) color space. A color trajectory (CT) is then derived by encoding a trajectory with the RGB colors. With the spatial and temporal properties extracted from the CT, spatio-temporal outliers are detected by a novel offline detection method. We then conduct GIS map fusion to obtain insights for better understanding the traffic anomaly locations, and more importantly the influences on the road affected by the corresponding anomalies. Extended from the offline detection, an online detection method is developed for real-time detection of anomalous patterns. Our proposed methods were tested on 3 real-world bus trajectory datasets to demonstrate the performance of high accuracies, high detection rates and relatively low false alarm rates.

Regulatory governance in emerging technologies: The case of autonomous vehicles in Sweden and Norway

Vehicle producers, universities, and technology companies, among others, are today involved in the development of autonomous vehicles (AVs). Ongoing in several countries are experimental activities in actual traffic situations. The legal conditions for autonomous vehicles, however, vary by country. A number of countries have introduced, or are considering introducing, rules for such activities. Some countries see autonomous vehicles as prohibited unless otherwise stated in the regulations, while other countries take exactly the opposite view, i.e., anything not explicitly prohibited is allowed. This paper introduces a regulatory governance perspective on autonomous vehicles. It describes how new regulatory standards are being shaped for emerging technologies in the transport sector using the case of autonomous vehicles in Sweden and Norway. The findings show how regulations are shaped by external pressures and that international conventions are influencing regulatory design. It also concludes that the final regulations hold some degree of flexibility but also that specific restrictions may be hindering advanced experiments. By introducing a theoretical perspective on regulatory governance of AVs, the paper contributes to a further understanding of how to analyse the shape of new regulation in the transport sector generally.

Microscopic Traffic Simulation Calibration Level for Reliable Estimation of Vehicle Emissions

Vehicle emissions are largely determined by the details of driving behaviours. Accordingly, emissions are often estimated by integrating micro-scale emission models into traffic simulations. Under this approach, it is essential to replicate the actual traffic situation being considered in an emission evaluation using a proper calibration procedure. Most previous research with respect to traffic flow has primarily focused on adjusting the complex combinations of parameters evaluated in these models, but it is not guaranteed that the use of widely used calibration measures can lead more accurate emissions estimates. Accordingly, we propose a systematic guideline for calibration to ensure reliable micro-scale emissions estimates. A calibration procedure is thus established in this paper based on various measure of effect (MOE) compositions (i.e., calibration levels) consisting of aggregated traffic data to identify the level that most reliably estimates micro-scale emissions. Five calibration levels of progressively more detailed measurements are first defined, valid calibration levels are identified, and the reliable calibration level is finally selected based on the available traffic data. The effect of vehicle type (i.e., light vs. heavy vehicles) composition on the estimated emissions is also evaluated for a well-calibrated simulation. We expect that a highly reliable estimation of emissions is possible using this more detailed traffic simulation calibration measurement.

Quantitative analysis and calculation of traffic social cost during the construction of open-cut subway station

With the rapid development of rail transit, traffic problems caused by open-cut metro construction have become increasingly prominent. In order to quantify the negative impact of subway station construction, taking a metro stations under construction in Chengdu as example, collected relevant data such as traffic flow, congestion time, speed during congestion and noise decibel value at metro construction intersection by field test. Through quantitative calculation, the negative effects of open-cut construction are monetized from four aspects: extra fuel cost, time delay cost, environmental pollution cost and fatigue cost. It is found that the social cost is as high as 20 million yuan per year. Based on the calculation results and the actual traffic situation in Chengdu, the corresponding measures to reduce social costs are put forward.

Bifurcation Control in an Optimal Velocity Model via Double Time-Delay Feedback Method

In this paper, a double time-delay feedback control of an optimal velocity model (OVM) is investigated. Double time-delay means that there exist two different state feedback control signals in the controlled OVM system, which are related to the velocity difference and the optimal velocity difference, respectively. Through linear stability analysis, the critical condition of Hopf bifurcation for the controlled OVM is derived. Utilizing the characteristics of Hopf bifurcation and the improved definite integral method, appropriate double time-delay feedback control strategy is designed in term of the number of unstable eigenvalues of the characteristic equation to suppress the stop-and-go waves generated by the uncontrolled OVM. Note that when the number of unstable eigenvalues is equal to zero, the controlled OVM is stable, otherwise, it is unstable. Numerical simulations are executed to validate the accuracy and feasibility of the design of double time-delay control strategy. Finally, case studies approximating the actual traffic situation are given, and the appropriate combination of control parameters is selected through the verified design steps. In addition, the measured data from NGSIM are also considered.

A two-lane mixed traffic flow model with drivers' intention to change lane based on cellular automata

Aiming at the shortcomings of the classic NaSch model and the two-lane lane change model (STCA), the viaduct was used as the simulation object on the basis of improving the rules and lane changing rules. Under the open boundary condition, a two-lane mixed traffic flow model considering the intention to enter the lane is established. Aim at the willingness of driver to enter lane research, this model puts forward the vehicle enter rules, which take into account the relevant factors that affect driver's intention to enter the road and quantify the influencing factors through the method of average input rate in queuing theory. Computer numerical simulation shows that the average input rate λ is determined by input rate λ and output rate μ, and is affected by the sensitivity parameter β, and is not greater than μ. The passing state of a road vehicle is determined by the output rate μ and is affected by β. At the same time, compared with the model that ignores the driver's intention to enter the road, the model proposed in this paper is closer to the actual traffic situation.

Research on time-of-use pricing method of shared bicycles

Shared bicycles have become an important mode of urban slow traffic, and the operation and management of shared bicycles have attracted more and more attention. In order to alleviate the shortage of supply during peak period and accelerate the turnover rate during flat peak period, this paper proposes a differentiated pricing strategy of shared bicycles. By establishing a bilevel programming model of shared bicycles fare and using a particle swarm optimization-based global pricing model solution algorithm, the purpose of minimizing the generalized travel cost of travelers and maximizing the economic benefits of shared bicycles enterprises are achieved. Based on the actual traffic situation in Beijing, the model parameters are calibrated to verify the validity of the model and the algorithm. The proposed model can provide a reference for the pricing strategy of shared bicycles and provide a scientific basis for the operation and management of shared bicycles enterprises.

Damage detection of highway bridges based on long-gauge strain response under stochastic traffic flow

Abstract Highway bridges are essential infrastructure engineering. To monitor their conditions effectively, bridge structural health monitoring (SHM) techniques have been developed to substitute the manual inspection. Among SHM techniques, the damage detection method is the most promising one utilized for identifying damage location and extent. However, most frequency-domain signal based damage detection methods are not sufficiently sensitive to local damage, while other time-domain signal based methods with a sufficient sensitivity have many limitations, especially on working conditions where those methods are only feasible when just a single vehicle passes over the bridge. This restriction severely differs with actual traffic situations. Under these backgrounds, in this study, a damage detection method based on long-gauge fibre Bragg grating (FBG) was proposed, which can achieve the identification of the damage location and extent under stochastic traffic flow. This method’s feasibility was initially verified through a series of indoor bridge model experiments. Then, some numerical case studies were conducted to mimic actual stochastic traffic flow conditions. The results of experiment and numerical simulation demonstrated that this method performs well in detecting the damage location and extent under various designed situations, and it has the potential to be an alternative for the current methods.