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Found 13 papers, 2 papers with code
Nonparametric estimation of k-modal taste heterogeneity for group level agent-based mixed logit
We propose a group-level agent-based mixed (GLAM) logit approach that is estimated with inverse optimization (IO) and group-level market share.
A sequential transit network design algorithm with optimal learning under correlated beliefs
Prior knowledge is reproduced from the regional household travel survey data.
A deep real options policy for sequential service region design and timing
The goal is to determine the optimal selection and timing of a set of zones to include in a service region.
EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System
Emergency vehicles (EMVs) play a crucial role in responding to time-critical calls such as medical emergencies and fire outbreaks in urban areas.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Worldwide city transport typology prediction with sentence-BERT based supervised learning via Wikipedia
Despite the value of understanding a city's typology, labeled data (city and it's typology) is scarce, and spans at most a few hundred cities in the current transportation literature.
An electric vehicle charging station access equilibrium model with M/D/C queueing
The model is then applied to compare charging station investment policies of DCFCs to Level 2 charging stations based on two alternative criteria.
Computers and Society
Agent-based Simulation Model and Deep Learning Techniques to Evaluate and Predict Transportation Trends around COVID-19
The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cities are grappling with what policies can be effective for a phased reopening shaped by social distancing.
V2I Connectivity-Based Dynamic Queue-Jump Lane for Emergency Vehicles: A Deep Reinforcement Learning Approach
Emergency vehicle (EMV) service is a key function of cities and is exceedingly challenging due to urban traffic congestion.
A Real-Time Dispatching Strategy for Shared Automated Electric Vehicles with Performance Guarantees
To overcome this complexity, we employ an online minimum drift plus penalty (MDPP) approach for SAEV systems that (i) does not require a priori knowledge of customer arrival rates to the different parts of the system (i. e. it is practical from a real-world deployment perspective), (ii) ensures the stability of customer waiting times, (iii) ensures that the deviation of dispatch costs from a desirable dispatch cost can be controlled, and (iv) has a computational time-complexity that allows for real-time implementation.
Empirical validation of network learning with taxi GPS data from Wuhan, China
In prior research, a statistically cheap method was developed to monitor transportation network performance by using only a few groups of agents without having to forecast the population flows.
Forecasting e-scooter substitution of direct and access trips by mode and distance
An e-scooter trip model is estimated from four U. S. cities: Portland, Austin, Chicago and New York City.
Air Taxi Skyport Location Problem for Airport Access
Witnessing the rapid progress and accelerated commercialization made in recent years for the introduction of air taxi services in near future across metropolitan cities, our research focuses on one of the most important consideration for such services, i. e., infrastructure planning (also known as skyports).
Network learning via multi-agent inverse transportation problems
New inverse optimization models and supporting algorithms are proposed to learn the parameters of heterogeneous travelers' route behavior to infer shared network state parameters (e. g. link capacity dual prices).
