| 1. |
- Sun, Yan, et al.
(författare)
-
Multiagent Modelling and Simulation of a Physical Internet Enabled Rail-Road Intermodal Transport System
- 2018
-
Ingår i: URBAN RAIL TRANSIT. - : Springer Nature. - 2199-6687 .- 2199-6679. ; 4:3, s. 141-154
-
Tidskriftsartikel (refereegranskat)abstract
- Simulation-based analysis has been used for planning, control, and decision-making support of physical internet enabled logistics networks. However, multiagent modelling and simulation based on micro-level interactions have been rarely developed for the pre-studies of digital transformation of urban rail transit systems. This hinders a wider industrial deployment of agent technology in the physical internet enabled transport infrastructure. To fill in this knowledge gap, this work presents an agent-based simulation that explicitly models the micro-level protocols of mobile recourse units and their interaction with the physical infrastructure in a rail-road intermodal transport network. Parameterisation of the simulation model is changeable to examine the influences of different efficiency factors. This allows understanding of which structural functions and resource configuration would make an impact system-wide. Through a practical application, a multiagent system is developed for modelling and analysis of sustainable logistics with individually operated mobile resource units. An agent-based simulation assessment is performed to quantify the improvement options. The results reveal that the physical internet can prevent trucks from empty driving, which has a positive effect on the sustainable logistics operations. The proposed model can be used to support the deployment and planning of digital transformation that could be implemented in urban rail transit systems serving urban distribution and passenger transport.
|
|
| 2. |
- Liu, Tianyou, et al.
(författare)
-
Modeling the Duration of the Impact of Unplanned Disruptions on Passenger Trips Using Smartcard Data in Urban Rail Systems
- 2023
-
Ingår i: URBAN RAIL TRANSIT. - : Springer Nature. - 2199-6687. ; 9:3, s. 266-279
-
Tidskriftsartikel (refereegranskat)abstract
- Many urban rail systems operate near capacity given the rapid increase in passenger demand, and unplanned disruptions are unavoidable. From a passenger perspective, the duration of trip delays is a major concern, and passenger trip delays may be longer than the train delays. Several studies have focused on predicting train delays, but the research on the duration of the disruption impacts on passenger trips is limited given that the duration is not observed directly. This paper proposes a probabilistic method to estimate the disruption impact duration using smartcard data, explores statistical and machine learning models to predict the duration of impacts on passengers, and identifies influencing factors including incident characteristics, operating conditions, infrastructure, external factors, and demand. The results highlight that prediction accuracies are acceptable for multiple linear regression, accelerated failure time, and random forest models. Disruptions caused by power failures have longer impact durations than other causes, followed by platform screen doors. The fixed block signaling system leads to a larger disruption duration than the moving block system. The study provides, for the first time, a data-driven approach to understanding the duration of the impact of disruptions on passenger trips using smartcard data which can facilitate timely and informed decision-making under unplanned disruptions.
|
|
