'<LI><A HREF="'.$dir.'#85">Schematic representation of the mapping generated by the
      feedback iterations.  Traffic evolution as a function of
      time-of-day can be represented as a trajectory in a high
      dimensional phase space.  Iterations can be seen as mappings of
      this trajectory into a new one.
</A>
<LI><A HREF="'.$dir.'#113">Result of day-to-day learning in a test example.  The scenario
      is a test case, using a network of Switzerland, where 50000
      vehicles from all over the system start between 6am and 7am and
      simultaneously attempt to reach a destination within the circle.
      Green shows free flowing traffic; red shows traffic jams.  LEFT:
      Situation at 9:00am in the zeroth iteration. RIGHT: Situation
      at 9:00am in the 49th iteration.  Clearly, traffic has spread
      out and the system is using more different routes in the 49th
      iteration. 
</A>
<LI><A HREF="'.$dir.'#216">Different relaxation paths in day-to-day replanning.  The plot shows
  the sum of all travel times as a function of the iteration for
  different relaxation methods.  All methods seem to lead to the same
  relaxed state.  From&nbsp;[</A><A
 HREF="node8.html#Rickert:thesis">13</A>].

<LI><A HREF="'.$dir.'#217">Flatness of the strategic landscape for route choice behavior.
      LEFT: 
Ratio of the travel times of the fastest path to the
      <IMG
 WIDTH="13" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
 SRC="img1.png"
 ALT="$k$">-fastest path as a function of <IMG
 WIDTH="13" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
 SRC="img1.png"
 ALT="$k$">.  Clearly, this measure
      decreases more quickly in an uncongested than in a relaxed
      congested network, meaning that in a relaxed congested network,
      there are many more alternatives at about the same system
      performance.  
RIGHT: Under relaxed congested conditions, the additional options are
truly dissimilar.  Shows is the similarity to the best option as
function of the additional travel time.
- From&nbsp;[</A><A
 HREF="node8.html#Kelly:Nagel:ksp">12</A>].

<LI><A HREF="'.$dir.'#181">Individualization of plans and interaction with router
      artifacts.  LEFT: All vehicles are re-planned according to the
      same information; vehicles do not use the freeway (arrrows)
      although the freeway is empty.  As explained in the text, this
      happens because the router makes erroneous predictions about
      where a vehicle will be at what time.
RIGHT: Vehicles treat routing results as additional options,
      that is, they can revert to other (previously used) options.  -
      The arrows point to the freeway.  The time is 7pm.
</A>
<LI><A HREF="'.$dir.'#218">Predictability as function of within-day rerouting capabilities.
      The result was obtained in the context of a simulation study of
      route guidance systems.  The x-axis shows the fraction of
      equipped vehicles; the y-axis shows average travel time of all
      vehicles in the simulation. For each value of market saturation,
      five different simulations with different random seeds were run.
      When market saturation increases from zero to 40%, system
      performance improves.  Beyond that, the average system
      performance, and, more importantly, also the predictability
      (variance) of the system performance degrade.
      From&nbsp;[</A><A
 HREF="node8.html#Rickert:thesis">13</A>]. 
'
