Student Projects

Type of Project: To be discussed with the student
Supervisor: Gustav Nilsson ([email protected])
Student: open for application

In this project, you will implement and simulate ride-hailing operations (like Uber, Lyft) in an open-source micro simulator for traffic, SUMO https://eclipse.dev/sumo/. As a first part, to familiarize yourself with how to use the Python API for controlling SUMO, you will simulate the service in a toy example. For the second part, there is a fully calibrated scenario for the city of Turin without ride-hailing, https://github.com/marcorapelli/TuSTScenario . By assuming that a fraction of the trips are made through ride-hailing, different performance metrics of the ride-hailing service can be investigated. If time permits, further investigations can also be done, such as the benefits of pooling passengers.

For the project to be successful, you need to have decent general data science and scripting skills, e.g., you should be able on your own (and perhaps some googling) to write a Python script to plot a time series from data stored in an XML file reasonably quickly. Some Linux/Unix and Git experience is beneficial too. No previous experience with SUMO is required.

Type of Project: To be discussed with the student
Supervisor: Weijiang Xiong ([email protected]) 
Student: open for application

Forecasting the future traffic states is a fundamental task for the management of transportation systems. While many deep learning methods have been proposed to address this problem, most of them only focus on predicting most probable values for the traffic states. In reality, the evolution of traffic is highly dynamic, and the sensor measurements inevitably contains noises. As a result, a point estimation is often insufficient to represent the possible future traffic states and provide grounds for decision making in traffic management. The idea to overcome this problem is to predict an uncertainty score in addition to the expected value, with which we can know how much the predicted value may vary.

In this project, the student will learn to adapt existing uncertainty prediction methods into traffic forecasting problem, and implement both epistemic uncertainty and aleatoric uncertainty prediction (from [1]) for traffic forecasting models. The student will also learn to combine these uncertainties to compute confidence intervals, which will also be visualized and evaluated. Depending on the progress, the project can include more contents in neural network design (e.g., anchor-based regression), auxiliary learning tasks (e.g., missing value completion or imputation). To smoothly complete the project, the student is expected to have knowledge in deep learning and some hands-on experiences with python and deep learning libraries.

Reference: [1] What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?. Alex Kendall, Yarin Gal. https://arxiv.org/abs/1703.04977

Type of Project: To be discussed with the student
Supervisor: Marko Maljkovic ([email protected])
Student: open for application

In this project, we aim to investigate how the task of monitoring traffic phenomena in a city region can be tackled using a swarm of drones. For a simplified case study describing an urban environment via graph, we are interested in designing a patrolling algorithm [1] that helps increase the heterogeneous coverage of the area while taking into account the monitoring capabilities of the drones.

We will begin by adapting the existing simulation environment that operates one drone and then proceed to design, test, and compare different coordination algorithms to control a fleet of drones. Students will have the opportunity to investigate different rule-based algorithms and, if interested in Deep Learning, to gain hands-on experience in designing some learning-based methods. Therefore, good knowledge of Python is required.

[1] Drone swarm patrolling with uneven coverage requirements. C. Piciarelli, G.L. Foresti, https://arxiv.org/pdf/2107.00362.pdf

Type of Project: To be discussed with the student
Supervisor: Georg Anagnostopoulos ([email protected])
Student: open for application

Disruptions of the traffic flow due to lane-changing events have long been considered by researchers as a significant determinant of congestion. From a modeling perspective, changing lane is often simplified as a discrete and instantaneous action, typically using discrete choice models. While valid for lane-based systems, such as highways, the assumption of perfect lane-discipline is too strong in multispecies urban traffic where cars share the roadspace with lighter and more flexible vehicles, such as motorcycles, bicycles, e-scooters, and other two-wheelers. Hence the research question is how we could implement lane-changing in multispieces urban traffic.

In this project, the students will work with an existing multispecies traffic simulator developed in our lab and try to expand its functionality by allowing the cars to perform lane-changes. Some of the concepts that we are going to focus on are self-organization, collision-avoidance, anticipation and stability. Any previous exposure to the principles of simulation is highly appreciated, but not necessary. Very good knowledge of Python is required.

Type of Project: To be discussed with the student
Supervisor: Zhenyu Yang ([email protected])
Student: open for application

The use of curbsides, typically avaiable for both buses and ride-hailing vehicles, plays a significant role in urban transportation management. The temporary parking of ride-hailing vehicles during pickups and drop-offs often lead to bus delays. Understanding this dynamic is vital for effective curb management and promoting bus services. Presently, there’s a gap in detailed knowledge on this topic. The student is tasked with utilizing microscopic traffic simulation software, such as Vissim, SUMO, or Aimsun, to model the interplay between bus and ride-hailing vehicles. There’s also the option to approach this study analytically, based on our assumptions regarding the road network and drivers’ behaviors.

A basic understanding of car-following models and traffic flow theory is beneficial, though not essential. A preliminary knowledge of Python or another programming language is highly recommended for this project. All results obtained from this study would be reproducible.

