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The jam flow condition is one of the main traffic states in traffic flow theory and the most difficult state for sectional traffic information acquisition. Since traffic information acquisition is the basis for the application of an intelligent transportation system, research on traffic vehicle counting methods for the jam flow conditions has been worthwhile. A low-cost and energy-efficient type of multi-function wireless traffic magnetic sensor was designed and developed. Several advantages of the traffic magnetic sensor are that it is suitable for large-scale deployment and time-sustainable detection for traffic information acquisition. Based on the traffic magnetic sensor, a basic vehicle detection algorithm (DWVDA) with less computational complexity was introduced for vehicle counting in low traffic volume conditions. To improve the detection performance in jam flow conditions with a “tailgating effect” between front vehicles and rear vehicles, an improved vehicle detection algorithm (SA-DWVDA) was proposed and applied in field traffic environments. By deploying traffic magnetic sensor nodes in field traffic scenarios, two field experiments were conducted to test and verify the DWVDA and the SA-DWVDA algorithms. The experimental results have shown that both DWVDA and the SA-DWVDA algorithms yield a satisfactory performance in low traffic volume conditions (scenario I) and both of their mean absolute percent errors are less than 1% in this scenario. However, for jam flow conditions with heavy traffic volumes (scenario II), the SA-DWVDA was proven to achieve better results, and the mean absolute percent error of the SA-DWVDA is 2.54% with corresponding results of the DWVDA 7.07%. The results conclude that the proposed SA-DWVDA can implement efficient and accurate vehicle detection in jam flow conditions and can be employed in field traffic environments.

Abstract² This paper proposes three practical vehicle speed estimation methods by a single multifunction magnetic sensor. Compared with traditional methods, this algorithm is simple and convenient to be realized. The multifunction magnetic sensor is described and introduced in this work. Next, a vehicle detection algorithm with a linear time complexity is put forward. Through setting two windows and scanning the vehicle waveform, we obtain the points of vehicle approaching and vehicle leaving, which are the bases for vehicle count, headway time, time occupancy, stopping time, detection of vehicle stopping and presence. The detailed detection methods of vehicle stopping and presence are described. We next present some speed estimation methods in detail. According to three speed estimation methods of Vehicle Length based (VLB), Time Difference based (TDB) and Mean Value based (MVB) to get different reference speeds when a vehicle passes over the sensor, we then analyze the applicability of the three methods. At last, we test the speed estimation methods by adoption of field data. Through the comparison with the real speed of 45 vehicles, it shows that the mean absolute errors (MAE) of VLB, TDB and MVB methods are respectively 4.12km/h, 5.90km/h and 4.05km/h and the mean speed errors of the three methods are all less than 1km/h. These errors are suitable for traffic engineering.

Road traffic state prediction is one of the essential and vital issues in intelligence transportation system, but it is difficult to get high accuracy due to the complicated spatiotemporal characteristics of traffic flow data, especially under the Sydney coordinated adaptive traffic system. In this work, we represent the traffic road network as a graph and propose a novel traffic flow prediction framework named the graph embedding recurrent neural network (GERNN). It could tackle the difficulty in the road traffic state prediction. We conduct numerical tests to compare GERNN with other existing methods using a real-world dataset.

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This paper discusses the influence factors of detector spacing optimization. Through the corresponding mathematical conversion, it gets the model parameters which influence the result of detector spacing optimization. IDF (Information Degree Function) is proposed to describe the spatial distribution characteristics of traffic information. Then, this paper gives the calibration method of the model parameters. After a general study on all parameters, this paper proposes the MIVM (Maximal Integrated Value Model). And the SPA (Shortest Path Algorithm) is used to solve the problem. Through the example of the Second Ring Road, in Beijing, the model parameters are calibrated on the field data. According to the result of detector spacing optimization, this paper obtains the reasonable density which is fit for Beijing expressway, and provides the basis for the practical application. The MIVM is fit for other cities, too.