Abstract
Bluetooth-based traffic detection is an emerging travel time collection technique; however, its use on arterials has been limited due to several challenges. In particular, data missing not at random (MNAR) is a common data set problem caused by system network failure or sensor malfunctioning. Solving the MNAR problem requires travel-time decomposition (TTD) using complete travel times spanning successive links. Previous work has focused on TTD methodologies that use probe vehicle data. However, these approaches may be unsuitable for Bluetooth-based data. Therefore, this study proposes a machine learning-based approach to decomposing Bluetooth-based travel time. A modified hidden Markov model was developed to model travel-time distributions and traffic-state transitions. A genetic algorithm (GA) was applied to solve a numerical optimal decomposition based on maximum likelihood. Two real-world travel-time data sets were used for validation of the approach. The proposed hidden Markov chain with GA (HMMGA) approach and Gaussian mixture model with GA (GMMGA) were compared with a benchmark approach using distance-based allocation. The results showed that the HMMGA significantly outperformed both the GMMGA and benchmark approaches. Using the HMMGA, the average mean absolute percentage error was up to 72% lower compared to the benchmark approach.
| Original language | English (US) |
|---|---|
| Article number | 04018005 |
| Journal | Journal of Computing in Civil Engineering |
| Volume | 32 |
| Issue number | 3 |
| DOIs | |
| State | Published - May 1 2018 |
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Keywords
- Bluetooth-based travel time
- Gaussian mixture model
- Genetic algorithm
- Hidden Markov model
- Missing not at random
- Travel-time decomposition
ASJC Scopus subject areas
- Civil and Structural Engineering
- Computer Science Applications
Cite this
Machine Learning Approach to Decomposing Arterial Travel Time Using a Hidden Markov Model with Genetic Algorithm. / Yang, Shu; Chen, Ming; Wu, Yao-jan; An, Chengchuan.
In: Journal of Computing in Civil Engineering, Vol. 32, No. 3, 04018005, 01.05.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Machine Learning Approach to Decomposing Arterial Travel Time Using a Hidden Markov Model with Genetic Algorithm
AU - Yang, Shu
AU - Chen, Ming
AU - Wu, Yao-jan
AU - An, Chengchuan
PY - 2018/5/1
Y1 - 2018/5/1
N2 - Bluetooth-based traffic detection is an emerging travel time collection technique; however, its use on arterials has been limited due to several challenges. In particular, data missing not at random (MNAR) is a common data set problem caused by system network failure or sensor malfunctioning. Solving the MNAR problem requires travel-time decomposition (TTD) using complete travel times spanning successive links. Previous work has focused on TTD methodologies that use probe vehicle data. However, these approaches may be unsuitable for Bluetooth-based data. Therefore, this study proposes a machine learning-based approach to decomposing Bluetooth-based travel time. A modified hidden Markov model was developed to model travel-time distributions and traffic-state transitions. A genetic algorithm (GA) was applied to solve a numerical optimal decomposition based on maximum likelihood. Two real-world travel-time data sets were used for validation of the approach. The proposed hidden Markov chain with GA (HMMGA) approach and Gaussian mixture model with GA (GMMGA) were compared with a benchmark approach using distance-based allocation. The results showed that the HMMGA significantly outperformed both the GMMGA and benchmark approaches. Using the HMMGA, the average mean absolute percentage error was up to 72% lower compared to the benchmark approach.
AB - Bluetooth-based traffic detection is an emerging travel time collection technique; however, its use on arterials has been limited due to several challenges. In particular, data missing not at random (MNAR) is a common data set problem caused by system network failure or sensor malfunctioning. Solving the MNAR problem requires travel-time decomposition (TTD) using complete travel times spanning successive links. Previous work has focused on TTD methodologies that use probe vehicle data. However, these approaches may be unsuitable for Bluetooth-based data. Therefore, this study proposes a machine learning-based approach to decomposing Bluetooth-based travel time. A modified hidden Markov model was developed to model travel-time distributions and traffic-state transitions. A genetic algorithm (GA) was applied to solve a numerical optimal decomposition based on maximum likelihood. Two real-world travel-time data sets were used for validation of the approach. The proposed hidden Markov chain with GA (HMMGA) approach and Gaussian mixture model with GA (GMMGA) were compared with a benchmark approach using distance-based allocation. The results showed that the HMMGA significantly outperformed both the GMMGA and benchmark approaches. Using the HMMGA, the average mean absolute percentage error was up to 72% lower compared to the benchmark approach.
KW - Bluetooth-based travel time
KW - Gaussian mixture model
KW - Genetic algorithm
KW - Hidden Markov model
KW - Missing not at random
KW - Travel-time decomposition
UR - http://www.scopus.com/inward/record.url?scp=85041455353&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85041455353&partnerID=8YFLogxK
U2 - 10.1061/(ASCE)CP.1943-5487.0000749
DO - 10.1061/(ASCE)CP.1943-5487.0000749
M3 - Article
VL - 32
JO - Journal of Computing in Civil Engineering
JF - Journal of Computing in Civil Engineering
SN - 0887-3801
IS - 3
M1 - 04018005
ER -