Search Results for author:
Found 9 papers, 4 papers with code
Traffic estimation in unobserved network locations using data-driven macroscopic models
This paper leverages macroscopic models and multi-source spatiotemporal data collected from automatic traffic counters and probe vehicles to accurately estimate traffic flow and travel time in links where these measurements are unavailable.
Estimating and Mitigating the Congestion Effect of Curbside Pick-ups and Drop-offs: A Causal Inference Approach
Curb space is one of the busiest areas in urban road networks.
Statistical inference of travelers' route choice preferences with system-level data
This data must be collected from surveys or travel diaries, which may be labor intensive, costly and limited to a small time period.
Estimating probabilistic dynamic origin-destination demands using multi-day traffic data on computational graphs
The proposed framework is cast into the computational graph and a reparametrization trick is developed to estimate the mean and standard deviation of the probabilistic dynamic OD demand simultaneously.
From Twitter to Traffic Predictor: Next-Day Morning Traffic Prediction Using Social Media Data
In this paper, we propose to mine Twitter messages as a probing method to understand the impacts of people's work and rest patterns in the evening/midnight of the previous day to the next-day morning traffic.
Learning to Recommend Signal Plans under Incidents with Real-Time Traffic Prediction
The main question to address in this paper is to recommend optimal signal timing plans in real time under incidents by incorporating domain knowledge developed with the traffic signal timing plans tuned for possible incidents, and learning from historical data of both traffic and implemented signals timing.
High-Resolution Traffic Sensing with Autonomous Vehicles
The last decades have witnessed the breakthrough of autonomous vehicles (AVs), and the perception capabilities of AVs have been dramatically improved.
Estimating multi-class dynamic origin-destination demand through a forward-backward algorithm on computational graphs
Provided with some observations of vehicular flow for each class in a large-scale transportation network, how to estimate the multi-class spatio-temporal vehicular flow, in terms of time-varying Origin-Destination (OD) demand and path/link flow, remains a big challenge.
A deep learning approach to real-time parking occupancy prediction in spatio-temporal networks incorporating multiple spatio-temporal data sources
The case study also shows that, in generally, the prediction model works better for business areas than for recreational locations.
