Zheng Lu 2012a - Revision history

Scipediacontent at 15:07, 21 January 2021

2021-01-21T15:07:30Z

Scipediacontent: Scipediacontent moved page Draft Content 355470556 to Zheng Lu 2012a

2020-10-14T12:31:04Z

Scipediacontent moved page Draft Content 355470556 to Zheng Lu 2012a

Scipediacontent: Created page with " == Abstract == Traditional solutions to shortest path problems on time-varying transportation networks use traffic information only at precise moments regardless of consider..."

2020-10-14T12:31:02Z

Created page with " == Abstract == Traditional solutions to shortest path problems on time-varying transportation networks use traffic information only at precise moments regardless of consider..."

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== Abstract ==

Traditional solutions to shortest path problems on time-varying transportation networks use traffic information only at precise moments regardless of considering the fact that the travel time through any link is dependent on the time entering that link. In this study, travel speed rather than travel time on each link is used as the time period dependent parameter to model time-dependent transportation networks, and a First-In-First-Out (FIFO) condition satisfied computational function of link travel time is then deduced based on kinematics. Finally, a temporally adaptive A* shortest path algorithm on this FIFO network is presented, where the time factor is introduced into the evaluation function, and the Euclidean distance divided by the maximum possible travel speed is used as a heuristic evaluator. An experiment on a real road network shows that the proposed algorithm is capable of foreseeing and bypassing forthcoming traffic congestion, at a cost only about 10% more in computational time than the traditional algorithm. In addition, frequent path reoptimization required with use of the traditional algorithm is effectively avoided.

Document type: Part of book or chapter of book

== Full document ==
<pdf>Media:Draft_Content_355470556-beopen561-9767-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://www.isprs.org/proceedings/XXXVIII/part2/Papers/89_Paper.pdf http://www.isprs.org/proceedings/XXXVIII/part2/Papers/89_Paper.pdf]

← Older revision		Revision as of 15:07, 21 January 2021
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	Traditional solutions to shortest path problems on time-varying transportation networks use traffic information only at precise moments regardless of considering the fact that the travel time through any link is dependent on the time entering that link. In this study, travel speed rather than travel time on each link is used as the time period dependent parameter to model time-dependent transportation networks, and a First-In-First-Out (FIFO) condition satisfied computational function of link travel time is then deduced based on kinematics. Finally, a temporally adaptive A* shortest path algorithm on this FIFO network is presented, where the time factor is introduced into the evaluation function, and the Euclidean distance divided by the maximum possible travel speed is used as a heuristic evaluator. An experiment on a real road network shows that the proposed algorithm is capable of foreseeing and bypassing forthcoming traffic congestion, at a cost only about 10% more in computational time than the traditional algorithm. In addition, frequent path reoptimization required with use of the traditional algorithm is effectively avoided.		Traditional solutions to shortest path problems on time-varying transportation networks use traffic information only at precise moments regardless of considering the fact that the travel time through any link is dependent on the time entering that link. In this study, travel speed rather than travel time on each link is used as the time period dependent parameter to model time-dependent transportation networks, and a First-In-First-Out (FIFO) condition satisfied computational function of link travel time is then deduced based on kinematics. Finally, a temporally adaptive A* shortest path algorithm on this FIFO network is presented, where the time factor is introduced into the evaluation function, and the Euclidean distance divided by the maximum possible travel speed is used as a heuristic evaluator. An experiment on a real road network shows that the proposed algorithm is capable of foreseeing and bypassing forthcoming traffic congestion, at a cost only about 10% more in computational time than the traditional algorithm. In addition, frequent path reoptimization required with use of the traditional algorithm is effectively avoided.
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−	~~Document type: Part of book or chapter of book~~
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−	~~== Full document ==~~
−	~~<pdf>Media:Draft_Content_355470556-beopen561-9767-document.pdf</pdf>~~


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	* [http://www.isprs.org/proceedings/XXXVIII/part2/Papers/89_Paper.pdf http://www.isprs.org/proceedings/XXXVIII/part2/Papers/89_Paper.pdf]		* [http://www.isprs.org/proceedings/XXXVIII/part2/Papers/89_Paper.pdf http://www.isprs.org/proceedings/XXXVIII/part2/Papers/89_Paper.pdf]
		+
		+	* [http://www.springerlink.com/index/pdf/10.1007/978-3-642-25926-5_14 http://www.springerlink.com/index/pdf/10.1007/978-3-642-25926-5_14],
		+	: [http://dx.doi.org/10.1007/978-3-642-25926-5_14 http://dx.doi.org/10.1007/978-3-642-25926-5_14]
		+
		+	* [https://link.springer.com/chapter/10.1007/978-3-642-25926-5_14 https://link.springer.com/chapter/10.1007/978-3-642-25926-5_14],
		+	: [https://www.scipedia.com/public/Zheng_Lu_2012a https://www.scipedia.com/public/Zheng_Lu_2012a],
		+	: [https://rd.springer.com/chapter/10.1007/978-3-642-25926-5_14 https://rd.springer.com/chapter/10.1007/978-3-642-25926-5_14],
		+	: [https://doi.org/10.1007/978-3-642-25926-5_14 https://doi.org/10.1007/978-3-642-25926-5_14],
		+	: [https://dblp.uni-trier.de/db/conf/sdh/sdh2010.html#ZhengL10 https://dblp.uni-trier.de/db/conf/sdh/sdh2010.html#ZhengL10],
		+	: [https://academic.microsoft.com/#/detail/63403264 https://academic.microsoft.com/#/detail/63403264]

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