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25
Discovering regions of different functions in a city using human mobility and POIs
- In Proceedings of the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
"... The development of a city gradually fosters different functional re-gions, such as educational areas and business districts. In this paper, we propose a framework (titled DRoF) that Discovers Regions of different Functions in a city using both human mobility among re-gions and points of interests (P ..."
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The development of a city gradually fosters different functional re-gions, such as educational areas and business districts. In this paper, we propose a framework (titled DRoF) that Discovers Regions of different Functions in a city using both human mobility among re-gions and points of interests (POIs) located in a region. Specifically, we segment a city into disjointed regions according to major roads, such as highways and urban express ways. We infer the functions of each region using a topic-based inference model, which regards a region as a document, a function as a topic, categories of POIs (e.g., restaurants and shopping malls) as metadata (like authors, af-filiations, and key words), and human mobility patterns (when peo-ple reach/leave a region and where people come from and leave for) as words. As a result, a region is represented by a distribution of functions, and a function is featured by a distribution of mobility patterns. We further identify the intensity of each function in differ-ent locations. The results generated by our framework can benefit a variety of applications, including urban planning, location choos-ing for a business, and social recommendations. We evaluated our method using large-scale and real-world datasets, consisting of two POI datasets of Beijing (in 2010 and 2011) and two 3-month GPS trajectory datasets (representing human mobility) generated by over 12,000 taxicabs in Beijing in 2010 and 2011 respectively. The re-sults justify the advantages of our approach over baseline methods solely using POIs or human mobility.
Visual traffic jam analysis based on trajectory data
- IEEE Trans. Vis. Comput. Graphics
, 2013
"... Accepted for publication by IEEE. ©2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/ ..."
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Accepted for publication by IEEE. ©2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/
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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Cited by 7 (1 self)
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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.
Adaptive Collective Routing Using Gaussian Process Dynamic Congestion Models
"... We consider the problem of adaptively routing a fleet of cooperative vehicles within a road network in the presence of uncertain and dynamic congestion conditions. To tackle this problem, we first propose a Gaussian Process Dynamic Congestion Model that can effectively characterize both the dynamics ..."
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Cited by 4 (0 self)
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We consider the problem of adaptively routing a fleet of cooperative vehicles within a road network in the presence of uncertain and dynamic congestion conditions. To tackle this problem, we first propose a Gaussian Process Dynamic Congestion Model that can effectively characterize both the dynamics and the uncertainty of congestion conditions. Our model is efficient and thus facilitates real-time adaptive routing in the face of uncertainty. Using this congestion model, we develop an efficient algorithm for non-myopic adaptive routing to minimize the collective travel time of all vehicles in the system. A key property of our approach is the ability to efficiently reason about the long-term value of exploration, which enables collectively balancing the exploration/exploitation trade-off for entire fleets of vehicles. We validate our approach based on traffic data from two large Asian cities. We show that our congestion model is effective in modeling dynamic congestion conditions. We also show that our routing algorithm generates significantly faster routes compared to standard baselines, and achieves near-optimal performance compared to an omniscient routing algorithm. We also present the results from a preliminary field study, which showcases the efficacy of our approach.
Smarter outlier detection and deeper understanding of large-scale taxi trip records: a case study of NYC
- In SIGKDD ’12 Workshop on Urban Computing
"... Outlier detection in large-scale taxi trip records has imposed significant technical challenges due to huge data volumes and complex semantics. In this paper, we report our preliminary work on detecting outliers from 166 millions taxi trips in the New York City (NYC) in 2009 through efficient spatia ..."
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Outlier detection in large-scale taxi trip records has imposed significant technical challenges due to huge data volumes and complex semantics. In this paper, we report our preliminary work on detecting outliers from 166 millions taxi trips in the New York City (NYC) in 2009 through efficient spatial analysis and network analysis using a NAVTEQ street network with half a million edges. As a byproduct of large-scale shortest path computation in outlier detection, betweenness centralities of street network edges are computed and mapped. The techniques can be used to help better understand the connection strengths among different parts of NYC using the large-scale taxi trip records.
