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36
TDrive: Driving directions based on taxi trajectories
 ACM SIGSPATIAL GIS
, 2010
"... GPSequipped taxis can be regarded as mobile sensors probing traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge. In this paper, we mine smart driving directions from the historical GPS trajectorie ..."
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Cited by 96 (21 self)
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GPSequipped taxis can be regarded as mobile sensors probing traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge. In this paper, we mine smart driving directions from the historical GPS trajectories of a large number of taxis, and provide a user with the practically fastest route to a given destination at a given departure time. In our approach, we propose a timedependent landmark graph, where a node (landmark) is a road segment frequently traversed by taxis, to model the intelligence of taxi drivers and the properties of dynamic road networks. Then, a VarianceEntropyBased Clustering approach is devised to estimate the distribution of travel time between two landmarks in different time slots. Based on this graph, we design a twostage routing algorithm to compute the practically fastest route. We build our system based on a realworld trajectory dataset generated by over 33,000 taxis in a period of 3 months, and evaluate the system by conducting both synthetic experiments and inthefield evaluations. As a result, 60–70 % of the routes suggested by our method are faster than the competing methods, and 20 % of the routes share the same results. On average, 50 % of our routes are at least 20 % faster than the competing approaches.
Driving with Knowledge from the Physical World
 In Proc. of KDD 2011
"... This paper presents a Cloudbased system computing customized and practically fast driving routes for an end user using (historical and realtime) traffic conditions and driver behavior. In this system, GPSequipped taxicabs are employed as mobile sensors constantly probing the traffic rhythm of a c ..."
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Cited by 67 (13 self)
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This paper presents a Cloudbased system computing customized and practically fast driving routes for an end user using (historical and realtime) traffic conditions and driver behavior. In this system, GPSequipped taxicabs are employed as mobile sensors constantly probing the traffic rhythm of a city and taxi drivers’ intelligence in choosing driving directions in the physical world. Meanwhile, a Cloud aggregates and mines the information from these taxis and other sources from the Internet, like Web maps and weather forecast. The Cloud builds a model incorporating day of the week, time of day, weather conditions, and individual driving strategies (both of the taxi drivers and of the end user for whom the route is being computed). Using this model, our system predicts the traffic conditions of a future time (when the computed route is actually driven) and performs a selfadaptive driving direction service for a particular user. This service gradually learns a user’s driving behavior from the user’s GPS logs and customizes the fastest route for the user with the help of the Cloud. We evaluate our service using a realworld dataset generated by over 33,000 taxis over a period of 3 months in Beijing. As a result, our service accurately estimates the travel time of a route for a user; hence finding the fastest route customized for the user.
Discovering Popular Routes from Trajectories
 In ICDE
, 2011
"... Abstract—The booming industry of locationbased services has accumulated a huge collection of users ’ location trajectories of driving, cycling, hiking, etc. In this work, we investigate the problem of discovering the Most Popular Route (MPR) between two locations by observing the traveling behavior ..."
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Cited by 28 (0 self)
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Abstract—The booming industry of locationbased services has accumulated a huge collection of users ’ location trajectories of driving, cycling, hiking, etc. In this work, we investigate the problem of discovering the Most Popular Route (MPR) between two locations by observing the traveling behaviors of many previous users. This new query is beneficial to travelers who are asking directions or planning a trip in an unfamiliar city/area, as historical traveling experiences can reveal how people usually choose routes between locations. To achieve this goal, we firstly develop a Coherence Expanding algorithm to retrieve a transfer network from raw trajectories, for indicating all the possible movements between locations. After that, the Absorbing Markov Chain model is applied to derive a reasonable transfer probability foreachtransfernodeinthe network, which is subsequently used as the popularity indicator in the search phase. Finally, we propose a Maximum Probability Product algorithm to discover the MPR from a transfer network based on the popularity indicators in a breadthfirst manner, and we illustrate the results and performance of the algorithm by extensive experiments. I.
