Industrial land is an indispensable strategic resource in urban development that plays an indispensable role in ensuring the industrial space of urban construction and development. Measuring and analyzing the eco-efficiency of industrial land utilization (ECILU) can provide insights into how to maximize the input–output ratio of industrial land and ensure the sustainable development of land resources and economies. Based on the undesirable output slacks-based measure (SBM) model, choosing land, capital, and labor as input indicators, and the industrial added value and carbon emissions as desirable and undesirable output indicators, this study measured the ECILUs in 78 cities and 13 metropolitan areas in four Chinese major economic zones from 2007 to 2018, analyzed their spatial and temporal evolution characteristics and regional differences, and constructed a Tobit regression model to test the influence mechanism of each variable on the ECILUs in different regions. This has important theoretical and practical significance for the Chinese government in formulating relevant policies and realizing the green utilization of urban land in the future. Empirical results showed that the ECILUs in most cities were low and that the differences between regions were large. The ECILU in the Western Economic Zone was relatively high, followed by the Eastern, Central, and Northeastern Economic Zones. According to the ECILU value and urban synergy degree of each metropolitan area, this study divided the 13 metropolitan areas into four categories. The regression analysis results showed that the variables had different effects on the ECILUs of all cities and the four economic zones in China. It is suggested that all economic zones should reinforce the optimization of industrial structure, control industrial pollutant discharge, and solve the phenomenon of labor surplus. The Eastern Zone should maintain the growth of its economy while focusing on soil quality. The Central Zone should focus on the efficient use of infrastructure, and the Western, Northeastern, and Central Zones should balance the green coverage area and the industrial land area to ensure the efficient use of urban industrial land.

The road-crossing of pedestrians at unsignalized crosswalks is a major concern for road safety. Previous studies focused on explaining of the mechanism underlying this behavior, but a framework of prediction is missing. To predict this behavior, only variables measured before the decision is made should be considered. To explore whether historical data is able to predict the behavior, this paper investigates pedestrians’ wait-or-go (WOG) behavior based on trajectory data and a machine learning method, both of which have been rarely applied by previous studies. The use of trajectory data enables the analysis of several influential factors related to moving characteristics, which are critical for pedestrians’ decision making. The framework based on machine learning, combined with trajectory data, achieves good explanatory power and predictability of pedestrians’ WOG behavior. Moreover, a possible application of this study is the prediction of pedestrian road-crossing intention in the context of autonomous cars.

This study investigates the spatiotemporal distribution of electric vehicle (EV) charging demands and operating efficiency of the charging system. The travel behavior of EV drivers is analyzed by considering the heterogeneity of range anxiety and bound rationality. Building on existing charging choice models, our more holistic perspective of charging demand distribution is obtained through multiagent system (MAS) modeling. In our study, the charging demand distribution in different areas is compared by considering two charging price schemes. The performance of the charging system is then evaluated based on three indicators, the charging request rejection rate, charging pile utilization rate, and charging load deviation, thereby verifying the effectiveness of our charging demand prediction model. This is done using multiagent-based simulations applied to the National Household Travel Survey (NHTS) 2017 dataset. The results are analyzed according to different key indicators, mainly indicating (a) the limitations of the time-of-use (TOU) pricing strategy in reducing the peak-valley difference, (b) the transferability of charging demand among different functional areas, and (c) that there is less demand for charging piles in the workplace, as users mainly rely on home charging. These results facilitate further analyses to help the design and operation of the EV charging infrastructure.