Aiming to develop a theoretically consistent framework to estimate travel demand using
multiple data sources, this paper first proposes a multi-layered Hierarchical Flow Network …

Rapid urbanization is placing increasing stress on already burdened transportation
infrastructure. Ubiquitous mobile computing and the massive data it generates presents new …

Stochastic travel demand estimation is essential to support many resilience and reliability
based transportation network analyses. The problem of estimating travel demand based on …

This paper proposes a modified bi-level optimization algorithm to estimate the time-
dependent origin–destination trip matrices for large-scale networks with multiple vehicle …

Transportation networks are unprecedentedly complex with heterogeneous vehicular flow.
Conventionally, vehicles are classified by size, the number of axles or engine types, eg …

Using road GIS (geographical information systems) data and travel demand data for two US
urban areas, the dynamical driver sources of each road segment were located. A method to …

To estimate network flow and travel behavior under recurrent traffic conditions, we leverage
computational graphs and multi-source system-level data and solve a single-level …

In this work, deterministic and stochastic optimization methods are tested for solving the
dynamic demand estimation problem. All the adopted methods demonstrate difficulty in …

This paper proposes a deep architecture called residual spatio-temporal network (RSTN) for
short-term travel demand forecasting. It comprises fully convolutional neural networks …

A dynamic origin–destination demand estimation model for planning applications with real-
time link counts from multiple days is presented. Based on an iterative bilevel estimation …