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Found 36 papers, 19 papers with code
X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learner
The effectiveness of traffic light control has been significantly improved by current reinforcement learning-based approaches via better cooperation among multiple traffic lights.
SEVD: Synthetic Event-based Vision Dataset for Ego and Fixed Traffic Perception
In response to this gap, we present SEVD, a first-of-its-kind multi-view ego, and fixed perception synthetic event-based dataset using multiple dynamic vision sensors within the CARLA simulator.
eTraM: Event-based Traffic Monitoring Dataset
Event cameras, with their high temporal and dynamic range and minimal memory usage, have found applications in various fields.
Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape
This paper delves into the critical area of deep learning robustness, challenging the conventional belief that classification robustness and explanation robustness in image classification systems are inherently correlated.
Privacy-preserving Fine-tuning of Large Language Models through Flatness
The privacy concerns associated with the use of Large Language Models (LLMs) have grown recently with the development of LLMs such as ChatGPT.
Teaching MLP More Graph Information: A Three-stage Multitask Knowledge Distillation Framework
To the best of our knowledge, it is the first work to include hidden layer distillation for student MLP on graphs and to combine graph Positional Encoding with MLP.
CityFlowER: An Efficient and Realistic Traffic Simulator with Embedded Machine Learning Models
Addressing these limitations, we introduce CityFlowER, an advancement over the existing CityFlow simulator, designed for efficient and realistic city-wide traffic simulation.
Interpreting Graph Neural Networks with In-Distributed Proxies
A popular paradigm for the explainability of GNNs is to identify explainable subgraphs by comparing their labels with the ones of original graphs.
When eBPF Meets Machine Learning: On-the-fly OS Kernel Compartmentalization
Compartmentalization effectively prevents initial corruption from turning into a successful attack.
Uncertainty Regularized Evidential Regression
The Evidential Regression Network (ERN) represents a novel approach that integrates deep learning with Dempster-Shafer's theory to predict a target and quantify the associated uncertainty.
Open-TI: Open Traffic Intelligence with Augmented Language Model
Transportation has greatly benefited the cities' development in the modern civilization process.
Probabilistic Offline Policy Ranking with Approximate Bayesian Computation
In this paper, we present Probabilistic Offline Policy Ranking (POPR), a framework to address OPR problems by leveraging expert data to characterize the probability of a candidate policy behaving like experts, and approximating its entire performance posterior distribution to help with ranking.
Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks
An explanation function for GNNs takes a pre-trained GNN along with a graph as input, to produce a `sufficient statistic' subgraph with respect to the graph label.
Uncertainty-aware Traffic Prediction under Missing Data
However, most studies assume the prediction locations have complete or at least partial historical records and cannot be extended to non-historical recorded locations.
Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning
In this work, we leverage LLMs to understand and profile the system dynamics by a prompt-based grounded action transformation.
Safety in Traffic Management Systems: A Comprehensive Survey
Traffic management systems play a vital role in ensuring safe and efficient transportation on roads.
Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control
Traffic signal control (TSC) is a complex and important task that affects the daily lives of millions of people.
MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation
Driven by the generalized GIB, we propose a graph mixup method, MixupExplainer, with a theoretical guarantee to resolve the distribution shifting issue.
RegExplainer: Generating Explanations for Graph Neural Networks in Regression Task
Graph regression is a fundamental task and has received increasing attention in a wide range of graph learning tasks.
FDTI: Fine-grained Deep Traffic Inference with Roadnet-enriched Graph
Under this setting, traffic flow is highly influenced by traffic signals and the correlation between traffic nodes is dynamic.
Reinforcement Learning Approaches for Traffic Signal Control under Missing Data
In this work, we aim to control the traffic signals in a real-world setting, where some of the intersections in the road network are not installed with sensors and thus with no direct observations around them.
HumanLight: Incentivizing Ridesharing via Human-centric Deep Reinforcement Learning in Traffic Signal Control
Apart from adopting FRAP, a state-of-the-art (SOTA) base model, HumanLight introduces the concept of active vehicles, loosely defined as vehicles in proximity to the intersection within the action interval window.
SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control
Traffic signal control is safety-critical for our daily life.
LibSignal: An Open Library for Traffic Signal Control
This paper introduces a library for cross-simulator comparison of reinforcement learning models in traffic signal control tasks.
Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning
Compared to other environments studied in most previous work, ours presents new generalization challenges for competitive reinforcement learning.
Modeling Network-level Traffic Flow Transitions on Sparse Data
DTIGNN models the traffic system as a dynamic graph influenced by traffic signals, learns the transition models grounded by fundamental transition equations from transportation, and predicts future traffic states with imputation in the process.
Semi-Supervised Clustering with Contrastive Learning for Discovering New Intents
To make DCSC fully utilize the limited known intents, we propose a two-stage training procedure for DCSC, in which DCSC will be trained on both labeled samples and unlabeled samples, and achieve better text representation and clustering performance.
Boosting Offline Reinforcement Learning with Residual Generative Modeling
While most research focuses on the state-action function part through reducing the bootstrapping error in value function approximation induced by the distribution shift of training data, the effects of error propagation in generative modeling have been neglected.
Learning to Simulate on Sparse Trajectory Data
Simulation of the real-world traffic can be used to help validate the transportation policies.
How Do We Move: Modeling Human Movement with System Dynamics
To the best of our knowledge, we are the first to learn to model the state transition of moving agents with system dynamics.
A Probabilistic Simulator of Spatial Demand for Product Allocation
Moreover, learning important spatial patterns in offline retail is challenging due to the scarcity of data and the high cost of exploration and experimentation in the physical world.
Diagnosing Reinforcement Learning for Traffic Signal Control
In this paper, we propose to re-examine the RL approaches through the lens of classic transportation theory.
Learning Phase Competition for Traffic Signal Control
Increasingly available city data and advanced learning techniques have empowered people to improve the efficiency of our city functions.
CoLight: Learning Network-level Cooperation for Traffic Signal Control
To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication.
A Survey on Traffic Signal Control Methods
Traffic signal control is an important and challenging real-world problem, which aims to minimize the travel time of vehicles by coordinating their movements at the road intersections.
Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction
Although both factors have been considered in modeling, existing works make strong assumptions about spatial dependence and temporal dynamics, i. e., spatial dependence is stationary in time, and temporal dynamics is strictly periodical.
