Student projects

Low-cost air quality sensors are increasingly deployed in cities to complement sparse reference-grade monitoring stations. However, their measurements are affected by drift, environmental sensitivity, and manufacturing variability, which limits their direct use for reliable air quality assessment. Calibration is therefore essential to improve their accuracy and usability. This project aims to systematically explore and compare calibration techniques for low-cost air quality sensors, using open datasets that include both reference and low-cost measurements. The primary goal is to assess which calibration strategies are most effective under different urban conditions and pollutant types.

During this project, you are expected to:

• Collect and preprocess datasets containing co-located measurements from low-cost sensors and reference stations.
• Implement and test different calibration techniques, such as linear regression, polynomial mapping, Gaussian process regression, and machine learning methods (e.g., random forest, neural networks).
• Evaluate calibration performance across pollutants (e.g., PM2.5, NO₂) and conditions (e.g., seasons, temperature ranges).
• Summarize findings into practical recommendations, highlighting trade-offs between accuracy, robustness, and computational cost.

Contact: Anna Paulish ([email protected])

Mobile sensing via public transit systems offers a unique opportunity for large-scale urban environmental monitoring. Unlike private or on-demand mobility service vehicles, public transport vehicles (e.g., buses) follow fixed schedules, resulting in a highly predictable yet non-uniform spatiotemporal operational pattern. To effectively use buses for urban sensing, it is crucial to understand their spatiotemporal distribution and how it shifts with the number of buses and different bus routes. This project develops a data-analytic framework to model the spatiotemporal distribution of sensing buses and evaluate its variance through sampling and statistical analysis.

Student: Yvan Bruno

Contact: Qiuzi Chen

Postal vehicles hold significant potential as mobile sensing agents, given their door-to-door service and extensive spatial coverage. To effectively use them for urban mobile sensing, it’s crucial to understand their operational patterns. Specifically, the spatiotemporal distribution of vehicles. Since these patterns are ultimately shaped by the need to fulfill delivery demand, it can serve as a strong indicator for predicting such patterns. A primary challenge lies in the lack of quantitative representation for delivery demand in a geospatial context. To address this, the project analyzes vehicle trajectories to extract operational patterns of postal vehicles and investigate geographical features that can represent delivery demand through statistical analysis. The findings will provide a foundation for estimating vehicle distribution to support the design of mobile sensing fleets.

Student: Andrea Salvatore Francis Montoneri

Contact: Qiuzi Chen

Open air quality datasets released by cities worldwide offer valuable opportunities to understand urban pollution dynamics, but they vary widely in terms of sensor density, spatial coverage, and temporal consistency. This project performs a systematic exploratory analysis of such datasets, focusing on characterizing coverage, variability, and potential sources of uncertainty. The primary goal is to benchmark different datasets and provide practical insights into their suitability for spatio-temporal modeling.

Student: Fletcher Collis

Supervisor: Anna Paulish

Electric ride-hailing vehicles are increasingly prevalent. Because these vehicles rely heavily on public charging infrastructure, their charging behaviors impose substantial challenges on the power system. Current research often makes assumptions that electric ride-hailing vehicles get fully recharged when leaving the charging stations. In practice, however, drivers may partially recharge to reduce downtime or respond to nearby demand. Analyzing these behaviors with real-world data enables a more accurate understanding of charging demand and better informs infrastructure planning. This project examines the partial charging behavior using taxi and charging-station data from Shenzhen.

Student: Fletcher Collis, 

Supervisor: Xuhang Liu

Electric ride-hailing vehicles are increasingly prevalent. Because these vehicles rely heavily on public charging infrastructure, their charging behaviors impose substantial challenges on the power system. This project will examine the partial charging behavior using taxi and charging-station data from Shenzhen. Based on historical electric taxi charging data, this project explores the optimization allocation of charging stations in the city and cost-effective battery size. 

Students: Mohamed Iraqi Houssaini, Jules Christmann

Supervisor: Xuhang Liu

Mobility-as-a-Service (MaaS) aims to provide seamless multi-modal mobility options to travelers and has great potential to improve the accessibility of urban transportation systems. With the increasing availability of convenient transfers and easily accessible travel option information, travelers are now faced with a greatly enlarged set of possible multi-modal trips. Understanding travelers’ preferences for these trips, as well as the factors shaping their choices, is therefore essential. Such insights can guide the design of MaaS platforms, optimize service offerings, and enhance user satisfaction by aligning system design with actual traveler behavior.

Building on a previous student project that developed a multimodal travel option searching algorithm and data of collected multi-day GPS trajectories of individuals in Switzerland, this project seeks to investigate the primary factors influencing travelers’ mode combinations and route choices, providing a data-driven foundation for improving multi-modal mobility services.

Students: Oscar Van Haaren, Antoine Demoulin

Supervisor: Xinyu Ma

On-demand meal delivery service (e.g., UberEat) has developed rapidly in recent years, particularly during COVID lockdowns. Similar to ride-hailing platforms, on-demand meal delivery platforms promptly collect orders and assign them to couriers for pickup and delivery. Differently, some platforms take the bundling strategy that groups several orders with close pickup and delivery locations into bundles. Accordingly, each courier service trip consists of multiple pickups and deliveries. Typically, solving the order bundling requires extensive evaluations on candidate bundles and each evaluation involves solving a Pickup-and-Delivery-Route-Planning (PDRP) problem. This imposes great computational challenge for real-time operations. This project aims to develop a reinforcement learning framework to tackle the order bundling problem. 

