Estimation of local traffic conditions using Wi-Fi sensor technology

Real-time traffic data is fundamental for active traffic monitoring and control. Traditionally, traffic data are collected using location-based sensors and spatial sensors. However, both sensors have well-known limitations due to installation, operations, maintenance costs, and environmental factors. This study develops a methodology to use Wi-Fi sensors for traffic state characterization on urban roads to overcome these limitations. We examine the received signal strength indicator (RSSI) patterns and identify three distinct RSSI signature patterns. These patterns are used to develop methodologies to estimate (a) Whether the position of the end of the queue is upstream or downstream of the detector, (b) Whether the traffic conditions in the vicinity of the detector are uniformly uncongested or uniformly congested, and (c) The maximum queue length and the time is taken for the queue to grow to the maximum extent. The estimates from the methodology are validated with empirical data that showed good concurrence with field conditions, and the methods proposed in this article have the potential to estimate the traffic conditions using sparse data from Wi-Fi sensors.