Type of Project: To be discussed with the student
Supervisor: Yura Tak ([email protected])
Student: open for application

UAV (Unmanned Aerial Vehicles) surged recently as a promising solution for aerial traffic monitoring. However, the sensors face the occlusions that can occur due to high buildings, trees, and other obstacles in the target area. Such occlusions decrease the performance of the vehicle detector. To achieve accurate monitoring with an occlusion-aware vehicle detector, we aim to segment the occlusions present on the road from the drone videos. To do so, the student will leverage state-of-the-art segmentation model, such as SAM [1], to segment the roads and the occlusions.

The student should have a good programming skills in Python. Previous experience with machine learning and computer vision is a plus.

[1] Segment Anything, Kirillov et al., https://arxiv.org/abs/2304.02643

Type of Project: To be discussed with the student
Supervisor: Minru Wang ([email protected])
Student: open for application

With the introduction of ride-sourcing services in urban areas, this new market has notable influence on transit ridership and travel costs. While there is growing research on how these travel modes can coexist, we are very interested in a specific case where some spatial restriction on ride-sourcing vehicle access is in place. As part of this project, the student will model the operation of ride-pooling as a first-mile service to complement public transit service, possibly by extending our adapted implementation of [1] to include transit lines in a simplified network. We will then evaluate the system performance in a number of operational scenarios.

Good programming skills in Python and strong analytical skills are essential for this project. Experience in optimization is an asset.

[1] On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. Alonso-Mora et al. https://doi.org/10.1073/pnas.1611675114

Type of Project: Laboratory GC (MSc), 4 credits
Supervisor: Georg Anagnostopoulos ([email protected])
Student: Brian Alexis Salamin and Jordan Lucien Dessibourg

For many city dwellers, riding a two-wheeler (bike, e-bike, scooter, e-scooter, motorcycle) is an attractive alternative to driving a four-wheeler (car, e-car). Not only because of lower purchasing and/or operating cost, but also as a strategy to bypass congestion. Due to their smaller size and higher maneuverability, some of the faster two-wheelers, such as motorcycles, percolate forward by riding between the lanes. This phenomenon goes by various names, including “filtering”, “creeping”, “lane-splitting” or “virtual lane”, and its understanding requires a more generalized theoretical toolbox.

Lane-based traffic flow theory, as exemplified by car-following models, does not apply in the abscence of a clear following hierarchy and predetermined lanes. Inpired from research in pedestrian flow, we will investigate a hybrid model, where cars are treated as moving obstacles, and two-wheelers navigate between the four-wheelers by performing collision-avoidance. The students will have the opportunity to investigate and synthesize concepts, such as distance to collider, time to collision, and anticipation. Our objective is to simulate motorcycle dynamics in a hybrid environmment and to reproduce the formation of virtual lanes. Good programming skills are desirable.

Type of Project: Civil Systems (MSc), 4 credits
Supervisor: Marko Maljkovic ([email protected])
Student: Zachary Hansen and Lorenzo Ballinari

In this project, we aim to investigate how the task of traffic monitoring can be tackled using a swarm of drones. For a simplified case study describing an urban environment, we are interested in designing a patrolling algorithm that helps increase the heterogeneous coverage of the area while taking into account the monitoring capabilities of the drones. To make the algorithm map-invariant, we want to investigate if such a centralized agent operating the swarm can be trained via Reinforcement Learning [1].

Ideally, students should be familiar with (or show willingness to learn) Deep Learning and Reinforcement Learning concepts. Moreover, some hands-on experience with python and some deep learning libraries would be beneficial.

Reference: [1] Drone swarm patrolling with uneven coverage requirements. C. Piciarelli, G.L. Foresti, https://arxiv.org/pdf/2107.00362.pdf

Type of Project: Semester Project in minor in CSE (MSc), 8 credits
Supervisor: Yura Tak ([email protected])
Student: Thamin Maurer

UAV (Unmanned Aerial Vehicles) surged recently as a promising solution for aerial surveillance of multiple urban areas. However, the sensors face the occlusions that can occur due to high buildings, trees and other obstacles in the target area. Such partially occluded vehicle images decreases the performance of the vehicle detector. In order to achieve an accurate monitoring with an occlusion-aware vehicle detector, we aim at de-occluding the occluded vehicle images based on the drone videos.

The project will be articulated in two-steps. The first part consists of segmenting the target occlusions area of the vehicle images to de-occlude. The second part will focus on the generation of de-occluded vehicle images from the occluded vehicle images, exploiting GAN models.

The student should have programming experience in Python. Previous experience with machine learning and computer vision is a plus.

Type of Project: Civil Systems (MSc), 4 credits; Construction project (MSc), 4 credits
Supervisor: Minru Wang ([email protected])
Student: Sanad JOUHARI and Mya JAMAL LAHJOUJI

Matching algorithms for dial-a-ride services can provide good quality solutions that aim to serve as many requests as possible with short waiting time and a small detour. However, existing studies often focus on tactical aspects and analyze performance metrics resulting from simulations. To complement the theoretical analysis, the semester project will consider uncertainties associated with an operational station-based microtransit service.

