基于高时空辨识度实时数据的重庆二环内外区域车流特征差异分析

高时空辨识度的车流时空分布特征是研究区域机动车排放的重要基础，通过射频识别技术和车辆注册登记数据获得重庆市二环区域每10 min的车流量以及车辆技术特征信息，对比分析内环以内及以外区域的分车型、分道路类型、分排放标准和燃料类型的车流量时空变化特征.结果表明:①重庆市内环以内区域日均流量为1.8×104辆，约为内环以外区域的1.8倍.②内环以内区域小型客车、公交车、出租车的日均流量分别为内环以外区域的1.7、2.1和2.5倍，而重型货车的日均流量为内环以外区域的54.8%.③ 2个区域车辆的主要燃料类型为汽油、天然气、柴油、新能源，占比分别为71.7%~73.7%、15.1%~21.4%、5.5%~9.6%、1.3%~1.5%.④ 2个区域车辆的排放标准分布基本一致，主要排放标准为国Ⅳ（约占76.5%），国Ⅴ约占11.4%，国Ⅲ约占9.0%，国Ⅱ、国Ⅰ和国Ⅰ前的占比之和约为3.1%.⑤ 2个区域的小时总流量变化特征呈“M”型分布，早高峰时段为08:00—10:00，晚高峰时段为16:00—18:00.⑥ 2个区域小型客车、公交车的小时流量变化特征均与总流量变化特征基本一致，但出租车、轻型货车和重型货车在08:00仍保持明显的上升趋势，直到14:00才缓慢下降.⑦内环以内区域高速路、快速路、县道的高峰时段流量明显较高，分别为内环以外区域的5.5、2.5、6.2倍；而内环以外区域国道的高峰时段流量相对较高，约为内环以内区域的1.8倍.研究显示，重庆市二环内外区域的车流量和车辆技术特征信息的时空分布存在较大差异，建议完善城市实际道路车流的时空监测网络，为机动车排放清单的编制提供更好的数据支撑. 

Temporal-Spatial Distribution Characteristics of Traffic Volume and Technical Level in Chongqing

The detailed distribution of traffic flow with high temporal-spatial resolution is an important basis for research on motor vehicle emissions. This study obtained information on the vehicle volume and vehicle technical characteristics information in the second ring district of Chongqing City every 10 minutes according to the Radio Frequency Identification Device and vehicle registration data. In addition, the temporal-spatial distribution characteristics of vehicle volumes were compared and analyzed through various vehicle types, road types, emission standards and fuel types between the inner and outer districts. The main conclusions are as follows: The average daily traffic volume in the inner district was 1.8×104 vehicles per day, which was about 1.8 times that of the outer district. Specifically, the proportions of light passenger cars, buses and taxis in the inner district were 1.7, 2.1 and 2.5 times larger than that of the outer district, respectively, while the proportion of heavy trucks was 54.8% of the outer district. The main fuel types in the inner and outer districts were gasoline (71.7%-73.7%), natural gas (15.1%-21.4%), diesel (5.5%-9.6%) and new energy (1.3%-1.5%). The distribution of emission standards in the inner district was the same as that of the outer district, with China Ⅳ accounting for about 76.5%, China Ⅴ accounting for about 11.4%, China Ⅲ accounting for about 9.0%, the sum of the proportions of China Ⅱ, China Ⅰ and under China Ⅰ is about 3.1%. The hourly total volumes change characteristics of the two districts both presented an 'M' shape, and the peak hours occurred during 08:00-10:00 and 16:00-18:00, respectively. In addition, the hourly volume change characteristics of light passenger cars and buses in the two districts were consistent with the total volume flow characteristics, while taxis, light trucks and heavy trucks showed continuous upward trend from 08:00 to 14:00. Finally, the peak period volumes of different types of roads in the two districts were quite different. The peak period volumes of highways, expressways and county roads in inner district were about 5.5, 2.5 and 6.2 times larger than the outside district, respectively. On the contrary, the peak period volumes of national highways in the outer district was 1.8 times larger than that of the inner district. The results indicated that the vehicle flow and vehicle characteristics information are significantly different between the inner and outer districts. More attention should be paid to the refined temporal-spatial monitoring network of urban traffic flow to provide better data support for the compilation of vehicle emission inventories.