Relaxing door-to-door matching reduces passenger waiting times: a workflow for the analysis of driver GPS traces in a stochastic carpooling service

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

Analytical Performance Model for Poisson Wireless Networks with Pathloss and Shadowing Propagation

International audienceThe SINR (signal to interference plus noise ratio) is a key factor for wireless networks analysis. Indeed, the SINR distribution allows the derivation of performance and quality of service (QoS) evaluation. Moreover, it also enables the analysis of radio resources allocation and scheduling policies, since they depend on the SINR reached by a UE (User Equipment). Therefore, it is particularly interesting to develop an analytical method which allows to evaluate the SINR, in a simple and quick way, for a realistic environment. Considering a stochastic Poisson network model, we establish the CDF (cumulative distributed function) of the SINR. We show that the shadowing can beneglected, in many cases, as long as mobiles are connected to their best serving base station (BS), i.e. the BS which offers them the most powerful useful signal. As a consequence, the analysis of performance and quality of service, directly derived from the CDF of SINR, can be established by using a propagation model which takes into account only the pathloss. Moreover, we establish that the Fluid network model we have proposed can be used to analyze stochastic Poisson distributed network. Therefore, theanalysis of stochastic Poisson network can be done in an easy and quick way, by using the analytical expression of the SINR established thanks to the Fluid network model.</p

Quality of Service and Performance Evaluation: A Fluid Approach for Poisson Wireless Networks

International audienceThe evaluation of performance and quality of serviceneeds the modeling of wireless networks. Among the differentmodels usually considered, the hexagonal network is the mostpopular. However, it requires extensive numerical computations. The Poisson network model, for which the base stations (BS) locations form a stochastic spatial Poisson process, allows to consider a non constant distance between base stations. Therefore, it characterizes more realistically operational networks. The Fluid network model, for which the interfering BS are replacedby a continuum of infinitesimal interferers, allows to establish closed-form formula for the SINR (Signal on Interference plus Noise Ratio). This model was validated by comparison with an hexagonal network. The two models establish very close results. In this paper, we show that the Fluid network model can also be used to analyze Poisson networks. Therefore, the evaluation of quality of service and performance becomes very easy, whateverthe type of model, by using the analytical expression of the SINRestablished by considering the fluid model.</p