Applications of Artificial Intelligence Paradigms to Decision Support in ...

Applications of Artificial Intelligence Paradigms to Decision Support in Real-Time Traffic Management Mashrur Chowdhury, Adel Sadek, Yongchang Ma, Neeraj Kanhere, and Parth Bhavsar management systems quickly detect and verify incidents, deploy the right equipment to the scene, and attempt to manage traffic better during the incident by diverting traffic onto alternative routes, if such a diversion would help save travel time. Whether diversion strategies may be beneficial depends on the duration and severity of the incident as well as the attractiveness of alternative routes. For example, if an incident lasts only 10 min and blocks only one lane of a three-lane highway, then diverting traffic to an alternative route that is 10.0 mi longer is not necessarily an effective strategy. A key decision in the incident management process is whether diverting traffic is warranted for a given scenario. Several techniques have been proposed over the years to address this question. For example, simulation models such as VISSIM (1), DYNASMART (2), and DynaMIT (3) can evaluate traffic conditions under the diversion strategy and for a control scenario that involves no diversion. A comparison of traffic conditions under these two cases would help determine whether diversion is warranted. The problems with this approach are that running the model twice requires time—especially for large-scale, complex networks—and diversion strategies must be developed almost in a real-time fashion to be effective. One cannot afford to wait until the simulation runs are completed to decide on a course of action. To address this issue, the feasibility of using two artificial intelligence (AI) paradigms to generalize the results obtained from running a comprehensive microscopic traffic simulation models is examined. If successful, these tools could then be used to evaluate new traffic situations and make routing decisions similar to those that would have been reached using a simulation model, in a fraction of the time that running a simulation model would require. It therefore would make those paradigms appropriate for on-line, real-time applications, and allow them to be used for real-time decision support during an incident. The two paradigms examined in this paper are support vector regression (SVR) and case-based reasoning (CBR). The theory behind support vector machines (SVMs) and SVR is similar. SVM is used primarily for pattern classification, whereas SVR is used for regression or function estimation. So far, SVR has had limited applications in the transportation field; previous examples of its application to transportation problems include travel time, traffic speed and traffic flow predictions, and incident detection in the context of ITS applications (4–7 ). In addition, Sun et al. apply SVM for vehicle detection using extracted features from Gabor filters (8). They compare the integrated application of SVM and Gabor filters with a different approach involving neural networks and demonstrate that the SVM approach is superior. In contrast, CBR was used in earlier studies as a decision support tool for numerous ITS applications (9–11), including predicting the

Decision support for real-time traffic management is a critical component for the success of intelligent transportation systems. Theoretically, microscopic simulation models can be used to evaluate traffic management strategies in real time before a course of action is recommended. However, the problem is that the strategies would have to be evaluated in real time; this might not be computationally feasible for large-scale networks and complex simulation models. To address this problem, two artificial intelligence (AI) paradigms—support vector regression (SVR) and case-based reasoning (CBR)—are presented as alternatives to the simulation models as a decision support tool. Specifically, prototype SVR and CBR decision support tools are developed and used to evaluate the likely impacts of implementing diversion strategies in response to incidents on a highway network in Anderson, South Carolina. The performances of the two prototypes are then evaluated by a comparison of their predictions of traffic conditions with those obtained from VISSIM, a microscopic simulation model. Although the prototype systems’ predictions were comparable to those obtained by simulation, their run times were only fractions of the time required by the simulation model. Moreover, SVR performance is superior to that of CBR for most cases considered. The study results provide motivation for consideration of the proposed AI paradigms as potential decision support tools for real-time transportation management applications.

Since the early 1990s, the transportation community has turned to the use of intelligent transportation systems (ITS) to help address some of the nation’s toughest transportation problems. ITS has now been deployed in almost all major U.S. cities. One of the key ITS components is the real-time traffic and incident management system. This system is designed to optimize the use of the existing transportation capacity, especially during incidents. Highway incidents (e.g., traffic crashes, adverse weather conditions, hazardous material spills, and short-term construction work) can cause excessive traffic delays and may result in secondary incidents. The goal of incident management systems is to alleviate this kind of short-term congestion and to smooth highway traffic flow by managing the traffic in real time. To achieve this goal, incident M. Chowdhury, Y. Ma, and P. Bhavsar, Department of Civil Engineering, Lowry Hall Box 340911, and N. Kanhere, Department of Electrical and Computer Engineering, Riggs Hall, Clemson University, Clemson, SC 29634. A. Sadek, Department of Civil and Environmental Engineering, University of Vermont, 33 Colchester Ave., 213D Votey, Burlington, VT 05405. Transportation Research Record: Journal of the Transportation Research Board, No. 1968, Transportation Research Board of the National Academies, Washington, D.C., 2006, pp. 92–98.

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benefits of traffic routing on a Connecticut test network (9). In the current study, the feasibility of SVR and CBR in traffic management is evaluated by a comparison of their predictions of traffic state evolution against a comprehensive traffic simulation model. The test highway network used for evaluation is near Anderson, South Carolina.

while keeping the computational cost low. Figure 1 is an overview of SVR. In this study, a radial basis function (RBF) kernel was used. As in Figure 1, the SVR model depends on a subset S of the training samples, support vector coefficients Cs, and a constant b. Case-Based Reasoning

