Abstract

The effect of traffic congestion within an urban region can be eliminated by curbing the number of vehicles that use the congested part of the transport network. To do so, earlier work by the authors introduced a novel route-reservation architecture that manages the vehicle departure times and makes the appropriate routing decisions so that vehicles arrive at their destination at the earliest possible time avoiding the congested parts of the network Menelaou et al. (2017a,b). Under the proposed route-reservation architecture, the future state of each road segment is predicted based on the received reservations and assuming that all vehicles travel at a constant speed which is set equal with the free-flow speed. Even within a homogeneous region, this assumption is not always valid which can lead to non accurate estimation of the time that a vehicle occupies a road segment which in turn affects the accuracy of the overall architecture. Therefore, the key objective of this work is to investigate different prediction schemes that improve the prediction-accuracy of vehicles travel times within each road segment. In this paper we explore two prediction methods, an Exponential Moving Average (EMA) and a Multiple Linear Regression (MLR) method. The performance of the two prediction schemes is also presented. Finally, realistic simulation results across an urban region of San Francisco demonstrate the gains that can be achieved applying the proposed prediction methods. C. Menelaou and P. Kolios and S. Timotheou and C.G. Panayiotou, "Effective Prediction of Road Segment Occupancy for the Route-Reservation Architecture", 2018 15th IFAC Symposium on Control in Transportation Systems CTS 2018, IFAC-PapersOnLine, pp 470 - 475, doi: https://doi.org/10.1016/j.ifacol.2018.07.07. Copyrights belong to Elsevier 2018 LTD

Abstract

International audience; This article tackles the problem of the autonomous navigation and coordination of multiple driverless vehicles for the transport of persons or goods in outdoor environments. The system composed of fully automated road vehicles, capable of providing an effective transportation service, was recently tested at the city of La Rochelle. This same system was further improved, and a new demonstration was performed at Inria Rocquencourt, in order to demonstrate the validity of the concepts for a coordinated navigation in the presence of ambiguous and conflictual situations in a mixed environment. The originality of the approach relies on the use of new cooperative concepts and their combination with advanced perception tasks operating simultaneously on several robots. This system was developed in the context of the European project CityMobil.

Abstract

Traffic and transportation are ongoing digitalisation. Travellers always carry smartphones everywhere they go. Smartphone-based crowdsensing can be used to collect and aggregate traffic information for services that contribute to smoother and more sustainable transportation and traffic — but only if the business model is profitable in the long-term. We analyse two existing crowdsensing services in traffic and transportation context (Waze, Moovit) and one being developed (TrafficSense) using findings from business model (two-sided markets; data use), crowdsensing (technical overview, participant incentives), and transportation (efficiency, sustainable urban transportation) literature. Waze may alleviate traffic congestion by helping its millions of users to avoid traffic jams. Moovit makes public transport more attractive by making it easier and smoother to use for travellers. TrafficSense service is developed in a research project. It uses crowdsensing to learn regular, multimodal routes of travellers. The information can be used to predict the general traffic and congestion levels based on the predicted intents of the crowd of travellers. Our contribution is to combine distinct but complementary viewpoints from two-sided markets, business models, crowdsensing, and transportation research to analyse the potential business and sustainability impacts of the emerging crowdsensing-based smart transportation services.