The effects of in-car speed limiters: field studies

Várhelyi, András; Mäkinen, Tapani

doi:10.1016/s0968-090x(00)00025-5

Cited by 84 publications

(54 citation statements)

References 2 publications

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“…Recent studies have shown that drivers became more aggressive, such as keeping closer following, after they came out of ISA controlled region compared to their normal driving behaviour (e.g. Comte, 2000;Várhelyi & Mäkinen, 2001). This behaviour has not been incorporated in the current model.…”

Section: Driving Behaviourmentioning

confidence: 99%

“…The systems were designed originally as a speed management measure for the urban environment (e.g. Várhelyi & Mäkinen, 2001;Hoogendoorn & Minderhoud, 2002). However, there is no technical restriction to the systems being applied on motorways where they can work in similar manner as controls by variable speed signs (Webb, 1980).…”

Section: Introductionmentioning

confidence: 99%

“…On-road trials of ISA systems were first conducted in Sweden (Almquist et al 1991;Almquist & Nygård, 1997) and later in the Netherlands and Spain (Várhelyi & Mäkinen, 2001). The speed control proved very popular, especially near schools.…”

Section: Introductionmentioning

confidence: 99%

“…Comte, 2000). These studies have been carried out through driving simulator experiments and/or actual road trials which, by their very nature, are limited in system penetration rates and traffic conditions (Várhelyi & Mäkinen, 2001). Network simulation modelling, on the other hand, provides a cost-effective method and controlled environment for evaluating the control systems in a network context.…”

Section: Introductionmentioning

confidence: 99%

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Network effects of intelligent speed adaptation systems

Liu

Tate

2004

Transportation

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ABSTRACT. Intelligent Speed Adaptation (ISA) systems use in-vehicle electronic devices to enable the speed of vehicles to be regulated externally. They are increasingly appreciated as a flexible method for speed management and control, particularly in urban areas. On-road trials using a small numbers of ISA equipped vehicles have been carried out in Sweden, the Netherlands, Spain and the UK. This paper describes the developments made to enhance a traffic microsimulation model in order to represent ISA implemented across a network and their impact on the networks. The simulation modelling of the control system is carried out on a real-world urban network, and the impacts on traffic congestion, speed distribution and the environment assessed. The results show that ISA systems are more effective in less congested traffic conditions. Momentary high speeds in traffic are effectively suppressed, resulting in a reduction in speed variation which is likely to have a positive impact on safety. Whilst ISA reduces excessive traffic speeds in the network, it does not affect average journey times. In particular, the total vehicle-hours travelling at speeds below 10 km/hr have not changed, indicating that the speed control had not induced more slow-moving queues to the network. A significant, eight percent, reduction in fuel consumption was found with full ISA penetration. These results are in accordance with those from field trials and they provide the basis for cost-benefit analyses on introducing ISA into the vehicle fleet. Contrary to earlier findings from the Swedish ISA road trials, these network simulations showed that ISA had no significant effect on emission of gaseous pollutants CO, NOx and HC. Further research is planned to investigate the impact on emission with a more comprehensive and up to date modal emission factor database.

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Section: Driving Behaviourmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Network effects of intelligent speed adaptation systems

Liu

Tate

2004

Transportation

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“…visual, audio or vibration), or limiting the speed to the official limit with/without possibility of override. A field study [30] found that ISA had a significant speed suppressing effects on road with speed limits ranging 30-70 km/h. The ISA concept is under active R&D in Europe: Following small field trials in 1996-1997, more extensive road trials were carried out in four Swedish cities in 1999-2001 with 4000 equipped vehicles [31].…”

Section: Intelligent Speed Adaptation (Isa) Systemsmentioning

confidence: 99%

Review of urban traffic management and the impacts of new vehicle technologies

Hounsell

Shrestha

Piao

et al. 2009

IET Intell. Transp. Syst.

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Parametric modeling of the heteroscedastic traffic speed variance from loop detector data

Wang

Hurwitz

et al. 2013

J of Advced Transportation

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Traffic speed variance is defined as a measure of the dispersion of space mean speeds among drivers. Empirical speed-density observations exhibit a structured traffic speed variance, which has been found to be associated to the roadway crash rate, the fatality rate, and travel time variability. The objective of this paper is to propose a generalized traffic speed variance function to describe this structured variance. The proposed speed variance function is a response of the speed-density curve with two additional parameters. The estimation of the model parameters in the proposed traffic speed variance function can be carried out through an iterative nonlinear least-square algorithm (i.e., LevenbergMarquardt). A series of logistic speed-density curve with varying parameters are used in the proposed traffic speed variance function with different levels of performance. The proposed traffic speed variance model can potentially help to unveil the underlying mechanism of empirical traffic phenomenon such as spontaneous congestion or capacity reduction.Judging from the empirical observations of traffic speed variance in the previous section, we found that these two variance functions are not appropriate to model a parabola-shaped variance function. 285HETEROSCEDASTIC TRAFFIC SPEED VARIANCE in the speed-density curve and its corresponding variance function can be estimated by a least-square iterative procedure (this paper used the leastsq in scipy.optimize [27]). To evaluate the performance of the proposed traffic speed variance models, the authors will compare the 3PL, 4PL, and 5PL speed-density models to the empirical mean of a speed-density observation; simultaneously, the authors also compare the traffic speed variance functions based on the 3PL, 4PL, and 5PL models to the empirical traffic speed variances as can be seen from the left half in Figures 11 and 12. The EM represents the empirical mean of the scattered speed-density observation, whereas EV is the empirical traffic speed variance at this location. Similarly, the nPL (n = 3, 4, 5) represents the mean curve estimated from the n-parameter logistic speed-density models. On the right-hand side of Figures 11 and 12, the MR indicates the mean residual, which is the difference between the empirical mean and the mean Figure 11. Performance of the three-parameter, four-parameter, and five-parameter logistic speed-density models against the empirical mean and residuals between their corresponding variance models and empirical variance at station 4001119 and 4001120. 292H. WANG ET AL.

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The effects of in-car speed limiters: field studies

Cited by 84 publications

References 2 publications

Network effects of intelligent speed adaptation systems

Network effects of intelligent speed adaptation systems

Review of urban traffic management and the impacts of new vehicle technologies

Parametric modeling of the heteroscedastic traffic speed variance from loop detector data

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