Abstract
Carpooling has the potential to transform itself into a mass transportation mode by abandoning its adherence to deterministic passenger-driver matching for door-to-door journeys, and by adopting instead stochastic matching on a network of fixed meeting points. Stochastic matching is where a passenger sends out a carpooling request at a meeting point, and then waits for the arrival of a self-selected driver who is already travelling to the requested meeting point. Crucially there is no centrally dispatched driver. Moreover, the carpooling is assured only between the meeting points, so the onus is on the passengers to travel to/from them by their own means. Thus the success of a stochastic carpooling service relies on the convergence, with minimal perturbation to their existing travel patterns, to the meeting points which are highly frequented by both passengers and drivers. Due to the innovative nature of stochastic carpooling, existing off-the-shelf workflows are largely insufficient for this purpose. To fill the gap in the market, we introduce a novel workflow, comprising of a combination of data science and GIS (Geographic Information Systems), to analyse driver GPS traces. We implement it for an operational stochastic carpooling service in south-eastern France, and we demonstrate that relaxing door-to-door matching reduces passenger waiting times. Our workflow provides additional key operational indicators, namely the driver flow maps, the driver flow temporal profiles and the driver participation rates.
Highlights
Carpooling has seen an explosion of utilisation in recent years [Furuhata et al, 2013]
We propose an indirect comparison in three stages: (i) extract all driver GPS traces which connect two meeting points in a restrained time interval, as the meeting point matches, (ii) extract the largest hierarchical cluster of these GPS traces to serve as the door-to-door matches, and (iii) compute the driver flows using Equation (1) for both sets of matches, and convert them using Equation (2) to passenger waiting time predictions
We introduced a novel data science-GIS workflow for a stochastic carpooling service
Summary
Carpooling has seen an explosion of utilisation in recent years [Furuhata et al, 2013]. Stiglic et al [2015] and Li et al [2018] provide more complex synthetic models to affirm that meeting points are essential to the feasibility of the mass carpooling services, and assert that it is almost impossible for a carpooling service to be based on door-to-door spatio-temporal matching Whilst these examples demonstrate that incentivising drivers to converge to meeting points, rather than relying on door-to-door matching, increases the potential pool of mutualisable journeys, we have not yet demonstrated that this leads to reduced waiting times. Our approach is based on network analysis tools [Guidotti et al, 2017] and complexity reduction/harmonisation algorithms [Douglas and Peucker, 2011] This topological simplification is essential to be able to mutualise GPS traces which share common arrival times at the carpooling meeting points. Once these GPS traces are in a suitable format, we are able to produce the required outputs in the right rectangle, namely the predicted waiting times, the driver flow maps, the driver flow temporal profiles and the driver participation rates
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
