Noise perturbation introduced by differential privacy (DP) could degrade the quality of essential services like dynamic pricing and ride-matching in ride-on-demand (RoD) services. In this paper, we focus on RoD services under an honest-but-curious server, and propose a Pricing-Aware Differentially Private framework (PADP-RoD) to protect users’ location privacy while providing them with high-quality location-based services. Specifically, given that a price multiplier is subject to abrupt changes in response to shifts in supply and demand, especially near hotspots, we propose an adaptive supply and demand aware grid to capture the changes. Powered by the grid, we put forward two utility metrics for quantifying the quality loss of dynamic pricing and ride-matching services caused by perturbation, respectively. With those metrics, PADP-RoD is formulated as a minimization problem, aiming to minimize the quality loss of services given DP constraint. In this way, we can achieve an optimal balance between privacy and service quality. Due to the problem being a multi-objective optimization, we decompose it into a dynamic-pricing utility sub-problem and a ride-matching utility sub-problem, and solve them separately. To solve the dynamic pricing utility sub-problem, we propose a heuristic algorithm named the dynamic pricing mapping algorithm. Since the semi-infinite and non-differentiable nature of the ride-matching utility sub-problem, we transform this sub-problem into an unconstrained problem by the exact penalty function method, and solve it employing the particle swarm optimization algorithm. Our theoretical analysis demonstrates that PADP-RoD satisfies both$\varepsilon _{d}$-DP and$\varepsilon _{d}$-identifiability, and extensive experiments on a real-world dataset show that it can provide high-quality dynamic pricing and ride-matching services.

Region representations (also called embeddings) are useful for various urban computing tasks. While graph-based region representation learning methods have shown outstanding performance, they encounter two major challenges: 1) the pervasive data noise and missing data can affect the quality of the constructed region graphs; and 2) high-order relationships (i.e., group-wise relationships) among regions are often insufficiently modeled and sometimes entirely overlooked. To this end, we proposeHyperRegion, an unsupervised region representation learning framework that integrates graph and hypergraph contrastive learning to learn comprehensive region embeddings from multi-modal data. Built upon a region hybrid graph network, this framework models both pair-wise and group-wise dependencies involving POI semantics, mobility patterns, geographic neighbors, and visual semantics. To mitigate the impact of data noise and missing data, graph and hypergraph contrastive learning are performed in parallel, and a cross-module contrast is further introduced to facilitate information exchange and collaboration. Extensive experiments on real-world datasets across three downstream tasks demonstrate thatHyperRegionoutperforms all baselines, particularly improving check-in prediction by reducing MAE and RMSE by approximately 8.5% and 8.2%, respectively, and increasing$R^{2}$by about 7%.

Ride-on-Demand services (RoD) like Didi and Uber have become widely accepted among passengers, with dynamic pricing as their core feature. In passenger destination prediction task, it is common to consider the vehicle's prefix trajectory as the crucial feature. In this paper, we build upon existing studies by introducing dynamic pricing and proposing a conditional BiLSTM-attention model framework. The dynamic prices influence the training process of network's bidirectional information flow through the conditional recurrent mechanism. Experimental evaluation on a multi-source urban dataset shows that our model achieves a top-5 accuracy of 69.2 %, outperforming baseline models. Notably, considering dynamic prices in the early stages of the trajectory (e.g. the former 10% and 30% of the whole trajectory) improves the average accuracy by 48.0 % and 61.3 %, respectively. Our hope is that the study can provide insights for industries such as advertising and catering on how to better utilize dynamic prices to achieve their desired business objectives.

Recent years witness the increasing popularity of ride-on-demand (RoD) services such as Uber and Didi. Compared with traditional taxi, RoD service is more “data-driven” and adopts dynamic pricing to manipulate the supply and demand in real time. Dynamic price could be viewed as an accurate and quantitative indicator of the supply and demand, and could provide clues to drivers, passengers, and the service providers, possibly reshaping the ways in which some problems are solved. In this paper, we focus on the seeking route recommendation problem that aims at increasing driver revenue by recommending highly profitable seeking routes to drivers of vacant cars with the help of dynamic prices. We first justify our motivation by showing the importance of route recommendation and answering why it is necessary to consider dynamic prices, based on the analysis of real service data. We then design a dynamic price prediction model to generate the dynamic prices at any given time and location based on multi-source urban data. After that, a reinforcement learning model is adopted to perform seeking route recommendation based on predicted dynamic prices. We conduct extensive experiments in different spatio-temporal combinations and make comparisons with multiple baselines. Results first show that our dynamic price prediction model achieves an accuracy ranging from 83.82% to 90.67% under different settings. It also proves that considering the real-time predicted dynamic prices significantly increases driver revenue by, for example, 12% and 47.5% during weekday evening rush hours, than merely using the average prices or completely ignoring dynamic prices.

