AN INTEGRATED CONTROL MODEL FOR FREEWAY INTERCHANGES

This dissertation proposes an integrated control framework to deal with traffic congestion at freeway interchanges. In the neighborhood of freeway interchanges, there are six potential problems that could cause severe congestion, namely lane-blockage, link-blockage, green time starvation, on-ramp queue spillback to the upstream arterial, off-ramp queue spillback to the upstream freeway segments, and freeway mainline queue spillback to the upstream interchange. The congestion problem around freeway interchanges cannot be solved separately either on the freeways or on the arterials side. To eliminate this congestion, we should balance the delays of freeways and arterials and improve the overall system performance instead of individual subsystem performance. This dissertation proposes an integrated framework which handles interchange congestion according to its severity level with different models. These models can generate effective control strategies to achieve near optimal system performance by balancing the freeway and arterial delays. The following key contributions were made in this dissertation: 1. Formulated the lane-blockage problem between the movements of an arterial intersection approach as an linear program with the proposed sub-cell concept, and proposed an arterial signal optimization model under oversaturated traffic conditions; 2. Formulated the traffic dynamics of a freeway segment with cell-transmission concept, while considering the exit queue effects on its neighboring through lane traffic with the proposed capacity model, which is able to take the lateral friction into account; 3. Developed an integrated control model for multiple freeway interchanges, which can capture the off-ramp spillback, freeway mainline spillback, and arterial lane and link blockage simultaneously; 4. Explored the effectiveness of different solution algorithms (GA, SA, and SA-GA) for the proposed integrated control models, and conducted a statistical goodness check for the proposed algorithms, which has demonstrated the advantages of the proposed model; 5. Conducted intensive numerical experiments for the proposed control models, and compared the performance of the optimized signal timings from the proposed models with those from Transyt-7F by CORSIM simulations. These comparisons have demonstrated the advantages of the proposed models, especially under oversaturated traffic conditions

Synchronization of traffic signals through a heuristic-modified genetic algorithm with GLTM

Urban signal timing is a non-convex NLP problem. Finding an optimal solution on not very small and simple networks may take long time, wherever possible. The present paper focuses on signal synchronization, thus creating fast-flow corridors on one or more network road arterials. To do this, a genetic-like algorithm is applied, in which new solutions generation follows heuristic conceptions. This can be carried out thanks to the specific formulation adopted, suitable for synchronization problems. The objective function is evaluated by the General Link Transmission Model, a very fast macroscopic dynamic simulator referring to the kinematic waves theory. Through this, queues dynamic evolution, spillback phenomenon and vehicles travel times are explicitly taken into account. Key-words: traffic control, signal setting, synchronization, optimization, genetic algorithm, general link transmission model

Data Analytic Approach to Support the Activation of Special Signal Timing Plans in Response to Congestion

Improving arterial network performance has become a major challenge that is significantly influenced by signal timing control. In recent years, transportation agencies have begun focusing on Active Arterial Management Program (AAM) strategies to manage the performance of arterial streets under the flagship of Transportation Systems Management & Operations (TSM&O) initiatives. The activation of special traffic signal plans during non-recurrent events is an essential component of AAM and can provide significant benefits in managing congestion. Events such as surges in demands or lane blockages can create queue spillbacks, even during off-peak periods resulting in delays and spillbacks to upstream intersections. To address this issue, some transportation agencies have started implementing processes to change the signal timing in real time based on traffic signal engineer/expert observations of incident and traffic conditions at the intersections upstream and downstream of congested locations. This dissertation develops methods to automate and enhance such decisions made at traffic management centers. First, a method is developed to learn from experts’ decisions by utilizing a combination of Recursive Partitioning and Regression Decision Tree (RPART) and Fuzzy Rule-Based System (FRBS) to deal with the vagueness and uncertainty of human decisions. This study demonstrates the effectiveness of this method in selecting plans to reduce congestion during non-recurrent events. However, the method can only recommend the changes in green time to the movement affected by the incident and does not give an optimized solution that considers all movements. Thus, there was a need to extend the method to decide how the reduction of green times should be distributed to other movements at the intersection. Considering the above, this dissertation further develops a method to derive optimized signal timing plans during non-recurrent congestion that considers the operations of the critical direction impacted by the incident, the overall corridor, as well as the critical intersection movement performance. The prerequisite of optimizing the signal plans is the accurate measurements of traffic flow conditions and turning movement counts. It is also important to calibrate any utilized simulation and optimization models to replicate the field traffic states according to field traffic conditions and local driver behaviors. This study evaluates the identified special signal-timing plan based on both the optimization and the DT and FRBS approaches. Although the DT and FRBS model outputs are able to reduce the existing queue and improve all other performance measures, the evaluation results show that the special signal timing plan obtained from the optimization method produced better performance compared to the DT and FRBS approaches for all of the evaluated non-recurrent conditions. However, there are opportunities to combine both approaches for the best selection of signal plans