Students: Amélie Menoud, Pedro Rodrigues Da Costa Savini, Daniel Krief

Supervisor: Xuhang Liu

Mobility-as-a-Service (MaaS) is an emerging concept that integrates transport services from both public and private transport service providers (TSPs) through a unified platform, enabling users to plan, book, and pay for multi-modal trips via a single account. A common business model of MaaS is to operate as an intermediary: the platform purchases service capacities from TSPs, then designs and sells multi-modal trips to travelers. As a new market player, a MaaS platform must carefully design pricing schemes that not only attract users but also ensure the participation and cooperation of TSPs. This relationship is often referred to as coopetition.

Built upon previous semester project, this project continues to explore the evolutionary dynamics of coopetition between the MaaS platform and TNCs in the more complex market scenarios.

Student: Jan Janczak

Supervisor: Xinyu Ma

This thesis investigates the application of the CARMA mechanism to railway transportation systems. Railway operators face increasing congestion during peak hours, while first-class tickets constitute an important source of revenue. Motivated by the need to balance efficiency, fairness, and economic sustainability, this work studies how passenger redistribution from second to first class can reduce overcrowding and improve passenger comfort in congested railway networks. 

Student: Mathis Magnin

Supervisor: Kenan Zhang

Mobility-as-a-Service (MaaS) is an emerging concept that integrates transport services from both public and private transport service providers (TSPs) through a unified platform, enabling users to plan, book, and pay for multi-modal trips via a single account. A common business model of MaaS is to operate as an intermediary: the platform purchases service capacities from TSPs, then designs and sells multi-modal trips to travelers. As a new market player, a MaaS platform must carefully design pricing schemes that not only attract users but also ensure the participation and cooperation of TSPs. This relationship is often referred to as coopetition.

This project develops a simulation-based model of day-to-day dynamics, in which the MaaS platform and TNCs iteratively revise their pricing and capacity strategies in pursuit of profit maximization, while travelers adapt their service choices in response to evolving trip prices and service levels (e.g., pickup waiting times determined by available capacity). 

Student: Mathis Magnin

Supervisor: Xinyu Ma

Network design problem (NDP) refers to the optimization of link (node) location and attribute (e.g., capacity) subject to both physical and economic constraints. Classic NDPs often employ system-level aggregate metrics as their objectives, such as minimizing total travel time or maximizing total travel utility, whereas ignoring the variation in individual welfare and thus possibly resulting in discrimination on some travelers. To address this issue, this project will explore accessibility-based NDP that aims to provide sufficient accessibility for all travelers to reach most destinations within a certain time budget.  

Student: Henri Collet

Supervisor: Kenan Zhang

Commercial speed, defined as the average travel speed of public transport vehicles from one stop to another, is a crucial indicator of the efficiency and robustness (“health”) of public transit networks. Due to traffic congestion, lack of coordination with traffic signals, temporal high passenger volume, complex interactions with other vehicles on roads, and other unforeseen events, real-world operations of public transit often deviate from designed schedules and experience significant delays. 

In collaboration with TPG, the major transit operator in Geneva, this project aims to perform a data-driven diagnostics on the current state of public transit networks. We will leverage a large dataset that collects individual vehicles over the past ten years and focus our analysis on the commercial speed with a primary goal to identify where and why operational bottlenecks occur repeatedly. 

Students: Luca Liuzzi, Ching-Chi Chou

Supervisor: Cloe Cortes (TPG), Kenan Zhang

Traffic congestion has long been a critical issue in large cities. As the well-known solution, congestion pricing is however arguably unfair as it tends to favor wealthier travelers. To address such an equity issue, we proposed a mechanism named CARMA that makes travelers to bid for access to scarce transport resources with non-tradable mobility credits, in anticipation of other travelers’ decisions as well as their own future trips. In our preliminary study, we numerically showed the redistribution of credits plays a key role in the system efficiency and found its strong connection to classic road pricing schemes. This project aims to further investigate credit redistribution as a design problem and deepen the understanding of its impact on system performance. 

Student: Mathis Magnin

Supervisor: Kenan Zhang

Order bundling in on-demand meal delivery service (e.g., UberEat) requires extensive evaluations on candidate bundles and each evaluation involves solving a Pickup-and-Delivery-Route-Planning (PDRP) problem. This imposes great computational challenge for real-time operations. In this project, we analyzed a real-world dataset of meal delivery service and explored the key factors affecting the order bundling decisions. 

Student: Margot Chapalain, Malak Jria

Supervisor: Xuhang Liu

Order bundling in on-demand meal delivery service (e.g., UberEat) requires extensive evaluations on candidate bundles and each evaluation involves solving a Pickup-and-Delivery-Route-Planning (PDRP) problem. This imposes great computational challenge for real-time operations. In this project, we analyzed a real-world dataset of meal delivery service and explored the key factors affecting the order bundling decisions. 

