Inferring work locations for users based on spatiotemporal data is important for real-world applications ranging from product recommendation, precise marketing, transportation scheduling to city planning. However, the problem of location inference based on urban surveillance data has not been explored. Therefore, a work location inference method was proposed for vehicle owners based on the data of traffic surveillance with sparse cameras. First, the urban traffic periphery data such as road networks and Point Of Interests (POIs) were collected, and the preprocessing method of road network matching was used to obtain a real road network with rich semantic information such as POIs and cameras. Second, the important parking areas, which mean the candidate work areas for the vehicles were obtained by clustering Origin-Destination (O-D) pairs extracted from vehicle trajectories. Third, using the constraint of the proposed in/out visiting time pattern, the most likely work area was selected from multiple area candidates. Finally, by using the obtained road network and the distribution of POIs in the road network, the vehicle's reachable POIs were extracted to further narrow the range of work location. The effectiveness of the proposed method was demonstrated by comprehensive experimental evaluations and case studies on a real-world traffic surveillance dataset of a provincial capital city.

Real-time urban traffic monitoring has become an important part of modern urban management, and traffic big data collected by video monitoring is wildly applied to urban management and traffic control. However, such huge citywide monitoring traffic big data is rarely used for urban traffic and urban computing research. The vehicle type mining and application analysis were implemented on the citywide monitoring traffic big data of a provincial capital city. Firstly, three types of vehicles with important influence on urban traffic:periodic private car, taxi and public commuter bus were defined. And the corresponding mining method for each type of vehicles was proposed. Experiments on 120 million vehicle records collected from 1704 video monitoring points in Jinan demonstrated the effectiveness of the proposed definitions and mining methods. Secondly, with four communities as examples, the residents' traffic modes and the relationships between the modes and the distribution of surrounding Points of Interest (POI) were mined and analyzed. Moreover, the potential applications of the urban traffic big data incorporated with POI in urban planning, demand forecasting and preference recommendation were explored.

Software guard is a dynamic tamper proofing technique. However, the guard cannot guarantee its own security and is easy to be bypassed or removed by hacker. This paper studied this problem and implemented a dynamic tamper proofing method combining three-thread architecture with software guard, which used improved three-thread structure to protect the guard security. Compared to the traditional three-thread protection technique, the improved three-thread structure increased the protection and difficulty to attack by the mutual watch and protection between remote thread and watch thread. The experimental results show that the software guard protected by the improved three-thread structure can not only prevent software tampering attacks but also prevent the attack to the guard itself effectively.