Adjustable Preference Path Strategies for Use in Multicriteria, Stochastic, and Time-Varying Transportation Networks

In this paper, an exact algorithm is proposed for determining adjustable preference path strategies in multicriteria, stochastic, and time-varying (MSTV) networks. In MSTV networks, multiple arc attributes are associated with each arc, each being a time-varying random variable. Solution paths that seek to minimize the expected value of each of multiple criteria are sought from all origins to a specified destination for all departure times in a period of interest. These solution strategies allow a traveler to update a preference for which criterion of multiple criteria is of greatest importance and then adaptively select the best path for the selected criterion at each node in response to knowledge of experienced travel times on the arcs. Such adjustable preference path strategies are particularly useful for providing real-time path-finding assistance.

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Collision-free Real-time Path-planning In Time Varying Environment

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems ◽

10.1109/iros.1992.587374 ◽

2005 ◽

Cited By ~ 4

Author(s):

P. Adolphs ◽

H. Tolle

Keyword(s):

Path Planning ◽

Real Time ◽

Time Varying ◽

Time Path ◽

Time Varying Environment ◽

Varying Environment

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An Improved Sparse Hierarchical Lazy Theta* Algorithm for UAV Real-Time Path Planning in Unknown Three-Dimensional Environment

2020 IEEE 20th International Conference on Communication Technology (ICCT) ◽

10.1109/icct50939.2020.9295690 ◽

2020 ◽

Author(s):

Chengfang Wu ◽

Xiaoyan Huang ◽

Yuanlin Luo ◽

Supeng Leng ◽

Fan Wu

Keyword(s):

Path Planning ◽

Real Time ◽

Three Dimensional ◽

Time Path

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Real-Time Path Planning with Virtual Magnetic Fields

IEEE Robotics and Automation Letters ◽

10.1109/lra.2021.3063992 ◽

2021 ◽

pp. 1-1

Author(s):

Majda Moussa ◽

Giovanni Beltrame

Keyword(s):

Magnetic Fields ◽

Path Planning ◽

Real Time ◽

Time Path

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Real Time Path Planning of Robot using Deep Reinforcement Learning

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2020.12.2494 ◽

2020 ◽

Vol 53 (2) ◽

pp. 15602-15607

Author(s):

Jeevan Raajan ◽

P V Srihari ◽

Jayadev P Satya ◽

B Bhikkaji ◽

Ramkrishna Pasumarthy

Keyword(s):

Reinforcement Learning ◽

Path Planning ◽

Real Time ◽

Time Path

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Study on real-time thermal–mechanical–frictional coupling characteristics of ball bearings based on the inverse thermal network method

Proceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology ◽

10.1177/1350650121993564 ◽

2021 ◽

pp. 135065012199356

Author(s):

Tie-Jun Li ◽

Meng-Zhuo Wang ◽

Chun-Yu Zhao

Keyword(s):

Real Time ◽

Thermal Contact Resistance ◽

Thermal Contact ◽

Time Varying ◽

Ball Bearings ◽

The Real ◽

Thermal Network ◽

Network Method ◽

Thermal Network Method ◽

Coupling Characteristics

The real-time thermal–mechanical–frictional coupling characteristics of bearings are critical to the accuracy, reliability, and life of entire machines. To obtain the real-time dynamic characteristics of ball bearings, a novel model to calculate point contact dynamic friction in mixed lubrication was firstly presented in this work. The model of time-varying thermal contact resistance under fit between the ring and the ball, between the ring and the housing, and between the ring and the shaft was established using the fractal theory and the heat transfer theory. Furthermore, an inverse thermal network method with time-varying thermal contact resistance was presented. Using these models, the real-time thermal–mechanical–frictional coupling characteristics of ball bearings were obtained. The effectiveness of the presented models was verified by experiment and comparison.

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Optimal Scheduling of Emergency Resources for Major Maritime Oil Spills Considering Time-varying Demand and Transportation Networks

European Journal of Operational Research ◽

10.1016/j.ejor.2020.12.040 ◽

2020 ◽

Author(s):

Lingye Zhang ◽

Jing Lu ◽

Zaili Yang

Keyword(s):

Oil Spills ◽

Transportation Networks ◽

Optimal Scheduling ◽

Time Varying ◽

Varying Demand

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Estimation of Path Travel Time Distributions in Stochastic Time-Varying Networks with Correlations

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211018464 ◽

2021 ◽

pp. 036119812110184

Author(s):

Monika Filipovska ◽

Hani S. Mahmassani ◽

Archak Mittal

Keyword(s):

Time Dependence ◽

Travel Time ◽

Correlation Structure ◽

Time Varying ◽

Travel Times ◽

Trajectory Data ◽

Time Varying Networks ◽

Transportation Research ◽

Search Approach ◽

Stochastic Time

Transportation research has increasingly focused on the modeling of travel time uncertainty in transportation networks. From a user’s perspective, the performance of the network is experienced at the level of a path, and, as such, knowledge of variability of travel times along paths contemplated by the user is necessary. This paper focuses on developing approaches for the estimation of path travel time distributions in stochastic time-varying networks so as to capture generalized correlations between link travel times. Specifically, the goal is to develop methods to estimate path travel time distributions for any path in the networks by synthesizing available trajectory data from various portions of the path, and this paper addresses that problem in a two-fold manner. Firstly, a Monte Carlo simulation (MCS)-based approach is presented for the convolution of time-varying random variables with general correlation structures and distribution shapes. Secondly, a combinatorial data-mining approach is developed, which aims to utilize sparse trajectory data for the estimation of path travel time distributions by implicitly capturing the complex correlation structure in the network travel times. Numerical results indicate that the MCS approach allowing for time-dependence and a time-varying correlation structure outperforms other approaches, and that its performance is robust with respect to different path travel time distributions. Additionally, using the path segmentations from the segment search approach with a MCS approach with time-dependence also produces accurate and robust estimates of the path travel time distributions with the added benefit of shorter computation times.

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A New Method for Phase Difference Estimation Based on Time-Varying Signal Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.333-335.650 ◽

2013 ◽

Vol 333-335 ◽

pp. 650-655

Author(s):

Peng Hui Niu ◽

Yin Lei Qin ◽

Shun Ping Qu ◽

Yang Lou

Keyword(s):

Signal Processing ◽

Real Time ◽

Phase Difference ◽

Time Signal ◽

Time Interval ◽

Time Varying ◽

Signal Model ◽

Time Frequency ◽

Mass Flowmeter ◽

Coriolis Mass Flowmeter

A new signal processing method for phase difference estimation was proposed based on time-varying signal model, whose frequency, amplitude and phase are time-varying. And then be applied Coriolis mass flowmeter signal. First, a bandpass filtering FIR filter was applied to filter the sensor output signal in order to improve SNR. Then, the signal frequency could be calculated based on short-time frequency estimation. Finally, by short window intercepting, the DTFT algorithm with negative frequency contribution was introduced to calculate the real-time phase difference between two enhanced signals. With the frequency and the phase difference obtained, the time interval of two signals was calculated. Simulation results show that the algorithms studied are efficient. Furthermore, the computation of algorithms studied is simple so that it can be applied to real-time signal processing for Coriolis mass flowmeter.

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