PROPOSED ARTIFICIAL INTELLIGENCE APPROACHES Support Vector Regression In 1995, Vladimir Vapnik and colleagues at AT&T Bell Laboratories developed SVMs based on the statistical learning theory (12). The theory was developed to help characterize the properties of learning machines that enable the system to generalize predictive information. SVMs include a set of supervised learning algorithms from the field of machine learning applicable to classification as well as regression problems. They use kernels to map the input data into a high dimensional feature space where linear classification becomes feasible. SVM algorithms are based on the principal of structural risk minimization (SRM) and the statistical learning theory developed by Vapnik et al. (13). Although SVMs are more popular for their applicability in the problem of pattern classification, Smola and Schölkopf promoted SVR as a different formulation of SVM (14). The model produced by SVR depends on only a subset of the training samples, because the cost function for building the model ignores the training samples inside the epsilon tube (a certain threshold distance from the prediction). SVR has been successfully applied in diverse areas, such as haptic data prediction, illumination analysis, and financial forecasting (15–17). Regression algorithms based on the underlying theory of SVMs are called SVR algorithms. SVR achieves nonlinear regression by mapping the training samples into a high-dimensional kernel-induced feature space, followed by linear regression in that space. Because the kernel mapping is implicit (depends only on the dot product of the input data vectors), it is possible to map the data to a high dimension

CBR is a relatively new reasoning paradigm and computational problem-solving method that is attracting increased attention in the AI community (18). At a basic level, CBR is based on the observation that when people solve a new problem, they often base the solution on one that worked for a similar problem in the past. A complete CBR process can be represented as a cycle consisting of the following tasks (Figure 2): 1. Retrieve the most similar case(s), 2. Reuse the case(s) to solve the problem, 3. Revise the proposed solution if necessary, and 4. Retain the parts of this experience to be used for future applications. At the core of the CBR process is a case base that stores previous instances of problems and their derived solutions. When faced with a new problem, a CBR system matches the new problem against cases in the case base, and retrieves the most similar case(s). Because the retrieved case is likely to be somewhat different from the current case, a CBR system typically adapts the retrieved solution to closely suit the new problem during the reuse step. The proposed solution is then implemented and tested for success; any revisions are made, if needed. Finally, the new case is retained, allowing the system to learn and refine its knowledge with usage. CBR therefore uses specific knowledge of previously experienced situations or cases. It also allows for incremental, sustained learning, because a new experience can be saved each time a problem has been solved. This new case becomes available for future problems. In the beginning, a CBR system could depend on simulated scenarios. As

Training samples from y = f (x)

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Subset S of training samples (Support Vectors), coefficients cs and constant b (xs ,xn) + b f(xn) = cs each s in S

FIGURE 1

Concept of SVR.

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tools—one using SVR and the other using CBR—were developed. Finally, their performance was evaluated on the test cases that were not used in developing either model. The details of the method are described below.

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Test Network The study network was on the I-85 corridor in Anderson, South Carolina (Figure 4). The major route is I-85, and an alternate route includes US-76 and SC-187.

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CBR cycle (19).

it advances, cases with actual data or real-world information could be added to the database. As Aamodt and Plaza point out, no universal CBR methods can be applied for every domain of application (18). The real challenge of any CBR research effort is thus to come up with methods that are suited to the application environment under consideration.

METHOD As mentioned previously, this study’s approach was formulated to evaluate the feasibility of using SVR and CBR as real-time decision support tools, instead of running simulation models, to generate information about the impacts of alternative actions during an incident or other highway management situation. Figure 3 shows the research method. The first steps were to select a test network and develop a detailed microscopic simulation model for the network. The cases needed to develop and evaluate the two AI paradigms were then generated by running the simulation model. Next, two prototype decision support

The VISSIM (v. 3.7) traffic simulation model was used to model the network. VISSIM is a time-step, behavior-based microscopic traffic simulation model that can incorporate detailed network and traffic control information to provide a realistic estimate of alternative management and control scenarios. In this paper, the primary measure used to evaluate network performance was the total travel time of the network, which averages the travel time for both the main route and the alternate route (weighted according to traffic volume). Vehicle actuated programming (VAP) is an add-on module to provide a programmable simulation interface for advanced users to input customized traffic control strategy. This study applied VAP to model driver response to variable message signs (VMSs). The peak hour volumes were collected and assigned to a series of traffic-generating source nodes at the end of the roadway to generate the demand for the network. VISSIM allows the user to create incidents and specify location, start time, expected duration and severity (in terms of lane blockage number). After the incident occurs and when a certain number of lanes are temporally blocked, vehicles try to change lanes to pass the incident scene, and some vehicles approaching the unblocked lane may slow down because of other vehicle’s lane-changing operations. In the high-traffic-volume scenario, drivers need more time to find gaps to merge into the traveling flow. After

Select Network

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FIGURE 3

Research method.

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FIGURE 4

Study network.

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the incident is cleared, the blocked lane reopens, and the simulation continues to catch the full incident effect until the network is restored to normal condition. To improve efficiency, a VMS at the nearest upstream exit can be used to disseminate real-time traffic information and advise drivers to use an alternative route. VISSIM allows the user to control the percentage of drivers who will change their original routes. To simulate this traffic diversion phenomenon, the study assumed a percentage of diverted traffic. This study aims to show the accuracy of SVR and CBR prediction with selected attributes. Future work may include more attributes. For example, the driver response to VMSs was selected as an input variable in this paper, but it may be modified in future research as dependent on attributes of the alternative route, such as length, travel time, and driver familiarity.

ing and the remaining 35 cases for testing. The SVR model chose 78 cases as the support vectors essential for the predictive model. Before training, data were normalized by subtracting mean of all components of cases and dividing by their standard deviations. This study used LIBSVM, a software library for SVM, to train and test the SVR model (20). The type of SVM used was epsilon-SVR with a radial basis kernel. The training time of the SVR model was