A conceptual framework for using feedback control within adaptive traffic control systems

Existing adaptive traffic control strategies lack an effective evaluation procedure to check the performance of the control plan after implementation. In the absence of an effective evaluation procedure, errors introduced in the system such as inaccurate estimates of arrival flows, are carried forward in time and reduce the efficiency of the traffic flow algorithms as they assess prevalent traffic conditions. It is evident that the feed-forward nature of these systems cannot accurately update the estimated quantities, especially during oversaturated conditions. This research is an attempt to develop a conceptual framework for the application of feedback control within the basic operation of existing adaptive traffic control systems to enhance their performance. The framework is applied to three existing adaptive traffic control strategies (SCOOT, SCATS, and OPAC) to enable better demand estimations and queue management during oversaturated condition. A numerical example is provided to test the performance of an arterial in a feedback environment. The example involves the design and simulation test of Proportional (P) and Proportional-Integral (P1) controllers and their adaptability to adequately control the arterial. A sensitivity analysis is further performed to justify the use of a feedback control system on arterials and to choose the type of controller best suited under given demand conditions. The simulation results indicated that for the studied arterial, the P1 controller can handle demand estimation and queuing better than P controllers. It was determined that a well designed feedback control system with a PI controller can effectively overcome some of the deficiencies of existing adaptive traffic control systems

Integrated Approach for Diversion Route Performance Management during Incidents

Non-recurrent congestion is one of the critical sources of congestion on the highway. In particular, traffic incidents create congestion in unexpected times and places that travelers do not prepare for. During incidents on freeways, route diversion has been proven to be a useful tactic to mitigate non-recurrent congestion. However, the capacity constraints created by the signals on the alternative routes put limits on the diversion process since the typical time-of-day signal control cannot handle the sudden increase in the traffic on the arterials due to diversion. Thus, there is a need for proactive strategies for the management of the diversion routes performance and for coordinated freeway and arterial (CFA) operation during incidents on the freeway. Proactive strategies provide better opportunities for both the agency and the traveler to make and implement decisions to improve performance. This dissertation develops a methodology for the performance management of diversion routes through integrating freeway and arterials operation during incidents on the freeway. The methodology includes the identification of potential diversion routes for freeway incidents and the generation and implementation of special signal plans under different incident and traffic conditions. The study utilizes machine learning, data analytics, multi-resolution modeling, and multi-objective optimization for this purpose. A data analytic approach based on the long short term memory (LSTM) deep neural network method is used to predict the utilized alternative routes dynamically using incident attributes and traffic status on the freeway and travel time on both the freeway and alternative routes during the incident. Then, a combination of clustering analysis, multi- resolution modeling (MRM), and multi-objective optimization techniques are used to develop and activate special signal plans on the identified alternative routes. The developed methods use data from different sources, including connected vehicle (CV) data and high- resolution controller (HRC) data for congestion patterns identification at the critical intersections on the alternative routes and signal plans generation. The results indicate that implementing signal timing plans to better accommodate the diverted traffic can improve the performance of the diverted traffic without significantly deteriorating other movements\u27 performance at the intersection. The findings show the importance of using data from emerging sources in developing plans to improve the performance of the diversion routes and ensure CFA operation with higher effectiveness

Continuous and Discrete-Time Optimal Controls for an Isolated Signalized Intersection

A classical control problem for an isolated oversaturated intersection is revisited with a focus on the optimal control policy to minimize total delay. The difference and connection between existing continuous-time planning models and recently proposed discrete-time planning models are studied. A gradient descent algorithm is proposed to convert the optimal control plan of the continuous-time model to the plan of the discrete-time model in many cases. Analytic proof and numerical tests for the algorithm are also presented. The findings shed light on the links between two kinds of models

Models for dynamic network loading and algorithms for traffic signal synchronization

The effectiveness of optimization strongly relies on the underlying model of the phenomenon. According to this, a considerable effort has been spent in improving the General Link Transmission Model (Gentile, 2008) to address urban networks, intersection and lane modelling and multimodal simulation. A genetic algorithm with a formulation tailored on the signal coordination problem has been integrated with the simulation engine. So, a practical and effective multi-objective optimization tool for traffic signal coordination is here presented

Models for dynamic network loading and algorithms for traffic signal synchronization

The effectiveness of optimization strongly relies on the underlying model of the phenomenon. According to this, a considerable effort has been spent in improving the General Link Transmission Model (Gentile, 2008) to address urban networks, intersection and lane modelling and multimodal simulation. A genetic algorithm with a formulation tailored on the signal coordination problem has been integrated with the simulation engine. So, a practical and effective multi-objective optimization tool for traffic signal coordination is here presented