Network effects of intelligent speed adaptation systems

Liu, Ronghui; Tate, James

doi:10.1023/b:port.0000025394.78857.13

Cited by 44 publications

(25 citation statements)

References 9 publications

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“…We speculate that this is because, in the absence of the fast vehicles, there is less to 324 interfere with the smooth flow of traffic during low flow conditions than there is during the peak 325 (Liu and Tate, 2004). 326 The results of this test demonstrate how a change in the assumed compliance with speed limits 327 can affect the overall performance of the system and that this effect differs according to the time of 328 day in ways which might not have been predicted in advance.…”

Section: U N C O R R E C T E D P R O O Fmentioning

confidence: 81%

Modelling safety-related driving behaviour—impact of parameter values

Bonsall

Liu

Young

2005

Transportation Research Part A: Policy and Practice

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10Traffic simulation models make assumptions about the safety-related behaviour of drivers. These 11 assumptions may or may not replicate the real behaviour of those drivers who adopt seemingly unsafe 12 behaviour, for example running red lights at signalised intersections or too closely following the vehicles 13 in front. Such behaviour results in the performance of the system that we observe but will often result in 14 conflicts and very occasionally in accidents. The question is whether these models should reflect safe behav-15 iour or actual behaviour. Good design should seek to enhance safety, but is the safety of a design neces-16 sarily enhanced by making unrealistically optimistic assumptions about the safety of driversÕ behaviour? 17This paper explores the questions associated with the choice of values for safety-related parameters in 18 simulation models. The paper identifies the key parameters of traffic simulation models and notes that sev-19 eral of them have been derived from theory or informed guesswork rather than observation of real behav-20 iour and that, even where they are based on observations, these may have been conducted in circumstances 21 quite different to those which now apply. Tests with the micro-simulation model DRACULA demonstrate 22 the sensitivity of model predictions-and perhaps policy decisions-to the value of some of the key param-23 eters. It is concluded that, in general, it is better to use values that are realistic-but-unsafe than values that 24 are safe-but-unrealistic. Although the use of realistic-but-unsafe parameter values could result in the adop-25 tion of unsafe designs, this problem can be overcome by paying attention to the safety aspects of designs.0965-8564/$ -see front matter Ó

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Section: U N C O R R E C T E D P R O O Fmentioning

confidence: 81%

Modelling safety-related driving behaviour—impact of parameter values

Bonsall

Liu

Young

2005

Transportation Research Part A: Policy and Practice

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“…Simulation results can be obtained throughout the evolution and on not just the means but also variances and probability distributions both within-day and between days. The full details of the DRACULA suite of models and their applications have been reported elsewhere (e.g., Hollander and Liu, 2008;Liu et al, 2006;Liu and Tate, 2004;Panis et al, 2006) and will therefore not be detailed herein.…”

Section: Dracula -A Microscopic Simulation Dta Modelmentioning

confidence: 99%

Disaggregate Path Flow Estimation in an Iterated Dynamic Traffic Assignment Microsimulation

Flötteröd

Liu

2013

Journal of Intelligent Transportation Systems

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This text describes the first application of a novel path flow and origin/destination (OD) matrix estimator for iterated dynamic traffic assignment (DTA) microsimulations. The presented approach, which operates on a trip-based demand representation, is derived from an agent-based DTA calibration methodology that relies on an activity-based demand model (Flötteröd et al., 2011a). The objective of this work is to demonstrate the transferability of the agent-based approach to the more widely used OD matrix-based demand representation. 2The calibration (i) operates at the same disaggregate level as the microsimulation and (ii) has drastic computational advantages over conventional OD matrix estimators in that the demand adjustments are conducted within the iterative loop of the DTA microsimulation, which results in a running time of the calibration that is in the same order of magnitude as a plain simulation. We describe an application of this methodology to the trip-based DRACULA microsimulation and present an illustrative example that clarifies its capabilities.

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“…Since the CACC system takes over a part of the longitudinal movement, a reference acceleration of CACC has been implemented to determine acceleration when the system controls vehicles in the simulation. Liu et al (2004) has studied traffic related impacts of Intelligent Speed Adaption (ISA) by traffic simulation modelling. The functionality of ISA has been implemented in the traffic simulation model and the impacts of ISA on traffic congestion, speed distribution and environment has been studied.…”

Section: Traffic Simulation Of Adasmentioning

confidence: 99%

Simulation Based Evaluation of Advanced Driver Assistance Systems

Elyasi-Pour¹

2015

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Road transportation is an essential element of mobility in most countries and we can observe an increasing demand for both goods and passenger traffic. There are however important societal and economical problems related to road transportation in terms of congestions, traffic safety and environmental effects. During the last decades vehicles have increasingly been equipped with different types of Advanced Driver Assistance Systems (ADAS). These systems can to some extent compensate for human behaviour and errors that cause congestions, accidents and air pollution. Most studies conducted to evaluate ADAS have focused on ADAS impacts on the driver or on the vehicle. Since an ADAS might influence not only driving behaviour and vehicle dynamics, but also the interaction between equipped and non-equipped vehicles, it is also important to consider the resulting effect on the traffic system. A reliable and realistic evaluation approach needs to include estimations of drivers' decisions in different traffic situations with respect to the ADAS functionality and how such decisions affect the traffic system as a whole.The overall aim of the thesis is to develop a simulation based evaluation framework for investigations of impacts of different types of cruise controllers on the traffic system. The objective is also to apply the framework to evaluate a fuel minimizing cruise controller for trucks, the Look Ahead Cruise Control (LACC). The framework developed consists of a combination of a microscopic traffic simulation model, and a vehicle and ADAS simulation model. When applied for a specific ADAS, as for example the LACC, the framework needs to be complemented with a driver model that captures the changes in driving behaviour due to the system of interest. In this thesis a driver model for LACC equipped trucks was developed based on results from a driving simulator experiment, a field operational test, and a focus group study. Simulation experiments were carried out to observe the LACC impacts on the traffic system with respect to penetration rate, traffic density, and variation in the desired speed. Environmental effects were estimated using emission calculations.The results show that an increase in traffic flow influences LACC-trucks more than CC-trucks with respect to fuel consumption and emissions. However, the results also show lower fuel consumption and emissions for LACC-trucks despite increased traffic flow. Increased penetration rate of LACC-trucks does not show any negative effect on traffic efficiency. iv v Acknowledgments

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Network effects of intelligent speed adaptation systems

Cited by 44 publications

References 9 publications

Modelling safety-related driving behaviour—impact of parameter values

Modelling safety-related driving behaviour—impact of parameter values

Disaggregate Path Flow Estimation in an Iterated Dynamic Traffic Assignment Microsimulation

Simulation Based Evaluation of Advanced Driver Assistance Systems

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