Abstract
Traffic volume information is widely used in all aspects of Intelligent Transportation Systems (ITS), such as transportation planning, traffic states identification, traffic management, safety analysis, and so on. In recent years, acoustic sensors are gradually applied to the detection of various traffic parameters. In this paper, acoustic data sets acquired from acoustic sensors are utilized to estimate the road traffic volume. The short-term energy (STE) algorithm and the energy to zero crossing rate (EZCR) algorithm are usually applied to acoustic analysis, and they both perform well under some simple circumstances, however, some urgent problems remain unresolved under certain complex conditions. One of such issues occurs when adjacent vehicle-pass signals (VPSs) intersect partially, seriously decreasing the accuracy of endpoint detection of VPSs, hampering the algorithm ability to maintain satisfactory traffic volume estimation. Another difficulty arises while multiple lanes are considered: some special VPSs cannot be detected. To solve these problems, a novel acoustic characteristic is defined, and an acoustic-based characteristic extraction algorithm for traffic volume estimation, entitled triangular wave analysis (TWA), is proposed. Comparing the TWA algorithm with the STE and EZCR algorithms, experimental results demonstrate the viability of the proposed algorithm in the case of intersectant VPSs.
Highlights
Traffic volume estimation is beneficial for traffic planning and the law of traffic operation
If all valley values are equal to zero and the peak values tend to be larger, the parameters e1, e2, c1, and c2 can be set to zero, the limitations will be suppressed well enough. According to this idea, we present a detailed description of the proposed algorithm, entitled Triangular Wave Analysis (TWA), to improve the accuracies of traffic volume and endpoint detections
In this paper, we propose an acoustic-based characteristic extraction algorithm for traffic volume estimation which can solve the problem of intersectant vehicle-pass-signals (VPSs) recognition
Summary
Traffic volume estimation is beneficial for traffic planning and the law of traffic operation. One algorithm uses the ratio of loop detector counts on parallel links, and the other algorithm uses data from probe vehicles to estimate the traffic volumes from the dynamics of the queue length at signalized intersections. Z. Zou et al.: Novel Acoustic Characteristic Extraction Algorithm for Traffic Volume Estimation. Traffic acoustic signals, which are consist of vehicle-pass signals (VPSs) and environment signals (ESs), carry significant traffic parameters information and provide data support for the Intelligent Transportation System (ITS). The core idea of the proposed algorithm can be divided into three steps: 1) extract new characteristic in order to adapt to traffic volume estimation; 2) turn every VPS into a disjoint triangular waveform in the new characteristic curve; 3) Get the traffic volume and endpoints of VPSs according to the triangular waveform
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