Trip purpose is a meaningful aspect of travel behaviour for the understanding of urban mobility. However, it is non-trivial to automatically obtain trip purposes. On one hand, trip purposes are naturally diverse and complicated, but the available predictive data sources are limited in real-world scenarios. On the other hand, since trip purpose labeling is costly and the development levels of cities are unbalanced, it is infeasible to access large-scale labeled data in less developed cities to train advanced prediction models. To narrow the gaps, this article presents A new Graph Embedding Network and active Domain Adaptation based framework (AGENDA) that only requires open data sources and is capable of predicting in both label-rich cities and label-scarce cities. Specifically, in label-rich source cities, we first use the vehicle’s GPS trajectory and open POI check-ins to augment trip contexts. Then we establish a supervised graph embedding network with two attention mechanisms to extract the passenger’s latent activity semantics and a classifier to predict trip purpose. To enable the prediction in label-scarce target cities, we further devise an active domain adaptation framework, in which adversarial domain adaptation is used to transfer the source-learned knowledge, and active learning is used to integrate human intelligence in the model training. A group of experiments are conducted with real-world datasets in Beijing and Shanghai. Evaluation results demonstrate that the proposed framework significantly outperforms existing trip purpose prediction algorithms, and could make accurate trip purpose prediction in label-scarce cities with much fewer labeling efforts.

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.

Incentive mechanisms are essential to incentive workers carrying mobile handheld devices to participate in mobile crowd sensing and finally achieve good truth discovery performance. However, malicious workers may report false or malicious data to defraud rewards, resulting in service quality degradation. Moreover, the existing incentive mechanism is challenging to identify malicious workers when recruiting workers in reality, which results in low accuracy of truth discovery and waste of cost. In this paper, we propose an Incentive-based Truth Discovery (ITD) scheme to incentive credible workers to submit high-quality data, thereby enhancing the accuracy of truth discovery. In the ITD, an unmanned aerial vehicle (UAV)-assisted split-aggregation truth discovery mechanism is proposed firstly to infer the truth. The addition of the UAV can improve the accuracy of truth discovery and assist in evaluating workers’ trust. Then, we evaluate the quality of participants’ data and propose a data quality-based trust meter to update each worker’s trust to guide future recruitment efforts. Finally, a Quality-aware Trustworthy Incentive (QTI) mechanism is proposed to select credible workers for data collection and provide them with reasonable payments. The experimental results show that ITD improves the accuracy of truth discovery by 98.47%, over the state-of-the-art, at a sensing cost reduced by as high as 43.89%.

In recent years, ride-on-demand (RoD) services such as Uber and DiDi are becoming increasingly popular. Different from traditional taxi services, RoD services adopt dynamic pricing mechanisms to manipulate the supply and demand on the road, and such mechanisms improve service capacity and quality. Seeking route recommendation has been widely studied in taxi service. In RoD service, the dynamic price is a new and accurate indicator describing the supply and demand, but it is yet rarely studied in providing clues for drivers to seek for passengers. In this paper, we propose to incorporate the impacts of dynamic prices as a key factor in recommending seeking routes to drivers. We first justfiy why it is necessary to recommend seeking routes and consider dynamic prices, by analyzing real service data from a typical RoD service. We then design a reinforcement learning model based on order and GPS trajectories datasets, and take into account dynamic prices in the design. Results prove that our model improves both driver earnings and seeking strategies. On driver earnings, the reinforcement learning model increases revenue efficiency by up to 34.52%, and considering dynamic prices leads to another increase of 6.19%. On seeking strategies, drivers are encouraged to serve local demand first, and they are redistributed more evenly and effectively.

