GPS Trajectory Completion Using End-to-End Bidirectional Convolutional Recurrent Encoder-Decoder Architecture with Attention Mechanism

Nawaz, Asif; Huang, Zhiqiu; Wang, Senzhang; Akbar, Azeem; AlSalman, Hussain; Gumaei, Abdu

doi:10.3390/s20185143

Cited by 20 publications

(9 citation statements)

References 46 publications

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“…Nawaz et al [13] have introduced a deep learning-based convolutional recurrent encoder-decoder architecture to address the GPS trajectory imputation problems. There have been temporal and spatial components to GPS trajectory data.…”

Section: Related Workmentioning

confidence: 99%

HTIM: A Hybrid Deep Learning Approach For Pedestrian Trajectory Imputation

Barua,

Halder,

Shopnil

et al. 2024

Preprint

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Pedestrian trajectories are crucial for self-driving cars to plan their paths effectively. The sensors implanted in these self-driving vehicles, despite being state-of-the-art ones, often face inaccuracies in the perception of surrounding environments due to technical challenges for adverse weather conditions, interference from other vehicles sensors and electronic devices, and signal reception failure, leading to incompleteness in trajectory data. But for real-time decision making for autonomous driving, the trajectory imputation is no less crucial. Previous attempts to address this issue such as statistical inference and machine learning approaches have shown promise. Yet, the landscape of deep learning is rapidly evolving with new and more robust models emerging. In this research, we have proposed an encoder-decoder architecture, Human Trajectory Imputation Model, coined as HTIM, to tackle these challenges. This architecture aims to fill in the missing parts of pedestrian trajectories. The model has been evaluated using the Intersection drone inD dataset, containing trajectory data at suitable altitudes preserving naturalistic pedestrian behavior with varied dataset sizes. To assess the effectiveness of our model, we have utilized L1, MSE, Quantile and ADE loss. Our experiments have demonstrated that HTIM outperforms the majority of the state-of-the-art methods in this field, thus indicating its superior performance in imputing pedestrian trajectories.

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Section: Related Workmentioning

confidence: 99%

HTIM: A Hybrid Deep Learning Approach For Pedestrian Trajectory Imputation

Barua,

Halder,

Shopnil

et al. 2024

Preprint

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“…The Geolife trajectory dataset has been used in different research fields such as in privacy-preserving location data [28], measuring trajectory stops and moves [29], user identification [30], trajectory completion [31], and transport mode detection [32].…”

Section: Walkmentioning

confidence: 99%

Modeling the Impact of Weather and Context Data on Transport Mode Choices: A Case Study of GPS Trajectories from Beijing

Otim¹,

Dörfer²,

Ahmed³

et al. 2022

Sustainability

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Over the years, researchers have been studying the effects of weather and context data on transport mode choices. Existing research studies are predominantly designed around travel surveys, but the accuracy of their findings relies on how travelers give accurate and honest answers. The proliferation of smartphones, however, now offers the possibility of utilizing GPS positioning data as an alternative information source, opening the potential to accurately model and better understand factors which influence transport mode choices, compared to travel surveys. The objective of this work is to develop a model to predict the transport mode choices based on GPS trajectories, weather and context data. We use 2671 GPS trajectories from the Geolife GPS trajectories dataset, weather data, such as temperature and air quality, and context data, such as rush hour, day/night time and onetime events, such as the Olympics. In the statistical analysis, we apply both descriptive and statistical models, such as the multinomial logit and probit models. We find that temperature has the most prominent effect among weather conditions. For instance, for temperatures greater than 25 °C, the walking share increases by 27%, and the bike share reduces by 21%, which is line with the results from several survey-based studies. In addition, the evidence of government policy on transport regulation is revealed when the air quality becomes hazardous, as people are encouraged to use environmentally friendly transport mode choices, such as the bike instead of the bus or car, which are known CO2 emitters. Our conclusion is that GPS trajectories can be used as a means to model passenger behavior, e.g. the choice of transport mode, in a quantitative way, which will support transport mode operators and policy makers in their efforts to design and plan the transport mode infrastructure to best suit the passengers’ needs.

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“…The Geolife trajectory dataset has been used in different research fields such as in privacy preserving location data [26], measuring trajectory stops and moves [27], user identification [28], trajectory completion [29], and transport mode detection [30].…”

Section: Gps Trajectoriesmentioning

confidence: 99%

Modelling the Impact of Weather and Context Data on Transport Mode Choices: A Case Study of GPS Trajectories From Beijing

Otim¹,

Dörfer²,

Ahmed³

et al. 2022

Preprint

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Over the years, researchers have been studying the effect of weather and context data on the transport mode choice. The majority of these works are based on survey data, however the accuracy of their findings relies on how respondents give accurate and honest answers. In this paper, the potential of using GPS trajectories as an alternative to travel surveys in studying the impact of weather and context data on transport mode choices is investigated in Beijing city. In the analysis, we apply both descriptive and statistical models such as the MNL and MNP models. Our findings indicate that temperature has the most prominent effect among weather conditions. For instance, for temperatures greater than 25 °C, the walking share increases by 27% and the bike share reduces by 21%, which is line with the results from several survey studies. In addition, the evidence of government policy on transport regulation is revealed when the air quality becomes hazardous as people are encouraged to use environmentally friendly travel mode choices such as the bike instead of the bus and car, which are known CO2 emitters. Moreover, due to a series of traffic restrictions introduced by the Beijing government during the 2008 summer Olympics, a decrease of 17.5% in the car share and an increase of 13% and 10% in the walking and bus shares, respectively are observed. These findings provide a scientific basis for effective transport regulation and planning purposes.

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GPS Trajectory Completion Using End-to-End Bidirectional Convolutional Recurrent Encoder-Decoder Architecture with Attention Mechanism

Cited by 20 publications

References 46 publications

HTIM: A Hybrid Deep Learning Approach For Pedestrian Trajectory Imputation

HTIM: A Hybrid Deep Learning Approach For Pedestrian Trajectory Imputation

Modeling the Impact of Weather and Context Data on Transport Mode Choices: A Case Study of GPS Trajectories from Beijing

Modelling the Impact of Weather and Context Data on Transport Mode Choices: A Case Study of GPS Trajectories From Beijing

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