EcoSky: Reducing vehicular environmental impact through eco-routing

Guo, Chenjuan; Yang, Bin; Andersen, Ove; Jensen, Christian S.; Torp, Kristian

doi:10.1109/icde.2015.7113389

Cited by 41 publications

(16 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Large-scale environmental problems involve managing and processing large datasets [2,3]. A prominent example is anthropogenic litter data, which is data about waste that originates from human activities such as food waste, diapers, construction materials, used motor oil, and hypodermic needles.…”

Section: Introductionmentioning

confidence: 99%

Scalable Multi-resolution Spatial Visualization for Anthropogenic Litter Data

Kang¹,

Zhao

Magdy

et al. 2019

Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

View full text Add to dashboard Cite

This paper demonstrates CleanUpOurWorld; a research spatial database that is designed and deployed to collect, process, query, and visualize anthropogenic litter data. Such data has a signicant importance in the eld of environmental sciences due to its important use cases. We make a major on-going eort to collect and maintain such data worldwide from dierent sources through a community of environmental scientists and partner organizations. With the increasing volume of data, existing software packages, such as GIS software, do not scale to process, query, and visualize such data. To overcome this, CleanUpOurWorld digests datasets from dierent sources, with dierent formats, in a scalable backend that cleans, integrates, and unies them in a structured form in a relational spatial database. Frontend applications are built to visualize litter data at multiple spatial resolutions.

show abstract

“…Traditional routing is cost-centric and aims at returning paths with minimal costs, e.g., distance, travel time, or fuel consumption. The cost of a path is computed from edge costs in edge-based cost modeling [6]- [11] or sub-path costs in path-based cost modeling [12]- [15]. In such routing, trajectory data is often used for annotating the edges or sub-paths with travel costs such as travel times; and routing services employ shortest path algorithms, e.g., Dijkstra's algorithm or contraction hierarchies [16], to return fastest, or simply shortest, paths.…”

Section: Introductionmentioning

confidence: 99%

Learning to Route with Sparse Trajectory Sets

Guo

Yang

et al. 2018

2018 IEEE 34th International Conference on Data Engineering (ICDE)

Self Cite

View full text Add to dashboard Cite

Motivated by the increasing availability of vehicle trajectory data, we propose learn-to-route, a comprehensive trajectory-based routing solution. Specifically, we first construct a graph-like structure from trajectories as the routing infrastructure. Second, we enable trajectory-based routing given an arbitrary (source, destination) pair.In the first step, given a road network and a collection of trajectories, we propose a trajectory-based clustering method that identifies regions in a road network. If a pair of regions are connected by trajectories, we maintain the paths used by these trajectories and learn a routing preference for travel between the regions. As trajectories are skewed and sparse, many region pairs are not connected by trajectories. We thus transfer routing preferences from region pairs with sufficient trajectories to such region pairs and then use the transferred preferences to identify paths between the regions. In the second step, we exploit the above graph-like structure to achieve a comprehensive trajectorybased routing solution. Empirical studies with two substantial trajectory data sets offer insight into the proposed solution, indicating that it is practical. A comparison with a leading routing service offers evidence that the paper's proposal is able to enhance routing quality. This is an extended version of "Learning to Route with Sparse Trajectory Sets" [1], to appear in IEEE ICDE 2018.

show abstract

EcoSky: Reducing vehicular environmental impact through eco-routing

Cited by 41 publications

References 9 publications

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

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

Scalable Multi-resolution Spatial Visualization for Anthropogenic Litter Data

Learning to Route with Sparse Trajectory Sets

Contact Info

Product

Resources

About