ABSTRACT

The foundation of traffic operations and management is the ability to monitor traffic conditions. One approach to traffic monitoring is to sample conditions by “tracking” a limited number of probe vehicles as they traverse a network. An emerging technology known as wireless location technology (WLT) has been developed to allow for the geolocation of mobile wireless devices (the most common of which are cellular telephones). Over the past decade, a number of research studies and operational tests have attempted to develop probe traffic monitoring systems based on WLT. However, there still exists significant confusion and misperceptions concerning WLT-based traffic monitoring. To address this problem, this paper seeks to provide a comprehensive assessment of WLT-based traffic monitoring. To do so, the specific purposes of this paper are to: (a) fully describe the concept of WLT-based traffic monitoring, (b) present a critical assessment of past studies of WLT-based traffic monitoring, (c) document the evaluation of one of the most recent operational tests – the 2001 Virginia Department of Transportation (VDOT), Maryland State Highway Administration (MSHA), and US Wireless Corporation (USWC) effort in the Washington, D.C. region, and (d) discuss the unique challenges that these systems pose to the field of traffic engineering.

INTRODUCTION

Since the intelligent transportation systems (ITS) program began to take shape in the early 1990s, transportation agencies at the federal, state, and local levels have focused significant resources on using information technology to improve surface transportation. While ITS has evolved and now takes a myriad of forms, the foundation of nearly every ITS initiative is the ability to measure traffic conditions on the network – generally referred to as traffic condition monitoring. Without knowledge of traffic conditions, transportation professionals can do little to manage traffic or provide traveler information. Furthermore, traffic condition data collected by ITS are now being used in a wide range of transportation applications, such as planning and infrastructure management.

Transportation professionals generally measure traffic conditions using a network of “point” sensors installed at strategic locations throughout the network. While this approach is functional, it suffers from practical limitations that have resulted in incomplete and erratic traffic condition data. The communications, installation, and maintenance costs of the sensor networks have forced transportation agencies to install them only on the most critical routes – often with widely spaced sensors. This leaves many “holes” in the system that lack traffic condition data.

An alternative approach to traffic condition monitoring is to sample a portion of vehicles as they traverse the network. This approach is generally referred to as probe-based monitoring. An emerging information technology, wireless location technology (WLT), holds great promise to provide the platform for a probe-based traffic condition monitoring system. This technology allows wireless devices to be geolocated while in use. WLT supports “location-based services” (LBSs) in which wireless subscribers (whether using cellular phones, automobile-based telematic devices, mobile computing products, etc.) are provided with targeted information content based on their specific location (i.e. latitude/longitude). Thus, WLT offers an existing and growing infrastructure that can conceptually be tapped to provide probe-based traffic information, without the need for transportation agencies to install extensive infrastructures solely devoted to traffic monitoring.

Given the appeal of this concept, it is no surprise that WLT-based monitoring has captured the interest of the ITS community over the past decade. In fact, there have been a number of relatively large-scale operational tests undertaken in an effort to accelerate development of WLT-based traffic monitoring systems. While these tests have generally been categorized as unsuccessful, the project participants have gained significant insight into the abilities of WLT-based traffic monitoring. Unfortunately, relatively little of this insight has made its way into formal research papers or reports. This has led to significant confusion in the transportation community, and a lack of information on which to base future decisions concerning this technology.

To address this need, this paper seeks to provide a comprehensive introduction to the concepts, experiences with, and performance of early-generation WLT-based traffic monitoring systems. The specific purposes of this paper are to (a) describe the concept of WLT-based traffic monitoring, (b) present a critical assessment of past studies of WLT-based traffic monitoring, (c) document the evaluation of one of the most recent operational tests – the Virginia Department of Transportation (VDOT), Maryland State Highway Administration (MSHA), and US Wireless Corporation (USWC) effort in the Washington, D.C. region, and (d) discuss the challenges that these systems pose to the field of traffic engineering.

