Traffic sign recognition (TSR) systems on the vehicles can collect posted speed limit sign information and have been in commercial usage since 2008. A daily-updated auto-pilot map can be constructed based on the massive amounts of TSR observations from multiple consumer vehicles; the data is then aggregated, filtered and processed, and the learned posted speed limit signs can be finally transferred to vehicles with high-coverage and real-time speed limit information. Compared with the direct sign detection by TSR systems, the auto-pilot map can complement the current detection errors, reduce the camera cost and provide a continuous speed limit information for autonomous vehicle applications. A pipeline of methods are specifically designed to deliver our research purpose by making full utilization of TSR observations and HERE map. Experimental results indicate that our proposed algorithms and methods can construct an auto-pilot map with an overall accuracy of 95.8%. It is also expected to update the speed limit information in a map at a faster pace than the traditional map since we are using sensors of customer vehicles instead of dedicated map construction vehicles. The utility of our proposed auto-pilot map opens a new perspective in autonomous driving.

Estimating urban traffic conditions of an arterial network with GPS probe data is a practically important while substantially challenging problem, and has attracted increasing research interests recently. Although GPS probe data is becoming a ubiquitous data source for various traffic related applications currently, they are usually insufficient for fully estimating traffic conditions of a large arterial network due to the low sampling frequency. To explore other data sources for more effectively computing urban traffic conditions, we propose to collect various traffic events such as traffic accident and jam from social media as complementary information. In addition, to further explore other factors that might affect traffic conditions, we also extract rich auxiliary information including social events, road features, Point of Interest (POI), and weather. With the enriched traffic data and auxiliary information collected from different sources, we first study the traffic co-congestion pattern mining problem with the aim of discovering which road segments geographically close to each other are likely to co-occur traffic congestion. A search tree based approach is proposed to efficiently discover the co-congestion patterns. These patterns are then used to help estimate traffic congestions and detect anomalies in a transportation network. To fuse the multisourced data, we finally propose a coupled matrix and tensor factorization model named TCE_R to more accurately complete the sparse traffic congestion matrix by collaboratively factorizing it with other matrices and tensors formed by other data. We evaluate the proposed model on the arterial network of downtown Chicago with 1,257 road segments whose total length is nearly 700 miles. The results demonstrate the superior performance of TCE_R by comprehensive comparison with existing approaches.

This paper studies the novel problem of more accurately estimating urban traffic congestions by integrating sparse probe data and traffic related information collected from social media. Limited by the lack of reliability and low sampling frequency of GPS probes, probe data are usually not sufficient for fully estimating traffic conditions of a large arterial network. To address the data sparsity challenge, we extensively collect and model traffic related data from multiple data sources. Besides the GPS probe data, we also extensively collect traffic related tweets that report various traffic events such as congestion, accident, and road construction from both traffic authority accounts and general user accounts from Twitter. To further explore other factors that might affect traffic conditions, we also extract auxiliary information including road congestion correlations, social events, road features, as well as point of interest (POI) for help. To integrate the different types of data coming from different sources, we finally propose a coupled matrix and tensor factorization model to more accurately complete the very sparse traffic congestion matrix by collaboratively factorizing it with other matrices and tensors formed by other data. We evaluate the proposed model on the arterial network of downtown Chicago with 1257 road segments. The results demonstrate the effectiveness and efficiency of the proposed model by comparison with previous approaches.

Conventional traffic congestion estimation approaches require the deployment of traffic sensors or large-scale probe vehicles. The high cost of deploying and maintaining these equipments largely limits their spatial-temporal coverage. This paper proposes an alternative solution with lower cost and wider spatial coverage by exploring traffic related information from Twitter. By regarding each Twitter user as a traffic monitoring sensor, various real-time traffic information can be collected freely from each corner of the city. However, there are two major challenges for this problem. Firstly, the congestion related information extracted directly from real-time tweets are very sparse due both to the low resolution of geographic location mentioned in the tweets and the inherent sparsity nature of Twitter data. Secondly, the traffic event information coming from Twitter can be multi-typed including congestion, accident, road construction, etc. It is non-trivial to model the potential impacts of diverse traffic events on traffic congestion. We propose to enrich the sparse real-time tweets from two directions: 1) mining the spatial and temporal correlations of the road segments in congestion from historical data, and 2) applying auxiliary information including social events and road features for help. We finally propose a coupled matrix and tensor factorization model to effectively integrate rich information for Citywide Traffic Congestion Eestimation (CTCE). Extensive evaluations on Twitter data and 500 million public passenger buses GPS data on nearly 700 mile roads of Chicago demonstrate the efficiency and effectiveness of the proposed approach.

