Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment

Abstract

The growing need of the driving public for accurate traffic information has spurred the deployment of large scale dedicated monitoring infrastructure systems, which mainly consist in the use of inductive loop detectors and video cameras. On-board electronic devices have been proposed as an alternative traffic sensing infrastructure, as they usually provide a cost-effective way to collect traffic data, leveraging existing communication infrastructure such as the cellular phone network. A traffic monitoring system based on GPS-enabled smartphones exploits the extensive coverage provided by the cellular network, the high accuracy in position and velocity measurements provided by GPS devices, and the existing infrastructure of the communication network. This article presents a field experiment nicknamed Mobile Century, which was conceived as a proof of concept of such a system. Mobile Century included 100 vehicles carrying a GPS-enabled Nokia N95 phone driving loops on a 10-mile stretch of I-880 near Union City, California, for 8 h. Data were collected using virtual trip lines, which are geographical markers stored in the handset that probabilistically trigger position and speed updates when the handset crosses them. The proposed prototype system provided sufficient data for traffic monitoring purposes while managing the privacy of participants. The data obtained in the experiment were processed in real-time and successfully broadcast on the internet, demonstrating the feasibility of the proposed system for real-time traffic monitoring. Results suggest that a 2–3% penetration of cell phones in the driver population is enough to provide accurate measurements of the velocity of the traffic flow. Data presented in this article can be downloaded from http://traffic.berkeley.edu.

Introduction

Before the era of the mobile internet, characterized in particular by the emergence of location based services heavily relying on GPS, the traffic monitoring infrastructure has mainly consisted of dedicated equipment, such as loop detectors, cameras, and radars. Installation and maintenance costs prevent the deployment of these technologies for the entire arterial network and even for highways in numerous places around the world. Moreover, inductive loop detectors are prone to errors and malfunctioning (daily in California, 30% out of 25,000 detectors do not work properly according to the PeMS system²).

For this reason, the transportation engineering community has looked for new ways to collect traffic data to monitor traffic. Electronic devices traveling onboard cars are appealing for this purpose, as they usually provide a cost-effective and reliable way to collect traffic data.

Radio-frequency identification (RFID) transponders, such as Fastrak in California or EZ-Pass on the East Coast,³ can be used to obtain individual travel times based on vehicle re-identification (Wright and Dahlgren, 2001, Ban et al., 2009). Readers located on the side of the road keep record of the time the transponder (i.e. the vehicle) crosses that location. Measurements from the same vehicle are matched between consecutive readers to obtain travel time. The fundamental limitations of this system is the cost to install the infrastructure (readers), its limited coverage, and the fact that only travel time between two locations can be obtained.

License plate recognition (LPR) systems are composed of cameras deployed along the roadway which identify license plates of vehicles using image processing techniques. When a vehicle is successfully identified crossing two sensors, a measurement of the vehicle’s travel time is obtained. Example deployments include TrafficMaster’s passive target flow management (PTFM) on trunk roads in the United Kingdom,⁴ and Oregon DOT’s Frontier Travel Time project (Bertini et al., 2005). Like RFID systems, LPR system coverage is limited by the cost to deploy the cameras.

Global positioning system (GPS) devices found in the market can obtain position and instantaneous velocity readings with a high accuracy, which can be used to obtain traffic information. Sanwal and Walrand (1995) addressed some of the key issues of a traffic monitoring system based on probe vehicle reports (position, speeds, or travel times), and concluded that they constitute a feasible source of traffic data. Zito et al. (1995) also investigated the use of GPS devices as a source of data for traffic monitoring. Two tests were performed to evaluate the accuracy of the GPS as a source of velocity and acceleration data. The accuracy level found was good, even though the selective availability⁵ feature was still on. The main drawback of this technology is that its low penetration in the population is not sufficient to provide an exhaustive coverage of the transportation network. Dedicated probe vehicles equipped with a GPS device represent added cost that cannot be applied at a global scale. An example of such program at a small scale is HICOMP⁶ in California, which uses GPS devices in dedicated probe vehicles to monitor traffic for some freeways and major highways in California. However, as pointed out by Kwon et al. (2007), the penetration of HICOMP is low and the collected travel times are not as reliable as other systems such as PeMS. Other approaches have investigated the possibility of using dedicated fleets of vehicles equipped with GPS or automatic vehicle location (AVL) technology to monitor traffic (Moore et al., 2001, Schwarzenegger et al., 2008, Bertini and Tantiyanugulchai, 2004), for example FedEx, UPS trucks, taxis, buses or dedicated vehicles. While industry models have been successful at gathering substantial amounts of historical data using this strategy, for example Inrix, the use of dedicated fleets always poses issues of coverage, penetration, bias due to operational constraints and specific travel patterns. Nevertheless, it appears as a viable source of data, particularly in large cities.

In the era of mobile internet services, and with the shrinking costs and increased accuracy of GPS, probe based traffic monitoring has become one of the next arenas to conquer by industries working in the field of mobile sensing. Increasing penetration of mobile phones in the population makes them attractive as traffic sensors, since an extensive spatial and temporal coverage could potentially soon be achieved. GPS-enabled cellular phone-based traffic monitoring systems are particularly suitable for developing countries, where there is a lack of resources for traffic monitoring infrastructure systems, and where the penetration rate of mobile phones in the population is rapidly increasing. By the end of 2007, the penetration rate of mobile phones in the population was over 50% in the world, ranging from 30% to 40% in developing countries (with an annual growth rate greater than 30%) to 90% to 100% in developed countries⁷.

