Road Network Traffic Flow Research Articles

Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm

In recent years, Intelligent Transportation Systems (ITS) have developed a lot. More and more sensors and communication technologies (e.g., cloud computing) are being integrated into cars, which opens up a new design space for vehicular-based applications. In this paper, we present the Spatial Optimized Dynamic Path Planning algorithm. Our contributions are, firstly, to enhance the effective of loading mechanism for road maps by dividing the connected sub-net, and building a spatial index; and secondly, to enhance the effect of the dynamic path planning by optimizing the search direction. We use the real road network and real-time traffic flow data of Karamay city to simulate the effect of our algorithm. Experiments show that our Spatial Optimized Dynamic Path Planning algorithm can significantly reduce the time complexity, and is better suited for use as a real-time navigation system. The algorithm can achieve superior real-time performance and obtain the optimal solution in dynamic path planning.

Carbon emissions tax policy of urban road traffic and its application in Panjin, China.

How to effectively solve traffic congestion and transportation pollution in urban development is a main research emphasis for transportation management agencies. A carbon emissions tax can affect travelers’ generalized costs and will lead to changes in passenger demand, mode choice and traffic flow equilibrium in road networks, which are of significance in green travel and low-carbon transportation management. This paper first established a mesoscopic model to calculate the carbon emissions tax and determined the value of this charge in China, which was based on road traffic flow, vehicle speed, and carbon emissions. Referring to existing research results to calibrate the value of time, this paper modified the traveler’s generalized cost function, including the carbon emissions tax, fuel surcharge and travel time cost, which can be used in the travel impedance model with the consideration of the carbon emissions tax. Then, a method for analyzing urban road network traffic flow distribution was put forward, and a joint traffic distribution model was established, which considered the relationship between private cars and taxis. Finally, this paper took the city of Panjin as an example to analyze the road traffic carbon emissions tax’s impact. The results illustrated that the carbon emissions tax has a positive effect on road network flow equilibrium and carbon emission reduction. This paper will have good reference value and practical significance for the calculation and implementation of urban traffic carbon emissions taxes in China.

Nonconvex <inline-formula> <tex-math notation="LaTeX">$l_p$ </tex-math> </inline-formula>-Norm Regularized Sparse Self-Representation for Traffic Sensor Data Recovery

Recovering missing values from incomplete traffic sensor data is an important task for intelligent transportation system because most algorithms require data with complete entries as input. Self-representation-based matrix completion attempts to optimally represent each sample by linearly combining other samples when conducting missing values recovery. Typically, it implements sparse or dense combination through imposing either $l_{1}$ -norm or $l_{2}$ -norm regularization over the representation coefficients, which is not always optimal in practice. To permit more flexibility, we propose in this paper a novel approach termed as $l_{p}$ -norm regularized sparse self-representation (SSR- $l_{p}$ ) by incorporating nonconvex $l_{p}$ -norm with $0 as regularization. In such a way, it is able to produce more sparsity than $l_{1}$ -norm and in turn facilitates the accurate recovery of missing data. We further develop an efficient iterative algorithm for solving SSR- $l_{p}$ . The performance of this method is evaluated on a real-world road network traffic flow data set. The experimental results verify the advantage of our method over other competing algorithms in recovering missing values.

Quantitative Analysis of Urban Regional Traffic Status

In order to monitor the real-time operation condition of urban region traffic flow, and to quickly identify regional traffic status, this paper adopts CNM (Clauset-Newman-Moore) Community Division Method of Complex Network to analyze traffic status information deeply implied from the regional road network traffic flow data, which aims to objectively develop the reasonable classification of regional traffic state with no classification criteria of determining regional traffic state. Combined with the regional road network traffic data from a certain city, the example analysis shows that this proposed method can easily provide the reasonable division of regional traffic condition and verifies the feasibility of the regional traffic state classification method. Besides, the example analysis gives the rough regional traffic status determination standard, laying theoretical basis for accurately judging the regional traffic state.

