Jinzhong Wang

Time-Location-Relationship Combined Service Recommendation Based on Taxi Trajectory Data

Recently, urban traffic management has encountered a paradoxical situation which is the empty carrying phenomenon for taxi drivers and the difficulty of taking a taxi for passengers. In this paper, through analyzing the quantitative relationship between passengers’ getting on and off taxis, we propose a time-location-relationship (TLR) combined taxi service recommendation model to improve taxi drivers’ profits, uncover the knowledge of human mobility patterns, and enhance passengers’ travel experience. Moreover, the TLR model uses Gaussian process regression and statistical approaches to acquire passenger volume, mean trip distance, and average trip time in functional regions during every period on weekdays and weekends, and allows drivers to pick up more passengers within a short time frame. Finally, we compare our proposed model with the autoregressive integrated moving average model, the back-propagation neural network model, the support vector machine model, and the gradient boost decision tree model by using the real taxi GPS data in Beijing. The experimental results show that our optimizing taxi service recommendation can predict more accurately than others by considering the 3-D properties.

Vehicular Social Networks: A survey

Abstract A Vehicular Social Network (VSN) is an emerging field of communication where relevant concepts are being borrowed from two different disciplines, i.e., vehicular ad-hoc networks (VANETs) and mobile social networks (MSNs). This emerging paradigm presents new research fields for content sharing, data dissemination, and delivery services. Based on social network analysis (SNA) applications and methodologies, interdependencies of network entities can be exploited in VSNs for prospective applications. VSNs involve social interactions of commuters having similar objectives, interests, or mobility patterns in the virtual community of vehicles, passengers, and drivers on the roads. In this paper, considering social networking in a vehicular environment, we investigate the prospective applications of VSNs and communication architecture. VSNs benefit from the social behaviors and mobility of nodes to develop novel recommendation systems and route planning. We present a state-of-the-art literature review on socially-aware applications of VSNs, data dissemination, and mobility modeling. Further, we give an overview of different recommendation systems and path planning protocols based on crowdsourcing and cloud-computing with future research directions.

LoTAD: long-term traffic anomaly detection based on crowdsourced bus trajectory data

As the development of crowdsourcing technique, acquiring amounts of data in urban cities becomes possible and reliable, which makes it possible to mine useful and significant information from data. Traffic anomaly detection is to find the traffic patterns which are not expected and it can be used to explore traffic problems accurately and efficiently. In this paper, we propose LoTAD to explore anomalous regions with long-term poor traffic situations. Specifically, we process crowdsourced bus data into TS-segments (Temporal and Spatial segments) to model the traffic condition. Later, we explore anomalous TS-segments in each bus line by calculating their AI (Anomaly Index). Then, we combine anomalous TS-segments detected in different lines to mine anomalous regions. The information of anomalous regions provides suggestions for future traffic planning. We conduct experiments with real crowdsourced bus trajectory datasets of October in 2014 and March in 2015 in Hangzhou. We analyze the varieties of the results and explain how they are consistent with the real urban traffic planning or social events happened between the time interval of the two datasets. At last we do a contrast experiment with the most ten congested roads in Hangzhou, which verifies the effectiveness of LoTAD.

IS2Fun: Identification of Subway Station Functions Using Massive Urban Data

Urbanization and modernization accelerate the evolution of urban morphology with the formation of different functional regions. To develop a smart city, how to efficiently identify the functional regions is crucial for future urban planning. Differed from the existing works, we mainly focus on how to identify the latent functions of subway stations. In this paper, we propose a semantic framework (IS2Fun) to identify spatio-temporal functions of stations in a city. We apply the semantic model Doc2vec to mine the semantic distribution of subway stations based on human mobility patterns and points of interest (POIs), which sense the dynamic (people’s social activities) and static characteristics (POI categories) of each station. We examine the correlation between mobility patterns of commuters and travellers and the spatio-temporal functions of stations. In addition, we develop the POI feature vectors to jointly explore the functions of stations from a perspective of static geographic location. Subsequently, we leverage affinity propagation algorithm to cluster all the stations into ten functional clusters and obtain the latent spatio-temporal functions. We conduct extensive experiments based on the massive urban data, including subway smart card transaction data and POIs to verify that the proposed framework IS2Fun outperforms existing benchmark methods in terms of identifying the functions of subway stations.