Experimental evidence supporting simpler Action Point paradigms for car-following

Abstract

ReuseUnless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. IntroductionModelling driving behaviour represents a crucial task for many applications in transportation. Three main areas can particularly benefit from an enhanced knowledge of driving behaviour: accident analysis and prevention, microscopic simulation of traffic, and Intelligent Transportation Systems (ITS). Benefits for ITS are mainly expected in the field of Advanced Driver Assistance Systems (ADAS), where some assistance/control logics interact with drivers (and their behaviour) and where both drivers' expectations, and impacts of the innovations on drivers' behaviour have to be considered in order to improve: a) the effectiveness of the solutions; b) driving (and traffic) safety and c) acceptance of technological solutions.Modelling of driving behaviour is based on two fundamental requirements. On the one hand, theoretical frameworks and paradigms are needed. On the other, observation tools and data are required in order both to develop/validate theories and to identify modelling parameters for practical applications. If the research focus is on disaggregate driving behaviour rather than aggregate traffic behaviour, the best source of information is based on individual vehicle data (IVD), as typically obtained by instrumented vehicles (IVs). An IV can be described as a standard vehicle where the kinematics, the interaction with surrounding vehicles and the vehicle-driver interaction are recorded for subsequent analysis. The possibility of observing only the kinematics of IVs, as allowed by some camera-based microscopic roadside observation systems like in the NGSIM project (Alexiadis et al., 2004), can lead to a reduced understanding of driving behaviour. Indeed, the possibility of observing the kinematics of an IV is just a prerequisite and IVs are usually equipped with a large number of sensors. Multisensing approaches not only enhance the estimation of the ego-kinematics of the controlled vehicle (Bifulco et al., 2011), but also allow detection of the surrounding traffic conditions and direct monitoring of on-board interaction between the driver and the vehicle, generally via the controlled area network (CAN). The overall result is a more comprehensive observation and enhanced understand...

A Linear Model for the Estimation of Fuel Consumption and the Impact Evaluation of Advanced Driving Assistance Systems

“…Our effort toward simplification of approaches is well justified by the need for adopting online estimation and control logics in the development of ADAS. Indeed, more critical aspects in ADAS, those related to human behavior, have been proved to be effectively approachable with simple and linear models, as shown in [34,35].…”

Distressed in the queue? Psychophysiological and behavioral evidence for two alternative car-following techniques

“…DD drivers’ visual behavior to keep safety distance relied substantially on BRC and drivers are normally good at estimating time to crash based on upon stereoscopic depth perception and visual angles subtended by a lead vehicle ( Gray & Regan, 1998 ). In the Action Point paradigm, visual angle models define the follower action in terms of the perceived horizontal visual angle between the followers’ retina and the width of the leader’s vehicle ( Pariota and Bifulco, 2015 , Saifuzzaman and Zheng, 2014 ). This concept is adopted to examine the driver’s ability to scale the relative velocity between the vehicles ( Yousif & Al-Obaedi, 2011 ).…”

“…Drivers move amidst a perennial oscillation either following a swinging leader, a stable leader or when driving with no traffic at all. This was implicit in early CF theories under the Action Point (AP) paradigm ( Brackstone et al, 2002 , Pariota and Bifulco, 2015 ) and pictured by the characteristic close-following spirals in different studies ( Pariota et al, 2016 , Wagner, 2011 ). Other CF models describe instability typical of transition phases between free-flow and congestion ( Orosz, Wilson, & Krauskopf, 2004 ), especially when the leader’s speed varies ( Pariota et al, 2016 ).…”

“…Driving (CF in particular) is a preeminently visual task ( Lee, Lee, & Boyle, 2007 ), demanding focused attention on the traffic ahead. Early in the 1960s, the AP model proposed a specific psychophysiological mechanism to explain CF discontinuities in the acceleration and deceleration phases: a lead vehicle’s visual extent (size) is the specific stimulus for drivers during CF ( Pariota & Bifulco, 2015 ). Besides this elementary psychophysical component, a consequence of speed variations during the CF process, visual patterns shown by expert vs novel drivers matter.…”

A neighbourhood search algorithm for determining optimal intervention strategies in the case of metro system failures

“…The models in question can be classified into service frequency optimization (Salzborn [16], yu et al [17] and gallo et al [18,19]), joint design of frequencies and routes (laporte et al [20] and Van Nes et al [21]) or elastic demand approach models (lee & Vuchic [22] and marìn & garcìa-ròdenas [23]). However, there are also several contributions related to specific themes such as the budget reductions of public transport (D'acierno et al [24]), the definition of energy-efficient driving profiles (gallo et al [25]), effect of information on user choices (Pariota & bifulco [26] and bifulco et al [27]) or safety of transportation systems (Dell'acqua et al [28]). …”