Multiple technological solutions exist to the localization problem using cell phones. Historically, the seminal approach chosen for monitoring vehicle motion using cell phones (prior to the rapid penetration of GPS in cellular devices) uses cell tower signal information to identify handset’s location. This technique usually relies on triangulation, trilateration, tower hand-offs, or a combination of these. Several studies have investigated the use of mobile phones for traffic monitoring using this approach (Westerman et al., 1996, Ygnace et al., 2000, Lovell, 2001, Fontaine and Smith, 2007, Bar-Gera, 2007). The fundamental challenge in using cell tower information for estimating position and motion of vehicles is the inherent inaccuracy of the method, which poses significant difficulties to the computation of speed. Several solutions have been implemented to circumvent this difficulty, in particular by the company Airsage, which historically developed its traffic monitoring infrastructure based on cell tower information (Liu et al., 2008, STL, 2006). Based on the time difference between two positions, average link travel time and speed can be estimated. Yim and Cayford (2001) conducted a field experiment to compare the performance of cell phones and GPS devices for traffic monitoring. The study concluded that GPS technology is more accurate than cell tower signals for tracking purposes. In addition, the low positioning accuracy of non-GPS based methods prevents its massive use for monitoring purposes, especially in places with complex road geometries. Also, while travel times for large spatio-temporal scales can be obtained from such methods, other traffic variables of interest, such as instantaneous velocity are more challenging to obtain accurately.

A second approach is based on GPS-enabled smartphones, leveraging the fact that increasing numbers of smartphones or PDAs come with GPS as a standard feature. This technique can provide more accurate location information, and thus more accurate traffic data such as speeds and/or travel times. Additional quantities can potentially be obtained from these devices, such as instantaneous velocity, acceleration, and direction of travel. Fontaine and Smith (2007) used cell phone for traffic monitoring purposes, and mentioned the need of having a GPS-level accuracy for position to compute reasonable estimates of travel time and speed. Yim and Cayford, 2001, Yim, 2003 concluded that if GPS-equipped cell phones are widely used, they will become more attractive and realistic alternative for traffic monitoring. GPS-enabled mobile phones can potentially provide an exhaustive spatial and temporal coverage of the transportation network when there is traffic, with a high positioning accuracy achieved by a GPS receiver. Some concerns regarding this technology include the need of a specifically designed handset, and the fact that the method requires each phone to send information to a center (Rose, 2006, Qiu et al., 2007), which could potentially increase the communication load on the system and the energy consumption of the handset.⁸ Another issue is the knowledge of vehicle position and velocity provided by this technology, which needs to be used in a way which does not infringe privacy.

The impact of these concerns (communication load, handset energy consumption, and privacy) can be handled with the appropriate sampling strategy. Sampling GPS data in the transportation network can be handled in at least two ways:

• Temporal sampling: Equipped vehicles report their information (position, velocity, etc.) at specific time intervals T, regardless of their positions.

• Spatial sampling: Equipped vehicles report their information (time, velocity, etc.) as they cross some spatially defined sampling points. This strategy is similar to the one used by inductive loop detectors, RFID transponders or license plate readers, in which data are obtained at fixed locations. It has the advantage that the phone is forced to send data from a given location of interest.

From a traffic estimation perspective, it is desirable to have a substantial amount of information available. Therefore, with a satisfying GPS accuracy, small T or very closely placed fixed measurements would yield more accurate estimates of traffic. However, these objectives conflict with the communication load constraints and privacy preservation.

As suggested in the literature (Ygnace et al., 2000, Yim, 2003, Qiu et al., 2007, Krause et al., 2008), field tests are needed to assess the potential of new technologies such as GPS-enabled mobile phones. Test deployments to assess the potential of traffic monitoring using cell phones go back to the advent of GPS on phones. In particular, the study of Demers et al. (2006) investigates the deployment of 200 vehicles for an extended period of three months and the potential data which can be gathered from it. As appears in light of that study, one of the main issues in experiments or pilot tests is the problem of penetration, i.e. percentage of vehicles equipped vs. total number of vehicles on the road.

This article presents the results of a large scale field experiment conducted in the San Francisco Bay Area, California, and aimed at assessing the feasibility of a traffic monitoring system using GPS-enabled mobile phones for freeway. The specificity of this field experiment is the penetration rate achieved during the test, which the authors believe is representative of upcoming GPS-equipped phones penetration in the population within a few months from the experiment. The performance of the system was sustained for a long enough time to show the feasibility of such a monitoring system. In addition to the data gathered, which is among the first in its kind, the article also briefly summarizes the prototype system which was built to gather the data, and which was recently extended for a pilot deployment in Northern California⁹ (Work and Bayen, 2008).

The rest of the article is organized as follows. Section 2 describes the system used to collect traffic data, along with the sampling strategy. Section 3 explains the goals of the experiment and its design. Section 4 presents the main results obtained from the data. Finally, Section 5 states the main conclusions obtained from the experiment.