arXiv subjects

Wai Wong

Publications and source records attributed to Wai Wong.

3 recordsLinked to original sources

arXiv2025

Real-time Bus Travel Time Prediction and Reliability Quantification: A Hybrid Markov Model

Accurate and reliable bus travel time prediction in real-time is essential for improving the operational efficiency of public transportation systems. However, this remains a challenging task due to the limitations of existing models and data sources. This study proposed a hybrid Markovian framework for real-time bus travel time prediction, incorporating uncertainty quantification. Firstly, the bus link travel time distributions were modeled by integrating various influential factors while explicitly accounting for heteroscedasticity. Particularly, the parameters of the distributions were estimated using Maximum Likelihood Estimation, and the Fisher Information Matrix was then employed to calculate the 95\% uncertainty bounds for the estimated parameters, ensuring a robust and reliable quantification of prediction uncertainty of bus link travel times. Secondly, a Markovian framework with transition probabilities based on previously predicted bus link travel times was developed to predict travel times and their uncertainties from a current location to any future stop along the route. The framework was evaluated using the General Transit Feed Specification (GTFS) Static and Realtime data collected in 2023 from Gainesville, Florida. The results showed that the proposed model consistently achieved better prediction performance compared to the selected baseline approaches (including historical mean, statistical and AI-based models) while providing narrower uncertainty bounds. The model also demonstrated high interpretability, as the estimated coefficients provided insights into how different factors influencing bus travel times across links with varying characteristics. These findings suggest that the model could serve as a valuable tool for transit system performance evaluation and real-time trip planning.

stat.AP↗

arXiv2020

Impact Evaluation of Falsified Data Attacks on Connected Vehicle Based Traffic Signal Control

Connected vehicle (CV) technology enables data exchange between vehicles and transportation infrastructure and therefore has great potentials to improve current traffic signal control systems. However, this connectivity might also bring cyber security concerns. As the first step in investigating the cyber security of CV-based traffic signal control (CV-TSC) systems, potential cyber threats need to be identified and corresponding impact needs to be evaluated. In this paper, we aim to evaluate the impact of cyber attacks on CV-TSC systems by considering a realistic attack scenario in which the control logic of a CV-TSC system is unavailable to attackers. Our threat model presumes that an attacker may learn the control logic using a surrogate model. Based on the surrogate model, the attacker may launch falsified data attacks to influence signal control decisions. In the case study, we realistically evaluate the impact of falsified data attacks on an existing CV-TSC system (i.e., I-SIG).

eess.SY↗

arXiv2019

Various methods for queue length and traffic volume estimation using probe vehicle trajectories

The rapid development of connected vehicle technology and the emergence of ride-hailing services have enabled the collection of a tremendous amount of probe vehicle trajectory data. Due to the large scale, the trajectory data have become a potential substitute for the widely used fixed-location sensors in terms of the performance measures of transportation networks. Specifically, for traffic volume and queue length estimation, most of the trajectory data based methods in the existing literature either require high market penetration of the probe vehicles to identify the shockwave or require the prior information about the queue length distribution and the penetration rate, which may not be feasible in the real world. To overcome the limitations of the existing methods, this paper proposes a series of novel methods based on probability theory. By exploiting the stopping positions of the probe vehicles in the queues, the proposed methods try to establish and solve a single-variable equation for the penetration rate of the probe vehicles. Once the penetration rate is obtained, it can be used to project the total queue length and the total traffic volume. The validation results using both simulation data and real-world data show that the methods would be accurate enough for assistance in performance measures and traffic signal control at intersections, even when the penetration rate of the probe vehicles is very low.

physics.soc-ph↗