To assess how a theoretical matching simulation can adapt to real-time operation, we invite interested students to incorporate aspects such as dwell time uncertainty and parking space constraints, and investigate how the service platform can adapt to these operational uncertainties and constraints. Good Python programming skill is required.

Type of Project: Bachelor project, 6 credits; Laboratoire GC (Msc), 4 credits
Supervisor: Minru Wang ([email protected])
Student: Manon Bertola and Yasser Tahiri

In this semester project, the student will work with a simplified ride-sourcing network where spatial demand imbalance patterns can be observed, for example, high flow into the city centre during morning peak hours. The objective is to study a dynamic model of the system, and optimize the service level by controlling the proportion of pooled trips, either by optimizing for an entire study period, or through a receding horizon approach. Good programming knowledge and experience with Python is essential. The project can be adapted according to the student’s skills and interests; therefore, no prior knowledge in control is required.

Type of Project: Civil Systems (MSc), 4 credits
Supervisor: Pengbo Zhu ([email protected])
Student: Jonas Affentranger and Kanamori Yuki

Mobility-on-Demand system is an emerging service within urban scenarios which shows its potential to reduce congestion at the same time optimize service quality for customers. A critical operational challenge is the problem of imbalance between vehicle supply and customer demand. In this project, we will investigate a bi-level control structure for repositioning empty vehicles. It benefits from efficient coordination between the actions of upper-level controllers which operate the aggregated traffic components (e.g. how many empty vehicles should move from one subregion to another according to estimated future demands), at the same time the self-management of individual vehicles at lower-level which can give relatively precise position guidance.

The objective is to develop the ability to formulate problems, design different control schemes, verify the performances and present comparisons and conclusions of what we find. Please note that good Matlab programming skill is required and highly appreciated.

Type of Project: Laboratoire GC (MSc), 4 credits; Bachelor Project, 6 credits
Supervisor: Georg Anagnostopoulos ([email protected])
Student: Sanad Jouhari and Evangelia Gkola

For many city dwellers, riding a two-wheeler (bike, e-bike, scooter, e-scooter, motorcycle) is an attractive alternative to driving a four-wheeler (car, e-car). Not only because of lower purchasing and/or operating cost, but also as a strategy to bypass congestion. Due to their smaller size and higher maneuverability, some of the faster two-wheelers, such as motorcycles, percolate forward by riding between the lanes. This phenomenon goes by various names, including “filtering”, “creeping”, “lane-splitting” or “virtual lane”, and its understanding requires a more generalized theoretical toolbox.

Lane-based traffic flow theory, as exemplified by car-following models, does not apply in the abscence of a clear following hierarchy and predetermined lanes. Inpired from research in pedestrian flow, we will investigate a hybrid model, where cars are treated as moving obstacles, and two-wheelers navigate between the four-wheelers by performing collision-avoidance. The students will have the opportunity to investigate and synthesize concepts, such as distance to collider, time to collision, and anticipation. Our objective is to simulate motorcycle dynamics in a hybrid environmment and to reproduce the formation of virtual lanes. Good programming skills are desirable.

Type of Project: Bachelor Project; Laboratory GC (MSc), 4 credits
Supervisor: Lynn Fayed ([email protected])
Student: Milesi Riccardo and Anne-Valérie Preto

On-demand micro-transit is a transportation alternative sharing similarities with both ride-hailing/ride-splitting from one side and public transit from the other. The relatively high capacity feature of the micro-transit vehicles is inherited from the classical public transit service operation. Nevertheless, the dynamic and flexible route and schedule of the operating micro-transit vehicles accentuates the great resemblance this service has with ride-hailing. However, even if the service structure of public transit and ride-hailing is well-established in the literature, our understanding of the on-demand micro-transit market dynamics is still deficient.

The aim of this project is therefore to use a simulation-based approach to understand the main features of this type of service. We will mainly focus on assessing the intricate relationships between demand, service rate, micro-transit vehicle occupancies, passenger detour, and driver trip length per passenger. The ultimate goal is to determine under what market conditions these services are efficient, and what is the network structure in which they could be beneficial. For this project, a good Python programming level is required.

Type of Project: Bachelor Project
Supervisor: Yura Tak ([email protected])
Student: Ogay Xavier and Mahmoud Dokmak

UAV (Unmanned Aerial Vehicles) surged recently as a promising solution for aerial surveillance of multiple urban areas. However, the sensors face the occlusions that can occur due to high buildings, trees and other obstacles in the target area. Such partially occluded vehicle images decreases the performance of the vehicle detector. In order to achieve an accurate monitoring with an occlusion-aware vehicle detector, we aim at de-occluding the occluded vehicle images based on the drone videos.

The project will be articulated in two-steps. The first part consists of segmenting the target occlusions area of the vehicle images to de-occlude. The second part will focus on the generation of de-occluded vehicle images from the occluded vehicle images, exploiting GAN models.