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《城市交通》杂志
2016年 第6期
出租汽车驾驶员生态驾驶行为培训方法 ——以北京市为例
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文章编号: 1672-5328(2016)06-0036-04

伍毅平1,程颖2,刘莹2,赵晓华1,荣建1
(1.北京工业大学城市交通学院,北京100124;2.北京市交通行业节能减排中心,北京100073)

摘要: 驾驶行为是影响机动车能耗和尾气排放的主要因素之一,生态驾驶行为已在众多发达国家推 广实施,并取得显著的节能减排效益。选取北京市同一车型的60 名出租汽车驾驶员实施生态驾驶 行为培训,利用OBD+北斗/GPS逐秒采集车辆油耗和运行数据。通过对比培训前后车辆平均百公里 油耗改变量,明确生态驾驶培训的节能效果,形成面向出租汽车驾驶员行为矫正的生态驾驶培训方 法。培训方案包括三种形式:基于培训手册和宣传视频的静态培训、基于驾驶模拟器的实操动态培 训、先静态后动态的综合培训。培训结果表明:生态驾驶行为培训平均降低车辆百公里油耗 8.6%;对出租汽车驾驶员实施动态生态驾驶培训更合理有效。由于车辆本身油耗的差异,生态驾驶 行为培训对于改善公共交通、货运交通及长途客运汽车等行业的能耗现状可能更为显著。

关键词: 节能减排;出租汽车;生态驾驶;驾驶培训;驾驶模拟器;行为矫正

中图分类号: U491

文献标识码:A

Eco-Driving Training for Taxi Drivers: A Case Study in Beijing

Wu Yiping1, Cheng Ying2, Liu Ying2, Zhao Xiaohua1, Rong Jian1
(1.College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China; 2.Beijing Transport Energy & Environment Centre, Beijing 100073, China)

Abstract: As a key factor influencing vehicle fuel consumption and emissions, eco- driving method has been implemented in many developed countries and made a significant effect in energy conservation and emissions reduction. This study uses OBD and BeiDou/GPS devices to collect vehicle fuel consumption and operational data for 60 Beijing taxi drivers with driving the same type of taxi before and after Eco-driving training. The effectiveness of such a training is obtained by comparing the change of average fuel consumption. Three optimized training method are further proposed, including static training model via brochure and video, dynamic training via driving simulator, and an integrated training method of combing the above two. The results show that an average percentage of fuel consumption reduction after eco- driving training is 8.6%, and dynamic eco driving training is more effective to taxi drivers. Considering the change of fuel consumption among different types of vehicles, the effects of eco-driving training is more significant to public transit, freight, and coach transport.

Keywords: traffic management; residents' travel characteristics; road traffic operation; severe air pollution; smog