GIS-based Decision Support Tool for the Evaluation and Selection of Adaptive Traffic Control Strategies on Transportation Networks
Sandeep Mudigonda, Kaan M. A. Özbay, Harsh Doshi · 2008
ABSTRACT With a number of adaptive signal strategies to choose from and an ever increasing cost of deploying new technologies, there is a need for transportation professionals to carefully determine optimal locations of a specific adaptive signal control strategy in order to maximize its benefits. An interoperable decision support system which not only gives a recommendation about the best network location for deployment, but also provides a seamless data exchange between various data sources will be extremely helpful. In this study the development of a prototype geographical information system (GIS) based decision support system (DSS) to address some of these issues is presented. First, a novel software bridge is implemented to ensure data exchange between the most widely used traffic signal optimization and analysis software, Synchro and the developed GIS-based DSS prototype. A macroscopic simulator, a rule-based expert system built up using various sources, and a benefit-cost analysis module that are integrated parts of this unique GIS-based DSS tool are then described in detail. Case studies that make use of the developed tool are also presented. INTRODUCTION One of the key elements of Intelligent Transportation Systems (ITS) is traffic adaptive signal control systems. The traffic engineering community has more than 40 years of experience with computer-controlled traffic signals. Although potential benefits of adaptive traffic signal control strategies have long been recognized by the traffic community, the lack of dependable implementation strategies and a wide-range of performance results (shown in Figure 1a) created skepticism towards these systems among traffic engineers. Figure 1a adapted from Gartner et al. (1995) (1) shows an enlarging performance envelope for different control generations (GC) that become more traffic adaptive with the increasing number of generation. Each generation envelopes the capabilities of the lower generation as shown in Figure 1b. Gartner et al (1995) (1) attributes this large envelope of performance, which covers both losses and gains in performance, to the problems associated with the internal modeling accuracy and logic of the control strategies, as well as to the implementation location and traffic-specific factors. (2)