Incorporating Reliability Performance Measures into the Transportation Planning and Programming Processes: Technical Reference (2014)

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151 Agencies can use five broad categories of traffic data sources to monitor travel time reliability: 1. Infrastructure-based detectors that can sense volume, occupancy, speed, and other data; 2. Automated vehicle identification (AVI) systems; 3. Automated vehicle location (AVL) systems; 4. Private-sector–based sources of traffic data; and 5. Event/incident data. Public agencies typically own and operate the infrastructure-based detectors and the AVI systems used for tolls, whereas private, third-party sources often own and operate the AVL systems or collect data from other AVL sources. This section describes the use of each of these data sources for evaluating reliability. E.1 INFRASTRUCTURE-BASED SOURCES Infrastructure-based detectors, which include loop and radar detectors, are already a common component of traffic management systems in many regions. Some can mea- sure vehicle speeds directly, while others use post-processing algorithms to estimate speeds based on counts and occupancy. The ones that can directly measure speeds are more valuable for measuring reliability. While prevalent, these technologies have a drawback in that they only provide data at fixed locations along the roadway, meaning that they can only report spot speeds. Consequently, they cannot provide information on an individual vehicle’s route or time of travel between two points. As a result, the data they transmit require E DATA COLLECTION METHODS

152 INCORPORATING RELIABILITY PERFORMANCE MEASURES INTO THE TRANSPORTATION PLANNING AND PROGRAMMING PROCESSES: TECHNICAL REFERENCE some processing and extrapolation before travel times can be calculated. This also means that the accuracy of the travel time measures they produce is a function of how frequently detectors are spaced along the roadway. If existing deployments have detec- tors spaced at a frequency of ½ mi or less, they are suitable for inclusion in a reliability monitoring system. If detectors are placed less frequently on key routes, agencies may want to consider either installing more detectors or supplementing the existing detec- tion with AVI sensors. The following types of technologies are considered infrastructure-based sources: • Loop Detectors: Loop detectors are located in the pavement on many roadway facilities. They have historically been the most common traffic-monitoring tool because of their relatively low installation cost and high performance. Coverage, however, varies greatly among cities and states. In many urban locations, they are common on freeway facilities. Many arterials also use loop detectors to con- trol actuated and adaptive traffic signals. However, it should be noted that loop detectors used in traffic-responsive signal systems are usually not well adapted to providing the data required to support reliability monitoring. In some cases it is possible for agencies to modify the existing signal system sensors to collect additional data and transmit them to a centralized location to support reliabil- ity monitoring. Loop detectors typically measure traffic volumes and occupancies and send data to a centralized location every 20 to 60 seconds. From these data, spot speeds can be calculated with a reasonable accuracy and used to extrapolate travel times. Loop detectors in a dual configuration (two closely spaced loops) can directly report speed values. Two drawbacks with loop detectors are their intrusive installation and their significant maintenance requirements. For this reason, it is typically recommended that agencies only use loop detectors for reliability moni- toring in locations where they already exist. • Wireless Magnetometer Detectors: Like loop detectors, wireless magnetometer detectors are located in the road but can be installed simply by drilling a hole into the pavement, eliminating the need for cutting pavement during installation and reducing maintenance requirements. These sensors use radio signals to communi- cate with access points located on the roadside, usually on poles or the cabinet, preventing the need to hardwire a detector to a controller cabinet. Like loop detec- tors, they report volume and occupancy data with a granularity that depends on the sensor’s setting. Sensors in a dual configuration can also directly report speed values. The data accuracy of wireless magnetometer detectors is similar to that of loops. Where agencies would like to install additional in-road infrastructure detectors, wireless magnetometer sensors are a good alternative to loop detectors. Recent developments have also adapted some wireless magnetometer detectors to re-identify vehicles at a second detector, giving them AVI capabilities. • Video Image Processors: Many agencies have begun installing video image pro- cessors, on both arterial and freeway facilities, as an alternative for loop detec- tion. Video image processing can retrieve volume, occupancy, and speed data from cameras on the roadway. This technology usually requires users to manually set up

