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A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards
Machine Learning · Submitted 18 Jun 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Experiment results based on taxi GPS data from Shanghai, China validate the superior prediction performance of the proposed model over conventional DCMs and other imitation learning baselines, even for destinations unseen in the training data.
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The abstract, sentence by sentence
Route choice modeling is a fundamental task in transportation planning and demand forecasting. Classical methods generally adopt the discrete choice model (DCM) framework with linear utility functions and high-level route characteristics. While several recent studies have started to explore the applicability of deep learning for route choice modeling, they are limited to path-based models with relatively simple model architectures and relying on predefined choice sets. Existing link-based models can capture the dynamic nature of link choices within the trip without the need for choice set generation, but still assume linear relationships and link-additive features. To address these issues, this study proposes a general deep inverse reinforcement learning (IRL) framework for link-based route choice modeling, which is capable of incorporating diverse features (of the state, action and trip context) and capturing complex relationships. Specifically, we adapt an adversarial IRL model to the route choice problem for efficient estimation of context-dependent reward functions without value iteration. Experiment results based on taxi GPS data from Shanghai, China validate the superior prediction performance of the proposed model over conventional DCMs and other imitation learning baselines, even for destinations unseen in the training data. Further analysis show that the model exhibits competitive computational efficiency and reasonable interpretability. The proposed methodology provides a new direction for future development of route choice models. It is general and can be adaptable to other route choice problems across different modes and networks.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Route choice modeling is a fundamental task in transportation planning and demand forecasting. | background 1.00 | 0.04 | 0.01 | asserted 1.00 |
| s1 | Classical methods generally adopt the discrete choice model (DCM) framework with linear utility functions and high-level route characteristics. | background 1.00 | 0.04 | 0.01 | asserted 1.00 |
| s2 | While several recent studies have started to explore the applicability of deep learning for route choice modeling, they are limited to path-based models with relatively simple model architectures and relying on predefined choice sets. | gap 0.68 | 0.04 | 0.02 | asserted 0.99 |
| s3 | Existing link-based models can capture the dynamic nature of link choices within the trip without the need for choice set generation, but still assume linear relationships and link-additive features. | background 0.50 | 0.04 | 0.02 | asserted 1.00 |
| s4 | To address these issues, this study proposes a general deep inverse reinforcement learning (IRL) framework for link-based route choice modeling, which is capable of incorporating diverse features (of the state, action and trip context) and capturing complex relationships. | contribution 0.98 | 0.81 | 0.17 | asserted 0.98 |
| s5 | Specifically, we adapt an adversarial IRL model to the route choice problem for efficient estimation of context-dependent reward functions without value iteration. | method 0.96 | 0.91 | 0.05 | asserted 0.99 |
| s6 | Experiment results based on taxi GPS data from Shanghai, China validate the superior prediction performance of the proposed model over conventional DCMs and other imitation learning baselines, even for destinations unseen in the training data. | result 1.00 | 0.96 | 0.68 | asserted 1.00 |
| s7 | Further analysis show that the model exhibits competitive computational efficiency and reasonable interpretability. | result 0.98 | 0.94 | 0.09 | asserted 1.00 |
| s8 | The proposed methodology provides a new direction for future development of route choice models. | future work 0.74 | 0.31 | 0.20 | asserted 0.78 |
| s9 | It is general and can be adaptable to other route choice problems across different modes and networks. | future work 0.49 | 0.38 | 0.15 | asserted 0.86 |
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The paper
Original abstract
Route choice modeling is a fundamental task in transportation planning and demand forecasting. Classical methods generally adopt the discrete choice model (DCM) framework with linear utility functions and high-level route characteristics. While several recent studies have started to explore the applicability of deep learning for route choice modeling, they are limited to path-based models with relatively simple model architectures and relying on predefined choice sets. Existing link-based models can capture the dynamic nature of link choices within the trip without the need for choice set generation, but still assume linear relationships and link-additive features. To address these issues, this study proposes a general deep inverse reinforcement learning (IRL) framework for link-based route choice modeling, which is capable of incorporating diverse features (of the state, action and trip context) and capturing complex relationships. Specifically, we adapt an adversarial IRL model to the route choice problem for efficient estimation of context-dependent reward functions without value iteration. Experiment results based on taxi GPS data from Shanghai, China validate the superior prediction performance of the proposed model over conventional DCMs and other imitation learning baselines, even for destinations unseen in the training data. Further analysis show that the model exhibits competitive computational efficiency and reasonable interpretability. The proposed methodology provides a new direction for future development of route choice models. It is general and can be adaptable to other route choice problems across different modes and networks.
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How the literature uses it
1 arXiv paper cite this in 1 sentence
What it cites · 10 sentences without a confident label
Uses the method or tool of 1 paper
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The clip function $clip(*)$ truncates $r_G(a |s, c)$ within the range of $[1-ϵ, 1+ϵ]$ and $ϵ$ is a pre-defined hyperparameter set as 0.2 in our case. $Â(s,a |c)$ denotes the estimated advantage of state-action pair $(s, a)$ conditional on the context $c$ and is calculated by adapting the generalized advantage estimator [ref] .
High-Dimensional Continuous Control Using Generalized Advantage Estimation · Training Algorithm · 1.00
Cites as background 2 papers
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More recently, a similar adversarial architecture was adopted in Adversarial Inverse Reinforcement Learning (AIRL), which is able to recover reward functions in large, high-dimensional problems [ref] .
Learning Robust Rewards with Adversarial Inverse Reinforcement Learning · Inverse Reinforcement Learning for Route Choice Modeling · 0.82
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As a result, recent works have investigated theories and methods for interpretable deep learning [ref] , though most related transportation studies focus on mode choice analysis.
Towards A Rigorous Science of Interpretable Machine Learning · Interpretable Deep Learning · 0.87
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