The Internet of Vehicles (IoV) connects roadside units, vehicles, and pedestrians into an efficient communication network. However, ensuring honesty among vehicles in such a decentralized system remains a challenge, especially for intersection management. To address this issue, we propose a blockchain-based IoV credit system. Drivers’ rushing behavior is a primary contributor to accidents at intersections. To solve this problem and enhance traffic efficiency, we propose a strategy that allows time-sensitive convoys to pass through an intersection ahead of others by paying traffic tokens via IoV while still complying with traffic regulations. To protect the privacy of the yielding vehicles, we develop an optimal pricing strategy for the rushing convoys using a dynamic game model with incomplete information, which is solved using a refined Bayesian Nash equilibrium. Furthermore, we introduce a blockchain consensus mechanism that employs a sequence chain to ensure a unique and consistent order for passing. Both theoretical analysis and simulations show that the proposed strategy is feasible and effective in meeting the practical need for low latency at intersections. It increases the likelihood of successful transactions, protects yielding convoys from lower benefits, and encourages broader participation.

Eye movement data convey a wealth of information that can be used to probe human behaviour and cognitive processes. To date, eye tracking studies have mainly focused on laboratory-based evaluations of cartographic interfaces; in contrast, little attention has been paid to eye movement data mining for real-world applications. In this study, we propose using machine-learning methods to infer user tasks from eye movement data in real-world pedestrian navigation scenarios. We conducted a real-world pedestrian navigation experiment in which we recorded eye movement data from 38 participants. We trained and cross-validated a random forest classifier for classifying five common navigation tasks using five types of eye movement features. The results show that the classifier can achieve an overall accuracy of 67%. We found that statistical eye movement features and saccade encoding features are more useful than the other investigated types of features for distinguishing user tasks. We also identified that the choice of classifier, the time window size and the eye movement features considered are all important factors that influence task inference performance. Results of the research open doors to some potential real-world innovative applications, such as navigation systems that can provide task-related information depending on the task a user is performing.

Motivated by many practical applications in logistics and mobility-as-a-service, we study the top-k optimal sequenced routes (KOSR) querying on large, general graphs where the edge weights may not satisfy the triangle inequality, e.g., road network graphs with travel times as edge weights. The KOSR querying strives to find the top-k optimal routes (i.e., with the top-k minimal total costs) from a given source to a given destination, which must visit a number of vertices with specific vertex categories (e.g., gas stations, restaurants, and shopping malls) in a particular order (e.g., visiting gas stations before restaurants and then shopping malls). To efficiently find the top-k optimal sequenced routes, we propose two algorithms PruningKOSR and StarKOSR. In PruningKOSR, we define a dominance relationship between two partially-explored routes. The partially-explored routes that can be dominated by other partially-explored routes are postponed being extended, which leads to a smaller searching space and thus improves efficiency. In StarKOSR, we further improve the efficiency by extending routes in an A manner. With the help of a judiciously designed heuristic estimation that works for general graphs, the cost of partially explored routes to the destination can be estimated such that the qualified complete routes can be found early. In addition, we demonstrate the high extensibility of the proposed algorithms by incorporating Hop Labeling, an effective label indexing technique for shortest path queries, to further improve efficiency. Extensive experiments on multiple real-world graphs demonstrate that the proposed methods significantly outperform the baseline method. Furthermore, when k = 1, StarKOSR also outperforms the state-of-the-art method for the optimal sequenced route queries.

The classical K Shortest Paths (KSP) problem, which identifies the k shortest paths in a directed graph, plays an important role in many application domains, such as providing alternative paths for vehicle routing services. However, the returned k shortest paths may be highly similar, i.e., sharing significant amounts of edges, thus adversely affecting service qualities. In this paper, we formalize the K Shortest Paths with Diversity (KSPD) problem that identifies top-k shortest paths such that the paths are dissimilar with each other and the total length of the paths is minimized. We first prove that the KSPD problem is NP-hard and then propose a generic greedy framework to solve the KSPD problem in the sense that (1) it supports a wide variety of path similarity metrics which are widely adopted in the literature and (2) it is also able to efficiently solve the traditional KSP problem if no path similarity metric is specified. The core of the framework includes the use of two judiciously designed lower bounds, where one is dependent on and the other one is independent on the chosen path similarity metric, which effectively reduces the search space and significantly improves efficiency. Empirical studies on 5 real-world and synthetic graphs and 5 different path similarity metrics offer insight into the design properties of the proposed general framework and offer evidence that the proposed lower bounds are effective.

The classical K Shortest Paths (KSP) problem, which identifies the k shortest paths in a directed graph, plays an important role in many application domains, such as providing alternative paths for vehicle routing services. However, the returned k shortest paths may be highly similar, i.e., sharing significant amounts of edges, thus adversely affecting service qualities. In this paper, we formalize the K Shortest Paths with Diversity (KSPD) problem that identifies top-k shortest paths such that the paths are dissimilarwith each other and the total length of the paths is minimized. We first prove that the KSPD problem is NP-hard and then propose a generic greedy framework to solve the KSPD problem in the sense that (1) it supports a wide variety of path similarity metrics which are widely adopted in the literature and (2) it is also able to efficiently solve the traditional KSP problem if no path similarity metric is specified. The core of the framework includes the use of two judiciously designed lower bounds, where one is dependent on and the other one is independent on the chosen path similarity metric, which effectively reduces the search space and significantly improves efficiency. Empirical studies on five real-world and synthetic graphs and five different path similarity metrics offer insight into the design properties of the proposed general framework and offer evidence that the proposed lower bounds are effective.

Bare-bones Particle Swarm Optimization (BPSO) is a simplified PSO variant, which has shown potential performance on many multimodal optimization problems. However, BPSO is also possible to be trapped into local optima for high-dimensional and complicated optimization problems. In order to enhance the performance of BPSO, this paper presents a modified BPSO, called NMBPSO. It combined the ideas of the traditional PSO and a modified BPSO to improve the capacity of balancing exploration and exploitation during the search process. To verify the effect and benefit of the proposed algorithm, a set of well known benchmark functions are employed and compared against some competitive PSO variants. Experiment results indicate that NMBPSO performs better than the traditional PSO, BPSO and a modified BPSO algorithm.

With higher spatial resolution, the image classification of remote sensing is always a hot research field. Besides spectral information, texture information from remote sensing image of higher spatial resolution has become an important data source to improve the classification accuracy. The image classification approach adopts an improved neural network, which contains two steps connected by the refusal principle. Two steps of input neurons are spectral information, using 3times3 window size, and texture information from gray co-occurrence matrix, which is selected by the genetic algorithms. The final result which is to overlay of above results get higher accuracy that the traditional method that ANN combine simply all of information from different source as input neurons.

We select Beijing City as the study area, focusing on the urban expansion in the past decade based on remotely sensed data (TM and SPOT of year 1997, 2001, 2004 and 2007) and GIS technology. Land use change is detected by means of post classification comparison. We obtain the distribution map of urban land during three periods for ten years. Then, spatial model of urban expansion is analyzed by GIS technology. Markov chains is used to gain the percentage for each type of land use convert to urban land; and urban expansion in given size of window is discussed. The main results are: 1) The work flow of multi-temporal land use change detection proved to be efficient. The outcomes were maps of land use pattern, urban ratio and expansion intensity maps; 2) From central city to suburb, the land use variation takes on strong spatio-temporal change. Urban expansion cores and traffic are two urban expansion spatial influence factors.