Traffic State Identification Research Articles

Big Data-Driven Based Real-Time Traffic Flow State Identification and Prediction

With the rapid development of urban informatization, the era of big data is coming. To satisfy the demand of traffic congestion early warning, this paper studies the method of real-time traffic flow state identification and prediction based on big data-driven theory. Traffic big data holds several characteristics, such as temporal correlation, spatial correlation, historical correlation, and multistate. Traffic flow state quantification, the basis of traffic flow state identification, is achieved by a SAGA-FCM (simulated annealing genetic algorithm based fuzzyc-means) based traffic clustering model. Considering simple calculation and predictive accuracy, a bilevel optimization model for regional traffic flow correlation analysis is established to predict traffic flow parameters based on temporal-spatial-historical correlation. A two-stage model for correction coefficients optimization is put forward to simplify the bilevel optimization model. The first stage model is built to calculate the number of temporal-spatial-historical correlation variables. The second stage model is present to calculate basic model formulation of regional traffic flow correlation. A case study based on a real-world road network in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling and computing methods.

Traffic Congestion Forecasting Based on Possibility Theory

Traffic congestion state identification is one of the most important tasks of ITS. Traffic flow is a nonlinear complicated system. Traffic congestion state is affected by many factors, such as road channelization, weather condition, drivers’ different driving behavior and so on. It is difficult to collect all necessary traffic information. Traffic congestion auto identification result based on incomplete traffic information exists uncertain. Little work has been done to analyze the uncertainty. Possibility theory introduced by Zadeh is an efficient means to present incomplete knowledge. Possibility distribution determination is an important task of possibility theory. In this paper, possibility theory is used to describe the uncertainty of traffic state. The possibility distribution of traffic state is determined according to the probability distribution of traffic flow parameters, such as volume, speed and occupancy and so on. The multi-variable distribution of traffic flow parameters is determined with large-scale traffic flow data. Large-scale traffic congestion samples are generated with parallel k-mean clustering method. Traffic congestion forecasting is based on the forecasting of traffic flow parameters with SVM (Support Vector Machines). At last, a practical example is analyzed with the proposed method.

Models on Real-Time State Identification for Unban Traffic Based on Fixed Detector

For the advantages and disadvantages of the traffic state identification based on fixed detector, a kind of method on the real-time state identification for the unban traffic was presented in order to improve accuracy and practical level of the traffic state identification. From analysis on detectors distribution and data collection methods, this paper carried out data preprocessing which collected from fixed detectors, then established the identification methods and thresholds for traffic state, developed the evaluation models of urban traffic congestion. Finally, the practicality of models was validated according to the traffic data collected by fixed detectors on typical roads in Qingdao city. The results show that the traffic state identification of the models is effective and with high precision.

Research on the comprehensive traffic state evaluation model linked with drivers’ perception under the vehicle networking

Unified judgment standards and methods are often adopted to identify traffic state in certain road network based on traffic flow parameters. However, drivers often have different perceptions about the traffic state on different road sections, since their expectations on traffic state vary more or less from each other on different road sections. In particular, under the vehicle networking, out of considerations for safety and other relevant factors, requirements for the correlation and coordination of running vehicles have also raised significantly. Therefore, it is necessary to take driver's perception about the driving conditions of certain road sections into consideration to adjust the release of traffic state. This paper has provided a comprehensive traffic state evaluation model linked with driver's perception under the vehicle networking. The authors first establish an ANFIS model based on the T---S model and then conduct statistical analysis on drivers' perceptions about certain traffic state. At last, the authors use the results of statistical analysis as regulatory factors to amend the parameters input through ANFIS. Through simulation, this paper has demonstrated that the model established has a high rate of convergence, a high identification precision and the generalization ability to conduct researches on the identification of traffic state.

