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An increasing number of vehicles are now equipped with GPS devices to facilitate fleet management and send their GPS locations continuously, generating a huge volume of trajectory data. Sending and storing such vehicle trajectory data cause sustainable communication and storage overheads. Trajectory data compression becomes a promising way to alleviate overhead issues. However, previous solutions are commonly carried out at the side of the data center after data having been received, thus saving the storage cost only. Here, we bring the idea of mobile edge computing and transfer the computation-intensive data compression task to the mobile devices of drivers. As a result, the trajectory data is reduced at the side of data generators before being sent out; thus, it can lower data communication and storage costs simultaneously. We propose DAVT , an error-bounded trajectory data representation, and a compression framework. Specifically, the trajectory data is reformatted into three parts (i.e., D istance, A cceleration & V elocity, and T ime), and three compressors are wisely devised to compress each part. For D and AV parts, a similar Huffman tree-forest structure is exploited to encode data elements effectively, but with quite different rationales. For the T part, the large absolute timestamps are transformed to small time intervals firstly, and different encoding techniques are adopted based on the data quality. We evaluate our proposed system using a large-scale taxi trajectory dataset collected from the city of Beijing, China. Our results show that our compressors outperform other baselines.

International audience; 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.

Ride-on-demand (RoD) services use dynamic prices to balance the supply and demand to benefit both drivers and passengers, as an effort to improve service efficiency. However, dynamic prices also create concerns for passengers: the “unpredictable” prices sometimes prevent them from making quick decisions at ease. It is thus necessary to give passengers more information to tackle this concern, and predicting dynamic prices is a possible solution. We focus on fine-grained dynamic price prediction – predicting the price for every single passenger request. Price prediction helps passengers understand whether they could get a lower price in neighboring locations or within a short time, thus alleviating their concerns. The prediction is performed by learning the relationship between dynamic prices and features extracted from multi-source urban data. There are linear or non-linear models as candidates for learning, and using different models leads to varying implications on accuracy, interpretability, model training procedures, etc. We train one linear and one non-linear model as representatives, and evaluate their performance from different perspectives based on real service data. In addition, we interpret feature contribution, at different levels, based on both models and figure out what features or datasets contribute the most to dynamic prices. Finally, based on evaluation results, we provide discussions on model selection under different circumstances, and propose a way to combine the two models. Our hope is that the study not only serves as an accurate prediction for passengers, but also provides concrete guidance on how to choose between models to improve the prediction.

International audience; Recent years witness 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.

In emerging ride-on-demand (RoD) services, dynamic pricing plays an important role in regulating supply and demand and improving service efficiency. Despite this, it also makes passenger anxious: whether the current price is low enough, or otherwise, how to get a lower price. It is thus necessary to provide more information to ease the anxiety, and predicting the prices is one possible solution. In this study, the authors predict the dynamic prices to help passengers learn if there is a lower price around. They first use entropy of historical prices to characterize the predictability of prices in different locations and claim that different prediction algorithms should be used to balance between efficiency and accuracy. They present an ensemble learning approach to price prediction and compare it with two baseline predictors, namely a Markov and a neural network predictor. The performance evaluation is based on the real data from a major RoD service provider. Results verify that the two baseline predictors work well in locations with different levels of predictabilities, and that ensemble learning significantly increases the prediction accuracy. Finally, they also evaluate the effects of prediction, i.e., the probability that passengers could benefit from the prediction and get a lower price.

In emerging ride-on-demand (RoD) services such as Uber and Didi (in China), dynamic prices play an important role in regulating supply and demand, trying to improve the service quality for both drivers and passengers. In this paper, we take a new perspective to study RoD services besides the supply or demand, and focus on passengers' reaction to dynamic prices. Passengers' reaction can be regarded as a process of searching for the best price before getting on a car, and the searching process reflects passengers' demand elasticity -- “how eager they are requesting a ride”. We collect data of passengers' reaction from a real RoD service provider in China, and analyze the patterns of passengers' reaction. The analysis results show that both the dynamic prices and passengers' demand elasticity influence their reaction. We then adopt and extend a previous model for sequential search from a known distribution to understand passengers' reaction, and use our data to obtain the search costs under various circumstances, which could be interpreted as passengers' demand elasticity. Insights on the search cost and other relevant quantities are discussed. Our expectation is that the result of the study should be helpful not only for service providers in designing dynamic pricing algorithms, but also for passengers and policy makers in understanding the effects and implications of dynamic pricing.

