Work

Predicting Intra-City Mobility Flows without Using Historical Flow Data

Year: 2025

Type: conference-paper

Abstract: This work in progress proposes a new deep learning-based method to generate intra-city mobility flows without using historical flow data. The proposed model takes the population/traveler volume of individual regions as inputs, and assumes no further geographical or mobility information about these regions, making it an effective and simple approach to be applied to many cities.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15350524

Authors: Zhang, Xucai, Huang, Haosheng

Institution: Ghent University

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W6949553892

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