This paper presents a distributed optimization procedure for the cooperative eco-driving control problem of a platoon of electric vehicles subject to safety and travel time constraints. Individual optimal trajectories are generated for each platoon member to account for heterogeneous vehicles and for the road slope. By rearranging the problem variables, the Riccati recursion can be applied along the chain-like structure of the platoon and be used to solve the problem by repeatedly transmitting information up and down the platoon. Since each vehicle is only responsible for its own part of the computations, the proposed control strategy is privacy-preserving and could therefore be deployed by any group of vehicles to form a platoon spontaneously while driving. The energy efficiency of this control strategy is evaluated in numerical experiments for platoons of electric trucks with different masses and rated motor powers.

Estimating traffic flow states at unmeasured urban locations provides a cost-efficient solution for many ITS applications. In this work, a geostatistical framework, kriging is extended in such a way that it can both estimate and predict traffic volume and speed at various unobserved locations, in real-time. In the paper, different distance metrics for kriging are evaluated. Then, a new, data-driven one is formulated, capturing the similarity of measurement sites. Then, with multidimensional scaling the distances are transformed into a hyperspace, where the kriging algorithm can be used. As a next step, temporal dependency is injected into the estimator via extending the hyperspace with an extra dimension, enabling for short horizon traffic flow prediction. Additionally, a temporal correction is proposed to compensate for minor changes in traffic flow patterns. Numerical results suggest that the spatio-temporal prediction can make more accurate predictions compared to other distance metric-based kriging algorithms. Additionally, compared to deep learning, the results are on par while the algorithm is more resilient against traffic pattern changes.

Most complex machine learning and modelling techniques are prone to over-fitting and may subsequently generalise poorly to future data. Artificial neural networks are no different in this regard and, despite having a level of implicit regularisation when trained with gradient descent, often require the aid of explicit regularisers. We introduce a new framework, Model Gradient Similarity (MGS), that (1) serves as a metric of regularisation, which can be used to monitor neural network training, (2) adds insight into how explicit regularisers, while derived from widely different principles, operate via the same mechanism underneath by increasing MGS, and (3) provides the basis for a new regularisation scheme which exhibits excellent performance, especially in challenging settings such as high levels of label noise or limited sample sizes.

In order to alleviate the range anxiety of electric vehicle users (EVUs), several researches focus on facilitating the efficiency of fast-electric vehicle charging stations (fast-EVCSs) using artificial intelligence (AI). This paper first proposes a fast-EVCS revenue maximization pricing policy using an AI approach, and we argue that the AI algorithm can learn to abuse EVUs information for maximizing its revenue. In order to investigate the hypothesis, firstly, a simulation environment is developed using vehicle performance models and an EVU’s charging station selection game. Then, we formulate the charging station revenue maximization problem as a Markov decision process (MDP) and propose a personalized dynamic pricing policy using a model-free reinforcement learning (RL) algorithm. From numerical simulation results, it is found that if the RL approach focuses solely on increasing revenue of the fast-EVCSs, it can learn to misuse personal information without any human intervention. To prevent such abuse, we suggest intuitive guidelines for policymakers and urban planners via numerical experiments.

The placement of electric vehicle charging stations (EVCSs), which encourages the rapid development of electric vehicles (EVs), should be considered from not only operational perspective such as minimizing installation costs, but also user perspective so that their strategic and competitive charging behaviors can be reflected. This paper proposes a methodological framework to consider crowdedness and individual preferences of electric vehicle users (EVUs) in the selection of locations for fast-charging stations. The electric vehicle charging station placement problem (EVCSPP) is solved via a decentralized game theoretical decision-making algorithm and$k$-means clustering algorithm. The proposed algorithm, referred to as$k$-GRAPE, determines the locations of charging stations to maximize the sum of utilities of EVUs. In particular, we analytically present that 50% of suboptimality of the solution can be at least guaranteed, which is about 17% better than the existing game theoretical based framework. We show a few variants to describe the utility functions that may capture the difference in preferences of EVUs. Finally, we demonstrate the viability of the decision framework via three real-world data-based experiments. The results of the experiments, including a comparison with a baseline method are then discussed.

The problems of bus bunching mitigation and the energy management of groups of vehicles have traditionally been treated separately in the literature and been formulated in two different frameworks. The present work bridges this gap by formulating the optimal control problem of the bus line eco-driving and regularity control as a smooth, multi-objectivenonlinear program. Since this nonlinear program has only a few coupling variables, it is shown how it can be solved in parallel aboard each bus, such that only a marginal amount of computations need to be carried out centrally. This procedure leverages the structure of the bus line by enabling parallel computations and reducing the communication loads between the buses, which makes the problem resolution scalable in terms of the number of buses. Closed-loop control is then achieved by embedding this procedure in amodel predictive control. Stochastic simulations based on real passengers and travel times data are realized for several scenarios with different levels of bunching for a line of electric buses. Our method achieves fast recoveries to regular headways as well as energy savings of up to 9.3% when compared with traditional holding or speed control baselines.

Considered in this paper is a novel model-based, coordinated ramp metering strategy. It aims at maximizing the discharge flow in motorway networks by minimizing the divergence of the traffic density from its critical value caused by unknown demand flow. The suggested synthesis algorithm casts the traffic control objective into the form of an induced$\mathcal {L}_{2}$-norm minimization problem. Hence, we aim at rejecting the effect of disturbance on the overall network performance output while the ramp input flow is subjected to constraints. With such a problem formulation, it is not required to know the disturbance input in order to find the proper control input. Without any central decision unit (traffic control center), ramp meters coordinate by sharing their local variables with solely their neighbor units (upstream and downstream) to achieve the global performance goal. Under some network symmetry conditions, a compositionally inexpensive distributed flow control method is suggested to address scalability issues. The method is implemented in simulation environment and compared with other control algorithms in two comprehensive case studies.

City-wide control and coordination of traffic flow can improve efficiency, fuel consumption, and safety. We consider the problem of controlling traffic lights under fixed and adaptive routing of vehicles in urban road networks. Multicommodity back-pressure algorithms, originally developed for routing and scheduling in communication networks, are applied to road networks to control traffic lights and adaptively reroute vehicles. The performance of the algorithms is analyzed using a microscopic traffic simulator. The results demonstrate that the proposed multicommodity and adaptive routing algorithms provide significant improvement over a fixed schedule controller and a single-commodity back-pressure controller in terms of various performance metrics, including queue length, trips completed, travel times, and fair traffic distribution.

The aim of the presented research is to elaborate a traffic-responsive optimal signal split algorithm taking uncertainty into account. The traffic control objective is to minimize the weighted link queue lengths within an urban network area. The control problem is formulated in a centralized rolling-horizon fashion in which unknown but bounded demand and queue uncertainty influences the prediction. An efficient constrained minimax optimization is suggested to obtain the green time combination, which minimizes the objective function when worst case uncertainty appears. As an illustrative example, a simulation study is carried out to demonstrate the effectiveness and computational feasibility of the robust predictive approach. By using real-world traffic data and microscopic traffic simulator, the proposed robust signal split algorithm is analyzed and compared with well-tuned fixed-time signal timing and to nominal predictive solutions under different traffic conditions.