On Querying Historical Evolving Graph Sequences
"... In many applications, information is best represented as graphs. In a dynamic world, information changes and so the graphs representing the information evolve with time. We propose that historical graphstructured data be maintained for analytical processing. We call a historical evolving graph sequ ..."
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Cited by 20 (2 self)
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In many applications, information is best represented as graphs. In a dynamic world, information changes and so the graphs representing the information evolve with time. We propose that historical graphstructured data be maintained for analytical processing. We call a historical evolving graph sequence an EGS. We observe that in many applications, graphs of an EGS are large and numerous, and they often exhibit much redundancy among them. We study the problem of efficient query processing on an EGS and put forward a solution framework called FVF. Through extensive experiments on both real and synthetic datasets, we show that our FVF framework is highly efficient in EGS query processing. 1.
Efficient KNearest Neighbor Search in TimeDependent Spatial Networks
, 2010
"... The class of k Nearest Neighbor (kNN) queries in spatial networks has been widely studied in the literature. All existing approaches for kNN search in spatial networks assume that the weight (e.g., traveltime) of each edge in the spatial network is constant. However, in realworld, edgeweights a ..."
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Cited by 11 (1 self)
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The class of k Nearest Neighbor (kNN) queries in spatial networks has been widely studied in the literature. All existing approaches for kNN search in spatial networks assume that the weight (e.g., traveltime) of each edge in the spatial network is constant. However, in realworld, edgeweights are timedependent and vary significantly in short durations, hence invalidating the existing solutions. In this paper, we study the problem of kNN search in timedependent spatial networks where the weight of each edge is a function of time. We propose two novel indexing schemes, namely Tight Network Index (T NI) and Loose Network Index (LNI) to minimize the number of candidate nearest neighbor objects and, hence, reduce the invocation of the expensive fastestpath computation in timedependent spatial networks. We demonstrate the efficiency of our proposed solution via experimental evaluations with realworld datasets, including a variety of large spatial networks with real trafficdata.
Online Computation of Fastest Path in TimeDependent Spatial Networks
, 2011
"... The problem of pointtopoint fastest path computation in static spatial networks is extensively studied with many precomputation techniques proposed to speedup the computation. Most of the existing approaches make the simplifying assumption that traveltimes of the network edges are constant. Howe ..."
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Cited by 8 (5 self)
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The problem of pointtopoint fastest path computation in static spatial networks is extensively studied with many precomputation techniques proposed to speedup the computation. Most of the existing approaches make the simplifying assumption that traveltimes of the network edges are constant. However, with realworld spatial networks the edge traveltimes are timedependent, where the arrivaltime to an edge determines the actual traveltime on the edge. In this paper, we study the online computation of fastest path in timedependent spatial networks and present a technique which speedsup the path computation. We show that our fastest path computation based on a bidirectional timedependent A * search significantly improves the computation time and storage complexity. With extensive experiments using real datasets (including a variety of large spatial networks with real traffic data) we demonstrate the efficacy of our proposed techniques for online fastest path computation.
Towards Modeling the Traffic Data on Road Networks
"... A spatiotemporal network is a spatial network (e.g., road network) along with the corresponding timedependent weight (e.g., travel time) for each edge of the network. The design and analysis of policies and plans on spatiotemporal networks (e.g., path planning for locationbased services) require r ..."
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Cited by 6 (4 self)
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A spatiotemporal network is a spatial network (e.g., road network) along with the corresponding timedependent weight (e.g., travel time) for each edge of the network. The design and analysis of policies and plans on spatiotemporal networks (e.g., path planning for locationbased services) require realistic models that accurately represent the temporal behavior of such networks. In this paper, for the first time we propose a traffic modeling framework for road networks that enables 1) generating an accurate temporal model from archived temporal data collected from a spatiotemporal network (so as to be able to publish the temporal model of the spatiotemporal network without having to release the real data), and 2) augmenting any given spatial network model with a corresponding realistic temporal model custombuilt for that specific spatial network (in order to be able to generate a spatiotemporal network model from a solely spatial network model). We validate the accuracy of our proposed modeling framework via experiments. We also used the proposed framework to generate the temporal model of the Los Angeles County freeway network and publish it for public use. 1.