153 INCORPORATING RELIABILITY PERFORMANCE MEASURES INTO THE TRANSPORTATION PLANNING AND PROGRAMMING PROCESSES: TECHNICAL REFERENCE detection zones on a computer that are in the field of view of each camera, mean- ing that it is important that the cameras not be moved and the detection zones be set up correctly. Some specialized systems can also re-identify vehicles detected at two separate cameras, giving them AVI capabilities. This technology is a viable method for travel time reliability monitoring where agencies already have cameras installed. • Radar Detectors: To overcome the intrusive installation and maintenance of loop detectors, many agencies have deployed microwave radar detectors, which are placed overhead or roadside and measure volume and speed data. One drawback to radar detectors is that they can lose their speed calibrations. Additionally, they can be sensitive to bad weather conditions such as snow, fog, or temperature change. Radar detectors are a viable option for agencies that want to increase the frequency of data collection infrastructure along a roadway without installing more loop detectors. • Other Infrastructure-Based Sources: A number of additional overhead vehicle detection technologies have capabilities similar to microwave radar detectors: pas- sive infrared sensors, ultrasonic sensors, and passive acoustic array sensors. These technologies can be considered on a site-specific basis or used for travel time reli- ability monitoring where they have already been deployed. E.2 AUTOMATED VEHICLE IDENTIFICATION SOURCES Automated vehicle identification (AVI) data collection sources detect a passing vehicle at one sensor and then re-identify the vehicle at a second sensor, allowing the vehicle’s travel time between two points to be directly computed. The drawback of AVI tech- nologies is that while they provide the travel time between two points, they cannot in- form on the route taken by individual vehicles or whether the trip included any stops. Because there are often multiple ways to travel between two points, especially in urban areas, some processing and filtering is required to ensure that reliability computations are based on representative travel times for a given route. Inaccuracies can also be re- duced by deploying sensor readers at frequent intervals, to reduce the likelihood that a vehicle took a different route than the one assumed in the computation. The following technologies are sources for AVI travel time data. • Bluetooth: Bluetooth receiver technology has only recently been applied to traffic data collection, but it appears to be promising for measuring travel times. The technology will be especially useful for arterial data collection given that the more traditional methods are not effective on arterials. Bluetooth detectors record the public media access control (MAC) address of a driver’s mobile phone or other consumer electronic device as the vehicle passes a point. This recorded ID number (or a truncated version of it, to reduce privacy concerns) can then be matched as the vehicle passes subsequent detectors, allowing travel times between points to be calculated.

154 INCORPORATING RELIABILITY PERFORMANCE MEASURES INTO THE TRANSPORTATION PLANNING AND PROGRAMMING PROCESSES: TECHNICAL REFERENCE This technology is advantageous in that it is accurate, low-cost, and porta- ble. A drawback, however, is that currently only a small percentage of drivers have Bluetooth-enabled devices in their vehicles; recent (2010) study estimates range from 5% in the Washington, D.C., metropolitan area to 1% outside of Indianapolis. It can be assumed that these percentages will grow, as commercial Bluetooth applications, particularly smart phones, become more prevalent, mak- ing Bluetooth an important data collection alternative for future projects. A few issues with Bluetooth measurements need to be accounted for in the data filtration process. First, Bluetooth readers frequently record the same wireless network ID more than once as a vehicle passes, especially when vehicles are traveling slowly. These duplicate addresses need to be removed to avoid counting a vehicle’s travel time more than once. Second, Bluetooth readers have a wide detection range that could collect travel times that do not reflect actual conditions. For example, a Bluetooth sensor station on a freeway might detect a vehicle that is in a queue on an entrance ramp and as a result a longer than accurate travel time would be re- ported. These nonrepresentative travel times would have to be filtered out during data processing. Additionally, on arterial streets, Bluetooth readers report travel times from nonvehicular modes like walking or cycling, so these times would have to be removed in the data cleaning process. • License Plate Readers: License plate readers (LPR) employ cameras that capture a digital image of a vehicle’s license plate and use optical character recognition (OCR) to read the plate number. While primarily used for toll enforcement, LPR can also be used to calculate travel times for vehicles that pass by two or more cameras. The advantage of LPR is that it can collect travel time samples from ve- hicles without requiring the presence of any specific device within the vehicle. This method, however, is not well suited for data collection on high-speed freeways. Additionally, plate matching is not always accurate, especially during adverse weather conditions. The equipment needed is also costly, and there are privacy concerns that come with tracking a vehicle by its license plate number. The per- centage of successful license plate matches is about 5% to 20% in a given period. Due to LPR’s accuracy issues and high cost, it is recommended that only those locations that have already installed LPR infrastructure use it as a primary method of data collection for reliability monitoring. • Radio-Frequency Identification: Radio-Frequency Identification (RFID) technol- ogy is employed in electronic toll collection (ETC) and can be used to re-identify vehicles for travel time purposes. RFID is embedded in toll tags such as EZPass on the East Coast and FasTrak in the San Francisco Bay Area. More than 20 states currently have locations that use RFID toll tags. The iFlorida toll tag travel time project found that toll tag penetration is high in urban areas with toll roads, but much lower in other areas. This means that this data collection option is best suited for urban areas with a high toll tag saturation rate. The study found compa- rable rates of saturation between urban freeways and urban arterials; however, the percentage of vehicles that could be re-identified at a second sensor was lower for arterials because more vehicles enter and exit the facility between sensor stations.

