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The paper proposes a traffic responsive control framework based on a Model Predictive Control (MPC) approach. The framework focuses on a centralized method, which can simultaneously compute the network decision variables (i.e., the green timings at each junction and the offset of the traffic light plans of the network). Furthermore, the framework is based on a hybrid traffic flow model operating as a prediction model and plant model in the control procedure. The hybrid traffic flow model combines two sub-models: an aggregate model (i.e., the Cell Transmission Model; CTM) and a disaggregate model (i.e., the Cellular Automata model; CA), using a transition cell to connect them. The whole framework is tested on a signalized arterial, performing several analyses to calibrate the MPC strategy and evaluate the traffic control approach using fixed and adaptive control strategies. All analyses are made in terms of total time spent, network total delay, queue lengths and degree of saturation. [ABSTRACT FROM AUTHOR]

To contend traffic congestion on urban networks, existing studies have made great efforts to develop traffic-responsive signal timing algorithms in the last decade. More recently, as an alternative to conventional model-based algorithms, machine learning-based methods have been tested on traffic light timing problems and show promising potentials. However, many researchers and practitioners still questioned the feasibility and applicability of adopting machine learning techniques in the ATSC domain. One of the reasons is that these methods assumed flawless detectors and heavily relied on simulators for training and evaluations. To address such a critical concern, this article customizes a Deep Q-learning Learning (DQL) method to optimize traffic signal timings at urban intersections, where the partial observations from identity-based detectors are inputs, and the green splits are outputs. A simulation-free data-driven prediction model is also developed to train the DQL with reduced computational time. Then the machine learning-based methods are evaluated on a real-world case with Automatic Number-Plate Recognition (ANPR) data. Experiments show the proposed data-driven model can predict the traffic state in limited computational time, and the DQL algorithm is 3.9% better than the field experiment performance from the adaptive control system, SCOOT, and 22% better than the time-of-day plan by SYNCHRO. The results indicate the DQL methods can only yield marginal improvement with restrictive input and output settings in congested traffic flow in comparison to the conventional adaptive method. [ABSTRACT FROM AUTHOR]

String stability is important for understanding traffic flow dynamics, while its analytical study for mixed traffic is deficient. We focus on an analytical framework on string stability of mixed traffic consisting of cooperative adaptive cruise control (CACC), adaptive cruise control (ACC), and human vehicles. The analytical framework was conducted based on generalized car-following model formulations of the three vehicular types. Then string stability criterions of one-class traffic flow, mixed traffic flow, and local CACC/ACC platoons were derived. Taking into account the mixed flow with partial degenerations of CACC and three concrete car-following models, an example application was studied. The example application analyzed the string stability from the perspectives of the homogeneous flow, the mixed traffic flow, and the local CACC/ACC platoon, respectively. The example application also studied balance between stability and capacity for the mixed traffic with CACC vehicles. Results show the usefulness of the proposed analytical framework, in term of not only analyzing string stability but also providing suggestions for dynamic regulations of CACC/ACC management strategy to balance string stability and traffic capacity for the mixed CACC-human flow. [ABSTRACT FROM AUTHOR]