Yunpeng Wang

TL;DR: A copula-based approach that incorporates the stochastic characteristics of segments travel time is proposed to model arterial travel time distribution (TTD), which serves as a basis for TTR quantification and it was found that the estimated path TTR is more accurate than that by the convolution model.

TL;DR: A new approach which applies a recently developed data mining approach called gradient boosting logit model to handle different types of predictor variables, fit complex nonlinear relationships among variables, and automatically disentangle interaction effects between influential factors using high-resolution traffic and signal event data collected from loop detectors is proposed.

TL;DR: A predictive framework to capture the stop-level headway irregularity based on transit smart card data can provide timely and accurate information for potential bus bunching prevention and inform passengers when the next bus will arrive and will greatly increase transit ridership and reduce operating costs for transit authorities.

TL;DR: Results show that cycle-based signal control could improve both traffic mobility and energy saving of the EV system with eco-driving compared to a fixed signal timing plan.

TL;DR: This work investigates the possibility of using the travel behavioral information derived from mobile phone data to build such a validation measure, and compares the activity-travel profiles derived from the call data with statistics that stem from traditional activity- travel surveys.