155 INCORPORATING RELIABILITY PERFORMANCE MEASURES INTO THE TRANSPORTATION PLANNING AND PROGRAMMING PROCESSES: TECHNICAL REFERENCE As a result, in Orlando, toll tag readers usually only generated between 10 and 20 travel time estimates per hour. Agencies should thoroughly evaluate their re- gional saturation rate of RFID toll tags to determine whether this technology can supply the number of travel time samples needed to robustly estimate reliability measures over time. Aside from sample size concerns, privacy issues are raised, because RFID transmits data that are identifiable to an individual vehicle. There- fore, if RFID is used to collect travel times, the system will need to encrypt data to remove personal information. The iFlorida deployment does this by sending the DOT database an encrypted key that represents the toll tag number, rather than the actual toll tag number itself. • Vehicle Signature Matching: Vehicle signature matching refers to methods that match the unique magnetic signature of a vehicle as it passes over a loop to the same signature from an upstream loop. Single loop, double loop, and wireless magnetometer detectors all have this capability. While loops are not capable of matching every vehicle, research and testing of this method has shown that it can match enough vehicles to provide accurate travel time distributions for both free- ways and arterials. One advantage of this method is that it can use preexisting detectors in new ways that improve travel time data accuracy. For arterials, it is advantageous over tradi- tional detector data, since it estimates travel times without the need for signal phase information. It also offers an additional benefit over other AVI technologies: it avoids potential privacy concerns through anonymity. This technology has only seen limited use in practice thus far, with projects in a few locations in California, but it appears promising for measuring travel times on both freeways and arterials. E.3 AUTOMATED VEHICLE LOCATION SOURCES Automated Vehicle Location (AVL) refers to technologies that track a vehicle along its entire path of travel. These methods provide the most accurate and direct measure- ments of travel times, but have not yet seen deployment sufficient to provide reliable data on a regional scale. This will change as more vehicles become equipped with AVL technologies and agencies become more accustomed to using them for real-time data collection. • Global Positioning System (GPS): Any vehicle equipped with a GPS-based receiver can be tracked along its path of travel to calculate route-based travel times and other traffic data. GPS technology is well suited for accurate travel time calcula- tions because it can pinpoint a car’s location within a few meters and its speed within 3 mph. GPS has traditionally been used to calculate travel times through test probe vehicles equipped with GPS receivers. The value of these data is limited because of the small number of test probe vehicles typically deployed, and they do not provide real-time data on a permanent basis. However, even in a more advanced system that monitors all GPS-equipped vehicles in real time, the low market penetration rate of GPS technology will be a constraint on the ability to

