Wireless Address Matching

Anonymous Wireless Address Matching (AWAM) is an approach to using digital device signatures (such as car radios, and cell phones) to “read” device addresses and therefore identify and track such devices or the trucks that contain them. The address, known as Media Across Control (MAC), is a standardized component of modern digital devices with communications capabilities. Any device with wireless, Bluetooth or radio capability has a MAC address. Since most trucks contain at least one device with a MAC address, AWAM is a feasible approach for freight vehicle location and re-identification.

Bluetooth is a short-range radio link that was standardized in the late 1990s to connect Personal Computers to devices such as cell phones, Global Positioning Systems (GPS) units or vehicle on-board computers (OBC). It enables close-range communication without the need for wires.

The main goal of the AWAM approach in transportation is to provide high-quality traffic information that accurately depicts conditions on roadways instrumented with Bluetooth receivers or sensors (TTIa, 2017). Address matching enables the capture of observational data such as the time, location, speed and other information about trucks that pass within the range of sensors. The figure below depicts the flow of data from a vehicle with an onboard data transmitter (e.g. cell phone, GPS or OBC) through a Bluetooth enabled device to a receiver and the computer where the data can be analyzed to provide travel times and speeds.

AWAM provides a way to collect travel time by interpolating between successive observations and, potentially, vehicle identification, routing, classification and commodity data as the technology emerges. Most if not all commercial vehicles have MAC id enabled devices with Bluetooth or wireless capability, providing an easy and nonintrusive way for data to be collected. When the vehicle may not be equipped, the driver’s phone is also a device that is identified if its Bluetooth receiver is turned on. It is estimated that by 2018, 90 percent of mobile devices will have Bluetooth location enabled, and currently around 60 percent of people with a Bluetooth enabled device turn on location services (Coombs, 2017). Determining device penetration rates, however, is a challenge due to the characteristics of the roadway, how the detection equipment is installed (e.g. antennas), and the type of technology being used (Vickich, 2017). For AWAM, a Bluetooth device, Wi-Fi or Bluetooth Low Energy device will work and will all have different characteristics and penetration rates.

Additionally, the AWAM equipment or readers are inexpensive relative to the construction of in-ground devices or other probe equipment, easy to maintain, portable and do not involve proprietary equipment or protocols (TTIa, 2017).

In urban areas, AWAM helps to derive travel time data and speeds and has been used successfully in many cities since its inception. An emerging use of this technology is to glean additional information from the device about the type of vehicle and, eventually, what a vehicle is carrying, for use in understanding urban freight flows and commodity values for economic impacts.

AWAM using Bluetooth technology to gauge transportation performance emerged around 2009 as the result of work to support the City of Houston by the Texas A&M University Transportation Institute (TTI). The city sought ways to monitor traffic on major arterials and was considering AVI (tolling) technology and license-plate recognition. However, the use of these systems presented major costs to the city, so TTI and Houston considered a Bluetooth-based option and developed the AWAM system (TTIb, 2017). The AWAM system became a way to collect information needed for traveler information, traffic management, and planning systems (TTIc, 2017). Now, emerging uses may include information about the vehicle type and class, commodity, origin, and destination, as well as other characteristics.

To capture the data, each AWAM reader senses probe devices with MAC-ids as they pass a reader station. The device transmits the time and location to a central host system. As probes are detected at successive AWAM readers, the host system calculates average travel times and speeds for a roadway segment. The figure below depicts the setup of the data collection system using AWAM where a vehicle is passing the sensors, which are picking up data and transmitting it for transportation analysis.

Bluetooth is a wireless protocol for networking devices over short distances. Each device using Bluetooth has a unique identifier called a Media Access Control address, or MAC address. The MAC address is then read by the AWAM equipment. The software running inside the roadside AWAM equipment package then forwards the device addresses to a host software component (TTIc, 2017) (TTIa, 2017). Figure is an example of mobile data collection device. It can be easily installed along infrastructure and relocated as needed.

• Regulatory Environment: Facilitates data access and use.
• Ownership: Not tightly controlled.
• Privacy: AWAM typically protect private information

Privacy and confidentiality appear as the primary concerns when data is gleaned from devices inside privately owned vehicles. However, the MAC addresses read by AWAM are not directly associated with a specific user and do not contain any personal data or information that could be used to identify or “track” an individual’s whereabouts. In addition, all addresses collected by AWAM are anonymized through encryption immediately upon receipt. Users who have privacy concerns are also able to turn off the Bluetooth discovery function of their device, which prevents it from being read by AWAM at all (TTIc, 2017). The resulting trade-offs include a smaller fraction of devices, i.e. vehicle observations captured. Another result of encryption is that the same devices / vehicles are recognizable within only a short window of time, say 24 hours, as long as the devices remain powered on. This provides some safeguards to privacy with the trade-off being a lower sample size of observations, and within limited time horizons.

• Capacity: Fairly easy to work with data.
• Stewardship: Straightforward.
• Equity: Data representative of specific roadway users

Though the systems are low in cost for the equipment and maintenance, consideration as to capacity and financial resources to provide and maintain the system is needed. Additionally, analysts with technical capabilities to mine the data, analyze it and visualize it for decision-maker are also needed. Agencies will need to consider how this is handled within their organization and how to maintain the program continuously for optimal data collection.

• Completeness: Low device penetration rates.
• Accuracy: Limitations due to functionality issues.
• Verifiability: Access to raw data.
• Dynamism: Time from capture to analysis can be quite short.
• Durability: Low cost and ease of deployment and use ensure its future stability as a source of data.

Device penetration rates affect the completeness of the data and also the equity of its coverage. TTI has found that travel time samples can be generated from between two and 20 percent of the traffic stream (Vickich, 2017). In a typical installation for each of the technologies, the following rates are picked up from the traffic stream:

• Bluetooth: ~6 percent
• Wi-Fi: ~15 percent
• Bluetooth Low Energy: ~15 percent

There are a number of technical considerations with this technology. For example, accuracy is affected by how well the device is calibrated so an important first step is to calibrate the system so that it reads the intended devices at the roadside and captures the data desired for analysis. In addition, any invalid data that does make its way into the system must be screened and discarded (TTIa, 2017). To do this, there are some areas of continued and emerging research to develop best practices. These include:

• Field equipment optimization
• Data validity algorithm development
• Review and comparisons of other types of probe-based traffic data collection methods (Toll Tag, License Plate Recognition, Floating Car, etc.) to wireless address matching to compare for accuracy (TTIa, 2017).
  Looking forward, this technology may help to obtain more information about roadway vehicles and contents, potentially even providing commodity information and other attributes that may help in transportation decision-making. This would involve data transfer from the vehicle through Bluetooth to the receiving device that has more information than currently captured. There are some studies emerging on the use of transmitting signals or data through AWAM readers that could transmit more about the truck, operations, and commodities. Some of the interest in improved vehicle inventory and use information and commodity details are beginning to identify measures for transmitting data in addition to presence and speed.