A comparative study of algorithms utilizing predictive models in the scheduling of shared demand responsive transport

Abstract

The project investigates the effect of predicting future demand on the efficiency of Demand Responsive Transport (DRT) systems, defined as a service offered by a centralised operator that dispatches shared vehicles to serve on-demand travel requests. Efficient DRT systems promise to play a crucial part in mitigating climate change and traffic congestion in urban areas. A literature review of the research in this field is carried out. The methods reviewed are grouped into three classes; sampling-based approaches, routing-based approaches, and approaches with implicit consideration of future demand. The implementation of two variants of a sampling-based approach is described. To predict near-future demand, one variant uses a simple frequentist model and the other makes use of a state-of-the-art Neural Network model. These algorithms are then compared to the myopic baseline algorithm (re-implemented in this study) across a wide range of tests which consider variables such as the fleet size, vehicle capacity, and percentage of total ridesharing demand captured by the operator. The experiments are based on simulations using data from the NYC Taxi and Limousine Commission dataset. The simulations were carried out on the open-source Jargo simulator. The results show that in low-demand scenarios (when the operator captures around 5% of the demand in Manhattan), the demand-aware assignment algorithm can effectively replace algorithms that reactively rebalance the fleet. The demand-aware assignment algorithm was shown to maintain high service rates (>90%) while reducing the total distance travelled (when compared to the reactive rebalancing algorithm), therefore contributing to lower emissions and traffic congestion. On the other hand, in the case of simulations that consider higher demand scenarios, the demand-aware assignment algorithm did not fare any better than the rebalancing algorithm, with some cases even negatively affecting the overall efficiency. In conclusion, demand-aware algorithms are better suited during the low-demand periods, which constitute a rather understudied niche where machine learning could potentially be useful in improving the level of service by making more efficient use of the resources available.