Title: DFG Project: Value of Time estimation considering Automated Vehicle Impacts (VOTAVI)
Uiversity: TUM
Project description: The value of travel time (VOT) is crucial in various areas, such as travel demand modeling, transport policy, and investment evaluation. VOT is influenced by factors like trip purpose, transport mode, and distance traveled, allowing for a more accurate assessment of cost-benefit analyses. The emergence of autonomous modes, like autonomous vehicles (AVs) and Urban Air Mobility (UAM), has transformed the perception of VOT. AVs enable individuals to engage in various tasks while traveling, prompting new challenges in VOT estimation and attracting increased research attention
Traditional travel surveys used surveys and data loggers, later transitioning to smartphone apps for digital travel surveys. These apps offer advantages like high user penetration and extensive data collection. However, challenges remain, including limited use of Stated Preference (SP) surveys for individual trips. The VOTAVI project aims to address these challenges by developing an innovative smartphone app for joint Revealed Preference (RP)/ Stated Preference (SP) surveys in the Autonomous Vehicle era, contributing to improved user preference inference and unbiased Value of Time estimation
Supervisory team
Deadline: Review of applications will begin immediately and continue until the position is filled
:Requirements
Have an MSc degree in a relevant field (e.g., transportation engineering, data science, computer science)
Be enthusiastic about researching transport-related projects — understanding the fundamentals of transportation systems and modelling will be a plus
Have strong analytical skills
Have excellent research, academic writing, and presentation skills
Experience in Swift and CocoaPods is considered a plus
A desire to write understandable and robust code
Knowledge in building SDKs / Apps
Professional proficiency in English
Knowledge of the German language will be considered a plus
Driven, reliable, good level of self-organization, team player
Knowledge of machine learning is a plus
More information about the position: Link
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