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Plug-in electric bus (PEB) is an environmentally friendly mode of public transportation and plug-in electric bus fast charging stations (PEBFCSs) play an essential role in the operation of PEBs. Under effective control, deploying an energy storage system (ESS) within a PEBFCS can reduce the peak charging loads and the electricity purchase costs. To deal with the (integrated) scheduling problem of (PEBs charging and) ESS charging and discharging, in this study, we propose an optimal real-time coordinated charging and discharging strategy for a PEBFCS with ESS to achieve maximum economic benefits. According to whether the PEB charging loads are controllable, the corresponding mathematical models are respectively established under two scenarios, i.e., coordinated PEB charging scenario and uncoordinated PEB charging scenario. The price and lifespan of ESS, the capacity charge of PEBFCS and the electricity price arbitrage are considered in the models. Further, under the coordinated PEB charging scenario, a heuristics-based method is developed to get the approximately optimal strategy with computation efficiency dramatically enhanced. Finally, we validate the effectiveness of the proposed strategies, interpret the effect of ESS prices on the usage of ESS, and provide the sensitivity analysis of ESS capacity through the case studies.

This study proposes a deep generative adversarial architecture (GAA) for network-wide spatialtemporal traffic state estimation. The GAA is able to combine traffic flow theory with neural networks and thus improve the accuracy of traffic state estimation. It consists of two Long Short-Term Memory Neural Networks (LSTM NNs) which capture correlation in time and space among traffic flow and traffic density. One of the LSTM NNs, called a discriminative network, aims to maximize the probability of assigning correct labels to both true traffic state matrices (i.e., traffic flow and traffic density within a given spatial-temporal area) and the traffic state matrices generated from the other neural network. The other LSTM NN, called a generative network, aims to generate traffic state matrices which maximize the probability that the discriminative network assigns true labels to them. The two LSTM NNs are trained simultaneously such that the trained generative network can generate traffic matrices similar to those in the training data set. Given a traffic state matrix with missing values, we use back-propagation on three defined loss functions to map the corrupted matrix to a latent space. The mapping vector is then passed through the pretrained generative network to estimate the missing values of the corrupted matrix. The proposed GAA is compared with the existing Bayesian network approach on loop detector data collected from Seattle, Washington and that collected from San Diego, California. Experimental results indicate that the GAA can achieve higher accuracy in traffic state estimation than the Bayesian network approach.Keywords: deep learning, generative adversarial architecture, Long Short-Term Memory Neural Network, traffic state estimation With the rapid development of Intelligent Transportation Systems (ITS), enriched real-time traffic data can be collected by various types of sensors, such as loop detectors, Global Positioning Systems (GPS) and Remote Traffic Microwave Sensors (RTMS). Strategies for traffic management and control, developed based on the application of such data, are often not as effective as expected due to lag time (2). In addition, traffic data at some spatial and temporal positions may be missing due to sensor disruption. Therefore, traffic state estimation has become an important research topic in the transportation area and attracted much attention. Existing studies for traffic state estimation can be divided into parametric approaches, nonparametric approaches and hybrid integration methods (3). In parametric approaches, model structure is predetermined based on certain theoretical assumptions and model parameters are calibrated from empirical data. Typical methods of this type include cell-transmission-model-based methods (4) and dynamic-trafficassignment-based methods (5). In nonparametric approaches, both model structure and parameters are not fixed. This type of approaches can be further divided into classical statistical models and Computational Intelligence (CI) models (6). Typical m...

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