This paper proposes a market segmentation method applied in the field of transportation behavior change using GPS trajectories and socio-demographic data collected from the advanced demand management system “GoEzy” designed by Metropia. User attributes are extracted using several statistical methods such as dynamic time warping, density-based spatial clustering of applications with noise (DBSCAN), and signal processing method to infer users’ sensitivity to incentives, temporal, and spatial travel patterns. Ten personas were generated by K-means clustering, representing different types of people with various travel patterns and sensitivity to incentives. The experiment was conducted on 24 new users to test if the persona could be used as a tool to predict their willingness to change. The results showed that after creating personas for new users and providing them with new incentives, their modified departure time pattern according to the new incentives matched expectations from analysis of the 10 personas.
The growing, aging, and urbanizing traveling population of the United States requires different and more personal designed mobility options. The private sector, in association with public firms, has started to test and deploy innovative solutions to address these shifting needs. Unfortunately, the build-first-and-users-will-come theory does not hold much weight. Overnight success stories are often the result of years of hard work behind the scenes. Simply put, start-up marketing is often a unique challenge because of limited resources, whether it is time, money, or talent. This study, therefore, introduced an agent-based model that could be used to simulate the reaction of commuters and travelers on the introduction of an innovative mobility option and business model. The model was backed up with empirical data from an Austin, Texas–based innovative mobility solution, the Metropia app. The results of this study show the importance of how and when promotional activities are conducted, with respect to the characteristics of the concept involved, to achieve appropriate public engagement. Thus, decision makers are provided with a quantitative projection of the effect of different strategies and the associated public reaction. For instance, in the case of introducing innovative mobility options that require high public engagement, such as carpooling or crowdsourcing solutions, it is suggested that strategists focus their attention and marketing budget to make an initial splash.
An increasingly emphasized research area is the forecast of short-term traffic conditions for nonrecurring traffic dynamics caused by random highway incidents such as crashes or roadway closures. This research proposes a prediction framework which focuses on training a machine learning (ML) model to predict the speed heatmap associated with incidents. Heatmaps contain ideal information that depicts the spatiotemporal characteristics of incident-induced impacts and are suitable objects for ML models to understand and predict. Because of the sparsity of incident data in the real world, we proposed a simulation approach to rapidly expand the training dataset, thus speeding up the model training process. The conditional deep convolutional generative adversarial nets is employed to predict the speed heatmap and the mesoscopic dynamic traffic assignment model DynusT was used to generate many training data. The evaluation shows that the proposed model captures both the tonal and spatial distribution of pixel values at 80.19% similarity between the prediction and actual heatmaps. To the best of our knowledge, this is one of the first attempts in the literature to train ML to predict heatmap representation of incident-induced spatiotemporal impact, and speeding up the training via simulation.
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