基于分时MDP的出租车载客预测推荐技术研究

针对出租车盲目寻客导致空载率高的问题,提出了一种出租车载客热点推荐策略,以最大程度优化匹配乘客过程,提高寻客效率。基于出租车历史轨迹数据,结合热点乘客信息的时间序列特性,提出基于循环神经网络的分段预测(SPBR)算法,以及基于分时马尔可夫决策过程(TMDP)的载客推荐模型。实验表明,SPBR算法预测结果的RMSE比SVR、CART和BPNN等算法分别降低了67.6%、71.1%和64.5%;TMDP模型出租车期望回报比历史期望提升了35.9%。     

关于出租车载客地点序列推荐技术的研究

在大城市中,出租车已成为实现智能交通运输系统不可或缺的一环。然而,由于一些出租车司机的驾驶经验,和对城市活动的熟悉程度的不足,使得其在寻找乘客时会采取毫无目的的随机漫游策略。这就导致了出租车司机的收益不高,同时也造成了能源的消耗以及环境的污染。针对此问题,将提出出租车载客地点的推荐模型,使得模型给出的推荐地点序列能获得较高的期望收益。具体来说,将基于出租车GPS轨迹数据建立出租车载客地点的马尔科夫决策过程模型,并给出求解该模型的2种算法。仿真实验结果显示,与典型的TopK方法相比,给出的推荐结果能更好地提高单位时间内出租车司机的收益。     

基于稀疏轨迹数据的出租车载客区域推荐

基于短期出租车轨迹数据的载客区域推荐能极大减少系统开销,提高推荐效率,但常伴随着数据稀疏性的问题.针对该问题,本文提出了一种融合地理信息的隐语义模型-GeoLFM.该模型通过将出租车司机所处的客观地理环境信息,融合到司机-载客区域矩阵分解的过程中,从而弥补数据稀疏性带来的不足.同时,根据出租车实时的空间位置信息,为身处不同地点的出租车推荐不同的载客区域.实验证明,本文提出的方法与常用方法相比,推荐结果与真实的出租车司机载客情况间的平均绝对误差和均方根误差都得到大幅降低,较好的提升了推荐效果.     

Real Time Anomalous Trajectory Detection and Analysis

GPS-equipped taxis can be considered as pervasive sensors and the large-scale digital traces produced allow us to reveal many hidden facts about the city dynamics and human behaviors. In this paper we present a novel GPS-based taxi system which can detect ongoing anomalous passenger delivery behaviors leveraging our proposed iBOAT method. To achieve real time monitoring, we reduce the response time of iBOAT by more than five times with an inverted index mechanism adopted. We evaluate the effectiveness of the system with large scale real life taxi GPS records while serving 200,000 taxis. With this system, we obtain about 0.44 million anomalous trajectories out of 7.35 million taxi delivery trips, which correspond to 7600 taxis’ GPS records in one month time in the city of Hangzhou, China. Through further analysis of these anomalous trajectories, we observe that: (1) Over 60 % of the anomalous trajectories are “detours” that travel longer distances and time than normal trajectories; (2) The average trip length of drivers with high-detour tendency is 20 % longer than that of normal drivers; (3) The length of anomalous sub-trajectories is usually less than a third of the entire trip, and they tend to begin in the first two thirds of the journey; (4) Although longer distance results in a greater taxi fare, a higher tendency to take anomalous detours does not result in higher monthly revenue; and (5) Taxis with a higher income usually spend less time finding new passengers and deliver them in faster speed.     

基于隐式反馈数据的个性化游戏推荐

现有推荐系统通常采用评分、评论等显式反馈数据实现个性化推荐.然而,显式反馈数据由于在实际中难以获取或因质量问题而往往变得不可用,从而导致相关推荐算法的应用范围受到很大限制.与此相反,诸如点击行为、浏览记录等隐式反馈数据在现实中大量存在.本文提出了一种面向游戏玩家的基于隐式反馈数据的游戏推荐方法.该方法综合考虑了玩家操作次数、操作时长等隐式反馈数据及其时效性,构建了基于伪评分的玩家对游戏的偏好模型,而后通过改进了的SVD++（Singular Value Decomposition++）算法实现个性化游戏推荐.在大规模真实数据集上的实验结果表明本文提出的方法具有更高的推荐精确率和召回率.     