Student: Elias Rafoul

Supervisor: Anke Ye, Kenan Zhang

Mobility-as-a-service (MaaS) aims to provide seamless multi-modal mobility options to traveler and has great potentials to improve sustainability and accessibility of urban transportation systems. One critical factor in MaaS system design, however often overlooked, is the transfer cost, e.g., extra walk and wait due to mode transfers. Using the multi-day GPS trajectories of individuals collected in Switzerland, this project explored how travelers move through the multi-modal transport system, identify different segments of their trips, and detect mode changes and transfers. It contributes to a better understanding of real-life travel behaviors and provides useful insights to design MaaS systems.

Students: Hana Wermeille, Orange Koenga

Superviser: Rui Yao, Xinyu Ma

Mobility-as-a-service (MaaS) aims to provide seamless multi-modal mobility options to traveler and has great potentials to improve sustainability and accessibility of urban transportation systems. One critical factor in MaaS system design, however often overlooked, is the transfer cost, e.g., extra walk and wait due to mode transfers. With the operational and planning data provided by all operators in Switzerland, this project constructs a time-specific multi-modal public transport network and develops an algorithm to search for all feasible travel options given user-specified time constraints. 

Students: Emilien Ulrich, Oreste Challandes

Superviser: Rui Yao, Xuhang Liu

Although carpooling has been long celebrated as a promising solution to reducing vehicular traffic and emissions during peak hours, it is still limited within families and for long-distance trips. For instance, EPFL launched a carpooling platform to encourage its staff and students to carpool in their daily commute. However, limited trips are posted and very few users remain active. The inconvenient trip matching comes to be a major obstacle. In this project, we built an idealized carpooling system for daily commute trips that features flexible role assignment and centralized trip matching. 

Students: Christina Schmid, Fannata Sédiko, Nicolas Wakim

Supervisor: Zhenyu Yang, Kenan Zhang

“Braess Paradox” is a well-known phenomenon in traffic networks, where adding one road link to the network instead worsens traffic congestion. This discouraging consequence is rooted in the selfish routing behavior of private vehicles. For example, a newly built highway may attract a lot of people and finally intensify congestion on upstream and downstream roads. This project identified potential Braess links in the Sioux Falls network and explored their common properties. 

Student: Julien Ars

Supervisor: Hossein Farahani

This project analyzed the PubliBike network at EPFL and surrounding municipalities, including demand pattern and user satisfaction, using EPFL mobility survey and operational data. It also benchmarked current service against alternative solutions to identify key challenges and opportunities in promoting bikesharing. The project was led by the EPFL Mobility Office.

Student: Evangelia Gkola, Gregoire Ecuyer

Supervisor: Luca Fontana, Luca Pellandini, Kenan Zhang

This project studied the carpooling behaviors of EPFL staffs and students using the mobility survey and data of an EPFL carpooling platform. It also studied the implication of carpooling on EPFL’s parking and CO2 emission reduction strategies. The project was led by the EPFL Mobility Office. 

Student: Martin Simon, Marine Jacob

Supervisor: Luca Fontana, Alexia Couturier, Kenan Zhang

On-demand mobility covers a wide range of services including taxis, e-hailing (e.g., Uber), ride-pooling, and bike-sharing. Although different in detailed operations, these services share some fundamental characteristics regarding the movements of vehicles, their interactions with users, as well as the spatiotemporal distribution of demand and supply. This project investigated an open-source simulation of on-demand mobility and tested it potential for generalizing different service modes and market conditions.  

Student: Mathis Magnin

Supervisor: Kenan Zhang

The perturbed utility model (PUM) is a discrete choice model that represents an individual’s decision as a vector of choice probabilities and describes the utility of each alternative as the sum of systematic utility and a convex perturbation function of the choice probability vector. PUM has been shown to generalize a wide range of discrete choice models (e.g., MNL) meanwhile enjoying a higher computational efficiency in model inference. However, a challenge is the specification and estimation of the perturbation function. In this project, we explored the potential of learning the perturbation function using neural networks (NN).

Student: Jingren Tang

Supervisor: Rui Yao

Current navigation apps (e.g., Google Maps) usually recommend walking paths based on distance and elevation, while ignoring how much pollution the pedestrian may be exposed to along the walk. This project studied the pollution-aware path planning problem for pedestrians in the urban environment using the spatiotemporal environmental data collected by Sparrow.  

Student: Anne-Valérie Preto

Supervisor: Rui Yao

This these analyzes the practical and regulatory feasibility of integrating bike-sharing services into existing public transport pricing system in the city of Fribourg through policy analysis, benchmark case study, and in-depth stakeholder interviews. 

Student: Grégoire Ecuyer

Supervisor: Véronique de Sépibus (RR&A SA)

Leveraging the data of BlaBlaCar, a peer-to-peer ridesharing platform, this these studies key influential factors of meeting point selection and provides insights into ride-sharing service design to further enhance user experience. 

Student: Marine Jacob

Supervisor: Rui Yao, Xinyu Ma