Calibrating the Wiedemann 99 Car-Following Model for Bicycle Traffic (original) (raw)
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A bicycle simulator for experiencing microscopic traffic flow simulation in urban environments
2018 21st International Conference on Intelligent Transportation Systems (ITSC)
Urban environments often imply complex transportation infrastructures with manifold different traffic participant using various modes of transport. These traffic participants interact with each other in different ways, often in specific patterns of communication. One option for understanding these interactions may come from microscopic traffic flow simulations. Simulated traffic on modelled urban transportation infrastructures may deliver insights on general traffic-related problems or show specific locations of high risk of accidents or of low traffic quality. Besides having a general view on microscopic traffic flow simulation results, we propose one option for experiencing these simulations from a first-person perspective visualization as one interacting traffic participant on a non-moving physical bicycle. We introduce a procedure for implementing a bicycle simulator for testing various scenarios in three-dimensional environments. By including individual realtime bicycle movements of test subjects into ongoing traffic simulations, we are able to derive individual behavioral strategies to cope with the modelled transportation infrastructure and with simulated vehicle drivers, bicyclists and pedestrians from the point of view of an urban bicyclist. We aim to introduce a novel technique for (1) analyzing present problems of traffic and built infrastructural elements, and, (2) inspecting planned scenarios with variations in traffic compositions (participants and modes) and built infrastructure (inclusion of new design elements). One first test scenario is implemented for gaining first insights on the usefulness of the presented device.
International Journal of Advanced Research (IJAR), 2019
Microscopic simulation models have been widely used in both transportation operations and management analysis because simulation is safer, less expensive and faster than field implementation and testing. The usefulness of these models in making design and traffic control decisions will mainly depend on their accuracy and reliability. This paper describes the detail procedure for the calibration and validation of a microscopic model of highly congested intersection Mirpur-10 in Dhaka. Legs of this intersection is composed of both motorized vehicles (Bus, Passenger car etc.) and non-motorized vehicles (Rickshaws, Bicycles). Most cases, drivers are rarely concern about the lane based traffic operation. Addressing this phenomena, a micro-simulation VISSIM model with modified driving behavior parameters helps to create a virtual environment representing the traffic scenario, optimize the problems and visualize the outputs that is important to face the challenges of transportation system at present and future.
Analysis of the influence of car-following input parameters on the modelled travelling time
Tehnicki Vjesnik
The calibration process is a basic condition of traffic model application in local conditions. The choice of input parameters, which are used in calibration process, influences the success of the calibration process itself; therefore the goal is to choose parameters with a larger influence on the modelling process. This paper offers a detailed analysis of car-following input parameters and their influence on the modelled travelling time. The experimental basis was a one-lane roundabout, and the tool used for traffic simulation was the VISSIM microsimulation traffic model. The results show that the car-following input parameters should be a part of the set of input parameters which will enter the process of calibration. The examined car-following input parameters affect the capacity of intersections and results show that it is necessary to revise the range of input values of one of the observed car-following input parameters.
2016
Researchers have been questioning if traffic microsimulation tools can be used for road safety evaluations. This study examines the use traffic microsimulation to predict conflicts between right-turning vehicles and through cyclists at signalized intersections. Moreover, this study evaluates if calibrating these models to describe the driving behavior at signalized intersection significantly improves the conflicts prediction. It was found that VISSIM has the potential to predict traffic conflicts of interest. In particular, a moderate correlation was found between real conflicts and simulated conflicts of the default models (? = 0.525). A strong correlation was found between real conflicts and calibrated models’ simulated conflicts (? = 0.618). However, a one-way ANOVA test indicated that travel time calibration did not significantly affect VISSIM’s conflicts prediction accuracy. It was also found that VISSIM’s prediction accuracy is expected to decrease as either the cyclists’ volu...
