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Destination Prediction by Sub-Trajectory Synthesis and Privacy Protection Against Such Prediction
"... Abstract — Destination prediction is an essential task for many emerging location based applications such as recommending sightseeing places and targeted advertising based on destination. A common approach to destination prediction is to derive the probability of a location being the destination bas ..."
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Abstract — Destination prediction is an essential task for many emerging location based applications such as recommending sightseeing places and targeted advertising based on destination. A common approach to destination prediction is to derive the probability of a location being the destination based on historical trajectories. However, existing techniques using this approach suffer from the “data sparsity problem”, i.e., the available historical trajectories is far from being able to cover all possible trajectories. This problem considerably limits the number of query trajectories that can obtain predicted destinations. We propose a novel method named Sub-Trajectory Synthesis (SubSyn) algorithm to address the data sparsity problem. SubSyn algorithm first decomposes historical trajectories into sub-trajectories comprising two neighbouring locations, and then connects the sub-trajectories into “synthesised ” trajectories. The number of query trajectories that can have predicted destinations is exponentially increased by this means. Experiments based on real datasets show that SubSyn algorithm can predict destinations for up to ten times more query trajectories than a baseline algorithm while the SubSyn prediction algorithm runs over two orders of magnitude faster than the baseline algorithm. In this paper, we also consider the privacy protection issue in case an adversary uses SubSyn algorithm to derive sensitive location information of users. We propose an efficient algorithm to select a minimum number of locations a user has to hide on her trajectory in order to avoid privacy leak. Experiments also validate the high efficiency of the privacy protection algorithm. I.
Urban Computing: Concepts, Methodologies, and Applications
"... Urbanization’s rapid progress has modernized many people’s lives, but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities, e.g., traffic flow, human mobility and geo ..."
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Cited by 14 (7 self)
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Urbanization’s rapid progress has modernized many people’s lives, but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities, e.g., traffic flow, human mobility and geographical data. Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people’s lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology, in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Secondly, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety & security, presenting representative scenarios in each category. Thirdly, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we outlook the
Crowd Sensing of Traffic Anomalies based on Human Mobility and Social Media 1
"... The advances in mobile computing and social networking services enable people to probe the dynamics of a city. In this paper, we address the problem of detecting and describing traffic anomalies using crowd sensing with two forms of data, human mobility and social media. Traffic anomalies are caused ..."
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The advances in mobile computing and social networking services enable people to probe the dynamics of a city. In this paper, we address the problem of detecting and describing traffic anomalies using crowd sensing with two forms of data, human mobility and social media. Traffic anomalies are caused by accidents, control, protests, sport events, celebrations, disasters and other events. Unlike the existing traffic-anomaly-detection methods, we identify anomalies according to driversâĂ ´Z routing behavior on an urban road network. Here, a detected anomaly is represented by a sub-graph of a road network where peopleâĂ ´Zs routing behaviors significantly differ from their original patterns. We then try to describe a detected anomaly by mining representative terms from the social media that people posted when the anomaly happened. The system for detecting such traffic anomalies can benefit both drivers and transportation authorities, e.g., by notifying drivers approaching an anomaly and suggesting alternative routes, as well as supporting traffic jam diagnosis and dispersal. We evaluated our system with a GPS trajectory dataset generated by over 30,000 taxicabs over a period of 3 months in Beijing, and a dataset of tweets collected from WeiBo, a Twitter-like social site in China. The results demonstrate the effectiveness and efficiency of our system.
Exploiting Large-Scale Check-in Data to Recommend Time-Sensitive Routes
- Proceedings of the ACM SIGKDD International Workshop on Urban Computing
, 2012
"... Location-based services allow users to perform geo-spatial check-in actions, which facilitates the mining of the moving activities of human beings. This paper proposes to recommend time-sensitive trip routes, consisting of a sequence of locations with associated time stamps, based on knowledge extra ..."
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Location-based services allow users to perform geo-spatial check-in actions, which facilitates the mining of the moving activities of human beings. This paper proposes to recommend time-sensitive trip routes, consisting of a sequence of locations with associated time stamps, based on knowledge extracted from large-scale check-in data. Given a query location with the starting time, our goal is to recommend a time-sensitive route. We argue a good route should consider (a) the popularity of places, (b) the visiting order of places, (c) the proper visiting time of each place, and (d) the proper transit time from one place to another. By devising a statistical model, we integrate these four factors into a goodness function which aims to measure the quality of a route. Equipped with the goodness measure, we propose a greedy method to construct the time-sensitive route for the query. Experiments on Gowalla datasets demonstrate the effectiveness of our model on detecting real routes and cloze test of routes, comparing with other baseline methods. We also develop a system TripRouter as a real-time demo platform.
