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One of the core risk management tasks is to identify hidden high-risky states that may lead to system breakdown, which can provide valuable early warning knowledge. However, due to high dimensionality and nonlinear interaction embedded in large-scale complex systems like urban traffic, it remains challenging to identify hidden high-risky states from huge system state space where over 99% of possible system states are not yet visited in empirical data. Based on maximum entropy model, we infer the underlying interaction network from complicated dynamical processes of urban traffic, and construct system energy landscape. In this way, we can locate hidden high-risky states that have never been observed from real data. These states can serve as risk signals with high probability of entering hazardous minima in energy landscape, which lead to huge recovery cost. Our finding might provide insights for complex system risk management.

Shared electric vehicles (SEVs) have emerged as a promising solution to contribute to sustainable urban mobility. However, ensuring the efficient operation and effective battery management of SEV systems remains a complex challenge. This challenge stems from factors such as slow plug-in charging, the potential role of SEVs in balancing grid load pressure, and the optimization of SEV operations to ensure their economic viability. To tackle these challenges, this paper introduces an integrated strategy for optimizing various aspects of SEV systems, encompassing strategies like Vehicle-to-Grid (V2G), Battery-to-Grid (B2G), and battery swapping. This approach is built on a space-time-energy network model that facilitates the optimization of battery charging and discharging scheduling, SEV operations like relocations and battery swapping, battery swapping station selection and the number of batteries. The objective of this approach is to maximize profits while addressing operational constraints and the complexities of energy management within SEV systems. Given the substantial complexity that arises with large-problem scales, the paper introduces a column generation-based heuristic algorithm. Extensive experimental validation is conducted, including sensitivity analysis on different charging speeds and fleet sizes. The results illuminate the impact of varying charging rates and fleet sizes on performance indicators. Notably, it is observed that battery swapping is particularly effective as an auxiliary charging method when the number of vehicles is limited. Conversely, in scenarios with a large fleet, the necessity for battery swapping diminishes. Moreover, results show the effectiveness of V2G and B2G technologies in grid load balancing.

The importance of understanding human mobility patterns has led many studies to examine their spatial-temporal scaling laws. These studies mainly reveal that human travel can be highly non-homogeneous with power-law scaling distributions of distances and times. However, investigating and quantifying the extent of variability in time and space when traveling the same air distance has not been addressed so far. Using taxi data from five large cities, we focus on several novel measures of distance and time to explore the spatio-temporal variations of taxi travel routes relative to their typical routes during peak and nonpeak periods. To compare all trips using a single measure, we calculate the distributions of the ratios between actual travel distances and the average travel distance as well as between actual travel times and the average travel time for all origin destinations (OD) during peak and nonpeak periods. In this way, we measure the scaling of the distribution of all single trip paths with respect to their mean trip path. Our results surprisingly demonstrate very broad distributions for both the distance ratio and time ratio, characterized by a long-tail power-law distribution. Moreover, all analyzed cities have larger exponents in peak hours than in nonpeak hours. We suggest that the interesting results of shorter trip lengths and times, characterized by larger exponents during rush hours, are due to the higher availability of travelers in rush hours compared to non-rush hours...