On-line Control Architecture for Enabling Real-time Traffic System Operations Srinivas Peeta∗ & Pengcheng Zhang School of Civil Engineering, Purdue University, West Lafayette, IN 47907, U.S.A. Abstract: Advances in information technology and inexpensive high-end computational power are motivating a new generation of methodological paradigms for the efficient information-based real- time operation of large-scale traffic systems equipped with sensor technologies. Critical to their effectiveness are the control architectures that provide a blueprint for the efficient transmission and processing of large amounts of real-time data, and consistency-checking and fault tolerance mechanisms to ensure seamless automated functioning. However, the lack of low-cost, high- performance, and easy-to-build computing environments is a key impediment to the widespread deployment of such architectures in the real-time traffic operations domain. This paper proposes an Internet-based on-line control architecture that uses a Beowulf cluster as its computational backbone and provides an automated mechanism for real-time route guidance to drivers. To investigate this concept, the computationally intensive optimization modules are implemented on a low-cost sixteen-processor Beowulf cluster and a commercially available supercomputer, and the performance of these systems on representative computations is measured. The results highlight the effectiveness of the cluster in generating substantial computational performance scalability, and suggest that its performance is comparable to that of the more expensive supercomputer. 1 INTRODUCTION The utilization of advanced technologies in intelligent transportation systems (ITS) enables the implementation of closed-loop control architectures to improve the efficiency, accessibility, reliability and safety of transportation systems. A key ITS technology, advanced traveler information systems (ATIS), envisages the provision of route guidance information to drivers using dynamic traffic assignment (DTA) models for vehicular networks equipped with sensor and information dissemination devices. Hence, such systems have access to real-time traffic flow data from the sensors and can relay useful traffic information to drivers after processing this data. This motivates the use of closed-loop control architectures for deployment. They are preferable here to open-loop architectures due to the presence of several sources of randomness that significantly affect the control outputs, including origin-destination demand, driver behavior, supply conditions, and traffic flow interactions (Peeta and Yang, 2003). The control architecture provides a blueprint 2 for the efficient transmission and processing of time-dependent data and its usage across components, consistency checking modules and fault tolerance mechanisms to ensure seamless automated functioning, and information supply strategies to enhance system performance. Due to the need to process large amounts of traffic data and generate information supply strategies in real- time, the associated control architectures are computationally intensive, and can represent a key barrier to their deployment. Previous efforts to address the computational barriers to the deployment of real-time control strategies in transportation networks have targeted both the algorithmic logic and the computing environment. In the context of real-time route guidance, the algorithmic aspects, addressed under the DTA umbrella, have focused on: (i) developing centralized iterative frameworks with truncated horizons (Peeta and Mahmassani, 1995), (ii) decentralizing the solution procedure by decomposing the traffic network into small zones (Hawas and Mahmassani, 1997; Chiu and Mahmassani, 2002), (iii) developing reactive solution strategies (Hawas and Mahmassani, 1997; Pavlis and Papageorgiou, 1999; Peeta and Yang, 2003), (iv) developing computationally more efficient solutions (Mahmassani et al., 1998a), and (v) using hybrid models that combine computationally intensive off-line solutions with efficient real-time strategies (Peeta and Zhou, 2002). Centralized iterative frameworks utilize real-time system measurements along with detailed predictions of future network states for deployment decisions. They typically predict and use the projected (experienced) travel times rather than current (instantaneous) travel times in their algorithmic logic. Hence, they are generally computationally intensive and difficult to implement in real-time. By contrast, decentralized solution strategies typically limit their analysis to a small area by using approximate estimates of the traffic conditions beyond the area under consideration. Although this significantly reduces the computational complexity, it may underestimate the effects of congestion ∗ To whom correspondence should be addressed. E-mail: [email protected]. 3 outside that area. Reactive route guidance strategies attempt to address this issue by considering only current measurements rather than future conditions or historical data. Unlike iterative strategies, they typically use instantaneous travel times as approximations to experienced travel times. For the same reason, iterative strategies are generally more accurate than reactive strategies except under certain scenarios (Pavlis and Papageorgiou, 1999) when the instantaneous travel time approximation may be reasonable. Hybrid strategies (Peeta and Zhou, 2002) exploit the advantages of iterative and reactive frameworks by combining the computationally intensive off-line solutions with efficient on-line reactive strategies. They use historical data to generate robust initial solutions off-line that are efficiently updated in real-time based on the unfolding traffic conditions. There have been several efforts to address the computational aspects related to real-time traffic operations by focusing on the computational environments, both in terms of the hardware and the operating configuration. They include: (i) implementing the models on expensive specialized high performance computing hardware (for example, Chang et al., (1994) and Habbal et al., (1994) on the Connection Machine; Peeta and Mahmassani (1995) and Ziliaskopoulos et al., (1997) on the CRAY), (ii) configuring a cluster of individual workstations into a distributed system (Peeta and Chen, 1999) and (iii) using efficient enabling environments such as CORBA (Mahmassani et al., 1998b; Ziliaskopoulos and Waller, 2000). Although massively parallel and/or other sophisticated computing architectures may address the computational needs of real-time route guidance algorithms, they are usually prohibitively expensive and typically beyond the budget of most local transportation agencies. Also, even if such agencies could access these computing systems, the associated high costs preclude dedicated usage for their specific operations only. Hence, these specialized hardware architectures are typically not customizable and may not be optimally configured for use by these algorithms. In addition, they typically entail system-specific skills for monitoring and maintenance, and specialized software to operate. The advances in information 4 technology and the growing popularity of open source software, coupled with low-cost high-end computing power, provide opportunities to explore new paradigms for affordable and efficient high performance computing to enable the deployment of on-line control strategies in a wide range of transportation networks. This paper presents a real-time information-based traffic system control architecture for route guidance that uses a Beowulf cluster as its central computing unit. Section 2 introduces the control architecture, the control flow logic, and the associated computing and data storage paradigms. Section 3 first demonstrates the performance capabilities of the Beowulf cluster through a series of experiments. It then analyzes the performance of the control architecture in an off-line mode by using different parallel paradigms to execute an important component, the path processing algorithm. Concluding comments are presented in Section 4. 2 ON-LINE TRAFFIC SYSTEM CONTROL ARCHITECTURE Figure 1 shows the conceptual structure to enable the proposed information-based real-time traffic control architecture for route guidance. Here, one or more transportation agencies utilize the computing power of a single centrally located unit to generate information dissemination strategies that provide routing information to the network users. While the notion of a remotely located computing unit is not essential for the proposed architecture, there are some advantages to this configuration. First, it enables the system operators (for example, the traffic control centers) within a geographic region to pool resources so as to cut costs. Second, the maintenance of the computing unit can be performed by on-site specialists, leading to greater reliability in terms of the functioning of the hardware. Third, changes in the operational logic and/or traffic control strategies can be seamlessly performed by dedicated on-site developers of the algorithmic logic and software. As shown in Figure 1, real-time traffic data obtained from the installed sensors at each on-line network is fed through the Internet or other communication media to the control algorithms located 5 on the central computing unit for generating route guidance strategies. While sensor data can be first sent to the traffic control center (TCC) and then channeled to the computing unit,