156 INCORPORATING RELIABILITY PERFORMANCE MEASURES INTO THE TRANSPORTATION PLANNING AND PROGRAMMING PROCESSES: TECHNICAL REFERENCE accurately represent travel time variations. However, it can be reasonably assumed that more vehicles and devices will have GPS capabilities in the future. GPS is also used by many transit agencies to monitor bus locations and schedule adherence in real time. As such, another alternative for agencies looking to monitor reliability is to use equipped buses as travel time probes. By identifying and factoring out bus-specific activities, such as dwell times and different acceleration rates, arterial travel times can be estimated from bus AVL data. • Connected Vehicle Initiative: The Connected Vehicle Initiative, sponsored by the U.S. DOT, is focused on leveraging wireless technology to allow vehicles and road- way facilities to communicate with one another, with the aim of improving safety, monitoring conditions, and providing traveler information. The majority of con- nected vehicle research will be completed by 2013, so it is impossible to know the full scope of the contributions that connected vehicles will make to reliability monitoring efforts. At this point, however, it seems that connected vehicle tech- nologies could provide a rich source of travel time information, since the vehicle to infrastructure (V2I) communication channels implemented through the program could be used to send collected vehicle-specific location data to a central data server for travel time processing. • Urban Congestion Report: The Urban Congestion Report, sponsored by the FHWA Office of Operations, is produced on a quarterly basis and characterizes congestion and reliability trends at the national and city level. The reports are de- signed to provide timely congestion and reliability information to state and local agencies; demonstrate the use of archived traffic operations data for performance monitoring; and promote state and local performance monitoring to support transportation decision making. The reports are based on archived traffic opera- tions data gathered for 23 urban areas. However, the FHWA is examining the use of private sector travel time and speed data, as evidenced in their July 2011 report, Private Sector Data for Performance Management: Final Report. • Cellular Telephone: Cellular telephone networks track cell phones to hand them off to different base stations as they travel, and travel times can be calculated through this information. The precision of location data increases with the num- ber of cellular towers that a phone is in range of. In urban areas, location accuracy can be within 100 feet, which in some cases is too large to assign vehicles to a specific link, especially in dense urban networks. In rural areas, location accuracy can be wrong by more than a mile, which would negate the value of travel times estimated in this manner. To obtain cellular travel times for reliability monitoring, agencies must either collaborate with cell phone companies or buy data from a third-party provider. This technology is currently being used as part of the Trans- portation Technology Innovation and Demonstration (TTID) Program. The con- tractor, Traffic.com-NAVTEQ, combines information from multiple probe tech- nologies including a proprietary sensor network, commercial and consumer GPS and cellular phone probes, and incident and event data. The data are then fused to provide real-time travel time estimates and incident information.

157 INCORPORATING RELIABILITY PERFORMANCE MEASURES INTO THE TRANSPORTATION PLANNING AND PROGRAMMING PROCESSES: TECHNICAL REFERENCE E.4 PRIVATE-SECTOR–BASED SOURCES In addition to the public sector sources described previously, private sources of data can be used to support reliability analysis. SHRP 2 Project L02 conducted a series of focus group interviews on data collec- tion practices and business processes related to measuring, monitoring, and recording travel time reliability information. The interview results established that many agen- cies are interested in obtaining data from private sources, in order to save time and money on data collection and processing. While these private sources can provide data for facilities that are otherwise unmonitored (such as arterials), the lack of transpar- ency on their proprietary methods of data collection presents challenges for agencies seeking to monitor reliability. These companies provide data to public agencies as a sideline to their core busi- ness, providing travel time and other data to the traveler information market. For public agencies, most commercial vendors provide a speed range (e.g., 30 to 40 mph) for stretches of roadway defined by Traffic Message Channel (TMC) IDs during a fixed period (e.g., 5- or 15-min or hour-long increments). (TMCs represent a consistent location referencing method agreed upon by the traveler information industry.) These data are, by their very nature, opaque to agencies. For example, it is not clear where on that stretch of roadway the speeds were observed or when during the period they were observed. More importantly, little information is given on the methods used to calculate the speeds. For example, the speeds may have been calculated from multiple GPS probe readings on the roadway and thus may be highly accurate, or they may have been interpolated entirely from historical data because no real-time samples were collected during the period. Data Sources These private source firms collect data from a variety of ITS sources, including GPS probes, road sensors (both publically and privately owned), toll tags, and smart phones. Many of these firms also collect incident and event data. The simplest data these firms collect are fixed roadway sensors. These are largely the result of a series of public-private partnerships, stretching back to the mid-1990s, in which firms were allowed to install and maintain fixed detectors on public road- ways, usually in exchange for an exclusive concession to sell the traffic data to another market, such as the local media market. Typically, these data are available already to the public agency, as part of the concession. In some cases, the agency might procure these data or additional rights to data they already receive (as part of a new travel time reliability system, for example). Often the private firms also receive the publically available agency sensor data from traffic management agencies. Increasingly, private vendors are also collecting probe data. Probe data have his- torically been the purview of freight companies, who have the necessary cost incentives to equip their vehicles with GPS. For example, freight companies can rent or purchase tracking devices to place on vehicles and then pay a flat communication fee to receive web access and real-time alerts on vehicle locations. Thus, the first data sources for private providers were primarily freight carriers. However, in a world of cheaper GPS