The Evaluation of Node Importance in Urban Road Network Based on Complex Network Theory

Identifying and protecting traffic hub is vital to keep the stability and reliability of urban road traffic network. In this paper, a comprehensive evaluation method of node importance in urban road traffic network was introduced based on identification theory of key nodes in complex network. Firstly, taking into account the impact of road network topology and traffic flow characteristics on node’s capacity, node importance in topological network and betweenness, urban road grade and node traffic flow are selected as the evaluating indexes of node importance, and AHP is used to determine the weight of each index. Secondly, the associated value of the indexes are calculated and obtained based on SuperMap Deskpro and practical investigation, and then, combined with TOPSIS, a sequence of node importance is determined. Finally, we applied the method to part district of Hohhot road traffic network, it can be seen that the key nodes obtained with the method stay in the important position in the the traffic network and have greater traffic flow, the results showed it is effective and practical feasible.

Prediction of Traffic Flow Combination Model Based on Data Mining

It is an important to quickly and accurately forecasting road network traffic flow in intelligent transportation systems, Aiming at the forecasting problem of short-term traffic flow, this paper proposed a traffic flow prediction algorithm, which based on traffic flow sequence partition and neural network model. Firstly, the algorithm divided the traffic flow into different patterns and time sequence by clustering, secondly, described and predicted traffic flow model according to BP neural network. Finally, the experiment shows that based on combined model is much accurate.

Experimental Analysis of Various Types of Road Intersections for Interchange Detection

AbstractRoad interchanges are a major pattern type in road networks. Recognition of road interchanges benefits automated road network generalization, car navigation and traffic flow analysis. This study first investigated several existing approaches to automatically detecting interchanges in a road network, and determined that the recognition of characteristic road intersections is essential for the effective detection of interchanges. Several experiments were carried out to investigate nine types of road intersections for interchange detection and to analyze characteristic ones. Furthermore, an approach to the detection of both intersections and segments of interchanges was proposed and validated. The road networks across different scales were tested and results show that: (1) the T‐shaped and Cross‐shaped junctions are very common in road networks, but they are not the most characteristic ones for interchange detection; (2) the y‐shaped, Y‐shaped types, X‐shaped, Fork‐shaped and Multi‐leg may be the characteristic types for interchange detection; (3) the proposed approach to detecting interchanges is effective, and most of the intersections and segments of interchanges can be detected. In addition, taking multiple characteristic types into consideration for interchange detection is suggested.

Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing

To increase the efficiency and precision of large-scale road network traffic flow prediction, a genetic algorithm-support vector machine (GA-SVM) model based on cloud computing is proposed in this paper, which is based on the analysis of the characteristics and defects of genetic algorithm and support vector machine. In cloud computing environment, firstly, SVM parameters are optimized by the parallel genetic algorithm, and then this optimized parallel SVM model is used to predict traffic flow. On the basis of the traffic flow data of Haizhu District in Guangzhou City, the proposed model was verified and compared with the serial GA-SVM model and parallel GA-SVM model based on MPI (message passing interface). The results demonstrate that the parallel GA-SVM model based on cloud computing has higher prediction accuracy, shorter running time, and higher speedup.

Method of Traffic Analysis Zone Partition for Traffic Impact Assessment

AbstractIn order to predict and analyze the attributes of macro traffic flow on road network and micro traffic flow at intersection accurately during traffic impact assessment (TIA), a method of combining traffic volume distribution model with traffic simulation was developed. First, based on Geography Information System (GIS), Voronoi diagrams were used to traffic analysis zone (TAZ) partition, and the attribute data was also partitioned correspondingly. Then, multiplicative competitive interaction (MCI) model was adopted to estimate the new OD matrix. As a result, the output of traffic demand model can be input to micro traffic simulator directly to simulate and analyze the micro traffic flow. Finally, a numeric test was done to show the validity and accuracy of the presented method.