Dynamic prices are used in many scenarios, e.g., flight ticketing, hotel room booking and ride-on-demand (RoD) service such as Uber and DiDi, and while they are beneficial for service providers, practitioners or users, they lead to the concern of privacy leakage – the possibility of learning user information from dynamic prices. In this paper, we aim to study this possibility and choose trip purpose mining in RoD service as an attack example, based on real-world large datasets. We discuss the criteria of choosing datasets – ubiquitous, collective and easily accessible – from the perspective of an attacker, and extract features describing trip information, spatio-temporal and dynamic prices context. The trip purpose mining problem is then solved as a multi-class classification problem and multiple binary-class problems. In the multi-class problem, we verify that dynamic prices information results in a 17.1% improvement in classification accuracy; in the binary-class problems, we quantify feature contributions and explain the different extents of privacy leakage in identifying different trip purposes. Our hope is that the study not only serves as a case study demonstrating the privacy leakage problem in RoD service, but also sheds light on such privacy problem in other services using dynamic prices and triggers more research efforts.

Aspect-based sentiment analysis (ABSA) has attracted broad attention due to its commercial value.Natural Language Generation-based (NLG) approaches dominate the recent advance in ABSA tasks.However, current NLG practices are inefficient because most of them directly employ an autoregressive generation framework that cannot efficiently generate location information and semantic representations of ABSA targets.In this paper, we propose a novel framework, namely Efficient Hybrid Generation (EHG) to revolutionize traditions.Specifically, we leverage an Efficient Hybrid Transformer to generate the location and semantic information of ABSA targets in parallel.Besides, we design a novel global hybrid loss function in combination with bipartite matching to achieve end-to-end model training.Extensive experiments demonstrate that our proposed EHG framework greatly improves the efficiency of NLG-based methods and outperforms the competitive baselines in almost all cases.

The rapidly-growing business of ride-on-demand (RoD) service such as Uber, Lyft and Didi proves the effectiveness of their new service model – using mobile apps and dynamic pricing to coordinate between drivers, passengers and the service provider, to manipulate the supply and demand, and to improve service responsiveness as well as quality. Despite its success, dynamic pricing creates a new problem for drivers: how to seek for passengers to maximize revenue under dynamic prices. Seeking route recommendation has already been studied extensively in traditional taxi service, but most studies do not consider the effects of taxis and passengers on the seeking taxi simultaneously. Further, in RoD service it is necessary to consider more factors such as dynamic prices, the status of other transportation services, etc. In this paper, we employ a force-directed approach to model, by analogy, the relationship between vacant cars and passengers as that between positive and negative charges in electrostatic field. We extract features from multi-source urban data to describe dynamic prices, the status of RoD, taxi and public transportation services, and incorporate them into our model. The model is then used in route recommendation in every intersection so that a driver in a vacant RoD car knows which road segment to take next. We conduct extensive experiments based on our multi-source urban data, including RoD service operational data, taxi GPS trajectory data and public transportation distribution data, and results not only show that our approach outperforms existing baselines, but also justify the need to incorporate multi-source urban data and dynamic prices.

Recent years have witnessed the rapidly-growing business of ride-on-demand (RoD) services such as Uber, Lyft and Didi. Unlike taxi services, these emerging transportation services use dynamic pricing to manipulate the supply and demand, and to improve service responsiveness and quality. Despite this, on the drivers' side, dynamic pricing creates a new problem: how to seek for passengers in order to earn more under the new pricing scheme. Seeking strategies have been studied extensively in traditional taxi service, but in RoD service such studies are still rare and require the consideration of more factors such as dynamic prices, the status of other transportation services, etc. In this paper, we develop ROD-Revenue, aiming to mine the relationship between driver revenue and factors relevant to seeking strategies, and to predict driver revenue given features extracted from multi-source urban data. We extract basic features from multiple datasets, including RoD service, taxi service, POI information, and the availability of public transportation services, and then construct composite features from basic features in a product-form. The desired relationship is learned from a linear regression model with basic features and high-dimensional composite features. The linear model is chosen for its interpretability-to quantitatively explain the desired relationship. Finally, we evaluate our model by predicting drivers' revenue. We hope that ROD-Revenue not only serves as an initial analysis of seeking strategies in RoD service, but also helps increasing drivers' revenue by offering useful guidance.

Ride-on-demand (RoD) services such as Uber and Didi (in China) are becoming increasingly popular, and in these services dynamic price plays an important role in balancing the supply (i.e., the number of cars) and demand (i.e., the number of passenger requests) to benefit both drivers and passengers. However, the dynamic price also creates concerns for passengers: the "unpredictable" prices sometimes prevent them from making quick decisions at ease. One may wonder if it is possible to get a lower price if s/he chooses to wait a while. Giving passengers more information helps to tackle this concern, and predicting the prices is a possible solution.

