TY - GEN
T1 - Probabilistic speed profiling for multi-lane road networks
AU - Zhang, Bing
AU - Trajcevski, Goce
N1 - Funding Information:
The work was supported by the NSF grants III 1213038 and CNS 1646107, and the ONR grant N00014-14-10215.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/29
Y1 - 2017/6/29
N2 - We address the problem of incorporating uncertain location data in the generation of speed profiles for vehicles on roads with multiple lanes. Moving objects' location data can be obtained from different/multiple sources-e.g., GPS on-board the moving objects, roadside sensors, cameras. However, each source has inherent limitations that affect the precision-from pure measurement-errors, to sparsity of their distribution. Incorporating such imprecision is paramount in any query/analytics oriented system that deals with location data. The difficulties multiply when one needs to reason about localization with lane-awareness and attempts to use the location-in-time data to enable effective navigation systems. To tackle this problem, we take a step towards: (a) incorporating uncertainty of the objects' locations into traditional map-matching processes, thereby augmenting them with its impact on different lanes, (b) introducing an information theoretic distance function that can be used to decide when two 'units' qualify to belong to a same cluster. Our experiments demonstrate that the proposed approach offers a more effective way to generate spatio-temporal clusters with similar speed profiles which, in turn, enables more efficient routes generation.
AB - We address the problem of incorporating uncertain location data in the generation of speed profiles for vehicles on roads with multiple lanes. Moving objects' location data can be obtained from different/multiple sources-e.g., GPS on-board the moving objects, roadside sensors, cameras. However, each source has inherent limitations that affect the precision-from pure measurement-errors, to sparsity of their distribution. Incorporating such imprecision is paramount in any query/analytics oriented system that deals with location data. The difficulties multiply when one needs to reason about localization with lane-awareness and attempts to use the location-in-time data to enable effective navigation systems. To tackle this problem, we take a step towards: (a) incorporating uncertainty of the objects' locations into traditional map-matching processes, thereby augmenting them with its impact on different lanes, (b) introducing an information theoretic distance function that can be used to decide when two 'units' qualify to belong to a same cluster. Our experiments demonstrate that the proposed approach offers a more effective way to generate spatio-temporal clusters with similar speed profiles which, in turn, enables more efficient routes generation.
KW - Multi-lane roads
KW - Speed profiles
KW - Uncertain trajectories
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U2 - 10.1109/MDM.2017.30
DO - 10.1109/MDM.2017.30
M3 - Conference contribution
AN - SCOPUS:85026747523
T3 - Proceedings - 18th IEEE International Conference on Mobile Data Management, MDM 2017
SP - 164
EP - 173
BT - Proceedings - 18th IEEE International Conference on Mobile Data Management, MDM 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE International Conference on Mobile Data Management, MDM 2017
Y2 - 29 May 2017 through 1 June 2017
ER -