Most probe vehicle data is generated using satellite navigation systems, such as the Global Positioning System (GPS), Globalnaya navigatsionnaya sputnikovaya Sistema (GLONASS), or Galileo systems. However, because of their high cost, relatively high position uncertainty in cities, and low sampling rate, a large quantity of satellite positioning data is required to estimate traffic conditions accurately. To address this issue, we introduce a new type of traffic monitoring system based on inexpensive inertial measurement units (IMUs) as probe sensors. IMUs as traffic probes pose unique challenges in that they need to be precisely calibrated, do not generate absolute position measurements, and their position estimates are subject to accumulating errors. In this paper, we address each of these challenges and demonstrate that the IMUs can reliably be used as traffic probes. After discussing the sensing technique, we present an implementation of this system using a custom-designed hardware platform, and validate the system with experimental data.

Probe vehicles consist in mobile traffic sensor networks that evolve with the flow of vehicles, transmitting velocity and position measurements along their path, generated using GPSs. To address the urban positioning issues of GPSs, we propose to replace them with inertial measurement units onboard vehicles, to estimate vehicle location and attitude using inertial data only. While promising, this technology requires one to carefully calibrate the orientation of the device inside the vehicle to be able to process the acceleration and rate gyro data. In this article, we propose a scheme that can perform this calibration automatically by leveraging the kinematic constraints of ground vehicles, and that can be implemented on low-end computational platforms. Preliminary testing shows that the proposed scheme enables one to accurately estimate the actual accelerations and rotation rates in the vehicle coordinates.

Probe vehicle data is currently generated using satellite navigation systems such as the GPS, GLONASS or Galileo systems. However, because of their high cost and relatively high position uncertainty and low sampling rate, satellite positioning systems have a relatively low penetration rate among users. In addition, such sensors do not provide context in the traffic measurements. To address these issues, we introduce a new traffic monitoring concept based on inexpensive inertial measurement units in conjunction with a wireless sensor network deployed inside a city. After discussing the sensing technique, we present a preliminary implementation of this system using an open source robotic platform. Preliminary results show that this system can be used to generate traffic measurement data.

Floods are the most common type of natural disaster, causing thousands of casualties every year. Among these events, urban flash floods are particularly deadly because of the short timescales on which they occur, and because of the high concentration of population in cities. Since most flash flood casualties are caused by a lack of information, it is critical to generate accurate and detailed warnings of flash floods. However, deploying an infrastructure that solely monitor flash floods makes little economic sense, since the average periodicity of catastrophic flash floods exceeds the lifetime of a typical sensor network. To address this issue, we propose a new sensing device that can simultaneously monitor urban flash floods and another phenomenon of interest (traffic congestion on the present case). This sensing device is based on the combination of an ultrasonic rangefinder with one or multiple remote temperature sensors. We show an implementation of this device, and illustrate its performance in both traffic flow and flash flood sensing. Field data shows that the sensor can detect vehicles with a 99% accuracy, in addition to estimating their speed and classifying them in function of their length. The same sensor can also monitor urban water levels with an accuracy of less than 2 cm. Two of the sensors have been deployed in a flood prone area, where they captured the only (minor) flash flood that occurred over the one-year test period, with no false detection, and an agreement in the estimated water level estimate (during the flash flood event) of about 2 cm.

We propose a new sensing device that can simultaneously monitor urban traffic congestion and another phenomenon of interest (flash floods on the present case). This sensing device is based on the combination of an ultrasonic rangefinder with one or multiple remote temperature sensors. We show an implementation of this device, and illustrate its performance in both traffic flow sensing. Field data shows that the sensor can detect vehicles with a 99% accuracy, in addition to estimating their speed and classifying them in function of their length. The same sensor can also monitor urban water levels with an accuracy of less than 2 cm.