Toward personalized, context-aware routing

Yang, Bin; Guo, Chenjuan; Ma, Yu; Jensen, Christian S.

doi:10.1007/s00778-015-0378-1

Cited by 73 publications

(46 citation statements)

References 21 publications

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“…A few recent studies has used GPS trajectories to estimate travel time and fuel consumption for different roads in various settings [29]- [33]. GPS trajectories also enable advanced vehicle navigation systems, e.g., personalized navigation [34], [35] and ecorouting [36], [37].…”

Section: Related Workmentioning

confidence: 99%

Network-Matched Trajectory-Based Moving-Object Database: Models and Applications

Ding

Yang

Güting

et al. 2015

IEEE Trans. Intell. Transport. Syst.

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Tracking and managing the locations of moving objects are essential in modern intelligent transportation systems (ITSs). However, a number of limitations in existing methods make them unsuitable for real-world ITS applications. In particular, Euclidean-based methods are not accurate enough in representing locations and in analyzing traffic, unless the locations are frequently updated. Network-based methods require either digital maps to be installed in moving objects or transmission of prediction policies, which inevitably increase the cost. To solve these problems, we propose a network-matched trajectory-based moving-object database (NMTMOD) mechanism and a traffic flow analysis method using the NMTMOD. In the NMTMOD, the locations of moving objects are tracked through a dense sampling and batch uploading strategy, and a novel edge-centric networkmatching method, which is running at the server side, is adopted to efficiently match the densely sampled GPS points to the network. In addition, a deviation-based trajectory optimization method is provided to minimize the trajectory size. Empirical studies with large real trajectory data set offer insight into the design properties of the proposed NMTMOD and suggest that the NMTMOD significantly outperforms other mobile-map free-moving-object database models in terms of precision of both location tracking and network-based traffic flow analysis.Index Terms-Moving-object database, network-matched trajectory, spatiotemporal database, traffic flow analysis.

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Section: Related Workmentioning

confidence: 99%

Network-Matched Trajectory-Based Moving-Object Database: Models and Applications

Ding

Yang

Güting

et al. 2015

IEEE Trans. Intell. Transport. Syst.

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“…This allows personalized eco-routing to suggest routes according to each driver's preferences in different contexts [10]. For example, a driver may get the fastest route during peak hours, while getting a route that considers a trade-off between travel time and fuel consumption during a weekend trip.…”

Section: Eco-routingmentioning

confidence: 99%

“…Additional Demonstration Content: Participants will also learn details on how EcoSky derives and maintains timedependent and uncertain eco-weights and how EcoSky conducts routing on a graph with time-dependent and uncertain edge weights [7], and they will receive information on how to derive individual drivers' driving preferences based on their historical trajectories [10].…”

Section: Demonstration Outlinementioning

confidence: 99%

EcoSky: Reducing vehicular environmental impact through eco-routing

Guo

Yang

Andersen

et al. 2015

2015 IEEE 31st International Conference on Data Engineering

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Reduction in greenhouse gas emissions from transportation attracts increasing interest from governments, fleet managers, and individual drivers. Eco-routing, which enables drivers to use eco-friendly routes, is a simple and effective approach to reducing emissions from transportation. We present EcoSky, a system that annotates edges of a road network with time dependent and uncertain eco-weights using GPS data and that supports different types of eco-routing. Basic eco-routing returns the most eco-friendly routes; skyline eco-routing takes into account not only fuel consumption but also travel time and distance when computing eco-routes; and personalized eco-routing considers each driver's past behavior and accordingly suggests different routes to different drivers.

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“…The RD query is applied in spatial networks, since in a large number of practical scenarios, travelers move in spatial networks (e.g., road networks) rather than in a Euclidean space [25,26,19,21,20]. To enable efficient processing of the RD query, for each vertex p, we pre-compute the number of spatial objects that are covered by a circular region defined by (p, τ.r), where p is the center point and τ.r is the radius.…”

Section: Introductionmentioning

confidence: 99%

Finding regions of interest using location based social media

et al. 2016

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The discovery of regions of interest in city groups is increasingly important in recent years. In this light, we propose and investigate a novel problem called Region Discovery query (RD query) that finds regions of interest with respect to a user's current geographic location. Given a set of spatial objects O and a query location q, if a circular region ω is with high spatial-object density and is spatially close to q, it is returned by the query and is recommended to users. This type of query can bring significant benefit to users in many useful applications such as trip planning and region recommendation. The RD query faces a big challenge: how to prune the search space in the spatial and density domains. To overcome the challenge and process the RD query efficiently, we propose a novel collaboration search method and we define a pair of bounds to prune the search space effectively. The performance of the RD query is studied by extensive experiments on real and synthetic spatial data.

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Toward personalized, context-aware routing

Cited by 73 publications

References 21 publications

Network-Matched Trajectory-Based Moving-Object Database: Models and Applications

Network-Matched Trajectory-Based Moving-Object Database: Models and Applications

EcoSky: Reducing vehicular environmental impact through eco-routing

Finding regions of interest using location based social media

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