Calibration and Validation of Microscopic Traffic Flow Models
Transportation Research Record, 2004
Microscopic simulation models are becoming increasingly important tools in modeling transport systems. There is a large number of models used in many countries. The most difficult stage in the development and use of such models is the calibration and validation of the microscopic sub-models such as the car following and gap acceptance models. This difficulty is due to the lack of suitable methods for adapting models to empirical data. The aim of this paper is to present recent progress in calibrating a number of microscopic traffic flow models. Ten very different models have been tested using data collected via DGPS-equipped cars (Differential Global Positioning System) on a test track in Japan. To calibrate the models, the data of the leading car are fed into the model under consideration and the model is used to compute the headway time series of the following car. The deviations between the measured and the simulated headways are then used to calibrate and validate the models. The calibration results agree with earlier studies as there are errors of 12 % to 17% for all models and no model can be denoted to be the best. The differences between individual drivers are larger than the differences between different models. The validation process gives acceptable errors from 17 % to 22%. But for special data sets with validation errors up to 60% the calibration process has reached what is known as "overfitting": because of the adaptation to a particular situation, the models are not capable of generalizing to other situations.
Investigating and calibrating the dynamics of vehicles in traffic micro-simulations models
The accuracy of the micro-simulation modelʼs generated vehicle activity data used in the emissions modelling depends on how the dynamic behaviours of vehicles are being represented in the model. The dynamic behaviour of every single vehicle is constantly modelled during the simulation phase in accordance with different vehicle internal behaviour models. It is therefore imperative that the model reproduces the same variability of these behaviours in the real-world. This research paper investigated two main approaches in studying how car dynamics are represented in AIMSUN traffic micro-simulation model. The first approach was to use field trajectories data in the calibration of car dynamics parameters of the car-following internal behavioural model in AIMSUN, the second approach was to compare the simulated vehicles activity modelsʼ outputs with field vehicles activity data obtained from an Instrumented Vehicle (IV) driving along the study route. The field-obtained vehicle trajectories contained second-by-second speeds and acceleration data, which have been utilised in the evaluation of the AIMSUN model performance at both macro and micro levels. The findings showed that the calibration of vehicle dynamics in car-following models has reduced the values of accelerations and decelerations in the simulations. However, this did not influence the vehicle trajectories behaviour that continued to show sharp accelerations and decelerations, which are not representative of the real-world behaviours. The research showed that the use of IV real-world data to evaluate the car-following internal behaviour model provided an effective and computationally efficient validation methodology, which offered a further level of accuracy to the available standard validation procedures.
Researchers have been questioning if traffic microsimulation tools can be used for road safety evaluations. This thesis examines if these tools have the potential to predict conflicts between right-turning vehicles and through cyclists at signalized intersections. Moreover, this thesis evaluates if calibrating these models to describe the driving behaviour at signalized intersections significantly improves the conflicts' prediction. It was found that VISSIM has the potential to predict traffic conflicts of interest. In particular, a moderate correlation was found between real conflicts and simulated conflicts of the default models (= 0.525). Calibrating the model for travel time improved the correlation between real conflicts and simulated conflicts (= 0.618). However, a one-way ANOVA test indicated that the improvement caused by travel time calibration was not significant. It was also found that VISSIM's prediction accuracy is expected to decrease as either the cyclists' volume or the product of cyclists' volume and right-turning vehicles' volume increase.
Evaluation of Different Vehicle Following Models under Mixed Traffic Conditions
Car-following models replicate the behavior of a driver following another vehicle. These models are widely used in the development of traffic simulation models. Only fewer studies have been conducted to compare the performance of different car following models under mixed traffic conditions. The present study focuses on the evaluation of different car following models under mixed traffic conditions. Specifically, the following four cars following models were selected: 1. Gipps Model, 2. Intelligent Driver Model (IDM), 3. Krauss Model and 4. Das and Asundi Model. These models were implemented in a microscopic traffic simulation model for a mid block section. Each of these models is then calibrated for three states: non-steady state with constant parameters across classes, steady state parameter and non-steady state with classwise parameters. Then the models are evaluated using the performance measures such as error in hourly stream speeds and classwise speeds, critical parameters and Mean Absolute Percentage Error (MAPE) for speed and density values, obtained at one minute intervals.