9,579 research outputs found
Delays, Inaccuracies and Anticipation in Microscopic Traffic Models
We generalize a wide class of time-continuous microscopic traffic models to
include essential aspects of driver behaviour not captured by these models.
Specifically, we consider (i) finite reaction times, (ii) estimation errors,
(iii) looking several vehicles ahead (spatial anticipation), and (iv) temporal
anticipation. The estimation errors are modelled as stochastic Wiener processes
and lead to time-correlated fluctuations of the acceleration.
We show that the destabilizing effects of reaction times and estimation
errors can essentially be compensated for by spatial and temporal anticipation,
that is, the combination of stabilizing and destabilizing effects results in
the same qualitative macroscopic dynamics as that of the respectively
underlying simple car-following model. In many cases, this justifies the use of
simplified, physics-oriented models with a few parameters only. Although the
qualitative dynamics is unchanged, multi-anticipation increase both spatial and
temporal scales of stop-and-go waves and other complex patterns of congested
traffic in agreement with real traffic data. Remarkably, the anticipation
allows accident-free smooth driving in complex traffic situations even if
reaction times exceed typical time headways.Comment: Major revision of the model and the simulations. Particularly, the
number of model parameters has been reduce
A proposed psychological model of driving automation
This paper considers psychological variables pertinent to driver automation. It is anticipated that driving with automated systems is likely to have a major impact on the drivers and a multiplicity of factors needs to be taken into account. A systems analysis of the driver, vehicle and automation served as the basis for eliciting psychological factors. The main variables to be considered were: feed-back, locus of control, mental workload, driver stress, situational awareness and mental representations. It is expected that anticipating the effects on the driver brought about by vehicle automation could lead to improved design strategies. Based on research evidence in the literature, the psychological factors were assembled into a model for further investigation
Big data analytics:Computational intelligence techniques and application areas
Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
Driver Heterogeneity in Willingness to Give Control to Conditional Automation
The driver's willingness to give (WTG) control in conditionally automated
driving is assessed in a virtual reality based driving-rig, through their
choice to give away driving control and through the extent to which automated
driving is adopted in a mixed-traffic environment. Within- and across-class
unobserved heterogeneity and locus of control variations are taken into
account. The choice of giving away control is modelled using the mixed logit
(MIXL) and mixed latent class (LCML) model. The significant latent segments of
the locus of control are developed into internalizers and externalizers by the
latent class model (LCM) based on the taste heterogeneity identified from the
MIXL model. Results suggest that drivers choose to "giveAway" control of the
vehicle when greater concentration/attentiveness is required (e.g., in the
nighttime) or when they are interested in performing a non-driving-related task
(NDRT). In addition, it is observed that internalizers demonstrate more
heterogeneity compared to externalizers in terms of WTG
2nd Symposium on Management of Future motorway and urban Traffic Systems (MFTS 2018): Booklet of abstracts: Ispra, 11-12 June 2018
The Symposium focuses on future traffic management systems, covering the subjects of traffic control, estimation, and modelling of motorway and urban networks, with particular emphasis on the presence of advanced vehicle communication and automation technologies.
As connectivity and automation are being progressively introduced in our transport and mobility systems, there is indeed a growing need to understand the implications and opportunities for an enhanced traffic management as well as to identify innovative ways and tools to optimise traffic efficiency.
In particular the debate on centralised versus decentralised traffic management in the presence of connected and automated vehicles has started attracting the attention of the research community.
In this context, the Symposium provides a remarkable opportunity to share novel ideas and discuss future research directions.JRC.C.4-Sustainable Transpor
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