According to [1], over-roadway sensors are becoming more popular as sources of real-time data for traffic signal control and traffic management. This is because of their ability to provide multi-lane data from a single sensor, reduce maintenance costs, increase safety to installation personnel, richer data sets not available from loops or magnetometers, and competitive purchase and installation costs. When a sensor is installed directly over the lane of traffic that it is intended to monitor, its view and hence its ability to collect data are typically not obstructed. But when a sensor is mounted on the side of a roadway and views multiple lanes of traffic at a perpendicular or oblique angle to the direction of traffic flow, tall vehicles can block its view of distant lanes, potentially causing an undercount or false average speed measurement [3].

Some over-roadway sensors can be affected by weather conditions, such as wind, fog, blowing snow and rain. Another disadvantage is that installation and maintenance can require lane closure for safety purposes when it is mounted above the road.In order to overcome the limitations of both the in-roadway and over-roadway sensors, the use of seismic signals for moving vehicle detection is proposed. In this paper, a detection configuration based on two seismic sensors installed on the road shoulder is designed. This technology may be deployed as an alternative to traditional in-roadway and over-roadway sensors. Because such sensors are installed at ground level but outside the travel lanes, installation and maintenance can be performed without diverting traffic or altering the road surface, and thus can substantially reduce costs.

By recording seismic signals in each interval, we believe the time delay of arrival (TDOA) can be estimated using a generalized cross-correlation Brefeldin_A approach with phase transform (GCC-PHAT). The slope of the TDOA curve in the linear region may be used to estimate axle speed. Various kinds of vehicle characterization information, including vehicle speed, axle spacing, and driving direction, should also be extracted from the collected seismic signals. To realize these data, however, suitable algorithms must be developed to process the observed ground waves at the sensor pair, and this is the primary focus of this paper.The remainder of this paper is organized as follows.

Section 2 explains the mechanism of seismic waves caused by moving vehicles, and presents theories relevant to source localization. In particular, GCC-PHAT method is introduced to estimate the TDOA of seismic sources. Section 3 describes the basic seismic propagation model for moving vehicles that defines fundamental geometric and vehicle characteristic parameters. In Section 4, estimation methods for vehicle information, including vehicle speed, axle spacing, axle detection, and driving direction, are investigated.