The problem of locating automatic vehicle identification (AVI) readers on a transportation network is one worth considering. AVI readers are strategically located to catch a maximum number of trips and cover a maximum number of origin–destination (O-D) pairs using a minimum number of AVI readers. There are three possible objectives when decid- ing locations for AVI readers: (a) a minimum number of AVI readers, (b) maximum O-D coverage, and (c) a maximum number of trips (or AVI readings). To satisfy all three objectives as much as possible, the prob- lem is formulated as a multiobjective integer-optimization problem. A distance-based genetic algorithm is applied to solve this multiobjective AVI reader-location problem by explicitly generating the nondominated solutions. Numerical results are presented to demonstrate the feasibility of the proposed multiobjective model. The procedure proposed holds great promise for the development of a well-configured AVI system that can achieve a balance between quality and cost of coverage (i.e., trade-off between cost and coverage requirements). In many cities, traffic congestion is a serious problem because of the limited road space and the ever-increasing travel demands. Intelligent transportation systems (ITS) are considered useful for dealing with traffic congestion, protecting the environment, and improving trans- portation safety. ITS take advantage of the latest sensor, communica- tion, and traffic-control technologies to combat traffic congestion. These technologies hold promise in assisting state, county, and local governments in meeting the increasing travel demands on the surface transportation system. These methods of traffic surveillance, which are integral parts of ITS, have been described as the eyes of ITS to provide knowledge of existing networkwide traffic conditions (1). Traffic management and information systems usually rely on a system of sensors for traffic surveillance. Currently, the dominant technology for this purpose are inductive-loop detectors, which are buried underneath road pavement to count vehicles passing over them. The reliability and accuracy of traffic data collected by loop detectors are critical factors influencing the performance of traffic- control and -management systems. Because of the harsh environment in which loop detectors operate, malfunctioning is a common prob- lem. Recent research (2) has showed that the percentage of good sam- ples can range from 20% to 80% in California. Other studies (3, 4) also reported similar results, in which more than 50% of the loops can be malfunctioning or producing erroneous traffic data at any one time. With advances in traffic-surveillance technologies, new types of sen- sors have become available to provide new data with more reliability and accuracy. The automatic vehicle-identification (AVI) system is one new sensor technology designed to measure travel times and to facilitate toll operations. In addition, it can provide other types of information unavailable from loop detectors. [See Dixon (5) for a list of potential data that can be collected by an AVI system]. An AVI sys- tem consists of fixed AVI reader stations placed at various locations in a transportation network and transponders (or AVI tags) placed in individual vehicles. Various traffic data can be collected by read- ing unique identification numbers from the AVI tags of individual vehicles as they pass through the AVI reader stations. A central problem in the deployment of the emerging AVI sur- veillance technology is to determine the number and locations of reading stations that would best cover the network. Sherali et al. (6 ) defined best in terms of providing a maximum degree of information about traffic variability in the network subject to certain resource constraints. On the other hand, Teodorovic et al. (7 ) proposed a two- objective model by defining best in terms of the total number of read- ings along the shortest path for each origin–destination (O-D) pair and the total number of O-D pairs covered. A reading here means that the same vehicle is registered at two different AVI stations in the net- work. Both studies considered the best coverage in terms of one or more criteria for a given number of AVI readers (or a fixed cost). No consideration is given to the trade-off between the quality of AVI coverage and the total cost (density of AVI readers) of the AVI sys- tem. In this paper, we extend the AVI reader-location problem of Teodorovic et al. (7 ) to explicitly include cost as another objective in addition to the total number of O-D pairs covered and the total num- ber of trips registered. The cost of an AVI system can be specified as the number of AVI readers installed. A multiobjective model is for- mulated for locating AVI readers in a network to catch a maximum number of trips and cover a maximum number of O-D pairs using a minimum number of AVI readers. When solving a multiobjective opti- mization problem, there may not exist a single best solution that satis- fies all objectives. It is necessary to develop a solution procedure that explicitly generates and retains the nondominated solutions. Instead of specifying the priority (or weight) for each objective as in the study by Teodorovic et al. (7 ), the distance-based genetic algorithm (GA) is applied in this study to generate a set of nondominated solutions. The remainder of this paper is organized as follows. In the next section, we formulate the multiobjective AVI location problem. The distance-based GA is presented in the next section, which is fol- lowed by a discussion of numerical results and finally by a section containing the conclusion and suggested future research. FORMULATION OF MULTIOBJECTIVE AVI LOCATION PROBLEM Recently, there have been some studies concerned with the impor- tance of the sensor-location problem to some transportation appli- cations. Yang and Miller-Hooks (8) proposed a model to select the Multiobjective Model for Locating Automatic Vehicle Identification Readers Anthony Chen, Piya Chootinan, and Surachet Pravinvongvuth Transportation Research Record: Journal of the Transportation Research Board, No. 1886, TRB, National Research Council, Washington, D.C., 2004, pp. 49–58. Department of Civil and Environmental Engineering, Utah State University, Logan, UT 84322-4110. 49