Data forwarding incentive mechanism based on auction model in mobile social network

Abstract:A data forwarding incentive mechanism based on auction model in mobile social network was proposed.In this incentive mechanism,the first-price sealed auction mode was extended,the transaction mode of virtual currency payment was adopted,and the procedure of data forwarding between nodes was abstracted into the auction transaction model.Based on the node's resource state,the virtual currency and the data property,the evaluation function of data forwarding transaction was given,and then the node gives the corresponding price according to the evaluation function and game strategy.Through the game analysis,the Nash equilibrium solution of AMIM was found,and the lowest bidder,of which the bid price was lower than the evaluation of data forwarding request node would been selected as the service provider for this data forwarding.In this incentive mechanism,the rational mobile nodes were enforced to voluntarily participate in data forwarding cooperation to maximize their own interests.The simulation experiment shows that AMIM mechanism can effectively reduce the energy consumption and improve the success rate and efficiency of data forwarding in the whole network system.     

基于2.4 GHz载频的“翼支付”安全认证研究

本文结合上海电信近期推出的2.4GHz翼支付手机刷卡试点业务,在研究中国电信现有通信网络、终端通信机制和安全认证协议的基础上,提出在现有通信系统上构建高安全、可管控、强认证的应用系统架构,以达到支付安全级别要求的体系结构,从而更好地支持上海电信手机刷卡应用系统的后续加载,并可作为目前复杂、多变的网络环境的支付安全模型,以期对2.4GHz手机刷卡支付应用的发展方向提供借鉴。     

基于随机游走和多样性图排序的个性化服务推荐方法

针对传统服务推荐算法由于数据稀疏性而导致推荐准确性不高,以及推荐结果缺乏多样性等缺陷,提出基于随机游走和多样性图排序的个性化服务推荐方法（PRWDR）.在分析直接相似关系稀疏性的基础上提出带权重的随机游走模型,通过在用户网络上进行随机游走来挖掘更多的相似关系;基于所有相似用户预测服务的QoS值,并给出服务图模型构建方法,以过滤大量性能过低的候选服务;提出最优节点集合选取策略,利用贪婪算法得到兼具推荐准确性和功能多样性的服务推荐列表.在公开发布的数据集上进行实验,并与多个经典算法进行比较,验证了本算法的有效性.     

Trust expansion and listwise learning-to-rank based service recommendation method

In view of the problem of trust relationship in traditional trust-based service recommendation algorithm,and the inaccuracy of service recommendation list obtained by sorting the predicted QoS,a trust expansion and listwise learning-to-rank based service recommendation method (TELSR) was proposed.The probabilistic user similarity computation method was proposed after analyzing the importance of service sorting information,in order to further improve the accuracy of similarity computation.The trust expansion model was presented to solve the sparseness of trust relationship,and then the trusted neighbor set construction algorithm was proposed by combining with the user similarity.Based on the trusted neighbor set,the listwise learning-to-rank algorithm was proposed to train an optimal ranking model.Simulation experiments show that TELSR not only has high recommendation accuracy,but also can resist attacks from malicious users.     

VANET-based secure taxi service

Taxi service is an important type of point-to-point transportation in many cities. One of the major concerns is the safety of both passengers and taxi drivers. In this paper, based on the framework of VANET (Vehicular Ad-hoc NETwork), we propose a novel secure taxi service scheme to tackle the problem. First, a driver needs to authenticate himself using a password before he can provide any service. Second, after picking up a passenger, the taxi’s OBU (on-board unit) needs to continuously send its current location to the trusted server. This location is then double-verified with RSU (road-side unit) signatures. We also design a cooperative tracking mechanism to locate a taxi in case of emergency. In some occasions, taxi passengers as well as taxi drivers are concerned with their privacy (such as travelling route). We incorporate privacy-preserving measures into our scheme. The taxi’s OBU uses pseudo identities instead of real identity for all ongoing transmissions so that a passenger’s travelling route (or the driver’s travelling route when he/she is not carrying any passengers) cannot be traced by a third party easily. We show by simulation that the total delay (from 1 to 9 s) introduced by all security measures is small while the total number of messages required to be transmitted in the network is reasonable.     

