#6398. Interpretable data-driven demand modelling for on-demand transit services

November 2026publication date
Proposal available till 22-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Aerospace Engineering;
Business, Management and Accounting (miscellaneous);
Civil and Structural Engineering;
Management Science and Operations Research;
Transportation;
Places in the authors’ list:
place 1place 2place 3place 4
FreeFreeFreeFree
2350 $1200 $1050 $900 $
Contract №6398.1 Contract №6398.2 Contract №6398.3 Contract №6398.4
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)

More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
In recent years, with the advancements in information and communication technology, different emerging on-demand shared mobility services have been introduced as innovative solutions in the low-density areas, including on-demand transit (ODT), mobility on-demand (MOD) transit, and crowdsourced mobility services. However, due to their infancy, there is a strong need to understand and model the demand for these services. In this study, we developed trip production and distribution models for ODT services at Dissemination areas (DA) level using four machine learning algorithms: Random Forest (RF), Bagging, Artificial Neural Network (ANN) and Deep Neural Network (DNN). The data used in the modelling process were acquired from Bellevilles ODT operational data and 20XX census data. Bayesian optimalization approach was used to find the optimal architecture of the adopted algorithms. Moreover, post-hoc model was employed to interpret the predictions and examine the importance of the explanatory variables.
Keywords:
Demand modelling; Machine learning; Model interpretability; On-demand service (ODT); Shared mobility; Trip generation and distribution

Contacts :
0