Abstract
Modular construction (MC) is a sound strategy to alleviate global issues such as housing crisis, labor shortage, and stagnant productivity. Project managers are aspired to achieve a Just-in-Time (JIT) delivery of their MC logistics. However, the efforts often fall short without the presence of a dedicated Estimated Time of Arrival (ETA) model. This study aims to bridge this gap by developing a MC-oriented ETA model. It does so by first identifying critical factors influencing ETA accuracy in general logistics and then developing an ETA model prototype, which is then calibrated using simulations and data collected from the Internet of Things (IoTs) applied in real-life MC projects in Hong Kong, China. Validated through a cross-border MC project in China’s Greater Bay Area, the MC-oriented ETA model achieved 90.6% prediction accuracy (±10 min), reduced transportation delays by 37.5%, and slashed daily planning time from 46.75 to 18.75 min. It is expected that the ETA model can be used in predictive planning of MC logistics delivery in the future. Ultimately, it can lead to the development of smart logistics planning and control systems to expedite MC housing delivery and alleviate urban congestion in high-density cities, offering valuable insights for policymakers, construction stakeholders, and supply chain managers.
| Original language | English |
|---|---|
| Pages (from-to) | 880-898 |
| Number of pages | 19 |
| Journal | Frontiers of Engineering Management |
| Volume | 12 |
| Issue number | 4 |
| Early online date | 22 Nov 2025 |
| DOIs | |
| Publication status | Published - Dec 2025 |
Bibliographical note
Publisher Copyright:© The Author(s) 2025.
Funding
This research was supported by the Innovation and Technology Fund (ITF) (Grant No. ITP/029/20LP) and the Public Sector Trial Scheme (PSTS) Fund (Grant No. ITT/004/24LP) of the Innovation and Technology Commission (ITC) of Hong Kong, China.
Keywords
- modular construction
- estimated time of arrival model
- high-density cities
- just-in-time delivery
- logistics planning