Arc travel time and path choice model estimation subsumed
We propose a method for maximum likelihood estimation of path choice model parameters and arc travel time using data of different levels of granularity. Hitherto these two tasks have been tackled separately under strong assumptions. Using a small example, we illustrate that this can lead to biased results. Results on both real (New York yellow cab) and simulated data show strong performance of our method compared to existing baselines.
Code (0)
등록된 구현이 없습니다.
Tasks
ARCMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Generalising Travel Time Prediction To Varying Route Choices In Urban Networks
Previous methods that predict system-wide travel time, predominantly grounded in graph neural networks, remain limited to typical and recurring demand patterns. While they successfully predict future congestion following…
DeepIST: Deep Image-based Spatio-Temporal Network for Travel Time Estimation
Estimating the travel time for a given path is a fundamental problem in many urban transportation systems. However, prior works fail to well capture moving behaviors embedded in paths and thus do not estimate the travel …
Travel Time EstimationDutyTTE: Deciphering Uncertainty in Origin-Destination Travel Time Estimation
Uncertainty quantification in travel time estimation (TTE) aims to estimate the confidence interval for travel time, given the origin (O), destination (D), and departure time (T). Accurately quantifying this uncertainty …
Deep Reinforcement LearningMixture-of-ExpertsTravel Time EstimationUncertainty QuantificationHTTE: A Hybrid Technique For Travel Time Estimation In Sparse Data Environments
Travel time estimation is a critical task, useful to many urban applications at the individual citizen and the stakeholder level. This paper presents a novel hybrid algorithm for travel time estimation that leverages his…
Travel Time EstimationSTAD: Spatio-Temporal Adjustment of Traffic-Oblivious Travel-Time Estimation
Travel time estimation is an important component in modern transportation applications. The state of the art techniques for travel time estimation use GPS traces to learn the weights of a road network, often modeled as a…
Travel Time Estimation