Surrogate model for real time signal control: theories and applications

Traffic signal controls play a vital role in urban road traffic networks. Compared with fixed-time signal control, which is solely based on historical data, real time signal control is flexible and responsive to varying traffic conditions, and hence promises better performance and robustness in managing traffic congestion. Real time signal control can be divided into model-based and model-free approaches. The former requires a traffic model (analytical or simulation-based) in the generation, optimisation and evaluation of signal control plans, which means that its efficacy in real-world deployment depends on the validity and accuracy of the underlying traffic model. Model-free real time signal control, on the other hand, is constructed based on expert experience and empirical observations. Most of the existing model-free real time signal controls, however, focus on learning-based and rule-based approaches, and either lack interpretability or are non-optimised. This thesis proposes a surrogate-based real time signal control and optimisation framework, that can determine signal decisions in a centralised manner without the use of any traffic model. Surrogate models offer analytical and efficient approximations of complex models or black-box processes by fitting their input-output structures with appropriate mathematical tools. Current research on surrogate-based optimisation is limited to strategic and off-line optimisation, which only approximates the relationship between decisions and outputs under highly specific conditions based on certain traffic simulation models and is still to be attempted for real time optimisation. This thesis proposes a framework for surrogate-based real time signal control, by constructing a response surface that encompasses, (1) traffic states, (2) control parameters, and (3) network performance indicators at the same time. A series of comprehensive evaluations are conducted to assess the effectiveness, robustness and computational efficiency of the surrogate-based real time signal control. In the numerical test, the Kriging model is selected to approximate the traffic dynamics of the test network. The results show that this Kriging-based real time signal control can increase the total throughput by 5.3% and reduce the average delay by 8.1% compared with the fixed-time baseline signal plan. In addition, the optimisation time can be reduced by more than 99% if the simulation model is replaced by a Kriging model. The proposed signal controller is further investigated via multi-scenario analyses involving different levels of information availability, network saturation and traffic uncertainty, which shows the robustness and reliability of the controller. Moreover, the influence of the baseline signal on the Kriging-based signal control can be eliminated by a series of off-line updates. By virtue of the model-free nature and the adaptive learning capability of the surrogate model, the Kriging-based real time signal control can adapt to systematic network changes (such as seasonal variations in traffic demand). The adaptive Kriging-based real time signal control can update the response surface according to the feedback from the actual traffic environment. The test results show that the adaptive Kriging-based real time signal control maintains the signal control performance better in response to systematic network changes than either fixed-time signal control or non-adaptive Kriging-based signal control.Open Acces

Macroscopic modelling and robust control of bi-modal multi-region urban road networks

The paper concerns the integration of a bi-modal Macroscopic Fundamental Diagram (MFD) modelling for mixed traffic in a robust control framework for congested single- and multi-region urban networks. The bi-modal MFD relates the accumulation of cars and buses and the outflow (or circulating flow) in homogeneous (both in the spatial distribution of congestion and the spatial mode mixture) bi-modal traffic networks. We introduce the composition of traffic in the network as a parameter that affects the shape of the bi-modal MFD. A linear parameter varying model with uncertain parameter the vehicle composition approximates the original nonlinear system of aggregated dynamics when it is near the equilibrium point for single- and multi-region cities governed by bi-modal MFDs. This model aims at designing a robust perimeter and boundary flow controller for single- and multi-region networks that guarantees robust regulation and stability, and thus smooth and efficient operations, given that vehicle composition is a slow time-varying parameter. The control gain of the robust controller is calculated off-line using convex optimisation. To evaluate the proposed scheme, an extensive simulation-based study for single- and multi-region networks is carried out. To this end, the heterogeneous network of San Francisco where buses and cars share the same infrastructure is partitioned into two homogeneous regions with different modes of composition. The proposed robust control is compared with an optimised pre-timed signal plan and a single-region perimeter control strategy. Results show that the proposed robust control can significantly: (i) reduce the overall congestion in the network; (ii) improve the traffic performance of buses in terms of travel delays and schedule reliability, and; (iii) avoid queues and gridlocks on critical paths of the network

An Efficient Requirement-Aware Attachment Policy for Future Millimeter Wave Vehicular Networks

The automotive industry is rapidly evolving towards connected and autonomous vehicles, whose ever more stringent data traffic requirements might exceed the capacity of traditional technologies for vehicular networks. In this scenario, densely deploying millimeter wave (mmWave) base stations is a promising approach to provide very high transmission speeds to the vehicles. However, mmWave signals suffer from high path and penetration losses which might render the communication unreliable and discontinuous. Coexistence between mmWave and Long Term Evolution (LTE) communication systems has therefore been considered to guarantee increased capacity and robustness through heterogeneous networking. Following this rationale, we face the challenge of designing fair and efficient attachment policies in heterogeneous vehicular networks. Traditional methods based on received signal quality criteria lack consideration of the vehicle's individual requirements and traffic demands, and lead to suboptimal resource allocation across the network. In this paper we propose a Quality-of-Service (QoS) aware attachment scheme which biases the cell selection as a function of the vehicular service requirements, preventing the overload of transmission links. Our simulations demonstrate that the proposed strategy significantly improves the percentage of vehicles satisfying application requirements and delivers efficient and fair association compared to state-of-the-art schemes.Comment: 8 pages, 8 figures, 2 tables, accepted to the 30th IEEE Intelligent Vehicles Symposiu

Social Equity Impacts of Congestion Management Strategies

This white paper examines the social equity impacts of various congestion management strategies. The paper includes a comprehensive list of 30 congestion management strategies and a discussion of equity implications related to each strategy. The authors analyze existing literature and incorporate findings from 12 expert interviews from academic, non-governmental organization (NGO), public, and private sector respondents to strengthen results and fill gaps in understanding. The literature review applies the Spatial – Temporal – Economic – Physiological – Social (STEPS) Equity Framework (Shaheen et al., 2017) to identify impacts and classify whether social equity barriers are reduced, exacerbated, or both by a particular congestion mitigation measure. The congestion management strategies discussed are grouped into six main categories, including: 1) pricing, 2) parking and curb policies, 3) operational strategies, 4) infrastructure changes, 5) transportation services and strategies, and 6) conventional taxation. The findings show that the social equity impacts of certain congestion management strategies are not well understood, at present, and further empirical research is needed. Congestion mitigation measures have the potential to affect travel costs, commute times, housing, and accessibility in ways that are distinctly positive or negative for different populations. For these reasons, social equity implications of congestion management strategies should be understood and mitigated for in planning and implementation of these strategies