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In this paper, a single-vehicle static partial repositioning problem (SPRP) is investigated, which distinguishes the user dissatisfaction generated by different stations. The overall objective of the SPRP is to minimize the weighted sum of the total operational time and the total absolute deviation from the target number of bikes at all stations. An iterated local search is developed to solve this problem. A novel loading and unloading quantity adjustment operator is proposed to further improve the quality of the solution. Experiments are conducted on a set of instances from 30 to 300 stations to demonstrate the effectiveness of the proposed customized solution algorithm as well as the adjustment operator. Using a small example, this paper also reveals that the unit penalty cost has an effect on the repositioning strategies. [ABSTRACT FROM AUTHOR]

Public security monitoring is a hot issue that the government and citizens pay close attention to. Multiobject tracking plays an important role in solving many problems for public security. Under crowded scenarios and emergency places, it is a challenging problem to predict and warn owing to the complexity of crowd intersection. There are still many deficiencies in the research of multiobject trajectory prediction, which mostly employ object detection and data association. Compared with the tremendous progress in object detection, data association still relied on hand-crafted constraints such as group, motion, and spatial proximity. Emergencies usually have the characteristics of mutation, target diversification, low illumination, or resolution, which makes multitarget tracking more difficult. In this paper, we harness the advance of the deep learning framework for data association in object tracking by jointly modeling pedestrian features. The proposed deep pedestrian tracking SSD-based model can pair and link pedestrian features in any two frames. The model was trained with open dataset, and the results, accuracy, and speed of the model were compared between normal and emergency or violent environment. The experimental results show that the tracking accuracy of mAP is higher than 95% both in normal and abnormal data sets and higher than that of the traditional detection algorithm. The detection speed of the normal data set is slightly higher than that of the abnormal data set. In general, the model has good tracking results and credibility for multitarget tracking in emergency environment. The research provides technical support for safety assurance and behavior monitoring in emergency environment. [ABSTRACT FROM AUTHOR]

Vehicular ad-hoc networks (VANETs) are the crucial part of intelligent transportation systems (ITS), which are brought to enhance the security, efficiency, and comfort of transportation. VANETs have aroused extensive attention in the world recently. One of the challenges in practice is real time and low delay, which strongly requires VANETs to be efficient. Existing schemes have properly solved the problem which is how to aggregate the signatures and verify the aggregated signature. However, few solutions are proposed to pinpoint all invalid signatures if existing. The algorithms that can find all invalid signatures are not efficient enough. Following consideration of the above deficiencies of existing approaches, this paper proposes a factorial bitwise divisions (FBD) algorithm and its optimized version and early-stopping factorial bitwise divisions (EFBD) algorithm. Both algorithms are parallel-friendly. Compared with the binary-based batch verification algorithm, the experimental results demonstrate that the proposed algorithms achieve better performance in both theory and practice at low invalid signatures' rate. Especially, in the parallel condition, when the number of invalid signatures is 1, the proposed algorithms cost only one aggregation-verification delay, while the comparison is more than log 2 n times. [ABSTRACT FROM AUTHOR]