TTDM: A Travel Time Difference Model for Next Location Prediction

• URL: http://arxiv.org/abs/2003.07781v1
• Date: Mon, 16 Mar 2020 05:16:43 GMT
• Title: TTDM: A Travel Time Difference Model for Next Location Prediction
• Authors: Qingjie Liu, Yixuan Zuo, Xiaohui Yu, Meng Chen
• Abstract summary: Next location prediction is of great importance for many location-based applications and provides essential intelligence to business and governments. In existing studies, a common approach to next location prediction is to learn the sequential transitions with massive historical trajectories based on conditional probability. We propose a novel method, called Travel Time Difference Model (TTDM), which exploits the difference between the shortest travel time and the actual travel time to predict next locations.
• Score: 14.93730951083916
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Next location prediction is of great importance for many location-based applications and provides essential intelligence to business and governments. In existing studies, a common approach to next location prediction is to learn the sequential transitions with massive historical trajectories based on conditional probability. Unfortunately, due to the time and space complexity, these methods (e.g., Markov models) only use the just passed locations to predict next locations, without considering all the passed locations in the trajectory. In this paper, we seek to enhance the prediction performance by considering the travel time from all the passed locations in the query trajectory to a candidate next location. In particular, we propose a novel method, called Travel Time Difference Model (TTDM), which exploits the difference between the shortest travel time and the actual travel time to predict next locations. Further, we integrate the TTDM with a Markov model via a linear interpolation to yield a joint model, which computes the probability of reaching each possible next location and returns the top-rankings as results. We have conducted extensive experiments on two real datasets: the vehicle passage record (VPR) data and the taxi trajectory data. The experimental results demonstrate significant improvements in prediction accuracy over existing solutions. For example, compared with the Markov model, the top-1 accuracy improves by 40% on the VPR data and by 15.6% on the Taxi data.

Related papers

• AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving [59.94343412438211]
  We introduce the GPT style next token motion prediction into motion prediction. Different from language data which is composed of homogeneous units -words, the elements in the driving scene could have complex spatial-temporal and semantic relations. We propose to adopt three factorized attention modules with different neighbors for information aggregation and different position encoding styles to capture their relations.
  arXiv  Detail & Related papers  (2024-03-20T06:22:37Z)
• Arrival Time Prediction for Autonomous Shuttle Services in the Real World: Evidence from Five Cities [3.6294895527930504]
  This study presents an AT prediction system for autonomous shuttles, utilizing separate models for dwell and running time predictions. To accurately handle the case of a shuttle bypassing a stop, we propose a hierarchical model combining a random forest classifier and a GNN. The results for the final AT prediction are promising, showing low errors even when predicting several stops ahead.
  arXiv  Detail & Related papers  (2024-01-10T18:41:39Z)
• 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)
• How do you go where? Improving next location prediction by learning travel mode information using transformers [6.003006906852134]
  We propose a transformer decoder-based neural network to predict the next location an individual will visit based on historical locations, time, and travel modes. In particular, the prediction of the next travel mode is designed as an auxiliary task to help guide the network's learning. Our experiments show that the proposed method significantly outperforms other state-of-the-art next location prediction methods.
  arXiv  Detail & Related papers  (2022-10-08T19:36:58Z)
• PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map [58.53373202647576]
  We propose PreTraM, a self-supervised pre-training scheme for trajectory forecasting. It consists of two parts: 1) Trajectory-Map Contrastive Learning, where we project trajectories and maps to a shared embedding space with cross-modal contrastive learning, and 2) Map Contrastive Learning, where we enhance map representation with contrastive learning on large quantities of HD-maps. On top of popular baselines such as AgentFormer and Trajectron++, PreTraM boosts their performance by 5.5% and 6.9% relatively in FDE-10 on the challenging nuScenes dataset.
  arXiv  Detail & Related papers  (2022-04-21T23:01:21Z)
• 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)
• SMART: Simultaneous Multi-Agent Recurrent Trajectory Prediction [72.37440317774556]
  We propose advances that address two key challenges in future trajectory prediction. multimodality in both training data and predictions and constant time inference regardless of number of agents.
  arXiv  Detail & Related papers  (2020-07-26T08:17:10Z)
• STINet: Spatio-Temporal-Interactive Network for Pedestrian Detection and Trajectory Prediction [24.855059537779294]
  We present a novel end-to-end two-stage network: Spatio--Interactive Network (STINet) In addition to 3D geometry of pedestrians, we model temporal information for each of the pedestrians. Our method predicts both current and past locations in the first stage, so that each pedestrian can be linked across frames.
  arXiv  Detail & Related papers  (2020-05-08T18:43:01Z)
• BusTime: Which is the Right Prediction Model for My Bus Arrival Time? [3.1761486589684975]
  This paper tries to fill this gap by proposing a general and practical evaluation framework for analysing various widely used prediction models. In particular, this framework contains a raw bus GPS data pre-processing method that needs much less number of input data points. We also present preliminary results for city managers by analysing the practical strengths and weaknesses in both training and predicting stages of commonly used prediction models.
  arXiv  Detail & Related papers  (2020-03-20T17:03:36Z)
• Ambiguity in Sequential Data: Predicting Uncertain Futures with Recurrent Models [110.82452096672182]
  We propose an extension of the Multiple Hypothesis Prediction (MHP) model to handle ambiguous predictions with sequential data. We also introduce a novel metric for ambiguous problems, which is better suited to account for uncertainties.
  arXiv  Detail & Related papers  (2020-03-10T09:15:42Z)
• Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction [57.56466850377598]
  Reasoning over visual data is a desirable capability for robotics and vision-based applications. In this paper, we present a framework on graph to uncover relationships in different objects in the scene for reasoning about pedestrian intent. Pedestrian intent, defined as the future action of crossing or not-crossing the street, is a very crucial piece of information for autonomous vehicles.
  arXiv  Detail & Related papers  (2020-02-20T18:50:44Z)

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.