Fuzzy cellular model for on-line traffic simulation

This paper introduces a fuzzy cellular model of road traffic that was intended for on-line applications in traffic control. The presented model uses fuzzy sets theory to deal with uncertainty of both input data and simulation results. Vehicles are modelled individually, thus various classes of them can be taken into consideration. In the proposed approach, all parameters of vehicles are described by means of fuzzy numbers. The model was implemented in a simulation of vehicles queue discharge process. Changes of the queue length were analysed in this experiment and compared to the results of NaSch cellular automata model.Comment: The original publication is available at http://www.springerlink.co

Phase Synchronization in Railway Timetables

Timetable construction belongs to the most important optimization problems in public transport. Finding optimal or near-optimal timetables under the subsidiary conditions of minimizing travel times and other criteria is a targeted contribution to the functioning of public transport. In addition to efficiency (given, e.g., by minimal average travel times), a significant feature of a timetable is its robustness against delay propagation. Here we study the balance of efficiency and robustness in long-distance railway timetables (in particular the current long-distance railway timetable in Germany) from the perspective of synchronization, exploiting the fact that a major part of the trains run nearly periodically. We find that synchronization is highest at intermediate-sized stations. We argue that this synchronization perspective opens a new avenue towards an understanding of railway timetables by representing them as spatio-temporal phase patterns. Robustness and efficiency can then be viewed as properties of this phase pattern

Calibrating Car-Following Models using Trajectory Data: Methodological Study

The car-following behavior of individual drivers in real city traffic is studied on the basis of (publicly available) trajectory datasets recorded by a vehicle equipped with an radar sensor. By means of a nonlinear optimization procedure based on a genetic algorithm, we calibrate the Intelligent Driver Model and the Velocity Difference Model by minimizing the deviations between the observed driving dynamics and the simulated trajectory when following the same leading vehicle. The reliability and robustness of the nonlinear fits are assessed by applying different optimization criteria, i.e., different measures for the deviations between two trajectories. The obtained errors are in the range between~11% and~29% which is consistent with typical error ranges obtained in previous studies. In addition, we found that the calibrated parameter values of the Velocity Difference Model strongly depend on the optimization criterion, while the Intelligent Driver Model is more robust in this respect. By applying an explicit delay to the model input, we investigated the influence of a reaction time. Remarkably, we found a negligible influence of the reaction time indicating that drivers compensate for their reaction time by anticipation. Furthermore, the parameter sets calibrated to a certain trajectory are applied to the other trajectories allowing for model validation. The results indicate that ``intra-driver variability'' rather than ``inter-driver variability'' accounts for a large part of the calibration errors. The results are used to suggest some criteria towards a benchmarking of car-following models

An empirical test for cellular automaton models of traffic flow

Based on a detailed microscopic test scenario motivated by recent empirical studies of single-vehicle data, several cellular automaton models for traffic flow are compared. We find three levels of agreement with the empirical data: 1) models that do not reproduce even qualitatively the most important empirical observations, 2) models that are on a macroscopic level in reasonable agreement with the empirics, and 3) models that reproduce the empirical data on a microscopic level as well. Our results are not only relevant for applications, but also shed new light on the relevant interactions in traffic flow.Comment: 28 pages, 36 figures, accepted for publication in PR

Optimised Traffic Flow at a Single Intersection: Traffic Responsive signalisation

We propose a stochastic model for the intersection of two urban streets. The vehicular traffic at the intersection is controlled by a set of traffic lights which can be operated subject to fix-time as well as traffic adaptive schemes. Vehicular dynamics is simulated within the framework of the probabilistic cellular automata and the delay experienced by the traffic at each individual street is evaluated for specified time intervals. Minimising the total delay of both streets gives rise to the optimum signalisation of traffic lights. We propose some traffic responsive signalisation algorithms which are based on the concept of cut-off queue length and cut-off density.Comment: 10 pages, 11 eps figs, to appear in J. Phys.

Effect of different rubber materials on husking dynamics of paddy rice

The conventional way to husk rice is to pass it between two rubber rollers that are rotating with a surface speed differential. The resulting normal pressure and shear stress causes the husk to be peeled away from the kernel. The process is suited to high-rice flow rates, but is energy intensive and can result in considerable wear to the surfaces of the rollers. The operating parameters for machines of this design are usually determined and set empirically. In this article, some experiments and calculations had been carried out in order to explore the mechanisms involved in husking rice grains using this method. A simple sliding friction rig with load cell and high-speed camera was used to observe the mechanisms that occur during husking. The husking performance of different rubbers was compared for changes in the applied normal load. It was found that grains rotate between the rubber counterfaces on initial motion before being husked. In addition, harder rubbers were found to husk a higher proportion of entrained grains at lower applied normal load. By measuring the coefficient of friction between rice and rubber samples, the shear force required to husk a given percentage of grains could be calculated and was shown to be constant regardless of rubber type. Based on the mechanism seen in the high-speed video, it was evident that there was a limiting shear stress that was the governing factor over the husked ratio

Effect of different rubber materials on husking dynamics of paddy rice

The conventional way to husk rice is to pass it between two rubber rollers that are rotating with a surface speed differential. The resulting normal pressure and shear stress causes the husk to be peeled away from the kernel. The process is suited to high-rice flow rates, but is energy intensive and can result in considerable wear to the surfaces of the rollers. The operating parameters for machines of this design are usually determined and set empirically. In this article, some experiments and calculations had been carried out in order to explore the mechanisms involved in husking rice grains using this method. A simple sliding friction rig with load cell and high-speed camera was used to observe the mechanisms that occur during husking. The husking performance of different rubbers was compared for changes in the applied normal load. It was found that grains rotate between the rubber counterfaces on initial motion before being husked. In addition, harder rubbers were found to husk a higher proportion of entrained grains at lower applied normal load. By measuring the coefficient of friction between rice and rubber samples, the shear force required to husk a given percentage of grains could be calculated and was shown to be constant regardless of rubber type. Based on the mechanism seen in the high-speed video, it was evident that there was a limiting shear stress that was the governing factor over the husked ratio