Individual Mobility Prediction: An Interpretable Activity-based Hidden Markov Approach

• URL: http://arxiv.org/abs/2101.03996v1
• Date: Mon, 11 Jan 2021 16:11:27 GMT
• Title: Individual Mobility Prediction: An Interpretable Activity-based Hidden Markov Approach
• Authors: Baichuan Mo, Zhan Zhao, Haris N. Koutsopoulos, Jinhua Zhao
• Abstract summary: This study develops an activity-based modeling framework for individual mobility prediction. We show that the proposed model can achieve similar prediction performance as the state-of-the-art long-term short-term memory (LSTM) model.
• Score: 6.1938383008964495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Individual mobility is driven by demand for activities with diverse spatiotemporal patterns, but existing methods for mobility prediction often overlook the underlying activity patterns. To address this issue, this study develops an activity-based modeling framework for individual mobility prediction. Specifically, an input-output hidden Markov model (IOHMM) framework is proposed to simultaneously predict the (continuous) time and (discrete) location of an individual's next trip using transit smart card data. The prediction task can be transformed into predicting the hidden activity duration and end location. Based on a case study of Hong Kong's metro system, we show that the proposed model can achieve similar prediction performance as the state-of-the-art long short-term memory (LSTM) model. Unlike LSTM, the proposed IOHMM model can also be used to analyze hidden activity patterns, which provides meaningful behavioral interpretation for why an individual makes a certain trip. Therefore, the activity-based prediction framework offers a way to preserve the predictive power of advanced machine learning methods while enhancing our ability to generate insightful behavioral explanations, which is useful for enhancing situational awareness in user-centric transportation applications such as personalized traveler information.

Related papers

• Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance [61.06245197347139]
  We propose a novel approach to explain the behavior of a black-box model under feature shifts. We refer to our method that combines concepts from Optimal Transport and Shapley Values as Explanatory Performance Estimation.
  arXiv  Detail & Related papers  (2024-08-24T18:28:19Z)
• Uncovering the human motion pattern: Pattern Memory-based Diffusion Model for Trajectory Prediction [45.77348842004666]
  Motion Pattern Priors Memory Network is a memory-based method to uncover latent motion patterns in human behavior. We introduce an addressing mechanism to retrieve the matched pattern and the potential target distributions for each prediction from the memory bank. Experiments validate the effectiveness of our approach, achieving state-of-the-art trajectory prediction accuracy.
  arXiv  Detail & Related papers  (2024-01-05T17:39:52Z)
• Vehicle Motion Forecasting using Prior Information and Semantic-assisted Occupancy Grid Maps [6.99274104609965]
  Motion is a challenging task for autonomous vehicles due to uncertainty in the sensor data, the non-deterministic nature of future, and complex behavior. In this paper, we tackle this problem by representing the scene as dynamic occupancy grid maps (DOGMs) We propose a novel framework that combines deep-temporal and probabilistic approaches to predict vehicle behaviors.
  arXiv  Detail & Related papers  (2023-08-08T14:49:44Z)
• Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation [66.86987509942607]
  We evaluate how such a paradigm should be done in imitation learning. We consider a setting where the pretraining corpus consists of multitask demonstrations. We argue that inverse dynamics modeling is well-suited to this setting.
  arXiv  Detail & Related papers  (2023-05-26T14:40:46Z)
• Context-aware multi-head self-attentional neural network model for next location prediction [19.640761373993417]
  We utilize a multi-head self-attentional (A) neural network that learns location patterns from historical location visits. We demonstrate that proposed the model outperforms other state-of-the-art prediction models. We believe that the proposed model is vital for context-aware mobility prediction.
  arXiv  Detail & Related papers  (2022-12-04T23:40:14Z)
• A Neural Active Inference Model of Perceptual-Motor Learning [62.39667564455059]
  The active inference framework (AIF) is a promising new computational framework grounded in contemporary neuroscience. In this study, we test the ability for the AIF to capture the role of anticipation in the visual guidance of action in humans. We present a novel formulation of the prior function that maps a multi-dimensional world-state to a uni-dimensional distribution of free-energy.
  arXiv  Detail & Related papers  (2022-11-16T20:00:38Z)
• Bootstrap Motion Forecasting With Self-Consistent Constraints [52.88100002373369]
  We present a novel framework to bootstrap Motion forecasting with Self-consistent Constraints. The motion forecasting task aims at predicting future trajectories of vehicles by incorporating spatial and temporal information from the past. We show that our proposed scheme consistently improves the prediction performance of several existing methods.
  arXiv  Detail & Related papers  (2022-04-12T14:59:48Z)
• Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion [88.45326906116165]
  We present a new framework to formulate the trajectory prediction task as a reverse process of motion indeterminacy diffusion (MID) We encode the history behavior information and the social interactions as a state embedding and devise a Transformer-based diffusion model to capture the temporal dependencies of trajectories. Experiments on the human trajectory prediction benchmarks including the Stanford Drone and ETH/UCY datasets demonstrate the superiority of our method.
  arXiv  Detail & Related papers  (2022-03-25T16:59:08Z)
• You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction [52.442129609979794]
  Recent deep learning approaches for trajectory prediction show promising performance. It remains unclear which features such black-box models actually learn to use for making predictions. This paper proposes a procedure that quantifies the contributions of different cues to model performance.
  arXiv  Detail & Related papers  (2021-10-11T14:24:15Z)
• Individual Mobility Prediction via Attentive Marked Temporal Point Processes [4.221871357181261]
  We propose a novel point process-based model to model human mobility and predict the whole trip $(t,o,d)$ in a joint manner. Experimental results on two large metro trip datasets demonstrate the superior performance of AMTPP.
  arXiv  Detail & Related papers  (2021-09-06T19:55:42Z)
• Episodic Memory for Learning Subjective-Timescale Models [1.933681537640272]
  In model-based learning, an agent's model is commonly defined over transitions between consecutive states of an environment. In contrast, intelligent behaviour in biological organisms is characterised by the ability to plan over varying temporal scales depending on the context. We devise a novel approach to learning a transition dynamics model, based on the sequences of episodic memories that define the agent's subjective timescale.
  arXiv  Detail & Related papers  (2020-10-03T21:55:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.