基于物联网的系统：通过机载污染气体分析进行城市交通大数据挖掘
An IoT-Based System: Big Urban Traffic Data Mining Through Airborne Pollutant Gases Analysis
AI总结：
本文提出一种基于物联网的城市交通拥堵检测系统，通过计算区域空气污染量来获取交通拥堵情况，实验结果令人满意。
摘要：
如今，在包括伊朗在内的发展中国家，由于人口增长，车辆数量不断增加。这最近导致了因交通拥堵浪费时间、日常通勤耗时增加以及事故增多。因此，有必要由交警控制交通拥堵，高效扩建道路，并由市民选择最佳路线以减少交通拥堵。因此，掌握每个车道的实时交通信息非常重要。如今，许多交通组织服务，如交警和城市交通控制系统，使用交通摄像头、电感传感器、卫星图像、雷达传感器、超声波技术和射频识别（RFID）进行城市交通诊断。但这种方法存在一些问题，如在受空气状况影响的拥堵交通中效率低下，以及无法检测平行交通。本文提出的方法基于物联网（IOT）检测交通拥堵，包含一个智能系统，通过计算该区域的空气污染量来获取交通拥堵情况。根据进行的实验，结果令人满意。
英文摘要：
Nowadays, in developing countries including Iran, the number of vehicles is increasing due to growing population. This has recently led to waste time getting stuck in traffic, take more time for daily commute, and increase accidents. So it is necessary to control traffic congestion by traffic police officers, expand paths efficiently and choose the best way for decreasing the traffic by citizens. Therefore, it is important to have the knowledge of instant traffic in each lane. Todays, many traffic organization services such as traffic police officer and urban traffic control system use traffic cameras, inductive sensors, satellite images, radar sensors, ultrasonic technology and radio-frequency identification (RFID) for urban traffic diagnosis. But this method has some problems such as inefficiency in heavy traffic influenced by condition of the air and inability to detect parallel traffic. Our method suggested in this article detects traffic congestion based on IOT containing a smart system that gives us traffic congestion by calculating the air pollution amount in that area. According to conducted experiment, the results were satisfied.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
