Open PhD position [TSE 130] - Safe and Comprehensive Urban development and transport management for Disruption-resilient Operations (SCUDO) project

At the Chair of Transportation Systems Engineering (TSE) of the Technical University of Munich (TUM), we seek an excellent, motivated doctoral researcher for our EU-funded SCUDO project.

Description of the TSE Chair

The Chair of Transportation Systems Engineering (TSE) conducts research in the transportation field with a particular focus on modeling and simulating transportation systems, implementing data science and data analytics in transport and human factors analysis, and applications of machine learning and deep learning. The TSE Chair researches multimodal and unimodal freight and passenger transport demand and supply modeling, allowing for contributions to the optimization, calibration, and validation of transport models. In this direction, the application of big data acquisition and analysis and flexible data-driven models are examined. The Chair also contributes to analyzing human factors in transport-related fields such as road safety modeling, behavioral economics applications, and modeling of factors that affect user engagement in transportation systems.

Project description

European transport systems face increasing challenges from global events, natural disasters, and geopolitical tensions. Disruptions, such as pandemic-driven declines in public transport and extreme weather damage, highlight vulnerabilities, especially for at-risk populations. Proactive monitoring, forecasting, and infrastructure planning strategies are essential to ensure resilience.

SCUDO’s vision is to identify and classify scenarios of disruptive changes, assess their impact on different socio-economic groups, improve monitoring disruptions and quantification of uncertainties, and develop methods for designing & managing transport systems based on the Safe System Approach. This will increase the resilience and safety of transport systems. Additionally, SCUDO aims to establish a Pan-European Platform that integrates digital solutions and data to comprehensively evaluate the impact of disruptions on different geographical areas and user groups. This will increase the resilience and safety of transport systems. Towards this vision, SCUDO will treat safety as an integral part of resilient systems and will innovate through the following actions:

→ Develop AI models for system monitoring and testing, aiming to quantify uncertainties and detect disruptions.

→ Develop AI-based predictive models and traffic forecasting approaches, using existing and opportunistic data sources to identify disruptions, classify them, and forecast their potential impact on different socio-economic groups.

→ Develop strategic/tactical planning and real-time decision models following a Safe System Approach, aiming to design resilient transport infrastructure and transport systems that are safe and energy efficient.

→ Combine the developed models into a Pan-European Platform and test them in 4 Use Cases related to resilient public transport management, resilient traffic management, resilient strategic planning of transport infrastructure networks in case of future housing developments, and resilient network management.

The Chair of Transportation Systems Engineering (TSE) is pivotal in SCUDO by developing advanced forecasting and operational control solutions. These include a forecasting framework for predicting disruptive events and a disruption simulation framework for assessing the impacts of transport disruptive events on different socio-economic groups. Additionally, TSE will lead the task of creating an AI-based recovery time forecasting algorithm and AI-based operational control algorithms for traffic operations, public transport, and networks. These innovations aim to enhance resilience and efficiency in transportation systems.

Prof. Dr. Constantinos Antoniou will supervise the PhD researcher at TUM. As this is a collaborative research project delivered by a consortium of academic and industry partners, the TUM PhD candidate will participate in regular joint meetings and formal workshops virtually and in person.

Requirements

• Have an MSc degree in a relevant field (e.g., transportation engineering, data science, computer science);
• Be enthusiastic about researching transport-related projects and understand the fundamentals of transportation systems optimization and machine learning models;
• Have strong analytical skills;
• Have excellent research, academic writing, and presentation skills;
• Have a strong programming background and experience using e.g. Python or R;
• Have excellent working knowledge (written and oral) of English (knowledge of German will be considered a plus);
• Be able to work with strict deadlines.

In addition to the points above, candidates must fulfil the general TUM admission requirements: www.gs.tum.de/en/gs/applicants/application/requirements/

Conditions of Employment

Following the Public Sector Collective Agreement of Länder (TV-L), TUM offers a competitive compensation package. This position is a 100% TV-L 13. More information on the offered wages can be found at oeffentlicher-dienst.info/tv-l/west/

TUM is an equal-opportunity employer. Qualified women are particularly encouraged to apply. Applicants with disabilities are treated with preference, given comparable qualifications.