藉由數位錄影資料辨識公路客運司機駕駛行為之研究

A Study on Recognizing Highway Bus Driver’s Behavior by Using Digital Video Data

羅家偉(Chia-Wei Lo)

指導教授 ： 陶治中

淡江大學/商管學院/運輸管理學系運輸科學碩士班/碩士(2021年)

https://doi.org/10.6846/TKU.2021.00590

摘要

目前多數客運業者多已裝設閉路電視攝影機於客運車輛，可針對司機行車行為進行錄影，本研究認為若能善用這些錄影資料來辨識司機異常之駕駛行為，則有助於輔導司機避免發生不良駕駛行為而提升行車安全。
在現場環境硬體與運算資源之條件下，本研究選定葛瑪蘭客運公司作為實證分析對象，經訪談該公司高階主管，決定採用後台駕駛影像資料進行司機駕駛行為影像辨識模型訓練與建構，並使用R語言開發工具建構卷積神經網路(CNN)模型，同時進行模型測試與修正，最終模型辨識率可達94%，具良好之辨識能力，顯示透過影像辨識方式分析客運司機行車間之行為，證明可行。
為使辨識結果可應用於管理輔助，本研究加入後台之營運資料，並以視覺化軟體Power BI進行呈現，最後建立一有助於客運業者管理、輔導司機正常駕駛之決策輔助機制，以此提升安全效益。例如：針對過勞人員進行溝通與了解是否個人私下作息不正常，又或身體健康發生問題，需要透過排班調整進行改善、或是針對不良駕駛行為之司機進行勸導並勒令改善不良行為等，進而提升行車安全。

關鍵字

駕駛行為 ； 數位錄影資料 ； 卷積神經網路 ； 資料視覺化

並列摘要

Current highway bus operators have installed in-vehicle CCTVs to record bus driver’s behavior during transportation. It will be meaningful to enhance safety if these digital video data can be used to help bus drivers to avoid from abnormal driving behavior.
Considering on-site hardware and computational resources this study has chosen Kamalan bus company as the use case for empirical analysis. Having discussed with top managers at Kamalan bus company, this study used R language to present a CNN-based model with continuous iterations and tests. Empirical results showed that the accurate rate reached 94% and achieved the goal of feasibility of recognizing bus driver’s behavior by using digital video data.
With the help of visualization tool Power BI this study used rear-end operation data to map with bus driver’s behavior results. It is proven that Kamalan bus company awards recognition to this model because of positive responses of bus drivers in company. For example, overloaded bus drivers will be interviewed to understand why they performed abnormal driving behavior. Personal persuade measures or rescheduling work shit may be taken to improve driving behavior for transportation safety.

並列關鍵字

Driver Behavior ； Digital Video Data ； Convolutional Neural Networks ； Data Visualization

參考文獻

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