Abstract
AbstractIn dynamic ride-sharing systems, intelligent repositioning of idle vehicles often improves the overall performance with respect to vehicle utilization, request rejection rates, and customer waiting times. In this work, we present a forecast-driven idle vehicle repositioning algorithm. Our approach takes a demand forecast as well as the current vehicle fleet configuration as inputs and determines suitable repositioning assignments for idle vehicles. The core part of our approach is a mixed-integer programming model that aims to maximize the acceptance rate of anticipated future trip requests while minimizing vehicle travel times for repositioning movements. To account for changes in current trip demand and vehicle supply, our algorithm adapts relevant parameters over time. We embed the repositioning algorithm into a planning service for vehicle dispatching. We evaluate our forecast-driven repositioning approach through extensive simulation studies on real-world datasets from Hamburg, New York City, Manhattan, and Chengdu. The algorithm is tested assuming a perfect demand forecast and applying a naïve forecasting model. These serve as an upper and lower bound on state-of-the-art forecasting methods. As a benchmark algorithm, we utilize a reactive repositioning scheme. Compared to this, our forecast-driven approach reduces trip request rejection rates by an average of 3.5 percentage points and improves customer waiting and ride times.
Highlights
While the popularity of mobility-on-demand (MOD) services such as Uber and Lyft has increased significantly in recent years, this growth has led to increased traffic congestion (Castiglione and Cooper 2018)
Concerning the customer rejection rate, forecast-driven repositioning algorithm (FDR) achieves the best results with an average improvement of around 3.5 percentage points compared to our benchmark algorithm REACT
FDR (N) even performs better in several scenarios. As this behavior may be observed for the other key performance indicators (KPI), we will focus our analysis on FDR (P) for most of this section
Summary
While the popularity of mobility-on-demand (MOD) services such as Uber and Lyft has increased significantly in recent years, this growth has led to increased traffic congestion (Castiglione and Cooper 2018). One way to tackle this problem is to increase the share of dynamic ride-sharing services such as UberPool or MOIA. Planning problems regarding MOD services in general and dynamic ride-sharing, in particular, have generated significant research attention. Most works focus on the vehicle routing aspect, i.e. solving the dynamic dial-a-ride-problem arising in these applications (AlonsoMora et al 2017a; Ma et al 2019). We focus on the idle vehicle repositioning problem, i.e. the problem of sending idle vehicles to a suitable location in anticipation of future demand
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