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Previous studies show that the popularity of bike-sharing systems has a significant impact on public transit ridership, mainly reflected in two aspects: substitution and supplement. However, a mode choice of the users between bike-sharing and bus for the last-mile connection to urban rails has not been sufficiently studied using actual traffic sensor data. Therefore, in this study, the binomial choice behavior between bike-sharing and buses as a last-mile connection to urban rails in Beijing, China, is analyzed and modeled using the feeder subchain information of bus and bike-sharing for 1 week that is extracted from transit smart card data, cycling record data, and station location information data. We specifically focus on the moderate distance (0.5–3 km), within which distance the bike-sharing is a rival and suitable alternative to the bus. A binomial logit model is constructed to examine the mode choice between buses and shared bikes as a last-mile connection to urban rail. In addition to the general explanatory variables (i.e., travel distance, travel time, and cost), built environments such as housing density, land uses, and bike-lane conditions are also considered in our study. Results show that the walking distance from the metro station has a stronger effect on the users’ mode choice than the total subchain distance. A longer travel time increases the preference for taking the bus. We also find that users are most likely to choose the bus in the evening rush hours, followed by off peak times and the morning rush hours. The existence of bike lanes, especially with isolation belts alongside them, increases the probability that people choose shared bikes. As bus and bike-sharing are the two most popular and economically competitive urban rail egress modes for a moderate distance in China, understanding the determinants of mode choice contributes to better planning and operational strategies for both feeder bus and bike-sharing systems. [ABSTRACT FROM AUTHOR]

Under the background of "carbon peaking and carbon neutrality goals" in China, the Highway Self-Consistent Energy System (HSCES) with renewable energy as the main body has become a key research object. To study the operational status of the HSCES in a specific region and realize the economically optimal operation of the HSCES, an HSCES model in a low-load, abundant-renewable-energy and no-grid scenario is established, and a two-stage optimal scheduling method for the HSCES is proposed. Moreover, in the day-ahead stage, uncertainty optimization scenarios are generated by Latin hypercube sampling, and a definition of the self-consistent coefficient is proposed, which is used as one of the constraints to establish a day-ahead economic optimal scheduling model. Through the case comparison analysis, the validity of the day-ahead scheduling model is confirmed and the optimal day-ahead scheduling plan is attained. Furthermore, in the intra-day stage, an intra-day rolling optimization method is proposed, which can effectively track the day-ahead scheduling plan and reduce the impact of forecast errors and energy fluctuations by coordinating the unit output within the HSCES system. It is verified that the HSCES can operate economically and safely in Western China, and self-consistently, without grid support. [ABSTRACT FROM AUTHOR]

As the power generation mode with the lowest carbon emissions, wind power generation plays an indispensable role in achieving the goal of carbon neutralization. To optimize the wind power density (WPD), forecasting is crucial to improve wind power utilization and power system stability. However, because near-surface wind is characterized by notable randomness, diversity, intermittence, and uncontrollability, accurately forecasting the WPD on wind farms remains a challenging task. In this study, we attempted to improve the WPD forecast in the middle- and high-latitude regions of China (wind energy resources are abundant there) by selecting the relatively optimal planetary boundary layer (PBL) scheme, as the PBL processes exert notable effects on the near-surface wind field directly. Based on a whole month in the summer (July 2021), seven PBL schemes were compared quantitatively by using the Weather Research and Forecasting (WRF) model for a total of 70 runs (for each run, the forecast period was 3 days). The results show that no PBL schemes could always show the best performance in forecasting all variables, and the forecast accuracy showed a notable dependence on the evolution of the weather systems. Among the seven PBL schemes, the Medium-Range Forecast (MRF) scheme showed the overall best performance in forecasting the 100 m wind speed, sea level pressure, and 2 m temperature, which ensured that it had the highest forecast skill for the WPD in the middle- and high-latitude regions of China. Further analyses indicate that the background conditions were also well forecasted by the MRF scheme (ranked first or second). This was a crucial reason why the WPD forecast was the best for the MRF scheme. [ABSTRACT FROM AUTHOR]