The TimeGeo modeling framework for urban mobility without travel surveys

By Shan Jiang, Yingxiang Yang, Siddharth Gupta, Daniele Veneziano, Shounak Athavale, Marta C. González. Proceedings of the National Academy of Sciences of the United States of America, 2016

336 citations recorded by the source corpus.

Subfields: Transportation

View Paper: https://www.semanticscholar.org/paper/72c91bfd0e6f544601dffa04e9b6fa5d6a5fcd9c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Transportation

1. Gianni Barlacchi
2. Luca Pappalardo
3. Massimiliano Luca
4. Bruno Lepri

Citations in the corpus

• A Survey on Deep Learning for Human Mobility (2020, background): “TimeGeo [89] and DITRAS [138] improve the temporal mechanism integrating into an EPR-like model a data-driven model that captures the circadian propensity to travel and out-of-routine trips.”