Origin-Aware Location Prediction Based on Historical Vehicle Trajectories

2022 ◽  
Vol 13 (1) ◽  
pp. 1-18
Author(s):  
Meng Chen ◽  
Qingjie Liu ◽  
Weiming Huang ◽  
Teng Zhang ◽  
Yixuan Zuo ◽  
...  

Next location prediction is of great importance for many location-based applications and provides essential intelligence to various businesses. In previous studies, a common approach to next location prediction is to learn the sequential transitions with massive historical trajectories based on conditional probability. Nevertheless, due to the time and space complexity, these methods (e.g., Markov models) only utilize the just passed locations to predict next locations, neglecting earlier passed locations in the trajectory. In this work, we seek to enhance the prediction performance by incorporating the travel time from all the passed locations in the query trajectory to each candidate next location. To this end, we propose a novel prediction method, namely the Travel Time Difference Model, which exploits the difference between the shortest travel time and the actual travel time to predict next locations. Moreover, we integrate the Travel Time Difference Model with a Sequential and Temporal Predictor to yield a joint model. The joint prediction model integrates local sequential transitions, temporal regularity, and global travel time information in the trajectory for the next location prediction problem. We have conducted extensive experiments on two real-world datasets: the vehicle passage record data and the taxi trajectory data. The experimental results demonstrate significant improvements in prediction accuracy over baseline methods.

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Li Wen ◽  
Xia Shi-xiong ◽  
Liu Feng ◽  
Zhang Lei

As there is great differences of movement patterns and social correlation between weekdays and weekends, we propose a fallback social-temporal-hierarchic Markov model (FSTHM) to predict individual’s future location. The division of weekdays and weekends is used to decompose the original state of traditional Markov model into two different states and distinguish the difference of the strength of social ties on weekdays and weekends. Except for the time division, the distribution of the visit time for each state is also considered to improve the predictive performance. In addition, in order to best suit the characteristics of Markov model, we introduce the modified cross-sample entropy to quantify the similarities between the individual and his friends. The experiments based on real location-based social network show the FSTHM model gives a 9% improvement over the Markov model and 2% improvement over the social Markov models which use cosine similarity or mutual information to measure the social correlation.


Author(s):  
Haoshu Cai ◽  
Yu Guo ◽  
Kun Lu

In the data-rich manufacturing environment, the production process of work-in-process is described and presented by trajectories with manufacturing significance. However, advanced approaches for work-in-process trajectory data analytics and prediction are comparatively inadequate. However, the location prediction of moving objects has drawn great attention in the manufacturing field. Yet most approaches for predicting future locations of objects are originally applied in geography domain. When applied to manufacturing shop floor, the prediction results lack manufacturing significance. This article focuses on predicting the next locations of work-in-process in the workshop. First, a data model is introduced to map the geographic trajectories into the logical space, in order to convert the manufacturing information into logical features. Based on the data model, a prediction method is proposed to predict the next locations using frequent trajectory patterns. A series of experiments are performed to examine the prediction method. The experiment results illustrate the impacts of the user-defined factors and prove that the proposed method is effective and efficient.


2020 ◽  
Vol 9 (12) ◽  
pp. 722
Author(s):  
Philippe Blais ◽  
Thierry Badard ◽  
Thierry Duchesne ◽  
Marie-Pier Côté

Usage-Based Insurance (UBI) is an insurance framework that has made its appearance in the last few years. It allows direct measurement of the traveling of policyholders, hence the growing interest of the industry to better understand driving behaviors. UBI generates large data volumes, from which events can be extracted, like harsh brakes or accelerations. Still, these events are measured without contextual information, which limits their explanatory power. Traffic is one of these types of contextual information that may have great potential, but access to such data remains an issue. Solutions exist, like traffic data from external providers, but for insurance companies that conduct business over large areas, this could result in very large costs. This paper demonstrates that data from insurance companies acquired via UBI can be used to model traffic. A method based on link travel time is proposed and tested on four Canadian cities. Then, routes created with the model are compared with those created using Google Maps. The results show that for 38 routes with an average length of around 5 km, the difference between the travel time of the routes of the proposed model and Google Maps is as small as one second on average.