Segmentation of Urban Areas Using Road Networks ∗
"... Region-based analysis is fundamental and crucial in many geospatialrelated applications and research themes, such as trajectory analysis, human mobility study and urban planning. In this paper, we report on an image-processing-based approach to segment urban areas into regions by road networks. Here ..."
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Cited by 4 (2 self)
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Region-based analysis is fundamental and crucial in many geospatialrelated applications and research themes, such as trajectory analysis, human mobility study and urban planning. In this paper, we report on an image-processing-based approach to segment urban areas into regions by road networks. Here, each segmented region is bounded by the high-level road segments, covering some neighborhoods and low-level streets. Typically, road segments are classified into different levels (e.g., highways and expressways are usually high-level roads), providing us with a more natural and semantic segmentation of urban spaces than the grid-based partition method. We show that through simple morphological operators, an urban road network can be efficiently segmented into regions. In addition, we present a case study in trajectory mining to demonstrate the usability of the proposed segmentation method. Categories and Subject Descriptors H.2.8 [Database Management]: spatial databases and GIS, data mining.
On Mining Anomalous Patterns in Road Traffic Streams
- In 7th International Conference on Advanced Data Mining and Applications
, 2011
"... Abstract. Large number of taxicabs in major metropolitan cities are now equipped with a GPS device. Since taxis are on the road nearly twenty four hours a day (with drivers changing shifts), they can now act as reliable sensors to monitor the behav-ior of traffic. In this paper we use GPS data from ..."
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Abstract. Large number of taxicabs in major metropolitan cities are now equipped with a GPS device. Since taxis are on the road nearly twenty four hours a day (with drivers changing shifts), they can now act as reliable sensors to monitor the behav-ior of traffic. In this paper we use GPS data from taxis to monitor the emergence of unexpected behavior in the Beijing metropolitan area. We adapt likelihood ratio tests (LRT) which have previously been mostly used in epidemiological studies to describe traffic patterns. To the best of our knowledge the use of LRT in traffic domain is not only novel but results in accurate and rapid detection of anomalous behavior. Key words: Spatio-temporal outlier, persistent, emerging, upper-bounding 1
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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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%.
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.
Mining the Situation: Spatiotemporal Traffic Prediction With Big Data
"... Abstract—With the vast availability of traffic sensors fromwhich traffic information can be derived, a lot of research effort has been devoted to developing traffic prediction techniques, which in turn improve route navigation, traffic regulation, urban area planning, etc. One key challenge in traff ..."
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Abstract—With the vast availability of traffic sensors fromwhich traffic information can be derived, a lot of research effort has been devoted to developing traffic prediction techniques, which in turn improve route navigation, traffic regulation, urban area planning, etc. One key challenge in traffic prediction is how much to rely on prediction models that are constructed using historical data in real-time traffic situations, which may differ from that of the his-torical data and change over time. In this paper, we propose a novel online framework that could learn from the current traffic situation (or context) in real-time and predict the future traffic by matching the current situation to the most effective prediction model trained using historical data. As real-time traffic arrives, the traffic context space is adaptively partitioned in order to effi-ciently estimate the effectiveness of each base predictor in different situations. We obtain and prove both short-term and long-term performance guarantees (bounds) for our online algorithm. The proposed algorithm also works effectively in scenarios where the true labels (i.e., realized traffic) are missing or become available with delay. Using the proposed framework, the context dimension that is the most relevant to traffic prediction can also be revealed, which can further reduce the implementation complexity as well as inform traffic policy making. Our experiments with real-world data in real-life conditions show that the proposed approach sig-nificantly outperforms existing solutions. Index Terms—Traffic prediction, big data, spatiotemporal, con-text-aware, online learning. I.
