Driving Behaviour: Models and Challenges

Toledo, Tomer

doi:10.1080/01441640600823940

Cited by 165 publications

(77 citation statements)

References 46 publications

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“…The stateof-the-art in lane-changing models includes Gipps (1986), Hidas (2002Hidas ( , 2005 and Toledo et al (2005Toledo et al ( , 2003. For an overview of lane changing models see Toledo (2007) or Janson Olstam (2005). The utilized lanechanging model is also based on the HUTSIM/TPMA (Kosonen, 1999) model, with some minor modifications; see Janson Olstam (2005) for details.…”

Section: Lane-changingmentioning

confidence: 99%

“…The most well known car-following model is probably the GHR-model (Chandler et al, 1958), the Gipps model (Gipps, 1981), and the Wiedemann model (Wiedemann, 1974;Wiedemann and Reiter, 1992). For an overview on car-following models, see for example Brackstone and McDonald (1998) or Toledo (2007). The utilized car-following model is based on the HUTSIM/TPMA (Kosonen, 1999) model with some modifications.…”

Section: Car-followingmentioning

confidence: 99%

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A framework for simulation of surrounding vehicles in driving simulators

Olstam

Lundgren

Adlers

et al. 2008

ACM Trans. Model. Comput. Simul.

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This article describes a framework for generation and simulation of surrounding vehicles in a driving simulator. The proposed framework generates a traffic stream, corresponding to a given target flow and simulates realistic interactions between vehicles. The framework is based on an approach in which only a limited area around the driving simulator vehicle is simulated. This closest neighborhood is divided into one inner area and two outer areas. Vehicles in the inner area are simulated according to a microscopic simulation model including advanced submodels for driving behavior while vehicles in the outer areas are updated according to a less time-consuming mesoscopic simulation model. The presented work includes a new framework for generating and simulating vehicles within a moving area. It also includes the development of an enhanced model for overtakings and a simple mesoscopic traffic model. The framework has been validated on the number of vehicles that catch up with the driving simulator vehicle and vice versa. The agreement is good for active and passive catch-ups on rural roads and for passive catch-ups on freeways, but less good for active catch-ups on freeways. The reason for this seems to be deficiencies in the utilized lane-changing model. It has been verified that the framework is able to achieve the target flow and that * Swedish National Road there is a gain in computational time of using the outer areas. The framework has also been tested within the VTI Driving simulator III.

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Section: Lane-changingmentioning

confidence: 99%

Section: Car-followingmentioning

confidence: 99%

A framework for simulation of surrounding vehicles in driving simulators

Olstam

Lundgren

Adlers

et al. 2008

ACM Trans. Model. Comput. Simul.

View full text Add to dashboard Cite

show abstract

“…Moreover, scarcity of travel information and database has reduced the accuracy of these developed models. As a result, driving behavior cannot be translated adequately, interdependently and rigorously [23].…”

Section: Systematic Literature Investigationmentioning

confidence: 99%

“…Limited exploration of drivers' competency in view of instantaneous decision making, planning, anticipation and driving goals as well as the scarcity of travel information makes it difficult to create fully accurate driving assessment models. Therefore, driver's travel behaviors could not be translated adequately, interdependently and rigorously through these models [23].…”