2020 ◽  
Vol 7 (2) ◽  
pp. 107-112
Author(s):  
Marian Manciu ◽  
Sorour Hosseini ◽  
Joscelyne Guzman-Gonzalez

Background: Statistical methods commonly used in survival analysis typically provide the probability that the difference between groups is due to chance, but do not offer a reliable estimate of the average survival time difference between groups (the difference between median survival time is usually reported). Objective: We suggest a Maximum-Entropy estimator for the average Survival Time Difference (MESTD) between groups. Methods: The estimator is based on the extra survival time, which should be added to each member of the group, to produce the maximum entropy of the result (resulting in the groups becoming most similar). The estimator is calculated only from time to event data, does not necessarily assume hazard proportionality and provides the magnitude of the clinical differences between the groups. Results: Monte Carlo simulations show that, even at low sample numbers (much lower than the ones needed to prove that the two groups are statistically different), the MESTD estimator is a reliable predictor of the clinical differences between the groups, and therefore can be used to estimate from (low sample numbers) preliminary data whether or not the large sample number experiment is worth pursuing. Conclusion: By providing a reasonable estimate for the efficacy of a treatment (e.g., for cancer) even for low sample data, it might provide useful insight in testing new methods for treatment (for example, for quick testing of multiple combinations of cancer drugs).


2020 ◽  
Vol 36 (9) ◽  
pp. 2690-2696
Author(s):  
Jarkko Toivonen ◽  
Pratyush K Das ◽  
Jussi Taipale ◽  
Esko Ukkonen

Abstract Motivation Position-specific probability matrices (PPMs, also called position-specific weight matrices) have been the dominating model for transcription factor (TF)-binding motifs in DNA. There is, however, increasing recent evidence of better performance of higher order models such as Markov models of order one, also called adjacent dinucleotide matrices (ADMs). ADMs can model dependencies between adjacent nucleotides, unlike PPMs. A modeling technique and software tool that would estimate such models simultaneously both for monomers and their dimers have been missing. Results We present an ADM-based mixture model for monomeric and dimeric TF-binding motifs and an expectation maximization algorithm MODER2 for learning such models from training data and seeds. The model is a mixture that includes monomers and dimers, built from the monomers, with a description of the dimeric structure (spacing, orientation). The technique is modular, meaning that the co-operative effect of dimerization is made explicit by evaluating the difference between expected and observed models. The model is validated using HT-SELEX and generated datasets, and by comparing to some earlier PPM and ADM techniques. The ADM models explain data slightly better than PPM models for 314 tested TFs (or their DNA-binding domains) from four families (bHLH, bZIP, ETS and Homeodomain), the ADM mixture models by MODER2 being the best on average. Availability and implementation Software implementation is available from https://github.com/jttoivon/moder2. Supplementary information Supplementary data are available at Bioinformatics online.


Author(s):  
Monika Filipovska ◽  
Hani S. Mahmassani ◽  
Archak Mittal

Transportation research has increasingly focused on the modeling of travel time uncertainty in transportation networks. From a user’s perspective, the performance of the network is experienced at the level of a path, and, as such, knowledge of variability of travel times along paths contemplated by the user is necessary. This paper focuses on developing approaches for the estimation of path travel time distributions in stochastic time-varying networks so as to capture generalized correlations between link travel times. Specifically, the goal is to develop methods to estimate path travel time distributions for any path in the networks by synthesizing available trajectory data from various portions of the path, and this paper addresses that problem in a two-fold manner. Firstly, a Monte Carlo simulation (MCS)-based approach is presented for the convolution of time-varying random variables with general correlation structures and distribution shapes. Secondly, a combinatorial data-mining approach is developed, which aims to utilize sparse trajectory data for the estimation of path travel time distributions by implicitly capturing the complex correlation structure in the network travel times. Numerical results indicate that the MCS approach allowing for time-dependence and a time-varying correlation structure outperforms other approaches, and that its performance is robust with respect to different path travel time distributions. Additionally, using the path segmentations from the segment search approach with a MCS approach with time-dependence also produces accurate and robust estimates of the path travel time distributions with the added benefit of shorter computation times.


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