Understanding urban human activity and mobility patterns using large-scale location-based data from online social media
- In UrbComp
, 2013
"... ABSTRACT Location-based check-in services enable individuals to share their activity-related choices providing a new source of human activity data for researchers. In this paper urban human mobility and activity patterns are analyzed using locationbased data collected from social media applications ..."
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ABSTRACT Location-based check-in services enable individuals to share their activity-related choices providing a new source of human activity data for researchers. In this paper urban human mobility and activity patterns are analyzed using locationbased data collected from social media applications (e.g. Foursquare and Twitter). We first characterize aggregate activity patterns by finding the distributions of different activity categories over a city geography and thus determine the purpose-specific activity distribution maps. We then characterize individual activity patterns by finding the timing distribution of visiting different places depending on activity category. We also explore the frequency of visiting a place with respect to the rank of the place in individual's visitation records and show interesting match with the results from other studies based on mobile phone data.
1coRide: Carpool Service with a Win-Win Fare Model for Large-Scale Taxicab Networks ∗
"... Carpooling has long held the promise of reducing gas consumption by decreasing mileage to deliver co-riders. Al-though ad hoc carpools already exist in the real world through private arrangements, little research on the topic has been done. In this paper, we present the first systematic work to desi ..."
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Cited by 4 (1 self)
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Carpooling has long held the promise of reducing gas consumption by decreasing mileage to deliver co-riders. Al-though ad hoc carpools already exist in the real world through private arrangements, little research on the topic has been done. In this paper, we present the first systematic work to design, implement, and evaluate a carpool service, called coRide, in a large-scale taxicab network intended to reduce total mileage for less gas consumption. Our coRide system consists of three components, a dispatching cloud server, passenger clients, and an onboard customized de-vice, called TaxiBox. In the coRide design, in response to the delivery requests of passengers, dispatching cloud servers calculate cost-efficient carpool routes for taxicab drivers and thus lower fares for the individual passengers. To improve coRide’s efficiency in mileage reduction, we formulate a NP-hard route calculation problem under differ-ent practical constraints. We then provide (i) an optimal algorithm using Linear Programming, (ii) a 2 approximation algorithm with a polynomial complexity, and (iii) its corre-sponding online version. To encourage coRide’s adoption, we present a win-win fare model as the incentive mechanis-m for passengers and drivers to participate. We evaluate coRide with a real world dataset of more than 14,000 taxi-cabs, and the results show that compared with the ground truth, our service can reduce 33 % of total mileage; with our win-win fare model, we can lower passenger fares by 49% and simultaneously increase driver profit by 76%.
Crowdplanner: A crowd-based route recommendation system
- In ICDE
, 2014
"... Abstract — As travel is taking more significant part in our life, route recommendation service becomes a big business and attracts many major players in IT industry. Given a user specified origin and destination, a route recommendation service aims to provide users the routes with the best travellin ..."
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Abstract — As travel is taking more significant part in our life, route recommendation service becomes a big business and attracts many major players in IT industry. Given a user specified origin and destination, a route recommendation service aims to provide users the routes with the best travelling experience according to criteria such as travelling distance, travelling time, traffic condition, etc. However, previous research shows that even the routes recommended by the big-thumb service providers can deviate significantly from the routes travelled by experienced drivers. It means travellers ’ preferences on route selection are influenced by many latent and dynamic factors that are hard to be modelled exactly with pre-defined formulas. In this work we approach this challenging problem with a completely different perspective – leveraging crowds ’ knowledge to improve the recommendation quality. In this light, CrowdPlanner – a novel crowd-based route recommendation system has been developed, which requests human workers to evaluate candidates routes recommended by different sources and methods, and determine the best route based on the feedbacks of these workers. Our system addresses two critical issues in its core components: a) task generation component generates a series of informative and concise questions with optimized ordering for a given candidate route set so that workers feel comfortable and easy to answer; and b) worker selection component utilizes a set of selection criteria and an efficient algorithm to find the most eligible workers to answer the questions with high accuracy. I.
