Trustable public transit through integrated multimodal transit networks and trip planning
Lead Research Organisation:
Queen Mary University of London
Department Name: School of Engineering & Materials Scienc
Abstract
TO BE UPDATED IN JAN 2024 (at Year 2 Progression)
This project will investigate the following questions:
1. How to identify areas where there is an increased risk of congestion or crowding in real-time?
2. How to assess areas of high risk of transport-related COVID-19 exposure?
3. How to alert travellers to stay away until there is sufficient capacity available or disperse them to other transport means?
4. How to inform service providers the available options and assist them to better manage overcrowding?
5. How to integrate active/shared/micro/mass mobility solutions into a comprehensive transport ecosystem that would let travellers navigate seamlessly across different transit modes, and help transport authorities prioritise new infrastructures?
The project will create the pathways to such technology by modelling and integrating different transit networks, assessing the risk of transport-related COVID-19 exposure, planning alternative mobility options with the assessed risk, prioritising cycle infrastructure using crowding information and informing both travellers and service providers.
This project will investigate the following questions:
1. How to identify areas where there is an increased risk of congestion or crowding in real-time?
2. How to assess areas of high risk of transport-related COVID-19 exposure?
3. How to alert travellers to stay away until there is sufficient capacity available or disperse them to other transport means?
4. How to inform service providers the available options and assist them to better manage overcrowding?
5. How to integrate active/shared/micro/mass mobility solutions into a comprehensive transport ecosystem that would let travellers navigate seamlessly across different transit modes, and help transport authorities prioritise new infrastructures?
The project will create the pathways to such technology by modelling and integrating different transit networks, assessing the risk of transport-related COVID-19 exposure, planning alternative mobility options with the assessed risk, prioritising cycle infrastructure using crowding information and informing both travellers and service providers.
People |
ORCID iD |
| Jun Chen (Primary Supervisor) | |
| Nadeem Khondokar (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/V519935/1 | 30/09/2020 | 29/04/2028 | |||
| 2601315 | Studentship | EP/V519935/1 | 30/09/2021 | 30/03/2026 | Nadeem Khondokar |