Over the past decade Online Social Networks (OSNs) have been helping hundreds of millions of people develop reliable computer-mediated relations. However, many user profiles in OSNs contain misleading, inconsistent or false information. Existing studies have shown that lying in OSNs is quite widespread, often for protecting a user's privacy. In order for OSNs to continue expanding their role as a communication medium in our society, it is crucial for information posted on OSNs to be trusted. Here we define a set of analysis methods for detecting deceptive information about user genders in Twitter. In addition, we report empirical results with our stratified data set consisting of 174,600 Twitter profiles with a 50-50 breakdown between male and female users. Our automated approach compares gender indicators obtained from different profile characteristics including first name, user name, and layout colors. We establish the overall accuracy of each indicator and the strength of all possible values for each indicator through extensive experimentations with our data set. We define male trending users and female trending users based on two factors, namely the overall accuracy of each characteristic and the relative strength of the value of each characteristic for a given user. We apply a Bayesian classifier to the weighted average of characteristics for each user. We flag for possible deception profiles that we classify as male or female in contrast with a self-declared gender that we obtain independently of Twitter profiles. Finally, we use manual inspections on a subset of profiles that we identify as potentially deceptive in order to verify the correctness of our predictions.

Real-time parking availability information is important in urban areas, and if available could reduce congestion, pollution, and gas consumption. In this paper, we present a software solution called PhonePark for detecting the availability of on-street parking spaces. The solution uses the GPS and/or accelerometer sensors in a traveler's mobile phone to automatically detect when and where the traveler parked her car, and when she released a parking slot. PhonePark can also utilize the mobile phone's Bluetooth sensor or piggyback on street parking payment transactions for parking activity detection. Thus, the solution considers only mobile phones and does not rely on any external sensors such as cameras, wireless sensors embedded in the pavements, or ultrasonic sensors on vehicles. Further contributions include an algorithm to compute the historical parking availability profile for an arbitrary street block and algorithms to estimate the parking availability in real-time for a given street block. The algorithms are evaluated using real-time and real world street parking data.

Users of mass transit systems such as those of buses and trains normally rely on accurate route maps, stop locations, and service schedules when traveling.If the route map, service schedule, or stop location has errors it can reduce the transit agency's ridership.In this paper, the problem of deriving transit systems by mining raw GPS data is studied.Specifically, we propose and evaluate novel classification features with spatial and temporal clustering techniques that derive bus stop locations, route geometries, and service schedules from GPS data.Subsequently, manual and expensive field visits to record and annotate the initial or updated route geometries, transit stop locations, or service schedules is no longer required by transit agencies.This facilitates a massive reduction in cost for transit agencies.The effectiveness of the proposed algorithms is validated on the third largest public transit system in the United States.

Over the last few years, cloud services have been steadily gaining traction in their use by commercial and noncommercial entities. As more and more sensitive or valuable processes, business functions and data move into the cloud, the need to improve threat identification and response, via auditing cloud transactions, increases. At the same time, the need for cloud users to protect the security and privacy of their resources has also intensified. In this paper, the problem of simultaneously supporting privacy and auditing in cloud systems is studied. Specifically, the paper discusses the guiding principles, fundamental concepts, and threat models for current cloud computing systems. Finally, we propose infrastructure that exploits a novel thin layer between the client and the cloud service provider to ensure that data storage, operation, and auditing does not reveal sensitive client information.

Real-time street parking availability information is important in urban areas, and if available could reduce congestion, pollution, and gas consumption. In this paper, an advanced street parking system called PhonePark is presented. Using the GPS, accelerometer, and Bluetooth sensors on a traveler's mobile phone, in conjunction with geospatial data, we can automatically detect when and where the traveler parked her car, and when she released a parking slot.

The transportation mode such as walking, cycling or on a train denotes an important characteristic of the mobile user's context. In this paper, we propose an approach to inferring a user's mode of transportation based on the GPS sensor on her mobile device and knowledge of the underlying transportation network. The transportation network information considered includes real time bus locations, spatial rail and spatial bus stop information. We identify and derive the relevant features related to transportation network information to improve classification effectiveness. This approach can achieve over 93.5% accuracy for inferring various transportation modes including: car, bus, aboveground train, walking, bike, and stationary. Our approach improves the accuracy of detection by 17% in comparison with the GPS only approach, and 9% in comparison with GPS with GIS models. The proposed approach is the first to distinguish between motorized transportation modes such as bus, car and aboveground train with such high accuracy. Additionally, if a user is travelling by bus, we provide further information about which particular bus the user is riding. Five different inference models including Bayesian Net, Decision Tree, Random Forest, Naïve Bayesian and Multilayer Perceptron, are tested in the experiments. The final classification system is deployed and available to the public.

A major concern for deployment of location-based mobile systems is the ill-usage of mobile client's location data, which may imply sensitive and private personal information. Also, even if the location is exposed willingly by the mobile client the query should not be linked to the mobile client. Sti

With the rapid advancement of positioning and tracking capabilities (mobile phones, on-board navigation systems) location based services are rapidly increasing. Privacy in location based systems is addressed in many papers. Our work is focused on the trusted third party privacy framework that utilizes the concept of k-anonymity with or without l-diversity. In previous anonymization models k may be defined as a personalization parameter of the mobile user or as uniform system parameter for all mobile users . Clearly, k other users may not be available at the time of request in these systems. These requests are discarded because the quality of service (QoS) they require cannot be satisfied. In this paper we introduce a novel suite of algorithms called MobiPriv that guarantees a 100% success rate of processing a mobile request using k-anonymity with diversity considerations. We evaluated our suite of algorithms experimentally against previously proposed anonymization algorithms using real world traffic volume data, real world road network and mobile users generated realistically by a mobile object generator.