Nael Alsaleh

Nael Alsaleh, Tareq Alsaleh, Fayez Moutassem et al.

This study examines travelers' preferences for electric flying taxi services in the United Arab Emirates (UAE) under varying travel conditions and service configurations. A stated preference (SP) survey of 213 respondents was conducted to analyze behavior across multiple transport alternatives, including private vehicles, public transport, ground taxis, and both shared and exclusive flying taxi services. The analysis considered key attributes such as travel time and cost, along with contextual factors including travel distance, congestion conditions, day of travel, and trip purpose. In addition, follow-up questions were used to capture the underlying reasons for mode choice and to assess potential modal shifts under changes in travel conditions. The results show that flying taxi services account for 22.6% of total responses, with higher shares under congested conditions and declining shares as travel distance increases. Clear differences are observed between shared and exclusive services. Shared flying taxis achieve higher modal shares and exhibit greater responsiveness to travel conditions, particularly at moderate distances, during weekdays, and for leisure trips. In contrast, exclusive flying taxis maintain lower modal shares, decline with increasing travel distance, and are more associated with business and weekend travel. The modal shift analysis further indicates that ground taxi users exhibit the highest propensity to switch to shared flying taxi services, particularly under cost increases. These findings highlight the importance of pricing and service design in promoting the adoption of shared flying taxi services as a more sustainable mobility option. In particular, maintaining affordable shared services, ensuring clear price differentiation from exclusive services, and prioritizing deployment in congested corridors and medium-distance travel markets can enhance adoption.

Nael Alsaleh, Bilal Farooq

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.