Automatically Generating Empirical Speed-flow Traffic Parameters from Archived Sensor Data

Traffic parameters used in the prediction models, which is traditionally based on assumptions, is one of the error sources for prediction. With the availability of traffic data in nowadays, the traffic data is popularly considered to apply for the validation of demand models. It is a crucial step for convincing the model prediction results. The flow-density model, which provides relationships between traffic flow variables, is a well-established approach for traffic prediction. Robust implementations of long- range planning and microsimulation models require calibration and validation of facility and time-dependent parameters (e.g. free flow speed, capacity, wave speed, critical density) which are sensitive to infrastructure, weather and other external factors. Archived data from freeway sensors provide a large sample from which to calibrate these parameters. In this paper, a set of automated traffic state identification tool is developed and applied to historical data to automatically determine the traffic phases. Once the traffic phase is known, we then calibrate the flow-density parameters with the fundamental diagram. Using this tool, with the identified traffic state for many days’ data, the traffic parameters for free flow state and congestion state can be calibrated automatically in a fundamental diagram. As a case study, the calibrated traffic parameters in a dynamic traffic model (Cell Transmission Model) CTM are presented. The main findings of this paper is that an automated parameter calibration method is applicable for practical use, and this method provides a convincing result.

New Research for Traffic State Identification

The article proposed a new research. The method may avoid interference of reverse drive vehicle by installing back-to-back reader in section, adopting active electronic tags to expand the scope and accuracy about reading and writing, using the information including the number of electronic tags in scanning section, update ratio, residence time to build the linkage system between control system and vehicle-mounted electronic tags, and cooperating with the signal condition in intersection, to make the discrimination of traffic state, especially the discrimination of congestion state. The method could identify the traffic state effectively, which was verified by simulated experiment through secondary development of VISSIM.

The Method of Traffic State Identification and Travel Time Prediction on Urban Expressway

This study presented a method of real-time traffic condition identification based on the fuzzy c-means clustering and travel time prediction on urban expressway. The traffic flow characteristics of expressway were analyzed and the traffic flow states were divided into four classes. Then the fuzzy c-means clustering technique was used to classify the sampled historical data and the clustering center of different traffic condition was gotten. In the test module, the real-time traffic data were used to identify which state the traffic data belong to. Based on the analysis, a travel time prediction model of urban expressway was given by using fuzzy regression. According to the collecting real-time traffic dada and the result of traffic state identification, a method of predicting travel time was introduced. Finally, an urban expressway in Guanzhou was as an example and the result of traffic state identification was same with the results of actual measurement data and questionnaire survey through drivers. The result of travel time prediction show that the predicted results had better fitting degree and precision and the feasibility of this method was verified. It can provide the basis for urban expressway traffic control and traffic induction.

A clustering approach to online freeway traffic state identification using ITS data

Online traffic state identification plays an important role in achieving the potentials promised by Intelligent Transportation Systems (ITS) on various traffic applications, e.g., real time traffic monitoring systems. Traditional approaches have shown the limitations in either obtaining the necessary pre-determined information or having difficulties in their online implementation. This paper introduces an online agglomerative clustering procedure for freeway traffic state identification using ITS data, represented by three traffic condition variables of flow rate, speed, and occupancy. An optimal fit of the statistical characteristics is provided by maximizing the intra-cluster data point distances and minimizing inter-cluster data point distances through a joint utilization of the Bayesian Information Criterion and the ratio of change of the dispersion measurements. Test results show that the identified freeway traffic states through the proposed procedure are reasonable and consistent with the common understandings on freeway traffic operating conditions.

REAL TIME DATA MANAGEMENT FOR ESTIMATING PROBABILITIES OF INCIDENTS AND NEAR MISSES

Abstract. Advances in real-time data collection, data storage and computational systems have led to development of algorithms for transport administrators and engineers that improve traffic safety and reduce cost of road operations. Despite these advances, problems in effectively integrating real-time data acquisition, processing, modelling and road-use strategies at complex intersections and motorways remain. These are related to increasing system performance in identification, analysis, detection and prediction of traffic state in real time. This research develops dynamic models to estimate the probability of road incidents, such as crashes and conflicts, and incident-prone conditions based on real-time data. The models support integration of anticipatory information and fee-based road use strategies in traveller information and management. Development includes macroscopic/microscopic probabilistic models, neural networks, and vector autoregressions tested via machine vision at EU and US sites.