Abstract
This study demonstrates an incident detection algorithm that uses the meteorological and traffic parameters for improving the poor performance of the automatic incident detection (AID) algorithms under extreme weather conditions and for efficiently using the meteorological devices on advanced freeways. This algorithm comprises an incident detection module that is based on learning vector quantization (LVQ) and a meteorological influencing factor module. Field data are obtained from the Yuwu freeway in Chongqing, China, to verify the algorithm. Further, the performance of this algorithm is evaluated using commonly used criteria such as mean time to detection (MTTD), false alarm rate (FAR), and detection rate (DR). Initially, an experiment is conducted for selecting the algorithm architecture that yields the optimal detection performance. Additionally, a comparative experiment is performed using the California algorithm, exponential smoothing algorithm, standard normal deviation algorithm, and McMaster algorithm. The experimental results demonstrate that the algorithm proposed in this study is characterized by high DR, low FAR, and considerable suitability for applications in AID.
Highlights
The remainder of this paper can be divided into four sections
After dealing with the relation between the meteorological factors and occurrence of traffic incidents using fuzzy logic, the subsequent task is to combine meteorological factors with a large number of traffic parameters for detecting incidents on freeways. e learning vector quantization (LVQ) neural network is extensively applied as a key technique to solve this problem in the data fusion domain, and this method is considered to be an efficient classification method [24, 25]. erefore, we use LVQ to combine the traffic parameters and meteorological factors for performing incident detection to improve the performance of the automatic incident detection (AID) algorithms
An experiment was conducted to determine the LVQ network architecture having the optimal detection performance to test the performance of the traffic accident detection algorithm. ree LVQ models with traffic measures having time-series of different lengths and with the factor calculated in this study as the inputs are used in this experiment
Summary
Meteorological Influencing the Factor Model Based on Fuzzy Logic. Two major meteorological measures are used in this model, including six hours of rainfall and hourly visibility. Based on the linear distribution of visibility and rainfall, trapezoidal and triangular functions were selected to describe the fuzzy set. The e minimal difference between both the curves is selected for determining the range of the fuzzy set by comparing the different Rfuzzy curves with the real incident frequency in different ranges of rainfall according to Figure 1. UR and UV denote the membership functions of the fuzzy sets of rainfall and visibility, respectively. Because the relation between UR and UV can be described as “and” in the rule base, we used Mamdani reasoning [26] as the inference methodology for determining each selected rule. 2.00 Y
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