DOI: 10.5281/zenodo.2275646
title: Simulated probabilistic speed profiles for selected routes in Prague

description: <strong>Simulated probabilistic speed profiles for 1608 road segments in Prague, Czech Republic</strong>

This dataset contains simulated speed profiles for selected road segments in Prague, Czech Republic. The profiles describe changes in average driving speed on a particular segment over a given time interval discretised to a time steps of a fixed size. Number of the time steps is stored in the "<strong>IntervalsPerSegment</strong>" attribute of the root group. Size of a single time step in <strong>seconds</strong> is stored in the "<strong>TimeStep</strong>" attribute. Total length of time interval covered by the profiles is computed as IntervalsPerSegment x TimeStep.

The file contains two types of profiles.

The first type are plain speed profiles which provide just average driving speed assigned to each time step - dataset "<strong>speed_profile</strong>".

The second type are probabilistic speed profiles which contain discrete probability distribution of the driving speed for particular segment and given time step. The probability distribution in this case has 4 support points, corresponding to 4 Levels of Service (LoS). The first LoS corresponds to an optimal (freeflow) speed and the last one describes the worst case (accident, heavy traffic, etc.). The discrete distribution is stored in the "<strong>prob_profile</strong>" dataset with the following format:

Column 1 - Speed corresponding to the first LoS in <strong>km/h</strong><br>
Column 2 - Probability assigned to this speed (float between 0.0 - 1.0)<br>
...

Sampling from this distribution is implemented by the Vose's alias method. We provide precomputed tables for implementing this method for each segment in datasets "<strong>alias</strong>" and "<strong>probability</strong>".

Further applications of the probabilistic speed profiles are available in:

Golasowski, M., Tomis, R., Martinovič, J., Slaninová, K., & Rapant, L. (2016, September). Performance evaluation of probabilistic time-dependent travel time computation. In <em>IFIP International Conference on Computer Information Systems and Industrial Management</em> (pp. 377-388). Springer, Cham.

<strong>File structure</strong>

Profiles for the individual segments are stored as subgroups in the root group. Names of the subgroups correspond to unique road segments identifiers corresponding to a subset of road network of Prague.

The identifiers correspond to a routing index published here: 10.5281/zenodo.2275556

<pre><code class="language-javascript">HDF5 "profiles_8runs.h5" {
GROUP "/" {
   ATTRIBUTE "IntervalsPerSegment" {
      DATATYPE  H5T_STD_I32LE
      DATASPACE  SCALAR
      DATA {
      (0): 59
      }
   }
   ATTRIBUTE "NumberOfProfiles" {
      DATATYPE  H5T_STD_I32LE
      DATASPACE  SCALAR
      DATA {
      (0): 4
      }
   }
   ATTRIBUTE "TimeStep" {
      DATATYPE  H5T_STD_I32LE
      DATASPACE  SCALAR
      DATA {
      (0): 60
      }
   }
   ATTRIBUTE "Version" {
      DATATYPE  H5T_STRING {
         STRSIZE 4;
         STRPAD H5T_STR_NULLTERM;
         CSET H5T_CSET_ASCII;
         CTYPE H5T_C_S1;
      }
      DATASPACE  SCALAR
      DATA {
      (0): "1.0"
      }
   }
   GROUP "66678115" {
      DATASET "alias" {
         DATATYPE  H5T_IEEE_F64LE
         DATASPACE  SIMPLE { ( 59, 4 ) / ( 59, 4 ) }
      }
      DATASET "prob_profile" {
         DATATYPE  H5T_IEEE_F64LE
         DATASPACE  SIMPLE { ( 59, 8 ) / ( 59, 8 ) }
      }
      DATASET "probability" {
         DATATYPE  H5T_IEEE_F64LE
         DATASPACE  SIMPLE { ( 59, 4 ) / ( 59, 4 ) }
      }
      DATASET "speed_profile" {
         DATATYPE  H5T_IEEE_F64LE
         DATASPACE  SIMPLE { ( 1, 59 ) / ( 1, 59 ) }
      }
   }</code></pre>
description: {"references": ["Golasowski, M., Tomis, R., Martinovi\u010d, J., Slaninov\u00e1, K., & Rapant, L. (2016, September). Performance evaluation of probabilistic time-dependent travel time computation. In IFIP International Conference on Computer Information Systems and Industrial Management (pp. 377-388). Springer, Cham.", "Silvano, C., Agosta, G., Bartolini, A., Beccari, A. R., Benini, L., Besnard, L., ... & Cherubin, S. (2018, August). ANTAREX: A DSL-based Approach to Adaptively Optimizing and Enforcing Extra-Functional Properties in High Performance Computing. In 2018 21st Euromicro Conference on Digital System Design (DSD) (pp. 600-607). IEEE.", "Tomis, R., Rapant, L., Martinovi\u010d, J., Slaninov\u00e1, K., Vondr\u00e1k, I.: Probabilistic time-dependent travel time computation using Monte Carlo simulation, (2016) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9611, pp. 161-170"]}