In its narrowest sense, Route Guidance is the set o

1
Issues and Challenges in Route Guidance Mr.
Tremaine Rawls, Mr. Timothy Hulitt, Dr. Fatma
MiliNorfolk State University, Jackson State
University, Oakland University
About Route Guidance
Reactive
Experimental Comparison
Challenges
In its narrowest sense, Route Guidance is the set
of directions given to drivers to help them reach
their desired destination. The Department of
Transportation (DOT) defines Route Guidance in a
much broadly manner as a driver decision aid
that uses knowledge about a traffic network to
provide advice that facilitates travel between an
origin and a destination. Based on 2005 data
from research at Texas Transportation Institute
(TTI), Overall, traffic congestion costs the U.S.
economy 78 billion a year, wasting 2.9 billion
gallons of fuel and robbing commuters of 4.2
billion hours, the study found.

• The Farver algorithm is reactive and assumes that
  a driver already has an optimal route constructed
  using historical data.
• The algorithm receives regular updates about the
  state of the roads, and reacts to evidence that
  unexpected events made the selected route
  sub-optimal.
• The idea behind Farver to redirect drivers in
  response to unexpected slow downs without
  over-reacting.
• It uses a splitting method to spread out
  vehicles.

• TRANSIMS
• The Transportation Analysis Simulation System was
  developed at the Los Alamos National Laboratory.
• TRANSIMS consists of different modules such as a
  population synthesizer, activity generator, route
  planner and micro simulator. Also, each module
  correlates with each other.

• Scalability
• Criteria used to select the path
• Time-variation in the data ---regular traffic
  patterns
• Accounting for non-regular traffic disturbances
• Difficulty in evaluating solutions
• Consistency
• Infrastructure required

Los Angeles I-405 I-605 Interchange. This
intersection on the San Diego Freeway cost 18
million hours of delay
Conclusions and Future Work

• Have a clear understanding of the key issues in
  route guidance.
• Gain knowledge of the solutions proposed.
• Elicit the relationship between the different
  solutions.
• Develop criteria for comparing the different
  algorithms.
• Comparison through analysis and simulation.

Background
Predictive

• Route Guidance can be thought of as the problem
  of finding the shortest path in a directed graph.
  
• Given a geographical area of interest, the roads
  and their intersections are represented by edges
  and vertices.
• The vertices are points of intersections where
  drivers make decisions the edges are road
  segments between two intersection points.

Predictive algorithms construct the shortest path
based upon real-time information. These
algorithms, unlike the DOT, do not utilize
historical data to obtain the optimal route.
Predictive algorithms collect regular updates
about the state of the roads and if an event
takes place that might hinder traffic, such as a
congestion or accident, the algorithm changes the
path based on real-time communication.
Time Dependency

• The Decreasing Order of Time (DOT) Algorithm is
  time dependent, and the assumptions are
• Availability of historical data reflecting
  regular daily traffic patterns
• There is constant travel time starting from the
  last time interval
• The concept of First-In-First-Out (FIFO), that a
  trip starting at a later time will arrive later.

Partial Reference List

• DoT, Federal Highway Administration, Development
  of Human Factors Guidelines for Advanced Traveler
  Information Systems and Commercial Vehicle
  Operations Publication No FHWA-RD-96-145.
  http//www.fhwa.dot.gov/tfhrc/safety/pubs/96145/in
  dex.html
• Jing-Chao Chen Dijkstras Shortest Path
  Algorithm, Journal of Formalized Mathematics,
  Vol 15, 2003. http//mark uncs.shinshu-u.ac.jp/mir
  ror/mizar/JFM/pdf/graphsp.pdf
• Jennifer Farver and Ismail Chabini, A
  Vehicle-Centric Logic for Decentralized and
  Hybrid Route Guidance, Massachusetts Institute
  of Technology
• http//ieeexplore.ieee.org/xpl/freeabs_all.jsp?ar
  number1244396) www.tfhrc.gov/pubrds/marapr00/tran
  sims.htm
• http//www.nmsu.edu/confserv/images/AreaMap2.gif
  ltNew Mexico State Universitygt
• Fatma Mili and Bill Herbert State of the Art
  vs. State of Practice
• http//eprints.whiterose.ac.uk/224
• Kristina Höök, Route Guidance Issues
• Ismail Chabini, Algorithms High Performance
  Computing for Dynamic Shortest Paths and
  Analytical Dynamic Traffic Assignment Models,
  Massachusetts Institute of Technology
• Intelligent Transportation Systems of America
  (ITS) http//www.itsa.org/
• Texas Transportation Institute (TTI)
• Forbes Informative Website ltwww.forbes.comgt

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