Optimization of adaptive traffic signal control with transit signal priority at isolated intersections using parallel genetic algorithms

Optimization of adaptive traffic signal timing is one of the most complex problems in traffic control systems. This dissertation presents a new method that applies the parallel genetic algorithm (PGA) to optimize adaptive traffic signal control in the presence of transit signal priority (TSP). The method can optimize the phase plan, cycle length, and green splits at isolated intersections with consideration for the performance of both the transit and the general vehicles. Unlike the simple genetic algorithm (GA), PGA can provide better and faster solutions needed for real-time optimization of adaptive traffic signal control. ^ An important component in the proposed method involves the development of a microscopic delay estimation model that was designed specifically to optimize adaptive traffic signal with TSP. Macroscopic delay models such as the Highway Capacity Manual (HCM) delay model are unable to accurately consider the effect of phase combination and phase sequence in delay calculations. In addition, because the number of phases and the phase sequence of adaptive traffic signal may vary from cycle to cycle, the phase splits cannot be optimized when the phase sequence is also a decision variable. A "flex-phase" concept was introduced in the proposed microscopic delay estimation model to overcome these limitations. ^ The performance of PGA was first evaluated against the simple GA. The results show that PGA achieved both faster convergence and lower delay for both under- or over-saturated traffic conditions. A VISSIM simulation testbed was then developed to evaluate the performance of the proposed PGA-based adaptive traffic signal control with TSP. The simulation results show that the PGA-based optimizer for adaptive TSP outperformed the fully actuated NEMA control in all test cases. The results also show that the PGA-based optimizer was able to produce TSP timing plans that benefit the transit vehicles while minimizing the impact of TSP on the general vehicles. The VISSIM testbed developed in this research provides a powerful tool to design and evaluate different TSP strategies under both actuated and adaptive signal control. ^

Eyewitness memory: Balancing the accuracy, precision, and quantity of information through metacognitive monitoring and control

Historically, memory has been evaluated by examining how much is remembered, however a more recent conception of memory focuses on the accuracy of memories. When using this accuracy-oriented conception of memory, unlike with the quantity-oriented approach, memory does not always deteriorate over time. A possible explanation for this seemingly surprising finding lies in the metacognitive processes of monitoring and control. Use of these processes allows people to withhold responses of which they are unsure, or to adjust the precision of responses to a level that is broad enough to be correct. The ability to accurately report memories has implications for investigators who interview witnesses to crimes, and those who evaluate witness testimony. ^ This research examined the amount of information provided, accuracy, and precision of responses provided during immediate and delayed interviews about a videotaped mock crime. The interview format was manipulated such that a single free narrative response was elicited, or a series of either yes/no or cued questions were asked. Instructions provided by the interviewer indicated to the participants that they should either stress being informative, or being accurate. The interviews were then transcribed and scored. ^ Results indicate that accuracy rates remained stable and high after a one week delay. Compared to those interviewed immediately, after a delay participants provided less information and responses that were less precise. Participants in the free narrative condition were the most accurate. Participants in the cued questions condition provided the most precise responses. Participants in the yes/no questions condition were most likely to say “I don’t know”. The results indicate that people are able to monitor their memories and modify their reports to maintain high accuracy. When control over precision was not possible, such as in the yes/no condition, people said “I don’t know” to maintain accuracy. However when withholding responses and adjusting precision were both possible, people utilized both methods. It seems that concerns that memories reported after a long retention interval might be inaccurate are unfounded. ^

A Static Traffic Assignment Model Combined with an Artificial Neural Network Delay Model

As traffic congestion continues to worsen in large urban areas, solutions are urgently sought. However, transportation planning models, which estimate traffic volumes on transportation network links, are often unable to realistically consider travel time delays at intersections. Introducing signal controls in models often result in significant and unstable changes in network attributes, which, in turn, leads to instability of models. Ignoring the effect of delays at intersections makes the model output inaccurate and unable to predict travel time. To represent traffic conditions in a network more accurately, planning models should be capable of arriving at a network solution based on travel costs that are consistent with the intersection delays due to signal controls. This research attempts to achieve this goal by optimizing signal controls and estimating intersection delays accordingly, which are then used in traffic assignment. Simultaneous optimization of traffic routing and signal controls has not been accomplished in real-world applications of traffic assignment. To this end, a delay model dealing with five major types of intersections has been developed using artificial neural networks (ANNs). An ANN architecture consists of interconnecting artificial neurons. The architecture may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The ANN delay model has been trained using extensive simulations based on TRANSYT-7F signal optimizations. The delay estimates by the ANN delay model have percentage root-mean-squared errors (%RMSE) that are less than 25.6%, which is satisfactory for planning purposes. Larger prediction errors are typically associated with severely oversaturated conditions. A combined system has also been developed that includes the artificial neural network (ANN) delay estimating model and a user-equilibrium (UE) traffic assignment model. The combined system employs the Frank-Wolfe method to achieve a convergent solution. Because the ANN delay model provides no derivatives of the delay function, a Mesh Adaptive Direct Search (MADS) method is applied to assist in and expedite the iterative process of the Frank-Wolfe method. The performance of the combined system confirms that the convergence of the solution is achieved, although the global optimum may not be guaranteed.

Eyewitness Memory: Balancing the Accuracy, Precision, and Quantity of Information through Metacognitive Monitoring and Control

Historically, memory has been evaluated by examining how much is remembered, however a more recent conception of memory focuses on the accuracy of memories. When using this accuracy-oriented conception of memory, unlike with the quantity-oriented approach, memory does not always deteriorate over time. A possible explanation for this seemingly surprising finding lies in the metacognitive processes of monitoring and control. Use of these processes allows people to withhold responses of which they are unsure, or to adjust the precision of responses to a level that is broad enough to be correct. The ability to accurately report memories has implications for investigators who interview witnesses to crimes, and those who evaluate witness testimony. This research examined the amount of information provided, accuracy, and precision of responses provided during immediate and delayed interviews about a videotaped mock crime. The interview format was manipulated such that a single free narrative response was elicited, or a series of either yes/no or cued questions were asked. Instructions provided by the interviewer indicated to the participants that they should either stress being informative, or being accurate. The interviews were then transcribed and scored. Results indicate that accuracy rates remained stable and high after a one week delay. Compared to those interviewed immediately, after a delay participants provided less information and responses that were less precise. Participants in the free narrative condition were the most accurate. Participants in the cued questions condition provided the most precise responses. Participants in the yes/no questions condition were most likely to say “I don’t know”. The results indicate that people are able to monitor their memories and modify their reports to maintain high accuracy. When control over precision was not possible, such as in the yes/no condition, people said “I don’t know” to maintain accuracy. However when withholding responses and adjusting precision were both possible, people utilized both methods. It seems that concerns that memories reported after a long retention interval might be inaccurate are unfounded.