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. In this paper we predict the dynamic prices to help passengers understand whether they could get a lower price in neighboring locations or within a short time. We first divide a city into rectangular cells, and use entropy and the temporal correlation of prices to characterize the predictability of prices of each cell. Based on the predictability of prices, we claim that different prediction algorithms should be used in different city areas, to balance between efficiency and accuracy. We design and implement two predictors, namely a Markov predictor and a neural network predictor, and evaluate their performance based on the real data we collected from a major RoD service provider in China. The results verify that the Markov predictor works well enough in highly-predictable areas, and the neural network predictor, while requiring more computation time, works better in areas with lower predictabilities. Finally, we also evaluate the effects of our prediction, i.e., the probability that passengers (in different city areas) could benefit from the prediction and get a lower price.

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.

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.

We study the problem of managing high intradatacenter traffic in a chunk-based public cloud storage service. The high traffic volume is introduced by aggregating very large files from multiple chunk servers in a single edge server. We measure a commercial cloud storage service system, and observe that peak traffic volume overwhelms the network interface cards (NICs) significantly. In the scenario of delivering content based on Smart Home network, it can be expected the file downloading service could be delay-insensitive. Thus, we propose “Batching Smooth intra-datacenter Traffic” (BST) scheme to reduce the peak load to a specified upper bound by batching and delaying users' requests. We resort to a mathematical model to understand the necessity of batching strategy. To evaluate BST's effects, we implement trace-driven simulations with different scheduling policies. In the commercial cloud storage service system, we show that BST is capable of keeping the upper bound to approximately 75% of the original peak traffic by trading off an average delay of 8 minutes.

As one of most promising and exciting future internet technology OpenFlow is attracting more and more researchers. However the methods and proposal of the inter-domain communication is still a key problem. In this paper, a EOF-MPLS methods is proposed to solve this problem. In this new technology, OpenFlow controller is responsible for the generation of the FEC and label binding algorithm. Users can use the operation system API provided by the OpenFlow controller to customize the details about the FEC and LDP, thus generate the FEC combined with labels. Considering the message reaching the boundary, Boundary OpenFlow Switch is responsible for identifying and forwarding packets to the other AS and it will complete the label replacement. The boundary OpenFlow is quite important that it should identify multiple protocols from different ASs, even some AS that has different internet structure such as NDN network structure. RSVP and traffic engineering can also be added to the process of FEC generation operated by the OpenFlow Controller. As a consequence, an entropy is used to evaluate the various affecting factors about the EOF-MPLS to the OpenFlow. Calculate the B-indicator to the A-indicator and the A-indicator to the total target one. The result shows that the most complicated A-indicator is the FEC and label combining while the most complicated B-indicator is the label number and TOS.

Openflow is one of the most important and popular next generation internet structure and technology. Its thought of “software decide network” makes dispatch of controller and date layer. Controller uses the openflow protocol to communicate and control the openflow switch to generate flow table, thus to achieve the centralized control of the whole network. OpenRoads is the framework about the wireless Openflow environment. It successfully apply the thought of SDN on the wireless mobile environment. As a propose to improve the performance and quality of the OpenRoads, The new embedded Openflow-MPLS(EOF-MPLS) is presented. An important technology key point is that both traditional MPLS and Openflow support the thought of separation between controller and date layer which makes a solid foundation for the new EOF-MPLS. EOF-MPLS offered forward equal class(FEC), Label distribute Protocol(LDP) to improve the efficiency of the nodes forward. It makes tight coupling with Openflow structure and support the QoS and traffic engineering. At last an entropy is came up with to assess the communication effectiveness of the EOF-MPLS to the OpenRoads.