5,104 research outputs found

    An ontology-based approach to relax traffic regulation for autonomous vehicle assistance

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    Traffic regulation must be respected by all vehicles, either human- or computer- driven. However, extreme traffic situations might exhibit practical cases in which a vehicle should safely and reasonably relax traffic regulation, e.g., in order not to be indefinitely blocked and to keep circulating. In this paper, we propose a high-level representation of an automated vehicle, other vehicles and their environment, which can assist drivers in taking such "illegal" but practical relaxation decisions. This high-level representation (an ontology) includes topological knowledge and inference rules, in order to compute the next high-level motion an automated vehicle should take, as assistance to a driver. Results on practical cases are presented

    Motor proteins traffic regulation by supply-demand balance of resources

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    In cells and in vitro assays the number of motor proteins involved in biological transport processes is far from being unlimited. The cytoskeletal binding sites are in contact with the same finite reservoir of motors (either the cytosol or the flow chamber) and hence compete for recruiting the available motors, potentially depleting the reservoir and affecting cytoskeletal transport. In this work we provide a theoretical framework to study, analytically and numerically, how motor density profiles and crowding along cytoskeletal filaments depend on the competition of motors for their binding sites. We propose two models in which finite processive motor proteins actively advance along cytoskeletal filaments and are continuously exchanged with the motor pool. We first look at homogeneous reservoirs and then examine the effects of free motor diffusion in the surrounding medium. We consider as a reference situation recent in vitro experimental setups of kinesin-8 motors binding and moving along microtubule filaments in a flow chamber. We investigate how the crowding of linear motor proteins moving on a filament can be regulated by the balance between supply (concentration of motor proteins in the flow chamber) and demand (total number of polymerised tubulin heterodimers). We present analytical results for the density profiles of bound motors, the reservoir depletion, and propose novel phase diagrams that present the formation of jams of motor proteins on the filament as a function of two tuneable experimental parameters: the motor protein concentration and the concentration of tubulins polymerized into cytoskeletal filaments. Extensive numerical simulations corroborate the analytical results for parameters in the experimental range and also address the effects of diffusion of motor proteins in the reservoir.Comment: 31 pages, 10 figure

    AUTOMATIC PENALTY CHARGING FOR TRAFFIC REGULATION

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    The research, "Automatic Penalty Charging for Traffic Regulation", is an attempt to design a system which will automatically incur penalty to the car driver and owner for violation of traffic rules. There are 3 units to be designed. One will be a standalone system attached to the ignition mechanism of the car, the other will be a standalone system which will be attached to the traffic signal points and the third will be mainframe RTO unit. For car ignition the driver has to place his RFID driving license card near to the reader. The design aims to reduce bribery, corruption, pollution, congestion in a city

    Bimodal traffic regulation system: A multi-agent approach

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    International audienceThe development of surface public transportation networks is a major issue in terms of ecology, economy and society. Their quality in terms of punctuality and passengers services (regularity between buses) should be improved in order to improve their attractiveness. To do so, cities often use regulation systems at intersections that grant priority to buses. The problem is that each transportation mode has its own characteristics and a dedicated decision support system. Therefore, most of them hardly take into account both public transport vehicles such as buses and private vehicle traffic. This paper proposes a multi-agent model that supports bimodal regulation and preserves monomodal regulation. The objective is to improve global traffic, to reduce bus delays and to improve bus regularity in congested areas of the network. In our approach, traffic regulation is obtained thanks to communication, collaboration and negotiation between heterogeneous agents. We tested our strategy on a complex network of nine junctions. The results of the simulation are presented

    Blackspot Location and Recommendation to Reduce Number and Severity of Accidents on Purbaleunyi Toll Road

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    Toll roads, as land transportation infrastructure, have an important role in Indonesia. With a high number of road crashes in Indonesia, with about 40,000 people die on the road each year, the determination of blackspot locations is crucial. The aim of this study is to analyze blackspot location on a toll road in Indonesia and, furthermore, to provide recommendations in order to reduce number and severity of accident. A case study is carried out on a toll road, named Purbaleunyi Toll Road, in West Java. Accident rate value and UCL method are used in this study to determine blackspot locations. The results indicated that there are many blackspot locations along the toll road and recommended solutions provided are adherence to traffic regulation, adherence to vehicle worthiness,dissemination of road safety importance to road users, and the implementation of blackspot treatments continuously

    Achieving safer school travel in the UK

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    This paper summarises the current UK approach to improving child road safety, focusing particularly on measures to enhance the safety of the school journey. It highlights the importance of a safe road environment, and a number of different ways in which this can be achieved, including engineering measures, often introduced via partnership work between local authorities, schools, the police, the local community, parents and children. It also reports on supporting measures, such as on-road child pedestrian and cycle training, which are becoming an increasingly common part of school activity

    A multiscale model for traffic regulation via autonomous vehicles

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    Autonomous vehicles (AVs) allow new ways of regulating the traffic flow on road networks. Most of available results in this direction are based on microscopic approaches, where ODEs describe the evolution of regular cars and AVs. In this paper, we propose a multiscale approach, based on recently developed models for moving bottlenecks. Our main result is the proof of existence of solutions for open-loop controls with bounded variation

    Costs and Technology of Public Transit Systems in Italy:Some Insights to Face Inefficiency

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    This study provides fresh evidence about the characteristics of technology and cost structure of public transit systems in Italy. The aim is to suggest useful guidelines for facing detected inefficiencies. The analysis is carried out through the estimation of a translog variable cost function. The sample includes 45 Italian public companies. Firms are observed in the years 1996, 1997 and 1998, and operate both in the urban and extra-urban compartments. Results support previous evidence on the existence of natural monopoly at local level and stress the importance of the average speed of vehicles in explaining cost differences between companies. We conclude that cost benefits can be achieved by promoting mergers between firms (whenever possible), introducing some forms of "competition-for-the-market" (e.g., competitive tendering for the single license) and taking more care of the local traffic regulation.

    Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making

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    In multi-objective decision planning and learning, much attention is paid to producing optimal solution sets that contain an optimal policy for every possible user preference profile. We argue that the step that follows, i.e, determining which policy to execute by maximising the user's intrinsic utility function over this (possibly infinite) set, is under-studied. This paper aims to fill this gap. We build on previous work on Gaussian processes and pairwise comparisons for preference modelling, extend it to the multi-objective decision support scenario, and propose new ordered preference elicitation strategies based on ranking and clustering. Our main contribution is an in-depth evaluation of these strategies using computer and human-based experiments. We show that our proposed elicitation strategies outperform the currently used pairwise methods, and found that users prefer ranking most. Our experiments further show that utilising monotonicity information in GPs by using a linear prior mean at the start and virtual comparisons to the nadir and ideal points, increases performance. We demonstrate our decision support framework in a real-world study on traffic regulation, conducted with the city of Amsterdam.Comment: AAMAS 2018, Source code at https://github.com/lmzintgraf/gp_pref_elici
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