CallCab: A Unified Recommendation System for Carpooling and Regular Taxicab Services
"... Abstract—Carpooling taxicab services hold the promise of providing additional transportation supply, especially in extreme weather or rush hour when regular taxicab services are insufficient. Although many recommendation systems about regular taxicab services have been proposed recently, little rese ..."
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Abstract—Carpooling taxicab services hold the promise of providing additional transportation supply, especially in extreme weather or rush hour when regular taxicab services are insufficient. Although many recommendation systems about regular taxicab services have been proposed recently, little research, if any, has been done to assist passengers to find a successful taxicab ride with carpooling. In this paper, we present the first systematic work to design a unified recommendation system for both regular and carpooling services, called CallCab, based on a data driven approach. In response to a passenger’s request, CallCab aims to recommend either (i) a vacant taxicab for a regular service with no detour, or (ii) an occupied taxicab heading to the similar direction for a carpooling service with less detour, yet without assuming any knowledge of destinations of passengers already on occupied taxicabs. To analyze these unknown destinations of occupied taxicabs, CallCab generates and refines taxicab trip distributions based on GPS datasets and context information collected in the existing taxicab infrastructure. To improve CallCab’s efficiency to process such a big dataset, we augment the efficient MapReduce model with a Measure phase tailored for our application. We evaluate CallCab with a real world dataset of 14, 000 taxicabs, and results show that compared to ground truth, CallCab can reduce 64 % of the total mileage to deliver all passengers and 63 % of passenger’s waiting time. I.
Efficient Frequent Sequence Mining on Taxi Trip Records Using Road Network Shortcuts
"... Huge amounts of geo-referenced spatial location data and moving object trajectory data are being generated at ever increasing rates. Patterns discovered from these data are valuable in understanding human mobility and facilitating traffic mitigation. In this study, we propose a new approach to minin ..."
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Huge amounts of geo-referenced spatial location data and moving object trajectory data are being generated at ever increasing rates. Patterns discovered from these data are valuable in understanding human mobility and facilitating traffic mitigation. In this study, we propose a new approach to mining frequent patterns from large-scale GPS trajectory data after mapping GPS traces to road network segments. Different from applying association rule-based frequent sequence mining algorithms directly, which generally have high computation overhead and are not scalable, our approach utilizes hierarchies of road networks. After contracting nodes and creating shortcuts by contraction hierarchies algorithms, the original road segment sequences are transformed into sequences of shortcuts with much smaller data volumes. By using computed shortest paths as simulated GPS trajectories, our experiments on 17,558 selected taxi trip records in NYC in January 2009 have shown that runtimes of frequent sequence mining on shortcut sequences are orders of magnitude faster than on original road segment sequences. In addition, frequent subsequences in shortcuts are more informative and interpretable based on the betweenness centralities of the shortcuts than visualizing betweenness centralities of individual road segments. 1
Dmodel: Online Taxicab Demand Model from Big Sensor Data in a Roving Sensor Network
"... Abstract—Investigating passenger demand is essential for the taxicab business. Existing solutions are typically based on offline data collected by manual investigations, which are often dated and inaccurate for real-time analysis. To address this issue, we propose Dmodel, employing roving taxicabs a ..."
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Abstract—Investigating passenger demand is essential for the taxicab business. Existing solutions are typically based on offline data collected by manual investigations, which are often dated and inaccurate for real-time analysis. To address this issue, we propose Dmodel, employing roving taxicabs as real-time mobile sensors to (i) infer passenger arriving moments by interactions of vacant taxicabs, and then (ii) infer passenger demand by a customized online training with both historical and real-time data. Such huge taxicab data (almost 1TB per year) pose a big data challenge to us. To address this challenge,Dmodel employs a novel parameter called pickup pattern (accounts for various real world logical information, e.g., bad weather) to reduce the size of data to be processed. We evaluate Dmodel with a real world 450 GB dataset of 14, 000 taxicabs for a half year, and results show that compared to ground truth,Dmodel achieves a 76 % accuracy on the demand inference and outperforms a statistical model by 39%. We further present an application where Dmodel is used to dispatch vacant taxicabs to achieve an equilibrium between passenger demand and taxicab supply across urban regions. I.
