Tim Hillel research projects

This page reports only the academic work registered in the databases of the Transport and Mobility Laboratory, and is not necessarily a comprehensive list of the work by Tim Hillel.

More information may be available here

Intelligent digital twins for assessing and predicting bridge road traffic demands
Sponsor: School of Architecture, Civil and Environmental Engineering (ENAC), École polytechnique fédérale de Lausanne
Team: Tim Hillel (PI&PM)
Period: October 01, 2020-April 01, 2022
Road bridges are a vital part of transportation networks, forming crucial links in natural bottleneck locations and enabling the continual flow of people and goods into, out of, and across cities. However, the analysis used for design and maintenance planning of this community-critical infrastructure is typically carried out using static models and assuming generalized traffic patterns. This analysis represents only peak loading scenarios and does not reflect the spatial and temporal variations in real-world traffic loads. The resulting uncertainty in load prediction can lead to in overengineering in bridge design as well as sub-optimal maintenance planning. Furthermore, as current analysis techniques model only maximal loads, they cannot be used to predict the maintenance condition of bridges due to fatigue from repeated loading and unloading of the bridge over time. This research aims to address these limitations by developing intelligent digital twins which can simulate the response of a bridge to realistic traffic loading scenarios. These digital twin models combine two primary elements: (i) a traffic simulation model which exploits detailed traffic count and weigh-in-motion data to generate time-dependent traffic loadings, and (ii) a detailed structural model which predicts the compliance and maintenance condition of a bridge for different maximal and cyclic loading patterns. The intelligent digital twin is intended to be generalizable to any bridge or network of bridges for which relevant data exists. This will enable these models to be used within an integrated approach to study infrastructure vulnerability and multi-hazard risk management.
Optimization of individual mobility plans to simulate future travel in Switzerland
Sponsor: Innosuisse (Swiss Innovation Agency)
Team: Michel Bierlaire (PI), Tim Hillel (PM), Janody Pougala, Rico Krueger
Period: September 01, 2020-March 01, 2022
This project, joint with Swiss Federal Railways (SBB) will develop a new activity-based modelling approach based on optimization of individual daily mobility plans. This approach will be implemented within SBB's existing nationwide model for Switzerland for investment and service planning decisions for future transportation.
OrgVisionPro: Automated organizational design and optimization
Sponsor: Innosuisse (Swiss Innovation Agency)
Team: Michel Bierlaire (PI), Rico Krueger (PM), Tim Hillel (PM), Melvin Wong, Nour Dougui
Period: October 01, 2019-June 30, 2021
This project, joint with CLEAP S.A., is will develop advanced analytics algorithms to propose organization design (OD) scenarios based on the existing situation, constraints, and future needs of a business. These scenarios will support organizations in shaping their future by optimizing their structure and operating models.
Activity based travel demand forecasting
Sponsor: Swiss Federal Railways (SBB)
Team: Michel Bierlaire (PI), Tim Hillel (PM), Janody Pougala
Period: March 01, 2019-March 01, 2020
This research project aims to update and improve the microscopic activity-based demand model developed and maintained by SBB. Specifically the research intends to address the following questions: 1. Ownership of mobility instruments: Which metrics and specifications can be added to the current model, in order to improve its ability to forecast mid-and long-term ownership of mobility instruments? More specifically, how can the notion of accessibility be integrated to the current model to capture more complex mode interactions? 2. Mode choice model: Can a tour-based approach be used to model mode choice? In addition, how can the processes to estimate destination and mode choice (currently nested) be combined to generate results that are consistent with observed mobility behaviors at different time horizons (short, mid, and long-term)? 3. Tour and activity generation: How can the generation of tours and activity patterns be combined to allow modelling of joint decisions?
Tim Hillel