The GPS trajectories are rich with potential information that could be used to explore the regulation of traffic to serve the public. While that past approaches for short-term traffic prediction have existed for some time, emerging smart transportation technologies require the traffic prediction capability to be both fast and scalable to full urban networks. In this paper, we propose a novel Neural Network with spatial-temporal correlations, named L-CNN based on CNN and LSTM, and develop an effective real-time prediction model to forecast the most likely potential passenger for taxi drivers. It is noteworthy that our model can be easily extended to other real-time traffic prediction problem such as road traffic and flow prediction. Finally, we test our method based on GPS trajectories generated by Cheng Du taxi. The method presented provides passenger prediction over 15-min intervals for up to 1h in advance and the results prove the efficiency of our proposed prediction system.

Ride-hailing is a representative practice of rapidly developing sharing economy, which not only brings people convenience on mobility, but also benefits the utilization of vehicular resources. For ride-hailing service providers, it is necessary to supervise the demands of customers so that they could carry out reasonable dispatches. To achieve it, a number of data mining methods have been applied. But most of these predicting methods only consider this problem from the temporal aspect, neglecting the connection inter-and intra-blocks of the city. Blocks are the outputs of region partition, which divides the whole city into a series of divisions. At the same time, the partitioned map, specifying borders of blocks, is useful for platforms to improve the process of supervising, dispatching and recommending. However, the majority of current partition methods are static and cannot effectively utilize features extracted from historical and geographical operational data. In this work, we propose to conduct partition task with taking distinct situations in different blocks into consideration. In particular, we propose a novel Region- Partition-Assisted Long Short-term Memory Neural Network for ride-hailing service demand prediction. This is a two-phase model, consisting of the grid-merging region partition and the application of artificial neural network. In experiment part, we show how the region partition method assists LSTM to better forecast the demands, and compare it with other partition techniques. Finally, for evaluating the performance of RPA-LSTM, we conduct extensive experiments on a real-world dataset provided by Didi Chuxing. The experimental results clearly demonstrate that our method can effectively improve the accuracy of prediction.

An increasing number of users have been attracted by location‐based social networks (LBSNs) in recent years. Meanwhile, user‐generated content in online LBSNs like spatial, temporal, and social information provides an ever‐increasing chance to study the human behavior movement from their spatiotemporal mobility patterns and spawns a large number of location‐based applications. For instance, one of such applications is to produce personalized point of interest (POI) recommendations that users are interested in. Different from traditional recommendation methods, the recommendations in LBSNs come with two vital dimensions, namely, geographical and temporal. However, previously proposed methods do not adequately explore geographical influence and temporal influence. Therefore, fusing geographical and temporal influences for better recommendation accuracy in LBSNs remains potential. In this work, our aim is to generate a top recommendation list of POIs for a target user. Specially, we explore how to produce the POI recommendation by leveraging spatiotemporal information. In order to exploit both geographical and temporal influences, we first design a probabilistic method to initially detect users’ spatial orientation by analyzing visibility weights of POIs which are visited by them. Second, we perform collaborative filtering by detecting users’ temporal preferences. At last, for making the POI recommendation, we combine the aforementioned two approaches, that is, integrating the spatial and temporal influences, to construct a unified framework. Our experimental results on two real‐world datasets indicate that our proposed method outperforms the current state‐of‐the‐art POI recommendation approaches.

In this paper, we leverage unmanned aerial vehicles (UAVs) to enhance mobile crowd sensing (MCS) by addressing two critical challenges: uncontrollable data quality and inevitable unsensed points of interest (PoIs). We introduce a UAV-assisted method to deal with these challenges. To ensure the accuracy of sensing data contributed by human participants, the proposed truth discovery method utilizes UAV-collected sensing data as few-shot samples to train the truth discovery model, which is then employed to calibrate sensing data solely collected by human participants. Additionally, to meet the sensing coverage requirement, we present a method that predicts data values for unsensed PoIs by utilizing their historical sensing data and the sensed neighboring PoIs information. The method employs a graph neural network to capture spatio-temporal relationships of the sensing data, facilitating accurate estimation of unsensed PoIs. Through extensive simulations, our approaches demonstrate superior performance compared to existing methods, showcasing the potential of UAV-assisted MCS for overcoming challenges and enhancing data collection efficiency in various domains.

The capabilities of Large Language Models (LLMs) have been remarkably enhanced through constant development and display impressive capabilities in numerous Natural Language Processing (NLP) tasks. This paper introduced an effective method to utilize LLMs to extract relationships, environmental context, and storylines from character information to generate dialogue datasets that enhance the fine-tuned models’ persona consistency. Then, this research employed Low-Rank Adaptation (LoRA) to perform cost-effective fine-tuning of the LLMs based on these dialogue datasets, thus creating models that can act as the specific character. Finally, this paper evaluated the fine-tuned models using automated metrics for objective analysis and conducted an evaluation focusing on the ability of the models to maintain persona consistency, including self-awareness, robustness, and individuality, and the fine-tuned models demonstrated superior performance over baseline models.

For smartphone service providers, it is of vital importance to recognize characteristics of customers. The process of recognizing these characteristics is generally referred to as user profile, which provides knowledge basis for business decisions, enables intelligent services, and brings unique competitiveness. As a basic component of user profile, bedtime could reflect lifestyle, health condition, and occupation of people. This paper presents a flexible algorithm named BTP (Bedtime Prediction), which is designed for predicting wake time and bedtime by analysing screen status of smartphone. BTP first collects screen status log data of user’s smartphone and conducts preprocessing with a series of auxiliary user profiles. Then, it detects and records users’ wake time and bedtime of one day by searching and combining major screen extinguish periods in the past 24 hours. Finally, BTP predicts future bedtime by matching current screen status sequence with all historical records. By applying BTP, most of night and morning scenario‐based applications could provide more considerate services, rather than following fixed execution time like alarm clock. Experiments on practical applications prove that BTP can effectively predict wake time and bedtime without applying complicated machine learning algorithms or uploading data to server.