An evolutionary game theory–based security model in vehicular ad hoc networks

The word population is growing on a daily basis; consequently, the growth of commute and transport, developing efficient and intelligent transportation systems (ITS), has become one of the most popular requirements and the most significant attempts in modern urban areas containing large population. A key component of intelligent transportation systems is a vehicular ad hoc network. Devising internet‐based practical programs such as awareness of climatic conditions, geographical location, practical programs like on‐line payment services in the vehicular ad hoc network has led to safer driving, prevention of deadly accidents, transportation improvement, more welfare and convenience for passengers, and even offering more commercial opportunities. The special features of vehicular ad hoc network, such as intense activity, constantly‐changing topology, the vehicles' high speed, etc, will lead to challenges in gaining security. Therefore, providing vehicular ad hoc networks with security is of extreme importance in terms of users' anonymity, identification, and data privacy. In this paper, a security model is presented using a method based on evolutionary games. This method, in every vehicle, is applied as a node in the network while interacting with other vehicles; it aims at distinguishing some common attacks and defending against attackers. In this method, defending (honest) vehicles and attacking vehicles take part in an asymmetrical game; each vehicle aims at gaining the most utility and achieving its goals. The proposed method is simulated using various scenarios. The simulation results reveal that the proposed method is efficient and it reaches equilibrium and convergence at the end of the game in each scenario.     

一种P2P网络信任模型METrust

 Peer-to-Peer(P2P)网络的异构性、匿名性、自治性等特点导致了一些安全问题,比如伪造、诋毁、协同作弊等,影响了服务质量.提出了一种基于推荐的P2P网络信任模型METrust,节点在网络中拥有唯一的推荐可信度,引入了更新幅度和更新力度两个参数来更新推荐可信度.给出了节点推荐可信度的更新算法;节点根据评价标准的相似程度选择推荐,其中节点的评价标准通过AHP(Analytic Hierarchy Process)方法确定.仿真实验表明,METrust信任模型可以识别恶意节点,有效提高P2P网络的服务质量.     

Game-theoretic pricing for video streaming in mobile networks

Mobile phones are among the most popular consumer devices, and the recent developments of 3G networks and smart phones enable users to watch video programs by subscribing data plans from service providers. Due to the ubiquity of mobile phones and phone-to-phone communication technologies, data-plan subscribers can redistribute the video content to nonsubscribers. Such a redistribution mechanism is a potential competitor for the mobile service provider and is very difficult to trace given users' high mobility. The service provider has to set a reasonable price for the data plan to prevent such unauthorized redistribution behavior to protect or maximize his/her own profit. In this paper, we analyze the optimal price setting for the service provider by investigating the equilibrium between the subscribers and the secondary buyers in the content-redistribution network. We model the behavior between the subscribers and the secondary buyers as a noncooperative game and find the optimal price and quantity for both groups of users. Based on the behavior of users in the redistribution network, we investigate the evolutionarily stable ratio of mobile users who decide to subscribe to the data plan. Such an analysis can help the service provider preserve his/her profit under the threat of the redistribution networks and can improve the quality of service for end users.     

Internet pricing with a game theoretical approach: concepts andexamples

The basic concepts of three branches of game theory, leader-follower, cooperative, and two-person nonzero sum games, are reviewed and applied to the study of the Internet pricing issue. In particular, we emphasize that the cooperative game (also called the bargaining problem) provides an overall picture for the issue. With a simple model for Internet quality of service (QoS), we demonstrate that the leader-follower game may lead to a solution that is not Pareto optimal and in some cases may be "unfair," and that the cooperative game may provide a better solution for both the Internet service provider (ISP) and the user. The practical implication of the results is that government regulation or arbitration may be helpful. The QoS model is also applied to study the competition between two ISPs, and we find a Nash equilibrium point from which the two ISPs would not move out without cooperation. The proposed approaches can be applied to other Internet pricing problems such as the Paris Metro pricing scheme     

电子支付协议的博弈逻辑模型与形式化分析

提出用一种新的基于博弈逻辑ATL（Ahernating-time Temporal Logic）的电子支付协议建模与分析方法。新方法克服了传统时序逻辑把协议看成封闭系统进行分析的缺点．可以成功地对电子商务中的对抗与合作行为进行描述．利用新方法对Bolignano协议进行了严格的形式化分析．发现该协议不能满足公平性要求．     