Preference Queries in Large MultiCost Transportation Networks
, 2010
"... Research on spatial network databases has so far considered that there is a single cost value associated with each road segment of the network. In most realworld situations, however, there may exist multiple cost types involved in transportation decision making. For example, the different costs of ..."
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Cited by 6 (1 self)
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Research on spatial network databases has so far considered that there is a single cost value associated with each road segment of the network. In most realworld situations, however, there may exist multiple cost types involved in transportation decision making. For example, the different costs of a road segment could be its Euclidean length, the driving time, the walking time, possible toll fee, etc. The relative significance of these cost types may vary from user to user. In this paper we consider such multicost transportation networks (MCN), where each edge (road segment) is associated with multiple cost values. We formulate skyline and topk queries in MCNs and design algorithms for their efficient processing. Our solutions have two important properties in preferencebased querying; the skyline methods are progressive and the topk ones are incremental. The performance of our techniques is evaluated with experiments on a real road network.
On Distributed TimeDependent Shortest Paths over DutyCycled Wireless Sensor Networks
"... Abstract—We revisit the shortest path problem in asynchronous dutycycled wireless sensor networks, which exhibit timedependent features. We model the timevarying link cost and distance from each node to the sink as periodic functions. We show that the timecost function satisfies the FIFO propert ..."
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Cited by 5 (1 self)
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Abstract—We revisit the shortest path problem in asynchronous dutycycled wireless sensor networks, which exhibit timedependent features. We model the timevarying link cost and distance from each node to the sink as periodic functions. We show that the timecost function satisfies the FIFO property, which makes the timedependent shortest path problem solvable in polynomialtime. Using the βsynchronizer, we propose a fast distributed algorithm to build alltoone shortest paths with polynomial message complexity and time complexity. The algorithm determines the shortest paths for all discrete times with a single execution, in contrast with multiple executions needed by previous solutions. We further propose an efficient distributed algorithm for timedependent shortest path maintenance. The proposed algorithm is loopfree with low message complexity and low space complexity of O(maxdeg), where maxdeg is the maximum degree for all nodes. The performance of our solution is evaluated under diverse network configurations. The results suggest that our algorithm is more efficient than previous solutions in terms of message complexity and space complexity. I.
Travel Cost Inference from Sparse, SpatioTemporally Correlated Time Series Using Markov Models
"... The monitoring of a system can yield a set of measurements that can be modeled as a collection of time series. These time series are often sparse, due to missing measurements, and spatiotemporally correlated, meaning that spatially close time series exhibit temporal correlation. The analysis of su ..."
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Cited by 5 (2 self)
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The monitoring of a system can yield a set of measurements that can be modeled as a collection of time series. These time series are often sparse, due to missing measurements, and spatiotemporally correlated, meaning that spatially close time series exhibit temporal correlation. The analysis of such time series offers insight into the underlying system and enables prediction of system behavior. While the techniques presented in the paper apply more generally, we consider the case of transportation systems and aim to predict travel cost from GPS tracking data from probe vehicles. Specifically, each road segment has an associated travelcost time series, which is derived from GPS data. We use spatiotemporal hidden Markov models (STHMM) to model correlations among different traffic time series. We provide algorithms that are able to learn the parameters of an STHMM while contending with the sparsity, spatiotemporal correlation, and heterogeneity of the time series. Using the resulting STHMM, near future travel costs in the transportation network, e.g., travel time or greenhouse gas emissions, can be inferred, enabling a variety of routing services, e.g., ecorouting. Empirical studies with a substantial GPS data set offer insight into the design properties of the proposed framework and algorithms, demonstrating the effectiveness and efficiency of travel cost inferencing. 1.