Industrial cluster formation in European regions. U.S. cluster templates and Austrian evidence.

The paper will be organized in the following manner. We first provide a concise review of how industrial trade clusters were developed from available I/O coefficients (see box), including how regional industrial data may be embedded within their "templates". Second, we will review the steps taken, using available industrial concordances, that permit regional data from other advanced national industrial systems to be embedded within these templates. Third, we will illustrate the results of applying the U.S. template for the motor vehicle industrial trade cluster to regions in both Austria and North Carolina over 5-10 year time periods. Finally, we will offer some speculative observations about what the results may indicate about regional cluster development in these two regions. (authors' abstract, ed. M.Putz)Series: SRE - Discussion Paper

Determining the Distribution Route Using the Clustered Generalised Vehicle Routing Problem Model and Dijkstra's Algorithm

Distributions and logistics processes are the essential element activities integral to the industrial sector, one of them is the distribution of LPG 3 kg. The most common issue that occurs in the distribution process is determining the vehicle routes which are often called Vehicle Routing Problems. PT Amalia Yusri is one of the distributor agents of LPG 3 kg in Banda Aceh and also had the same issue to determine the distribution routes. The Clustered Generalized Vehicle Routing Problem model is used to determine the optimal distribution route on the vertex destinations that have been grouped. Based on the number of requests and vehicle capacity, the grouping and formation of clusters are divided into two days and split into five groups. The first day consists of group 1 and group 2; while on the second day are group 3, group 4, and group 5. The method that is used to optimise distribution distance is Dijkstra Algorithm. From the application of the CGVRP model and Dijkstra Algorithm obtained a total distance for group 1 is 25.582 m, group 2 is 24.650 m, group 3 is 39.350 m, group 4 is 27.500 m, and group 5 is 38.500 m

Probabilistic Description of Traffic Breakdowns

We analyze the characteristic features of traffic breakdown. To describe this phenomenon we apply to the probabilistic model regarding the jam emergence as the formation of a large car cluster on highway. In these terms the breakdown occurs through the formation of a certain critical nucleus in the metastable vehicle flow, which enables us to confine ourselves to one cluster model. We assume that, first, the growth of the car cluster is governed by attachment of cars to the cluster whose rate is mainly determined by the mean headway distance between the car in the vehicle flow and, may be, also by the headway distance in the cluster. Second, the cluster dissolution is determined by the car escape from the cluster whose rate depends on the cluster size directly. The latter is justified using the available experimental data for the correlation properties of the synchronized mode. We write the appropriate master equation converted then into the Fokker-Plank equation for the cluster distribution function and analyze the formation of the critical car cluster due to the climb over a certain potential barrier. The further cluster growth irreversibly gives rise to the jam formation. Numerical estimates of the obtained characteristics and the experimental data of the traffic breakdown are compared. In particular, we draw a conclusion that the characteristic intrinsic time scale of the breakdown phenomenon should be about one minute and explain the case why the traffic volume interval inside which traffic breakdown is observed is sufficiently wide.Comment: RevTeX 4, 14 pages, 10 figure

Anisotropic effect on two-dimensional cellular automaton traffic flow with periodic and open boundaries

By the use of computer simulations we investigate, in the cellular automaton of two-dimensional traffic flow, the anisotropic effect of the probabilities of the change of the move directions of cars, from up to right ($p_{ur}$) and from right to up ($p_{ru}$), on the dynamical jamming transition and velocities under the periodic boundary conditions in one hand and the phase diagram under the open boundary conditions in the other hand. However, in the former case, the first order jamming transition disappears when the cars alter their directions of move ($p_{ur}\neq 0$ and/or $p_{ru}\neq 0$). In the open boundary conditions, it is found that the first order line transition between jamming and moving phases is curved. Hence, by increasing the anisotropy, the moving phase region expand as well as the contraction of the jamming phase one. Moreover, in the isotropic case, and when each car changes its direction of move every time steps ($p_{ru}=p_{ur}=1$), the transition from the jamming phase (or moving phase) to the maximal current one is of first order. Furthermore, the density profile decays, in the maximal current phase, with an exponent $\gamma \approx {1/4}$.}Comment: 13 pages, 22 figure

A criterion for condensation in kinetically constrained one-dimensional transport models

We study condensation in one-dimensional transport models with a kinetic constraint. The kinetic constraint results in clustering of immobile vehicles; these clusters can grow to macroscopic condensates, indicating the onset of dynamic phase separation between free flowing and arrested traffic. We investigate analytically the conditions under which this occurs, and derive a necessary and sufficient criterion for phase separation. This criterion is applied to the well-known Nagel-Schreckenberg model of traffic flow to analytically investigate the existence of dynamic condensates. We find that true condensates occur only when acceleration out of jammed traffic happens in a single time step, in the limit of strong overbraking. Our predictions are further verified with simulation results on the growth of arrested clusters. These results provide analytic understanding of dynamic arrest and dynamic phase separation in one-dimensional traffic and transport models

Order parameter model for unstable multilane traffic flow

We discuss a phenomenological approach to the description of unstable vehicle motion on multilane highways that explains in a simple way the observed sequence of the phase transitions "free flow -> synchronized motion -> jam" as well as the hysteresis in the transition "free flow synchronized motion". We introduce a new variable called order parameter that accounts for possible correlations in the vehicle motion at different lanes. So, it is principally due to the "many-body" effects in the car interaction, which enables us to regard it as an additional independent state variable of traffic flow. Basing on the latest experimental data (cond-mat/9905216) we assume that these correlations are due to a small group of "fast" drivers. Taking into account the general properties of the driver behavior we write the governing equation for the order parameter. In this context we analyze the instability of homogeneous traffic flow manifesting itself in both of the mentioned above phase transitions where, in addition, the transition "synchronized motion -> jam" also exhibits a similar hysteresis. Besides, the jam is characterized by the vehicle flows at different lanes being independent of one another. We specify a certain simplified model in order to study the general features of the car cluster self-formation under the phase transition "free flow synchronized motion". In particular, we show that the main local parameters of the developed cluster are determined by the state characteristics of vehicle motion only.Comment: REVTeX 3.1, 10 pages with 10 PostScript figure