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Pay Your Trip for Traffic Congestion: Dynamic Pricing in Traffic-Aware Road Networks

AAAI 2020 · Architectures and efficiency › RNNs and normalization · approach: Dataset distillation and data-efficient training · 31 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add route pre-checking (PC) to GRM (ablation)GRM · BT and NTruntime: 1.05-1.21x fasterHelped
Use swap-based GRM instead of exact DRM as number of trips n growsGRM · BT and NTruntimeHelped
Increase number of route candidates k from 3 to 7GRM · BT and NTratio of congestion factor: 1.33 (BT), 1.36 (NT) at k=7Hurt
Increase number of route candidates kGRM · BT and NTruntimeNo clear effect
Increase number of congestion levels from 2 to 5GRM · BT and NTruntimeHurt
Increase number of congestion levelsDRM · BT and NTruntimeNo clear effect
Increase swap parameter epsilon from 5/n to 50/nGRM and GRM+PC · BT, NTruntime: 5 -> 50 (x n^-1), gains modest after 30/nHelped
Increase candidate selection parameter m from 5 to 20GRM · BT, NTratio of congestion factor: 5 -> 20Hurt
Increase k for DRMDRM · BT, NTruntimeHurt

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