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In light of the increasing interest to transform the fixed-route public transit (FRT) services into on-demand transit (ODT) services, there exists a strong need for a comprehensive evaluation of the effects of this shift on the users. Such an analysis can help the municipalities and service providers to design and operate more convenient, attractive, and sustainable transit solutions. To understand the user preferences, we developed three hybrid choice models: integrated choice and latent variable (ICLV), latent class (LC), and latent class integrated choice and latent variable (LC-ICLV) models. We used these models to analyze the public transit user's preferences in Belleville, Ontario, Canada. Hybrid choice models were estimated using a rich dataset that combined the actual level of service attributes obtained from Belleville's ODT service and self-reported usage behaviour obtained from a revealed preference survey of the ODT users. The latent class models divided the users into two groups with different travel behaviour and preferences. The results showed that the captive user's preference for ODT service was significantly affected by the number of unassigned trips, in-vehicle time, and main travel mode before the ODT service started. On the other hand, the non-captive user's service preference was significantly affected by the Time Sensitivity and the Online Service Satisfaction latent variables, as well as the performance of the ODT service and trip purpose. This study attaches importance to improving the reliability and performance of the ODT service and outlines directions for reducing operational costs by updating the required fleet size and assigning more vehicles for work-related trips.

The rapid increase in the cyber-physical nature of transportation, availability of GPS data, mobile applications, and effective communication technologies have led to the emergence of On-Demand Transit (ODT) systems. In September 2018, the City of Belleville in Canada started an on-demand public transit pilot project, where the late-night fixed-route (RT 11) was substituted with the ODT providing a real-time ride-hailing service. We present an in-depth analysis of the spatio-temporal demand and supply, level of service, and origin and destination patterns of Belleville ODT users, based on the data collected from September 2018 till May 2019. The independent and combined effects of the demographic characteristics (population density, working-age, and median income) on the ODT trip production and attraction levels were studied using GIS and the K-means machine learning clustering algorithm. The results indicate that ODT trips demand is highest for 11:00 pm-11:45 pm during the weekdays and 8:00 pm-8:30 pm during the weekends. We expect this to be the result of users returning home from work or shopping. Results showed that 39% of the trips were found to have a waiting time of smaller than 15 minutes, while 28% of trips had a waiting time of 15-30 minutes. The dissemination areas with higher population density, lower median income, or higher working-age percentages tend to have higher ODT trip attraction levels, except for the dissemination areas that have highly attractive places like commercial areas.

In recent years, with the advancements in information and communication technology, different emerging on-demand shared mobility services have been introduced as innovative solutions in the low-density areas, including on-demand transit (ODT), mobility on-demand (MOD) transit, and crowdsourced mobility services. However, due to their infancy, there is a strong need to understand and model the demand for these services. In this study, we developed trip production and distribution models for ODT services at Dissemination areas (DA) level using four machine learning algorithms: Random Forest (RF), Bagging, Artificial Neural Network (ANN) and Deep Neural Network (DNN). The data used in the modelling process were acquired from Belleville's ODT operational data and 2016 census data. Bayesian optimalization approach was used to find the optimal architecture of the adopted algorithms. Moreover, post-hoc model was employed to interpret the predictions and examine the importance of the explanatory variables. The results showed that the land-use type was the most important variable in the trip production model. On the other hand, the demographic characteristics of the trip destination were the most important variables in the trip distribution model. Moreover, the results revealed that higher trip distribution levels are expected between dissemination areas with commercial/industrial land-use type and dissemination areas with high-density residential land-use. Our findings suggest that the performance of ODT services can be further enhanced by (a) locating idle vehicles in the neighbourhoods with commercial/industrial land-use and (b) using the spatio-temporal demand models obtained in this work to continuously update the operating fleet size., Comment: Transportation Research Part A: Policy and Practice 2021

An estimated 180 papers focusing on deep learning and EHR were published between 2010 and 2018. Despite the common workflow structure appearing in these publications, no trusted and verified software framework exists, forcing researchers to arduously repeat previous work. In this paper, we propose Cardea, an extensible open-source automated machine learning framework encapsulating common prediction problems in the health domain and allows users to build predictive models with their own data. This system relies on two components: Fast Healthcare Interoperability Resources (FHIR) -- a standardized data structure for electronic health systems -- and several AUTOML frameworks for automated feature engineering, model selection, and tuning. We augment these components with an adaptive data assembler and comprehensive data- and model- auditing capabilities. We demonstrate our framework via 5 prediction tasks on MIMIC-III and Kaggle datasets, which highlight Cardea's human competitiveness, flexibility in problem definition, extensive feature generation capability, adaptable automatic data assembler, and its usability.