Incorporating trip chaining within online demand estimation

Autor: Constantinos Antoniou, Moeid Qurashi, Francesco Viti, Guido Cantelmo, A. Arun Prakash
Přispěvatelé: EU-FEDER [sponsor]
Rok vydání: 2018
Předmět:
FOS: Computer and information sciences
Mathematical optimization
Computer science
0211 other engineering and technologies
Demand estimation
Transportation
Context (language use)
02 engineering and technology
Interval (mathematics)
Management Science and Operations Research
Multidisciplinaire
généralités & autres [C99] [Ingénierie
informatique & technologie]
021105 building & construction
0502 economics and business
Computer Science - Data Structures and Algorithms
Data Structures and Algorithms (cs.DS)
Civil and Structural Engineering
Estimation
050210 logistics & transportation
021103 operations research
Basis (linear algebra)
05 social sciences
Work (physics)
Multidisciplinary
general & others [C99] [Engineering
computing & technology]
Kalman filter
Kalman Filtering
ddc
Demand Estimation
Chaining
Online Calibration
Zdroj: Transportation Research Procedia, 38, 462-481. Amsterdam, Netherlands: Elsevier (2019).
Transportation Research Procedia
Transportation Research Part B: Methodological
Popis: Time-dependent Origin-Destination (OD) demand flows are fundamental inputs for Dynamic Traffic Assignment (DTA) systems and real-time traffic management. This work introduces a novel state-space framework to estimate these demand flows in an online context. Specifically, we propose to explicitly include trip-chaining behavior within the state-space formulation, which is solved using the well-established Kalman Filtering technique. While existing works already consider structural information and recursive behavior within the online demand estimation problem, this information has been always considered at the OD level. In this study, we introduce this structural information by explicitly representing trip-chaining within the estimation framework. The advantage is twofold. First, all trips belonging to the same tour can be jointly calibrated. Second, given the estimation during a certain time interval, a prediction of the structural deviation over the whole day can be obtained without the need to run additional simulations. The effectiveness of the proposed methodology is demonstrated first on a toy network and then on a large real-world network. Results show that the model improves the prediction performance with respect to a conventional Kalman Filtering approach. We also show that, on the basis of the estimation of the morning commute, the model can be used to predict the evening commute without need of running additional simulations.
copyright 2019. This manuscript version is made available under the CC-BY-NC-ND 2.0 license http://creativecommons.org/licenses/by-nc-nd/2.0 Article presented at 23rd International Symposium on Transportation and Traffic Theory, ISTTT 23, 24-26 July 2019, Lausanne, Switzerland
Databáze: OpenAIRE
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