Section: Introductionmentioning

confidence: 99%

Green Driver: Travel Behaviors Revisited on Fuel Saving and Less Emission

et al. 2018

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Road transportation is the main energy consumer and major contributor of ever-increasing hazardous emissions. Transportation professionals have raised the idea of applying the green concept in various areas of transportation, including green highways, green vehicles and transit-oriented designs, to tackle the negative impact of road transportation. This research generated a new dimension called the green driver to remediate urgently the existing driving assessment models that have intensified emissions and energy consumption. In this regard, this study aimed to establish the green driver's behaviors related to fuel saving and emission reduction. The study has two phases. Phase one involves investigating the driving behaviors influencing fuel saving and emission reduction through a systematic literature review and content analysis, which identified twenty-one variables classified into four clusters. These clusters included the following: (i) FE f1 , which is driving style; (ii) FE f2 , which is driving behavior associated with vehicle transmission; (iii) FE f3 , which is driving behavior associated with road design and traffic rules; and (iv) FE f4 , which is driving behavior associated with vehicle operational characteristics. The second phase involves validating phase one findings by applying the Grounded Group Decision Making (GGDM) method. The results of GGDM have established seventeen green driving behaviors. The study conducted the Green Value (GV) analysis for each green behavior on fuel saving and emission reduction. The study found that aggressive driving (GV = 0.16) interferes with the association between fuel consumption, emission and driver's personalities. The research concludes that driver's personalities (including physical, psychological and psychosocial characteristics) have to be integrated for advanced in-vehicle driver assistance system and particularly, for green driving accreditation.

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“…"This has given rise to two main kinds of models of traffic dynamics, namely: microscopic representations, based on the description of the individual behaviour of each vehicle; and macroscopic representations describing traffic as a continuous flow obeying global rules" (Toledo, 2007). Driving cycles used for vehicle emission testing are also specified on a second-by-second speed-time profile.…”

Section: Introductionmentioning

confidence: 99%

Development of vehicle emissions models for Australian conditions

Zhu¹

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Microscopic simulation models such as AIMSUN, VISSIM and/or PARAMICS have the ability to output emissions based on default values for emission factors derived mainly from European test data. Emission algorithms in those models are based on overseas vehicle emissions datasets, which do not reflect the different Australian vehicles, fuels, climate and fleet composition. The proposed research provides a set of emission algorithms to be used in conjunction with traffic simulation modelling, to better represent local conditions. Macro level models based on average vehicle speeds may not be appropriate for use at a more localized and detailed level when vehicle speed profiles may change significantly. Emission rates for a number of vehicles were compared using Australian data based on dynamometer The thesis discusses the limitations of existing emissions estimation approaches at the micro level. A methodology to establish emission models for predicting emission pollutants other than CO 2 is proposed. The models adopt a genetic algorithm approach to select the predicting variables. The approach is capable of solving combinatorial optimisation problems. Overall, iii the emission prediction results reveal that the proposed new models outperform conventional equations.There is a need to match emission modelling estimation to the accuracy levels of confidence in the outputs of transport models. In order to quantify the likely level of uncertainty attached to forecasts of emissions, an analysis of errors needs to be undertaken. The two major sources of error are the deficiency inherent in the model structure itself and the uncertainty in the input data used. This thesis deals with both of these error types in relation to CO 2 emissions modelling using a case-study from Brisbane, Australia. To estimate input data uncertainty, an analysis of different traffic conditions using Monte Carlo simulation is shown here. Model structure induced uncertainties are also quantified by statistical analysis for a number of traffic scenarios. To arrive at an optimal overall CO 2 prediction, the interaction between the two components was taken into account. Since a more complex model does not necessarily yield higher overall accuracy, a balanced solution needs to be found. The results obtained suggest that the CO 2 model used in the analysis produces low overall uncertainty under free flow traffic conditions. However, when average traffic speeds approach congested conditions, there are significant errors associated with emissions estimates.Using different scenarios for different road configurations and traffic conditions, the results of applying the new approach are compared with those obtained by using default emissions parameters commonly found in a simulation package.The enhancement of emission predictions rests to a large extent on the further improvements to traffic micro-simulation models. The results obtained suggest that the new approach produces low overall errors under several traffic conditions. The accuracy of emissions p...

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Driving Behaviour: Models and Challenges

Cited by 165 publications

References 46 publications

A framework for simulation of surrounding vehicles in driving simulators

A framework for simulation of surrounding vehicles in driving simulators

Green Driver: Travel Behaviors Revisited on Fuel Saving and Less Emission

Development of vehicle emissions models for Australian conditions

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