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Found 19 papers, 2 papers with code
No-Regret Learning in Stackelberg Games with an Application to Electric Ride-Hailing
Instead, we treat the lower-level game as a black box, assuming only that the followers' interactions approximate a Nash equilibrium while the leader observes the realized cost of the resulting approximation.
On Learning-Based Traffic Monitoring With a Swarm of Drones
Efficient traffic monitoring is crucial for managing urban transportation networks, especially under congested and dynamically changing traffic conditions.
Multi-Source Urban Traffic Flow Forecasting with Drone and Loop Detector Data
Traffic forecasting is a fundamental task in transportation research, however the scope of current research has mainly focused on a single data modality of loop detectors.
Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery
This paper presents a framework for extracting georeferenced vehicle trajectories from high-altitude drone footage, addressing key challenges in urban traffic monitoring and limitations of traditional ground-based systems.
Ranked #1 on
Object Detection
on Songdo Vision
(using extra training data)
Receding-Horizon Games with Tullock-Based Profit Functions for Electric Ride-Hailing Markets
Leveraging the concept of receding-horizon games, we propose a method to optimize proactive dispatching of vehicles for recharging over a predefined time horizon.
Hierarchical Control for Vehicle Repositioning in Autonomous Mobility on Demand Systems
Balancing passenger demand and vehicle availability is crucial for ensuring the sustainability and effectiveness of urban transportation systems.
Deep Learning Methods for Adjusting Global MFD Speed Estimations to Local Link Configurations
In large-scale traffic optimization, models based on Macroscopic Fundamental Diagram (MFD) are recognized for their efficiency in broad analyses.
A Blotto Game Approach to Ride-hailing Markets with Electric Vehicles
When a centrally operated ride-hailing company considers to enter a market already served by another company, it has to make a strategic decision about how to distribute its fleet among different regions in the area.
Macroscopic pricing schemes for the utilization of pool ride-hailing vehicles in bus lanes
In this work, we analyze a network configuration where part of the urban transportation network is devoted to dedicated bus lanes.
On decentralized computation of the leader's strategy in bi-level games
Motivated by the omnipresence of hierarchical structures in many real-world applications, this study delves into the intricate realm of bi-level games, with a specific focus on exploring local Stackelberg equilibria as a solution concept.
A Coverage Control-based Idle Vehicle Rebalancing Approach for Autonomous Mobility-on-Demand Systems
As an emerging mode of urban transportation, Autonomous Mobility-on-Demand (AMoD) systems show the potential in improving mobility in cities through timely and door-to-door services.
A Dynamic Macroscopic Framework for Pricing of Ride-hailing Services with an Optional Bus Lane Access for Pool Vehicles
First, we develop a modal- and space-dependent aggregate model for private vehicles, ride-pooling, and buses, and we use this model to test different control strategies.
Learning How to Price Charging in Electric Ride-Hailing Markets
In this work, we assume that a central authority wants to control the distribution of the vehicles and can do so by selecting charging prices.
On Finding the Leader's Strategy in Quadratic Aggregative Stackelberg Pricing Games
This paper analyzes a class of Stackelberg games where different actors compete for shared resources and a central authority tries to balance the demand through a pricing mechanism.
Two-layer adaptive signal control framework for large-scale dynamically-congested networks: Combining efficient Max Pressure with Perimeter Control
However, its effectiveness under saturated conditions is questionable, while network-wide application is limited due to high instrumentation cost.
Hierarchical Pricing Game for Balancing the Charging of Ride-Hailing Electric Fleets
We provide a pricing mechanism that ensures the existence of a unique Nash equilibrium of the upper-level game that minimizes the external agent's objective at the same time.
A Pricing Mechanism for Balancing the Charging of Ride-Hailing Electric Vehicle Fleets
Both ride-hailing services and electric vehicles are becoming increasingly popular and it is likely that charging management of the ride-hailing vehicles will be a significant part of the ride-hailing company's operation in the near future.
Traffic signal prediction on transportation networks using spatio-temporal correlations on graphs
Multivariate time series forecasting poses challenges as the variables are intertwined in time and space, like in the case of traffic signals.
Ranked #7 on
Traffic Prediction
on PEMS-BAY
(RMSE metric)
Travel time prediction for congested freeways with a dynamic linear model
We compare our prediction accuracy of travel time for freeways in California (I210-E and I5-S) under highly congested traffic conditions with those of other methods: the instantaneous travel time, k-nearest neighbor, support vector regression, and artificial neural network.
