Due to the rapid development of the sharing economy, shared bikes have become one of the most popular and convenient traveling tools in intelligent transport systems. Aiming to save the time spent on waiting for or searching bikes at bike stations, the operators of bike-sharing systems need to dynamically dispatch bikes. Predicting the number of bikes for each station can help to optimize the repository of bikes. The usage of bikes is affected by several uncertain factors, so bike number prediction becomes a challenging and difficult problem. To manage this problem, we propose an algorithm called bimodal Gaussian inhomogeneous Poisson (BGIP) to predict the number of bikes. The BGIP includes three steps. First, the inhomogeneous Poisson process is adopted to describe the process that people arrive at a bike station to pick up or return bikes. Second, the bimodal Gaussian function is used to describe the intensity function of inhomogeneous Poisson process. In order to dynamically uncover the changing trend in the usage state of bikes, we propose a method to measure the influences of external factors on the usage of bikes. Third, the number of bikes is predicted by calculating the mean usage of bikes on the basis of checking-out and checking-in sequences. Experiments demonstrated that our algorithm outperformed the baseline algorithms in solving the bike prediction problem: accurately predicting the number of bikes and determining whether there is at least one bike available at a bike station.

Shared bikes have become popular traveling tools in our daily life. The successful operation of bike sharing systems (BSS) can greatly promote energy saving in a city. In BSS, stations becoming empty or full is the main cause of customers failing to rent or return bikes. Some truck-based rebalancing strategies are proposed to solve this problem. However, there are still challenges around the relocation of bikes. The truck operating costs also need to be considered. In this paper, we propose a customer-oriented rebalancing strategy to solve this problem. In our strategy, two algorithms are proposed to ensure the whole system is balanced for as long as possible. The first algorithm calculates the optimal state of each station through the one-dimensional Random Walk Process with two absorption walls. Based on the derived optimal state of each station, the second algorithm recommends the station that has the largest difference between its current state and its optimal state to the customer. In addition, a simulation system of shared bikes based on the historical records of Bay Area Bikeshare is built to evaluate the performance of our proposed rebalancing strategy. The simulation results indicate that the proposed strategy is able to effectively decrease the imbalance in the system and increase the system’s performance compared with the truck-based methods.

In this paper we study the deployment of an Unmanned Aerial Vehicle (UAV) network that consists of multiple UAVs to provide emergent communication service for people who are trapped in a disaster area, where each UAV is equipped with a base station that has limited computing capacity and power supply, and thus can only serve a limited number of people. Unlike most existing studies that focused on homogeneous UAVs, we consider the deployment of heterogeneous UAVs where different UAVs have different computing capacities. We study a problem of deploying $K$ heterogeneous UAVs in the air to form a temporarily connected UAV network such that the network throughput – the number of users served by the UAVs, is maximized, subject to the constraint that the number of people served by each UAV is no greater than its service capacity. We then propose a novel $O(\sqrt{\frac{s}{K}})$ -approximation algorithm for the problem, where $s$ is a given positive integer with $1 \le s\le K$ , e.g., $s=3$ . We also devise an improved heuristic, based on the approximation algorithm. We finally evaluate the performance of the proposed algorithms. Experimental results show that the numbers of users served by UAVs in the solutions delivered by the proposed algorithms are increased by 25% than state-of-the-arts.

Feature selection plays a significant role in the field of data mining and machine learning to reduce the data dimension, speed up the model building process and improve algorithm performance. Tree growth algorithm (TGA) is a recent proposed population-based metaheuristic, which shows great power of search ability in solving optimization of continuous problems. However, TGA cannot be directly applied to feature selection problems. Also, we find that its efficiency still leave room for improvement. To tackle this problem, in this study, a novel improved TGA (iTGA) is proposed, which can resolve the feature selection problem efficiently. The main contribution includes, (1) a binary TGA is proposed to tackle the feature selection problems, (2) a linearly increasing parameter tuning mechanism is proposed to tune the parameter in TGA, (3) the evolutionary population dynamics (EPD) strategy is applied to improve the exploration and exploitation capabilities of TGA, (4) the efficiency of iTGA is evaluated on fifteen UCI benchmark datasets, the comprehensive results indicate that iTGA can resolve feature selection problems efficiently. Furthermore, the results of comparative experiments also verify the superiority of iTGA compared with other state-of-the-art methods.

Tourist attraction and tour route recommendation are the key research highlights in the field of smart tourism. Currently, the existing recommendation algorithms encounter certain problems when making decisions regarding tourist attractions and tour routes. This paper presents a smart tourism recommendation algorithm based on a cellular geospatial clustering and weighted collaborative filtering. The problems are analyzed and concluded, and then the research ideas and methods to solve the problems are introduced. Aimed at solving the problems, the tourist attraction recommendation model is set up based on a cellular geographic space generating model and a weighted collaborative filtering model. According to the matching degree between the tourists’ interest needs and tourist attraction feature attributes, a precise tourist attraction recommendation is obtained. In combination with the geospatial attributes of the tourist destination, the spatial adjacency clustering model based on the cellular space generating algorithm is set up, and then the weighted model is introduced for the collaborative filtering recommendation algorithm, which ensures that the recommendation result precisely matches the tourists’ needs. Providing precise results, the optimal tour route recommendation model based on the precise tourist attraction approach vector algorithm is set up. The approach vector algorithm is used to search the optimal route between two POIs under the condition of multivariate traffic modes to provide the tourists with the best motive benefits. To verify the feasibility and advantages of the algorithm, this paper designs a sample experiment and analyzes the resulting data to obtain the relevant conclusion.