Dynamic Graph Mining for Multi-weight Multi-destination Route Planning with Deadlines Constraints

2021 ◽  
Vol 15 (1) ◽  
pp. 1-32
Author(s):  
Yu Huang ◽  
Josh Jia-Ching Ying ◽  
Philip S. Yu ◽  
Vincent S. Tseng
Keyword(s):  
Graph Mining ◽  
Route Planning ◽  
Dynamic Graph

scholarly journals Continuous Route Planning over a Dynamic Graph in Real-Time

2020 ◽  
Vol 174 ◽  
pp. 111-114
Author(s):  
Tianlun Dai ◽  
Wenchao Zheng ◽  
Jiayue Sun ◽  
Cun Ji ◽  
Tao Zhou ◽  
...  
Keyword(s):  
Real Time ◽  
Route Planning ◽  
Dynamic Graph

Tourism Route Planning Route Based on Tourist Category using Orientee ring Problem with Time Windows

2019 ◽  
Vol 1 (1) ◽  
pp. 42-49
Author(s):  
Indri Hapsari ◽  
◽  
Hazrul Is wadi ◽  
Yosvaldo Ongko Cahyadi ◽  
◽  
...  
Keyword(s):  
Time Windows ◽  
Route Planning ◽  
Ring Problem

Recent advances on urban taxi sharing systems with the path arrival probability

2020 ◽  
Vol 13 ◽  
Author(s):  
Dui Hongyan ◽  
Zhang Chi
Keyword(s):  
Failure Probability ◽  
Traffic Congestion ◽  
Optimal Strategy ◽  
Time Windows ◽  
Optimal Path ◽  
Route Planning ◽  
Driving Time ◽  
Load Rate ◽  
Travel Efficiency ◽  
Arrival Probability

Background : Taxi sharing is an emerging transportation arrangement that helps improve the passengers’ travel efficiency and reduce costs. This study proposes an urban taxi sharing system. Methods: Considering each side congestion of the transport network, their corresponding reliability and failure probability are analyzed. Under the constraints of the number of passengers and their own time windows, the analysis is performed on passengers whose optimal path is inclusive. Results: According to the optimal strategy, the different passengers can be arranged into the same taxi to realize the taxi sharing. Then the shared taxi route can be optimized. Conclusion: Due to the reasonable vehicle route planning and passenger combination, these can effectively alleviate the traffic congestion, save the driving time, reduce the taxi no-load rate, and save the driving distance. At last, a numerical example is used to demonstrate the proposed method.

A distributed location obfuscation method for online route planning

2020 ◽  
Vol 95 ◽  
pp. 101850
Author(s):  
Padraig Corcoran ◽  
Peter Mooney ◽  
Andrei Gagarin
Keyword(s):  
Route Planning ◽  
Location Obfuscation

Toward Greater Intelligence in Route Planning: A Graph-Aware Deep Learning Approach

2020 ◽  
Vol 14 (2) ◽  
pp. 1658-1669 ◽  
Author(s):  
Zirui Zhuang ◽  
Jingyu Wang ◽  
Qi Qi ◽  
Haifeng Sun ◽  
Jianxin Liao
Keyword(s):  
Deep Learning ◽  
Route Planning ◽  
Learning Approach
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