Analyzing non-linear contributions to predictive performance in a neural network based scheduling model

• This paper aims to investigate whether increasing non-linear opportunities in a neural network-based scheduling model improves its predictive performance. More specifically, this paper experiments on a trip distribution model that is part of an activity-based scheduling model called Skyline-seqNN from the ongoing thesis A neural network scheduling model. The motivation behind that model s proposed structure is to lay the groundwork for a neural network discrete choice model (DCM) that achieves to model travel demand on a detailed level while also being suitable for experimental analysis. Similar to a four-step model framework in the sequential aspect, the model system from the referenced paper utilizes the three sub-models; trip generation, trip distribution, and mode choice using a utility-maximizing micro-simulation approach. The trip generation model first decides whether, at every 10-minute interval between 05:00 am and 11:00 pm, an individual in the next time step should stay and continue the current activity or take an activity-defined trip. The distribution and mode choice models are used whenever a trip is selected. The trip distribution model decides the trip s destination by evaluating travel times and land use descriptions of each zone. The mode choice model learns the probability distribution of modes given each mode s travel time to the selected destination zone. Tests performed in this paper show how successive non-linear opportunities between input features in the trip distribution model increase its predictive performance. The data used for training and evaluation comes from a travel questionnaire from 2015 per-formed in Stockholm containing 10819 individuals and days.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Samhällsbyggnadsteknik -- Transportteknik och logistik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Civil Engineering -- Transport Systems and Logistics (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)

Nyckelord

Activity-based

Discrete Choice Model

Machine Learning

Neural Networks

Non-linear

Scheduling

Simulation

Publikations- och innehållstyp

ref (ämneskategori)

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