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High-voltage dc/dc converters play an important role in HVDC grids. Isolated modular dc/dc converters (IMDCCs) based on modular multilevel converter (MMC) technology provide a good solution to high-voltage applications. In order to reduce the size of the system, the IMDCC is required to be operated with a high ac link frequency, but this will lead to increased switching loss and thus degraded efficiency. This paper proposes a soft-switching operation scheme for such an IMDCC. In this scheme, a quasi-square-wave (QSW) modulation method is employed, where the chain-links generate quasi-square terminal voltages with reduced dv/dt. With such chain-link terminal voltages, the arm currents which provide good condition for the soft-switching operation of the QSW-IMDCC can be obtained. Since soft-switching can be achieved for the power switches, the proposed scheme will suffer less switching loss, thus improving the efficiency of the converter. Moreover, a capacitor voltage balancing control strategy is proposed. This strategy does not need any arm current sensors, thus reducing the cost. The proposed soft-switching operation scheme and capacitor voltage balancing control strategy are verified by the simulation results.

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Knowledge of vehicle headway distribution is very important for intelligent transportation and intelligent vehicle simulations. Various headway distribution models have been proposed, but most of them only fit for a certain traffic situation. To solve this problem, we study the dependence of headway distributions on traffic status in this paper. Results show that the log-normal distribution model is adequate in fitting headway data when the traffic is in free flow status; while the log-logistic distribution model is more suitable in fitting headway data when the traffic is in congestion status. This conclusion is useful in the traffic signal optimization algorithm, since it indicates that we should apply different delay estimation models during different traffic status so as to design optimal timing plan.

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Pedestrian safety is increasingly recognized as a major public health concern. Extensive safety studies have been conducted to examine the influence of multiple variables on the occurrence of pedestrian-vehicle crashes. However, the explicit relationship between pedestrian safety and road network characteristics remains unknown. This study particularly focused on the role of different road network patterns on the occurrence of crashes involving pedestrians. A global integration index via space syntax was introduced to quantify the topological structures of road networks. The Bayesian Poisson-lognormal (PLN) models with conditional autoregressive (CAR) prior were then developed via three different proximity structures: contiguity, geometry-centroid distance, and road network connectivity. The models were also compared with the PLN counterpart without spatial correlation effects. The analysis was based on a comprehensive crash dataset from 131 selected traffic analysis zones in Hong Kong. The results indicated that higher global integration was associated with more pedestrian-vehicle crashes; the irregular pattern network was proved to be safest in terms of pedestrian crash occurrences, whereas the grid pattern was the least safe; the CAR model with a neighborhood structure based on road network connectivity was found to outperform in model goodness-of-fit, implying the importance of accurately accounting for spatial correlation when modeling spatially aggregated crash data.

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