WLT-BASED TRAFFIC MONITORING

WLT can be classified into two general groups: handset-based systems and network-based systems. Handset-based systems rely on global positioning system (GPS)-enabled wireless phones. The GPS unit in the handset determines the location of a phone, and this information is relayed from the phone to a central processing system maintained by the wireless carrier. Network-based systems utilize signal information from cell phones to derive their location. In some cases, network-based systems require special equipment to be installed throughout a metropolitan area in order to analyze signal characteristics of calls. For example, some network-based systems determine positions by analyzing signal power and angle of arrival at multiple cellular towers. In other cases, network-based systems derive location estimates purely from signaling information already available at cellular towers. Since network-based systems do not require users to have GPS-enabled phones, they generally provide less spatial accuracy than handset-based systems.

There have been several attempts to use WLT data to generate traffic condition information. In order to produce this type of information, a WLT system has to be able to perform three basic tasks:

Location Determination. The location of the probe must be determined. These position estimates are usually inaccurate to some degree, and may not lie directly on the roadway network.
Map Matching. In map matching, the position estimates are matched to a specific road. Techniques for map matching vary from simple geometric methods to more complex statistical approaches that account for link connectivity and past travel history.
Determination of Traffic Information. The probes represent a small sample of the entire traffic stream. A WLT-based system must be able to use a set of these samples to estimate the speed or travel time for all traffic on a link.

Several evaluations of early generation WLT-based traffic condition monitoring systems have occurred, but none of these systems adequately performed all three of these tasks. Past evaluations have taken one of two forms: simulation studies or field operational tests. Major findings of these studies are summarized below.

Simulation Studies

Researchers have used simulation studies to explore the potential accuracy and effectiveness of WLT-based systems. While these studies do not replicate the actual conditions precisely, they do provide some indication of the potential performance of a WLT-based system.

French Simulation Study

A study conducted by the French transportation research organization, INRETS, focused on developing a discrete event simulation of traffic flow in order to determine the sample size requirements and accuracy of a hypothetical WLT system (Ygnace, et. al., 2000). The simulation examined the impact of varying levels of probe vehicle penetration on the accuracy of travel time estimates. A location error of 150 meters was assumed, and researchers examined a series of traffic and geometric conditions. The simulation results showed that freeway link travel times could be estimated to within 10 percent of their actual value if there is at least 5 percent penetration of wireless devices in the traffic stream. These promising results are based on relatively simple geometric conditions.

Berkeley Simulation Study

A recent evaluation by the Berkeley Institute for Transportation Studies examined factors that could affect the utility of WLT-based traffic monitoring systems (Cayford and Johnson, 2003). The researchers examined three variables in their simulation: location accuracy, frequency of locations of a single wireless device, and the total number of locations that could be determined per square mile per second. The variation in the number of roads that could be traversed by at least one vehicle within a five-minute period was used as the measure of effectiveness to compare different alternatives. The researchers did not attempt to address whether the observed sample sizes were sufficient to produce accurate estimates of speeds or travel times for the entire traffic stream, however. The major findings of this research effort were:

Assuming a network-based system accurate to within 100 meters, at least one probe speed sample can be generated on 85 percent of the roads every 5 minutes. This assumes that positions are updated every 30 seconds, and a maximum of 40 locations are determined every second per square mile.
Assuming a handset-based system accurate to within 50 meters, a probe speed sample can be generated on 90 percent of the roads every 5 minutes. This assumes that positions are updated every 30 seconds, and a maximum of 40 locations are determined every second per square mile.

Again, these results only show whether a tracked probe vehicle traveled on a particular road at least once during a 5-minute period. The researchers did not state whether this would be sufficient to determine actual speeds or travel times.

Field Operational Tests

Several field tests of WLT-based systems have been performed in the United States. Network-based systems have been used in all cases since GPS-enabled phones currently account for a relatively small portion of available probes.

CAPITAL Field Operational Test

The first major operational test of a WLT-based system was conducted over a 27-month period in the mid-1990’s on several interstates and state routes in Virginia (UMD, 1997). This project was named CAPITAL (Cellular APplied to ITS Tracking And Location), and was the result of a cooperative agreement between FHWA, VDOT, MSHA, and several private sector firms. This evaluation produced the following major findings:

By the end of testing, wireless telephones could be located within 100 meters of their actual position. The accuracy of the position estimates improved considerably as the number of cellular towers providing directional information increased. The evaluators noted that accuracies on the order of 5 to 25 meters might be needed to perform accurate speed estimation for a network.
In order to calculate speed, at least four position estimates had to be identified for each phone, and this occurred only 20 percent of the time. As a result, link speed estimates could not be generated for the network. This was due to the small number of data points, as well as a lack of well-developed algorithms to match vehicles to links.

