Variability in spatial accessibility of emergency medical services has become a major concern in evaluating the quality of emergency medical services in China. Unlike some other public services, response time is critical in the provision of emergency medical services. Traffic congestion may significantly affect response time, especially in large cities. This study uses a transportation simulation model to estimate the travel time under free-flow and congested road conditions and measure the corresponding spatial accessibility of emergency medical services for various hours of a day in inner-city Shanghai. When traffic congestion is considered, the overall spatial accessibility is significantly reduced, and the effect is further magnified in certain congested areas. The results help policy makers in planning the emergency medical services resource that is sensitive to the spatiotemporal variation of its accessibility.

Mobile cyber-physical systems (CPSs) are very hard to verify, because of asynchronous communication and the arbitrary number of components. Verification via model checking typically becomes impracticable due to the state space explosion caused by the system parameters and concurrency. In this paper, we propose a formal approach to verify the safety properties of parameterized protocols in mobile CPS. By using counter abstraction, the protocol is modeled as a Petri net. Then, a novel algorithm, which uses IC3 (the state-of-the-art model checking algorithm) as the back-end engine, is presented to verify the Petri net model. The experimental results show that our new approach can greatly scale the verification capabilities compared favorably against several recently published approaches. In addition to solving the instances fast, our method is significant for its lower memory consumption.

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Driving is an integral part of our everyday lives, but it is also a time when people are uniquely vulnerable. Previous research has demonstrated that not only does listening to suitable music while driving not impair driving performance, but it could lead to an improved mood and a more relaxed body state, which could improve driving performance and promote safe driving significantly. In this article, we propose SAfeDJ, a smartphone-based situation-aware music recommendation system, which is designed to turn driving into a safe and enjoyable experience. SAfeDJ aims at helping drivers to diminish fatigue and negative emotion. Its design is based on novel interactive methods, which enable in-car smartphones to orchestrate multiple sources of sensing data and the drivers' social context, in collaboration with cloud computing to form a seamless crowdsensing solution. This solution enables different smartphones to collaboratively recommend preferable music to drivers according to each driver's specific situations in an automated and intelligent manner. Practical experiments of SAfeDJ have proved its effectiveness in music-mood analysis, and moodfatigue detections of drivers with reasonable computation and communication overheads on smartphones. Also, our user studies have demonstrated that SAfeDJ helps to decrease fatigue degree and negative mood degree of drivers by 49.09% and 36.35%, respectively, compared to traditional smartphone-based music player under similar driving situations.

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