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On Scaling Time Dependent Shortest Path Computations for Dynamic Traffic Assignment Amit Gupta, Weijia Xu Texas Advanced Computing Center Kenneth Perrine,

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Presentation on theme: "On Scaling Time Dependent Shortest Path Computations for Dynamic Traffic Assignment Amit Gupta, Weijia Xu Texas Advanced Computing Center Kenneth Perrine,"— Presentation transcript:

1 On Scaling Time Dependent Shortest Path Computations for Dynamic Traffic Assignment Amit Gupta, Weijia Xu Texas Advanced Computing Center Kenneth Perrine, Dennis Bell, Natalia Ruiz-Juri Network Modeling Center

2 Dynamic Traffic Assignment Used to Model complex interactions between –Traveller   Traveller –Traveller   Road Infrastructure Usually Simulation Based Shortest Path calculations –Used to model traveller behavior to congestion –Intensive part of the process –Time dependent Traffic conditions change 1

3 Road Networks & Travel Time Turn Movement Delays Signal Delays 2 Prohibitions –(One Ways, Closed Roads) Tolls, Vehicle Types * Time Dependent *

4 3 Transportation: Road Networks

5 DTA Workflow 4

6 TDSP Is a “Label Correcting” algorithm Has 2 main parts –Update Labels Incorporates the activity of most recent simulation Changes the cost (label) values at nodes Causes upstream costs to change Maintains pointers to downstream nodes –Search for Routes Back traces the downstream pointers Constructs the shortest path topology 5

7 TDSP 6 Origin Destination

8 Update Labels 7 Origin Destination

9 TDSP Challenges For precision –Larger number of labels maintained –Implies Computational overhead Simulation activity changes travel time –Label values keep changing –Shortest paths also change –Stored on file and looked up –Small, Random reads. I/O overhead 8

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11 Our Approach VISTA –DTA and Simulation Framework –Popular in Transportation Research I/O Bottleneck –Initial profiling Upto 77% time spent here –Primarily Route File activity –Moved target to Ramdisk 10

12 Our Approach Computational Bottleneck –Profiling indicated TDSP( Update Labels ) is the main computational Load 11

13 Update Labels: Queuing Scheme Scan Eligible List –All Label Correcting algorithms use it –Intermediate structure to hold nodes for later VISTA used Dequeue scheme –“Visited nodes” inserted to the front –No prioritization otherwise –Order of Nodes depends on order in which Links are visited –“Feedback effect” during label updates 12

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15 Priority Queue Order of Node evaluation matters Prioritize Nodes during evaluation –Use the first time label –Nodes label values converge faster –Reduces overall time for Update Labels => TDSP finishes faster –Average case is improved Already congested link stays that way –Analysis periods like “Rush Hour” time windows –Common for many research investigations 14

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18 Datasets NetworkNodesLinksO/D PairsDemand (Vehicles) Downtown Austin 5461251364063322 South Austin 178497327255253097 Central Austin 1871400351984131425 Austin Region AM 11446237774407520697885 Austin Region PM 114462377744922801095899 17

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21 Conclusion Large performance gains (6x-12x) –Relatively minor code modifications –Changes to application architecture Methodology easily extended to other DTA frameworks besides VISTA Future Work –More general heuristic –Using Accelerators 20

22 Acknowledgement Funding –Texas Department of Transportation –CAMPO Capital Area Metropolitan Planning Organization –NSF Stampede Supercomputer https://www.tacc.utexas.edu/stampede/ Questions ?... 21


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