Projets étudiants

“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. In this project, we will identify the existence of Braess links in both stylized and general road networks, explore their common properties, and develop heuristics to identify them without extensive computations. 

During this project, you are expected to

• Learn the static traffic assignment model and implement its solution algorithms
• Conduct numerical experiments to recognize Braess links in different networks
• Generalize the similarity among recognized Braess links 
• Develop heuristics to identify Braess links

Contact: Dr. Hossein R. Farahani ([email protected])

Besides the imbalance between supply (e.g., road capacity) and demand (e.g., vehicular flows), traffic congestion is largely due to the selfish routing behaviors of private vehicles. Theoretically, if all vehicles choose their routes to minimize their own travel time, the system converges to a state called user equilibrium (UE). On the other hand, if all vehicles are centrally controlled and routed to minimize total travel time in the network, the system ends up at the system optimum (SO) state. Our previous study shows that we can actually approach SO by strategically controlling a small fraction of vehicles. Moreover, the controlled vehicles are found traveling between a limited number of origin-destination (OD) pairs. Motivated by this observation, this project aims to further study these critical OD pairs, explore their distribution in general networks, and design metrics to evaluate the “critical” level of an OD pair. 

During this project, you are expected to

• Learn ORCS and implement its solution algorithm
• Conduct numerical experiments to investigate critical OD pairs in different networks
• Generalize the similarity of recognized OD pairs 
• Propose features and metrics to evaluate the critical level of OD pairs

Contact: Dr. Hossein R. Farahani ([email protected])

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 aims to develop a general agent-based simulation framework that captures the common features of on-demand mobility. Meanwhile, it is structured such that the simulator can easily be specified for particular services. 

Main tasks to be expected in this project include:

• Design the simulation architecture
• Code and debug the main modules 
• Conduct case studies

Contact: Prof. Kenan Zhang ([email protected]) 

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 will explore the potential of learning the perturbation function using neural networks (NN). 

During this project, you are expected to 

• Learn the fundamentals of PUM 
• Formulate the learning problem of the perturbation function
• Train and test the NN-based perturbation function

Contact: Dr. Rui Yao ([email protected])

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 aims to study the pollution-aware path planning problem for pedestrians in the urban environment using the spatiotemporal environmental data collected by Sparrow.  

• Explore the spatiotemporal environmental data in the urban context
• Design the pollution index and integrate it into the classic path-planning algorithm
• Compare the pollution-aware paths to benchmarks over time and space
• Develop a webpage and user interface to visualize pollution-aware paths

Contact: Dr. Rui Yao ([email protected])