多主多从Stackelberg博弈下的边缘缓存资源分配算法

为鼓励视频服务提供商参与到缓存过程中,本文提出一种基于Stackelberg博弈的激励缓存资源分配算法。与传统激励缓存资源分配方案不同,本文考虑同时存在多个网络运营商和多个视频服务提供商,视频服务提供商从网络运营商处购买存储空间以缓存热门视频。针对该场景,本文将该激励缓存模型建模为多主多从Stackelberg博弈问题,分别构建主方和从方的效用函数,证明了在网络运营商价格确定的情况下,视频服务提供商之间的非合作博弈存在纳什均衡。文章利用分布式迭代算法对该博弈模型进行求解,获得了视频服务提供商的最优缓存策略和网络运营商的最优价格策略。仿真结果表明,本文提出的激励缓存机制可使视频服务提供商获得比其他缓存分配算法更高的单位成本收益。      

A model of user behavior in multi-service priced networks

Today’s IP networks must be able to serve many applications with different needs and goals and the traditional best effort approach is not sufficient to provide the various degrees of Quality of Service (QoS) required by these heterogeneous applications. In previous work, we have proposed a user-centric QoS management scheme within a game theoretic framework. In this paper, our goal is to model user behavior and to study the relationship between network performance, user satisfaction and choice of service class. Our results show that our user behavior model is realistic in the context of a user-controlled QoS: users can obtain a satisfactory service by choosing their service classes themselves. Their satisfaction is higher when they can choose their service classes dynamically rather than with a static allocation. The model also points out the importance of user actions on network stability.     

Dynamic game and reliable recommendation based transferring reputation mechanism for mobile cloud computing

The booming development of the mobile internet and cloud computing leads to the emerging of many mobile cloud platforms based services.However,since mobile users store lots of data and privacy information in the cloud when they are using the mobile cloud services,they are facing multiple increasingly serious security threats such as data leaks and privacy exposures.The data security and privacy protection was investigated in mobile cloud computing,aiming at the internal bad mouthing attacks and mobile attacks.A dynamic game and reliable recommendation based transferring reputation mechanism was proposed.First,a dynamic game based recommendation incentive mechanism was proposed.Secondly,a reliable recommendation reputation evaluation model was established based on the incentive mechanism.Last,a novel transferring reputation mechanism was proposed that combined the above mentioned incentive mechanism and reputation evaluation model.Simulation results demonstrate the proposed transferring reputation mechanism can defend against the internal bad mouthing attacks and mobile attacks effectively,enhance the credibility of mobile terminals and improve the data security and privacy protection of mobile cloud services.     

A routing protocol for urban vehicular ad hoc networks to support non-safety applications

The Urban Vehicular Ad Hoc Network (UVANET) provides non-safety applications like media sharing, internet service, file transfer, gaming, and so on. Routing plays an important role in providing better services to the users in UVANET. In this paper, a novel routing protocol is proposed for UVANET to support non-safety applications. We consider a non-safety application wherein the drivers and passengers of different parking lots can play multi-player games with each other. To play the games smoothly, the game data should reach the destination in a minimum time. Simulation results show that when the vehicle density in the city area is high, the proposed protocol meets the end-to-end delay tolerance of 100 ms. Finally, an experiment is performed to validate the proposed routing protocol by running a simple puzzle game in a UVANET prototype designed in an indoor laboratory environment and in an outdoor environment.     

Non-Cooperative Multicast and Facility Location Games

We consider a multicast game with selfish non- cooperative players. There is a special source node and each player is interested in connecting to the source by making a routing decision that minimizes its payment. The mutual influence of the players is determined by a cost sharing mechanism, which in our case evenly splits the cost of an edge among the players using it. We consider two different models: an integral model, where each player connects to the source by choosing a single path, and a fractional model, where a player is allowed to split the flow it receives from the source between several paths. In both models we explore the overhead incurred in network cost due to the selfish behavior of the users, as well as the computational complexity of finding a Nash equilibrium. The existence of a Nash equilibrium for the integral model was previously established by the means of a potential function. We prove that finding a Nash equilibrium that minimizes the potential function is NP-hard. We focus on the price of anarchy of a Nash equilibrium resulting from the best-response dynamics of a game course, where the players join the game sequentially. For a game with in players, we establish an upper bound of O(radicnlog² n) on the price of anarchy, and a lower bound of Omega(log n/log log n). For the fractional model, we prove the existence of a Nash equilibrium via a potential function and give a polynomial time algorithm for computing an equilibrium that minimizes the potential function. Finally, we consider a weighted extension of the multicast game, and prove that in the fractional model, the game always has a Nash equilibrium.