Abstract
Traffic Census Sensor Using Vibration Caused by Passing Vehicles
Highlights
Traffic censuses, which investigate traffic volumes and vehicle movements, provide essential data that will allow planners to develop more efficient road improvement plans and help reduce carbon dioxide (CO2) emissions
Since we found that the accuracy saturates at around eight dimensions, we used eight mel-frequency cepstral coefficients (MFCCs) dimensions in our machine learning (ML) process
These results show that ML-based linear discriminant analysis (LDA) made it possible to count passing vehicles with high accuracy
Summary
Traffic censuses, which investigate traffic volumes and vehicle movements, provide essential data that will allow planners to develop more efficient road improvement plans and help reduce carbon dioxide (CO2) emissions. In Japan, a national road/street traffic situation survey known as the “Traffic Census” is conducted once every five years. In this census, the traffic volumes of Japan’s highways, national roads, and prefectural roads are investigated. As of the latest (2015) road traffic census report, more than 50% of the censuses were still being counted manually.[1] Figure 1 shows the rates of the different vehiclecounting processes used in the last three censuses. Manual counting process declined by only 13% in the last 10 years, and that the contributions of mechanized counting processes remain low, primarily because such vehicle counters are expensive, require large machines, and have long setup times. It is difficult for camera systems to count vehicles correctly in dark locations or at night
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