Similarity join is a fundamental operation in data cleaning and integration. Existing similarity-join methods utilize the string similarity to quantify the relevance but neglect the knowledge behind the data, which plays an important role in understanding the data. Thanks to public knowledge bases, e.g., Freebase and Yago, we have an opportunity to use the knowledge to improve similarity join. To address this problem, we study knowledge-aware similarity join, which, given a knowledge hierarchy and two collections of objects (e.g., documents), finds all knowledge-aware similar object pairs. To the best of our knowledge, this is the first study on knowledge-aware similarity join. There are two main challenges. The first is how to quantify the knowledge-aware similarity. The second is how to efficiently identify the similar pairs. To address these challenges, we first propose a new similarity metric to quantify the knowledgeaware similarity using the knowledge hierarchy. We then devise a filter-and-verification framework to efficiently identify the similar pairs. We propose effective signature-based filtering techniques to prune large numbers of dissimilar pairs and develop efficient verification algorithms to verify the candidates that are not pruned in the filter step. Experimental results on real-world datasets show that our method significantly outperforms baseline algorithms in terms of both efficiency and effectiveness.

In emerging ride-on-demand (RoD) services such as Uber or Didi (in China), dynamic pricing plays an important role in regulating supply and demand, trying to make such service, to some extent, more convenient for passengers. Despite the convenience, dynamic pricing also exerts mental burden on passengers: they wonder whether the current price is low enough to accept, or if it is not, what they could do to get a lower price. Without extra information, passengers sometimes feel anxious and lose satisfaction. It is thus necessary to provide more information to relieve the anxiety, and price prediction is one of the solutions.

As the Internet-of-Vehicles (IoV) technology becomes an increasingly important trend for future transportation, designing large-scale IoV systems has become a critical task that aims to process big data uploaded by fleet vehicles and to provide data-driven services. The IoV data, especially high-frequency vehicle statuses (e.g., location, engine parameters), are characterized as large volume with a low density of value and low data quality. Such characteristics pose challenges for developing real-time applications based on such data. In this paper, we address the challenges in designing a scalable IoV system by describing CarStream, an industrial system of big data processing for chauffeured car services. Connected with over 30,000 vehicles, CarStream collects and processes multiple types of driving data including vehicle status, driver activity, and passenger-trip information. Multiple services are provided based on the collected data. CarStream has been deployed and maintained for three years in industrial usage, collecting over 40 terabytes of driving data. This paper shares our experiences on designing CarStream based on large-scale driving-data streams, and the lessons learned from the process of addressing the challenges in designing and maintaining CarStream.

Chauffeured car service based on mobile applications like Uber or Didi suffers from supply-demand disequilibrium, which can be alleviated by proper prediction on the distribution of passenger demand. In this paper, we propose a Zero-Grid Ensemble Spatio Temporal model (ZEST) to predict passenger demand with four predictors: a temporal predictor and a spatial predictor to model the influences of local and spatial factors separately, an ensemble predictor to combine the results of former two predictors comprehensively and a Zero-Grid predictor to predict zero demand areas specifically since any cruising within these areas costs extra waste on energy and time of driver. We demonstrate the performance of ZEST on actual operational data from ride-hailing applications with more than 6 million order records and 500 million GPS points. Experimental results indicate our model outperforms 5 other baseline models by over 10% both in MAE and sMAPE on the three-month datasets.

Similarity join is a fundamental operation in data cleaning and integration. Existing similarity-join methods utilize the string similarity to quantify the relevance but neglect the knowledge behind the data, which plays an important role in understanding the data. Thanks to public knowledge bases, e.g., Freebase and Yago, we have an opportunity to use the knowledge to improve similarity join. To address this problem, we study knowledge-aware similarity join, which, given a knowledge hierarchy and two collections of objects (e.g., documents), finds all knowledge-aware similar object pairs. To the best of our knowledge, this is the first study on knowledge-aware similarity join. There are two main challenges. The first is how to quantify the knowledge-aware similarity. The second is how to efficiently identify the similar pairs. To address these challenges, we first propose a new similarity metric to quantify the knowledge-aware similarity using the knowledge hierarchy. We then devise a filter-and-verification framework to efficiently identify the similar pairs. We propose effective signature-based filtering techniques to prune large numbers of dissimilar pairs and develop efficient verification algorithms to verify the candidates that are not pruned in the filter step. Experimental results on real-world datasets show that our method significantly outperforms baseline algorithms in terms of both efficiency and effectiveness.