While the CAPITAL test showed that WLT could provide reasonably accurate positional data, it was unsuccessful in producing traffic information.

U.S. Wireless Operational Tests

In addition to the VDOT/MSHA/USWC evaluation documented later in this paper, the U.S. Wireless Corporation (no longer in business) also participated in an operational test in Oakland, California (Yim and Cayford, 2001). Researchers at the University of California - Berkeley obtained 44 hours of wireless location data. The researchers found that the position estimates generally had a 60-meter accuracy, although 66 percent of all probe vehicle tracks had at least one data point that deviated from the caller’s actual position by more than 200 meters.

While the researchers were generally able to identify the location of a vehicle, they were not successful in matching vehicles to roads or in generating speed or travel time information. The researchers noted that the call lengths were generally very short, with a median call length of only 30 seconds. This made it impossible to estimate speeds on links since position estimates were not available for long distances. As a result, the researchers were not able to match 60 percent of vehicles to a roadway link.

The most recently completed large-scale field test of WLT-based traffic monitoring was the VDOT/MSHA/USWC effort that took place in the Washington D.C. region beginning in the year 2000. The next sections of this paper introduce this test, describe the evaluation methodology, and present a summary of the evaluation results.

DEPLOYMENT DESCRIPTION – VDOT/MSHA/USWC OPERATIONAL TEST

Beginning in 2000, VDOT and MSHA participated in a WLT demonstration project with USWC in the southern suburban region of Washington D.C.. This region includes the Capital Beltway, a heavily traveled 8-lane freeway that experiences significant congestion, and many major arterials. The purpose of the demonstration project was to demonstrate the feasibility of WLT-based traffic condition monitoring.

EVALUATION METHODOLOGY

The University of Virginia’s Center for Transportation Studies served as the traffic monitoring evaluator in this field test (the University of Maryland also served as an evaluator, focusing on location estimation of individual vehicles). This section details the methodology developed for the WLT-based traffic monitoring evaluation.

Data Collection

A key evaluation challenge was to collect accurate baseline traffic data to serve as the ground truth against which WLT-based system estimates of macroscopic traffic parameters could be compared. It was essential that the baseline data accurately represent the actual conditions on the facilities – and that this could be verified through manual means. Two possible approaches to collecting the baseline data were available to the evaluator: probe vehicles and point sensors.

Conceptually, the use of probe vehicles for baseline data collection is desirable. Given that the WLT-based system collects data on “probes” over the entire length of roadway links, baseline probes would allow for the same measurement. However, without tolled facilities in the project region, it was impractical to collect significant samples of baseline probe data. The use of a point sensor, on the other hand, allows the speed of every vehicle passing a point on a link to be collected. However, it requires the key assumption that conditions are uniform throughout the link in order to compare with the WLT-based system results. Traffic volumes can also be determined using point sensors, something that is impossible with a probe-based approach. This is important in that it allows for the assessment of the temporal adequacy of samples from the WLT-based system by providing true population data for the macroscopic traffic parameters. Given these advantages, as well as data collection constraints, it was determined that point sensors would be used as the baseline measurement approach in this evaluation.

A van-mounted video detection system was used for baseline data collection. The video detection system was used to derive point measures (spot speeds and counts) by processing video from a camera mounted on a 45-foot telescoping mast installed on the van. This system provided the flexibility to position the location of collection in the mid-point of WLT-based system defined links (relatively short, 0.4 miles in length), and allowed for manual verification of baseline data accuracy (using a hand-held Lidar unit for speed, and manual counts for volume).

Given that the focus of this evaluation was on the ability of WLT-based systems to support traffic monitoring applications, it was decided that a relatively short “polling” interval should be used in the effort. Based on information in the Highway Capacity Manual (TRB, 2000) and recent research on flow rate stability (Smith and Ulmer, 2003) 10 minutes was selected as the polling interval used.