158 INCORPORATING RELIABILITY PERFORMANCE MEASURES INTO THE TRANSPORTATION PLANNING AND PROGRAMMING PROCESSES: TECHNICAL REFERENCE and ubiquitous smart phones, this is rapidly changing. Currently, an estimated 35% of drivers have smart phones, many of whom use the device’s GPS capabilities in- vehicle for navigation assistance. Firms are increasingly acquiring data directly from con sumers as part of the growing personal navigation market. Consequently, the size and diversity of the probe data sets are exploding. Data Transparency While some providers may supply metadata on the data quality (e.g., a ranking scale), the methods for the quality assessment are also opaque. For the most part, these limi- tations are inherent to the business model of the data provider. Private source data providers have built their competitive advantage on their network of data sources and data fusion methodologies. Because of this, they are unlikely to reveal the underlying sources and methodologies to transportation agencies. This fact must be considered by agencies interested in using private source data to produce or supplement reliability information. The ability to accurately report on travel time reliability has improved considerably over the last few years as the number and coverage of data sources including private probe data increase. Several technical and institutional challenges are associated with using and integrating probe data. Technical challenges include validating the resulting speed measurements with actual speeds, ensuring that sample sizes are adequate, and geolocating data from the standard traffic message channel (TMC) to coincide with state linear referencing systems. Institutional challenges include licensing data, privacy concerns, ownership, rights, usage, and resale of data. The report Private Sector Data for Performance Management, prepared for FHWA in July 2011, describes the chal- lenges and examines issues surrounding blended traffic data. The report also discusses integration of private sector travel time data with public agency traffic volume data. Agencies may want to test the data quality issue by • Building travel time distributions out of the speed-binned data, to see if these sim- plified distributions were adequate to its needs; and • Purchasing a data sample from a firm and independently testing its quality. E.5 EVENT AND INCIDENT DATA COLLECTION Traffic data are not the only data that will inform transportation analysts on travel time reliability; other event and incident data also provide reliability information. Many agencies in the United States routinely track incidents and incident duration, weather, work zone lane closures, and special events. In most cases, staff working in a traffic management center (TMC) use tracking software to monitor these incidents and events. While it is possible to track these events manually in a spreadsheet, it is a time-consuming task. Most TMCs track incidents automatically, using the operator software. Additionally, a number of TMCs also log work zone lane closures by loca- tion and duration of the closure and special events in their traffic management plans. The most sophisticated TMCs track the duration and timeline of incidents as they are

159 INCORPORATING RELIABILITY PERFORMANCE MEASURES INTO THE TRANSPORTATION PLANNING AND PROGRAMMING PROCESSES: TECHNICAL REFERENCE happening by saving operator actions time stamps. These time stamps can be used to determine the time the lanes were closed for an incident, the agency response time, and what time the lanes were cleared. This information, along with the traffic data, provides a complete history of an incident’s impacts. E.6 DATA INTEGRATION Accessible and quality data are the foundation of performance management and tech- nical analysis that support investment decisions. Effective decision making in each element of the performance management framework requires that data be collected, cleaned, accessed, analyzed, and displayed. Therefore, the national and state focus on performance measurement has resulted in several states evaluating and improv- ing their data programs and systems. A variety of methods and tools are being used across the country to assess, evaluate, and prioritize data programs. At the same time, the information industry benefits from continued rapid changes in technology and in- frastructure for data sharing as the breadth of technologies for data management and dissemination continues to increase and the complexity and cost of deploying these tools continues to fall.