Bayesian Markov Chain Monte Carlo-Based Simultaneous Speed Prediction Model for Heterogeneous Traffic on Urban Primary Roads
Abstract
Objectives: The study designs a simultaneous speed prediction model based on Bayesian Markov Chain Monte Carlo (MCMC) methods for heterogeneous, non-lane-based traffic on urban primary roads, with the aim of determining the effects of vehicle-specific traffic densities on the operating speeds of the six vehicle types. Methods: The traffic data were obtained from videos recorded by unmanned aerial vehicles on five main urban roads in Dhaka, Bangladesh, and were then processed by a deep learning approach in order to derive information on vehicle trajectories, traffic volumes, densities, and space-mean speeds. Six simultaneous equations for speed prediction were developed using a Bayesian MCMC framework, covering standard cars, utility vehicles, heavy vehicles, three-wheelers, two-wheelers, and non-motorized vehicles. The performance of the models was assessed by means of goodness-of-fit measures. Findings: The coefficients of determination for the models varied from 0.922 to 0.956 and the root mean square errors were between 0.50 and 1.56 km/h for the six types of vehicle. Operating speeds decreased steadily as traffic volume increased, although traffic composition had a significant effect on speed when the demand was moderate or high. Operating speeds increased and congestion was delayed when non-motorized vehicles were excluded. The transferability study showed that the models had a reliable ability to make predictions on separate road sections.
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BibTeX
@misc{alam2026bayesian,
title = {{Bayesian Markov Chain Monte Carlo-Based Simultaneous Speed Prediction Model for Heterogeneous Traffic on Urban Primary Roads}},
author = {S. M Towhidul Alam and Md. Muhtashim Shahrier and Nazmul Haque and Md Asif Raihan and Md. Hadiuzzaman},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.05027},
eprint = {2610.05027},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.05027},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Bayesian Markov Chain Monte Carlo-Based Simultaneous Speed Prediction Model for Heterogeneous Traffic on Urban Primary Roads
AU - S. M Towhidul Alam
AU - Md. Muhtashim Shahrier
AU - Nazmul Haque
AU - Md Asif Raihan
AU - Md. Hadiuzzaman
PY - 2026
DA - 2026/10/04
PB - arXiv
DO - 10.48550/arxiv.2610.05027
UR - https://arxiv.org/abs/2610.05027
AB - Objectives: The study designs a simultaneous speed prediction model based on Bayesian Markov Chain Monte Carlo (MCMC) methods for heterogeneous, non-lane-based traffic on urban primary roads, with the aim of determining the effects of vehicle-specific traffic densities on the operating speeds of the six vehicle types. Methods: The traffic data were obtained from videos recorded by unmanned aerial vehicles on five main urban roads in Dhaka, Bangladesh, and were then processed by a deep learning approach in order to derive information on vehicle trajectories, traffic volumes, densities, and space-mean speeds. Six simultaneous equations for speed prediction were developed using a Bayesian MCMC framework, covering standard cars, utility vehicles, heavy vehicles, three-wheelers, two-wheelers, and non-motorized vehicles. The performance of the models was assessed by means of goodness-of-fit measures. Findings: The coefficients of determination for the models varied from 0.922 to 0.956 and the root mean square errors were between 0.50 and 1.56 km/h for the six types of vehicle. Operating speeds decreased steadily as traffic volume increased, although traffic composition had a significant effect on speed when the demand was moderate or high. Operating speeds increased and congestion was delayed when non-motorized vehicles were excluded. The transferability study showed that the models had a reliable ability to make predictions on separate road sections.
ER -