Adaptive transit design: Optimizing fixed and demand responsive multi-modal transportation via continuous approximation

In most cities, transit consists solely of fixed-route transportation, whence the inherent limited Quality of Service for travellers in suburban areas and during off-peak periods. On the other hand, completely replacing fixed-route (FR) with demand-responsive (DR) transit would imply a huge operational cost. It is still unclear how to integrate DR transportation into current transit systems to take full advantage of it. We propose a Continuous Approximation model of a transit system that gets the best from fixed-route and DR transportation. Our model allows deciding whether to deploy a FR or a DR feeder, in each sub-region of an urban conurbation and each time of day, and to redesign the line frequencies and the stop spacing of the main trunk service. Since such a transit design can adapt to the spatial and temporal variation of the demand, we call it Adaptive Transit. Numerical results show that, with respect to conventional transit, Adaptive Transit significantly improves user-related cost, by drastically reducing access time to the main trunk service. Such benefits are particularly remarkable in the suburbs. Moreover, the generalized cost, including agency and user cost, is also reduced. These findings are also confirmed in scenarios with automated vehicles. Our model can assist in planning future-generation transit systems, able to improve urban mobility by appropriately combining fixed and DR transportation.

Automated summary

In most cities, the transit system consists of fixed-route transportation, which results in limited service quality for suburban areas and off-peak periods. However, completely replacing fixed-route with demand-responsive transit would be costly. Our proposal is a Continuous Approximation model that combines both fixed-route and demand-responsive transportation, allowing for adaptive transit planning. Numerical results show that this model significantly improves user-related cost and reduces access time to the main trunk service, particularly in suburbs. This model can guide the planning of future transit systems by integrating fixed and demand-responsive transportation.