Knowing why people travel is meaningful for human mobility understanding and smart services development. Unfortunately, in real-world scenarios, trip purpose cannot be automatically collected on a large scale, thus calling for effective prediction models. Nevertheless, since passengers’ trip purposes in the city are diverse and complicated, the prediction is very difficult especially at a fine-grained level. Worse still, the informative data sources and real purpose-labels about trips are commonly limited for model learning. To resolve the dilemma, we propose a semi-supervised deep embedding framework for predicting fine-grained trip purposes on a large scale. Specifically, we first derive augmented trip contexts from the vehicle’s GPS trajectory and public POI check-in data, then convert POI contexts into the graph structure. We further establish aDual-AttentionGraphEmbedding Network withAutoencoder architecture (DAGE-A) to accomplish prediction and reconstruction simultaneously, in which category-aware graph attention networks are devised to model the POI semantics at trip’s origin/destination and extract complementary knowledge from unlabeled trips; and soft-attention is employed to aggregate different trip semantics appropriately for the final prediction. We conduct extensive experiments in Beijing and Shanghai, and results show our framework outperforms state-of-the-arts and could reduce labelling efforts by up to 20%. We also find that our model is generalized at different times and locations, and the performance varies for different trip purposes.

Although crowdsensing has emerged as a popular information collection paradigm, its security and privacy vulnerabilities have come to the forefront in recent years. However, one big limitation of previous research is that the security domain and the privacy domain are typically considered separately. Therefore, it is unclear whether the defense methods in the privacy domain will have unexpected impact on the security domain. To bridge this gap, in this paper, we propose a novel Disguise-based Data Poisoning Attack (DDPA) against the differentially private crowdsensing systems empowered with the truth discovery method. Specifically, we propose a novel stealth strategy, i.e., disguising the malicious behavior as privacy behavior, to avoid being detected by truth discovery methods. With this stealth strategy, the shortcoming of failing to maximize the attack effectiveness is avoided naturally through structuring a bi-level optimization problem, which can be solved with the alternating optimization algorithm. Moreover, we show that the differentially private crowdsensing systems are vulnerable to data poisoning attacks, and enhancing the level of privacy will bring more serious security threats. Finally, the evaluation results on the real-world dataset Emotion and the synthetic dataset SynData demonstrate that DDPA can not only achieve maximum utility damage but also remain undetected.

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.

Smart-home is becoming increasingly popular in recent years, and it introduces a new content retrieval paradigm - delay-insensitive downloading. In this new paradigm, users do not require the content retrieval task to finish as soon as possible, but only set a deadline for it. We study the role of this paradigm in the traffic engineering of a chunk-based cloud storage service. We propose that it could help to reduce the high intra-datacenter traffic at peak resulting from the chunk-based architecture, by delaying users' content requests when necessary. Because of the introduction of the new paradigm, we consider the co- existence of three applications (downloading, streaming, delay-insensitive downloading) in the service, and try to understand the best way to delay users' requests. We conduct an incentive-based study for the evaluation of schemes of delaying users' requests: the service provider pays incentives to users to promote this new paradigm. The incentive, as well as users' application-specific QoE on the service, is modelled, and a framework for the evaluation is presented. We then apply our framework to study several proposed delay schemes and present both quantitative and qualitative results.

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.

With recent proliferation of smart home hubs, public cloud storage service is facing a significant challenge of distributing very large content to end users. In these services, many delivered files are of size of tens of GB (even up to hundreds of GB for 4K resolution videos), and the fraction of very-large files keeps increasing. By observing a commercial cloud storage and distribution service, we notice the issue of excessive intra-datacenter network traffic for distributing very-large files, which seriously affects the quality of service and scalability. Based on the measurement results and the analysis of the issue, we propose cache-based methods (in both static and dynamic schemes) to help reduce the large volume of intra-datacenter traffic. Our proposals are evaluated on the chunk-level traces from one of the biggest commercial cloud storage and distribution service providers in China. The cache-based approach can reduce the maximum intra-datacenter traffic significantly in a cost-effective manner.

We study the problem of how to manage the high intra-datacenter traffic in a chunk-based public cloud storage service serving primarily smart home devices. The large volume of traffic is introduced by delivering very large content during busy hours in the cloud. Measurement of a commercial cloud service shows that the peak traffic volume (at its edge servers) overwhelms the network interface cards (NICs), resulting in serious congestion and packet losses. Since it can be expected the large content downloading requests in smart home environment could be delay-insensitive, we propose DST to keep the peak load under a specified upper bound, by delaying users' requests when necessary. By modelling DST as a queueing system, we derive the relation between the mean delay and the traffic upper bound. With trace-driven simulations, we evaluate the system performance and validate the analysis results. For the commercial cloud service we study, we show that it is possible to keep the traffic upper bound to about 80% of peak traffic rate by introducing a mean delay of around 48 minutes.