  The advent of Low Power Wide Area Networks (LPWAN) has enabled the
feasibility of wireless sensor networks for environmental traffic sensing
across urban areas. In this study, we explore the usage of LoRaWAN end nodes as
traffic sensing sensors to offer a practical traffic management solution. The
monitored Received Signal Strength Indicator (RSSI) factor is reported and used
in the gateways to assess the traffic of the environment. Our technique
utilizes LoRaWAN as a long-range communication technology to provide a
largescale system. In this work, we present a method of using LoRaWAN devices
to estimate traffic flows. LoRaWAN end devices then transmit their packets to
different gateways. Their RSSI will be affected by the number of cars present
on the roadway. We used SVM and clustering methods to classify the approximate
number of cars present. This paper details our experiences with the design and
real implementation of this system across an area that stretches for miles in
urban scenarios. We continuously measured and reported RSSI at different
gateways for weeks. Results have shown that if a LoRaWAN end node is placed in
an optimal position, up to 96% of correct environment traffic level detection
can be obtained. Additionally, we share the l
