Normal Acceleration Behavior of Passenger Vehicles Starting from Rest at All-Way Stop-Controlled Intersections

Wang, Jun; Dixon, Karen; Li, Hainan; Ogle, Jennifer

doi:10.3141/1883-18

Cited by 73 publications

(65 citation statements)

References 2 publications

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“…The dynamic speed profiles of three participants are illustrated in Figure 3. The results confirm the findings of Wang et al (2004), that the constant acceleration assumption does not reflect realistic, actual driver behaviour. After analysing 40 different driver simulator runs, it was found that the process consists of only two main operational states, namely, transition (state 1) and acceleration (state 2).…”

Section: B31 Data Collection and Vehicle Operation Classificationsupporting

confidence: 86%

“…Generally, an assumption of constant acceleration is implemented by most micro-simulation packages. However, Wang et al (2004) have assumed that drivers normally accelerate with a polynomial decreasing relationship with speed. Long (2000) has concluded that linearly decreasing acceleration rates better represent both maximum vehicle acceleration capabilities and actual motorist behaviour.…”

Section: A6mentioning

confidence: 99%

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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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Section: B31 Data Collection and Vehicle Operation Classificationsupporting

confidence: 86%

Section: A6mentioning

confidence: 99%

Development of vehicle emissions models for Australian conditions

Zhu¹

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“…The published results of Hirschmann [4], Rakha [6] and Wang [7] suggest that the most probable acceleration values for stop line accelerations are in the 1 to 2 mls 2 range with peak accelerations are in the 3 to 4 mls 2 range. Our results support that findings with a slightly higher peak acceleration observed.…”

Section: A Speed and Accelerationmentioning

confidence: 93%

“…The study showed the desired acceleration rates from the VISSIM simulation model are too high by default, and suggested that these parameters need to be calibrated against the real world measurement before the model can be used to calculate for emissions. The acceleration values determined from this study are from 0 to 4 mls 2 • Other research into the vehicle acceleration at signalized junctions ( [5], [6], [7]) has focused on developing different models for estimating vehicle acceleration levels; in [6],the study proposed a vehicle dynamic model for predicting maximum acceleration for passenger vehicle. The model predicted values of acceleration range between 0 -4.3 mls 2 for the tested vehicles.…”

Section: Introductionmentioning

confidence: 99%

Field investigation of vehicle acceleration at the stop line with a dynamic vision sensor

Simón¹,

Hu²,

Margherita³

et al. 2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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This article presents a study of vehicle acceleration measurements at the stop line of a traffic light in an urban environment. Accurate representation of vehicle acceleration behavior is an important parameter to traffic simulation tools especially when the emissions related to the simulated traffic need to be calculated. A smart eye traffic data sensor (TDS) system was used to record the vehicle trajectories. This device is based on dynamic vision sensor technology that features 1 millisecond temporal resolution, wide dynamic range of 120 dB of illumination, zero-redundancy and asynchronous data output. The trajectories of the detected vehicles from the vision sensor temporal contrast event data have been manually annotated in a space-time representation using a graphical tool. From these extracted trajectories the acceleration of the vehicles has been calculated. We present results of acceleration distributions obtained from over 300 passenger-car acceleration cycles observed in the field. The measurements focus on the first phase of acceleration from the stop line up to a maximum speed of 40 km/h. The results are compared to the results from a traffic micro simulation tool obtained for a similar stop line scenario. The results will be used in the traffic micro-simulation that serves as a basis for a decision support tool for adaptive traffic management developed in the CARBOTRAF FP7 EU project.

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“…This generic profile is set with a 2.4 m/s² deceleration rate, which is the average rate at 50 km/h according to [20]. This is a typical generic profile that may be used for ADAS applications.…”

Section: A Gaussian Processes Based Modellingmentioning

confidence: 99%

Modelling stop intersection approaches using Gaussian processes

Armand

Filliat

Ibañez‐Guzmán

2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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Abstract-Each driver reacts differently to the same traffic conditions, however, most Advanced Driving Assistant Systems (ADAS) assume that all drivers are the same. This paper proposes a method to learn and to model the velocity profile that the driver follows as the vehicle decelerates towards a stop intersection. Gaussian Processes (GP), a machine learning method for non-linear regressions are used to model the velocity profiles. It is shown that GP are well adapted for such an application, using data recorded in real traffic conditions. It consists of the generation of a normally distributed speed, given a position on the road. By comparison with generic velocity profiles, benefits of using individual driver patterns for ADAS issues are presented.

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Normal Acceleration Behavior of Passenger Vehicles Starting from Rest at All-Way Stop-Controlled Intersections

Cited by 73 publications

References 2 publications

Development of vehicle emissions models for Australian conditions

Development of vehicle emissions models for Australian conditions

Field investigation of vehicle acceleration at the stop line with a dynamic vision sensor

Modelling stop intersection approaches using Gaussian processes

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