Research map / Architectures and efficiency / RNNs and normalization
Pay Your Trip for Traffic Congestion: Dynamic Pricing in Traffic-Aware Road Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add route pre-checking (PC) to GRM (ablation) | GRM · BT and NT | runtime: 1.05-1.21x faster | Helped |
| Use swap-based GRM instead of exact DRM as number of trips n grows | GRM · BT and NT | runtime | Helped |
| Increase number of route candidates k from 3 to 7 | GRM · BT and NT | ratio of congestion factor: 1.33 (BT), 1.36 (NT) at k=7 | Hurt |
| Increase number of route candidates k | GRM · BT and NT | runtime | No clear effect |
| Increase number of congestion levels from 2 to 5 | GRM · BT and NT | runtime | Hurt |
| Increase number of congestion levels | DRM · BT and NT | runtime | No clear effect |
| Increase swap parameter epsilon from 5/n to 50/n | GRM and GRM+PC · BT, NT | runtime: 5 -> 50 (x n^-1), gains modest after 30/n | Helped |
| Increase candidate selection parameter m from 5 to 20 | GRM · BT, NT | ratio of congestion factor: 5 -> 20 | Hurt |
| Increase k for DRM | DRM · BT, NT | runtime | Hurt |
Closest papers by meaning
- Future Aware Pricing and Matching for Sustainable On-Demand Ride Pooling (AAAI 2023 · similarity 0.87)
- DyETC: Dynamic Electronic Toll Collection for Traffic Congestion Alleviation (AAAI 2018 · similarity 0.85)
- Neural Approximate Dynamic Programming for On-Demand Ride-Pooling (AAAI 2020 · similarity 0.85)
- GARLIC: GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching (AAAI 2025 · similarity 0.82)
- Hierarchical Optimization via LLM-Guided Objective Evolution for Mobility-on-Demand Systems (NeurIPS 2025 · similarity 0.82)
- SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem (NeurIPS 2025 · similarity 0.82)
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