Finally, it was important to clearly define the term “sample” in the data collection effort. In many cases, a WLT-based system may sample the speed of the same vehicle multiple times as it traverses a single link. While it is possible to argue the validity of treating each sample of the same vehicle as an independent sample of link speed (i.e. assuming a vehicle’s speed is solely governed by conditions over the entire length of the link), a conservative approach that most directly corresponds to traditional traffic monitoring practice is to consider the average speed of the multiple samples from the same vehicle as the single sample for that vehicle/link pair. This was the approach used in this research.

Comparative Analysis

The comparative analysis included two key components. First, the baseline link population data was used to compute confidence intervals on mean speed estimates to identify minimum required sample sizes for deriving traffic data of particular levels of “quality.” These were then compared with the WLT-based system samples to ascertain if the system is theoretically capable of providing sufficient numbers of samples. Second, link data from the baseline video detection system and WLT-based system were directly compared to determine if the WLT-based system produced link mean speed results that accurately reflect the ground truth.

Sample Size Adequacy Evaluation

Given that a WLT-based traffic monitoring system will produce individual vehicle speed samples on links, or samples of the random variable, U, and the system is attempting to estimate the mean link speed, μ, the Central Limit Theorem can be used to estimate the number of samples required to estimate μ to within some level of allowable error at an assumed confidence level. This approach is widely used in experimental design and has been used, for example, to determine the number of probes required for speed estimation in the Houston automatic vehicle identification (AVI) based traffic monitoring system (Turner and Holdener, 1996).

Based on the Central Limit Theorem, one can collect n probe samples and compute a confidence interval about the population mean as follows:

(1)

In other words, there is an a probability that the true mean link speed falls in the interval defined above. Note that this effort’s methodology allows for the direct computation of the speed population parameters since the van-based video detection system measured the speed of every vehicle traversing the link over the 10-minute interval.

Working with equation (1), if the “width” of the confidence interval is defined as 2d miles/hour (i.e., an error of ±d miles/hour can be accepted in the estimation of the true link mean speed, m) an equation can be derived to determine the minimum required sample size, n. Note that a confidence level, α, must be assumed.

(2)

Finally, it must be noted that this methodology assumes that a WLT-based traffic monitoring system measures the speed of each vehicle traversing a link without error. Certainly, this will not be the case. As such, the values of n presented in the results represent the absolute minimum number of samples required. A fielded system will likely require a somewhat larger number of samples to account for errors in the system’s ability to measure individual vehicle speeds.

Link Conditions Estimation Evaluation

For each 10-minute interval, mean speeds produced by the WLT-based system were compared with the baseline data to measure percentage error in the speed estimates of the WLT-based system. In addition, hypothesis tests were conducted to rigorously assess the difference in mean speed values. The Wilcoxon Rank-sum Test, the most widely used nonparametric alternative to the independent samples t-test, was selected for this purpose. The mean speed of baseline and WLT-based system samples were compared at the 95 percent confidence level using the following hypothesis test:

Ho: m = m_o

Ha: m ¹ m_o

Where,

m = WLT-based system mean 10-minute interval link speed

m_o= Baseline mean 10-minute interval link speed

RESULTS

Three major data collection efforts were conducted to gather the baseline data needed for the evaluation. These took place in the Fall of 2001, and included links that could be classified as freeway, high-speed major arterial, and low-volume/speed urban links. Data collection was conducted primarily during daylight hours, however, data were collected in the evening for several hours on a freeway link. It should first be noted that the WLT-based system was unable to reliably collect sufficient samples to estimate conditions on low-volume/speed urban links – therefore no results are available for this category of facility. Table 1 provides detail on the links analyzed in this research.

TABLE 1,

Link Descriptions

Link ID	Road Name	Direction	Speed Limit (mph)	AADT	# Lanes	Description
201	I-495	East	55	65,000	4	Between Telegraph Rd & US1
202	I-495	West	55	73,000	4	Between Telegraph Rd & US1
103	US-1	North	45	58,000 (Bidirectional)	3	Immediately south of I-495 interchange
104	US-1	South	45 </