travel times through smart algorithms may not only optimize the travel times
but also reduce carbon emissions, such as CO2, CO and Hydro-Carbons.
It can also result in reduced driver frustrations and can increase passenger
expectations of consistent travel times, which in turn points to benefits in
overall planning of day schedules. Fuel consumption savings are another benefit
from the same. However, attempts to elect the shortest path as an assumption of
quick travel times, often work counter to the very objective intended and come
with the risk of creating a “Braess Paradox” which is about congestion
resulting when several drivers attempt to elect the same shortest route. The
situation that arises has been referred to as the price of anarchy! We propose
algorithms that find multiple shortest paths between an origin and a
destination. It must be appreciated that these will not yield the exact number
of Kilometers travelled, but favourable weights in terms
of travel times so that a reasonable allowable time difference between the multiple
shortest paths is attained when the same Origin and Destinations are considered
and favourable responsive routes are
determined as variables of traffic levels and time of day. These routes are
selected on the paradigm of route balancing, re-routing algorithms and traffic
light intelligence all coming together to result in optimized consistent travel
times whose benefits are evenly spread to all motorist, unlike the Entropy
balanced k shortest paths (EBkSP) method which favours some motorists on the
basis of urgency. This paper proposes a Fully Balanced Multiple-Candidate
shortest path (FBMkP) by which we model in SUMO to overcome the computational
overhead of assigning priority differently to each travelling vehicle using
intelligence at intersections and other points on the vehicular network. The
FBMkP opens up traffic by fully balancing the whole network so as to benefit
every motorist. Whereas the EBkSP reserves some routes for cars on high
priority, our algorithm distributes the benefits of smart routing to all
vehicles on the network and serves the road side units such as induction loops
and detectors from having to remember the urgency of each vehicle. Instead,
detectors and induction loops simply have to poll the destination of the
vehicle and not any urgency factor. The minimal data being processed
significantly reduce computational times and the benefits all vehicles. The
multiple-candidate shortest paths selected on the basis of current traffic
status on each possible route increase the efficiency. Routes are fewer than
vehicles so possessing weights of routes is smarter than processing individual
vehicle weights. This is a multi-objective function project where improving one
factor such as travel times improves many more cost, social and environmental
Cite this paper
Chintu, S. , Anthony, R. , Roshanaei, M. and Ierotheou, C. (2014) Intelligent and Predictive Vehicular Networks. Intelligent Control and Automation
, 60-71. doi: 10.4236/ica.2014.52007
 Nagurney, A. (2009) Scientific American, The Braess Paradox and the Price of Anarchy “Detours by Design”. The Braess Paradox. http://annanagurney.blogspot.com/2009/01/scientific-american-braess-paradox-and.html
 Translation of the Braess 1968 Article from German to English Appears as the Article “On a Paradox of Traffic pLanning,” by Braess, D., Nagurney, A. and Wakolbinger, T., in the Journal Transportation Science, 39, 2005, 446-450.
 Sadek, A. and Basha, N. (2005) Self Learning Intelligent Agents for Dynamic Traffic Routing on Transportation Networks. In: Unifying Themes in Complex Systems, Springer, 503-511.
 Wolfson, O. and Xu, B. (2010) Spartial-Temporal Databases in Urban Transportation. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 33, 1-8.
 Jenelius, E. and Koutsopoulous, H.N. (2012) Impact of Sampling Protocol on Bias and Consistency in Travel Time Estimation on Probe Vehicle Data. http://home.abe.kth.se/~jenelius/JK_2013.pdf
 Lin, C.-H., Yu, J.-L., et al. (2009) Genetic Algorithm for Shortest Driving Time in Intelligent Transportation Systems. International Journal of Hybrid Technology, 2, 21-28.
 Shandiz, H.T., et al. (2009) Intelligent Transport System Based on Genetic Algorithms. World Applied Sciences Journal, 6, 908-913. http://www.idosi.org/wasj/wasj6(7)/7.pdf
 M. Gen and Lin, L. (2006) A New Approach for Shortest Path Routing Problem by Random Key-Based GA. Proceedings of Genetic and Evolutionary Computation, Washington, 8-12 July 2006, 1411-1412
 Abdullai, B. and Kattas, L. (2003) Reinforcement Learning: Introduction to Theory and Potential for Transport Applications. Canadian Journal of Civil Engineering, 30, 981-991. http://dx.doi.org/10.1139/l03-014
 Georgescu, L., Zeitler, D. and Standridge, C.R. (2012) Intelligent Transportation Systems Real Time Traffic Speed Prediction with Minimal Data. Grand Valley State University, Allendale.
 Pan, J., Khan, M.A., et al. (2009) Proactive Vehicular Re-routing Strategies for Congestion Avoidance. 2012 IEEE 8th International Conference on Distributed Computing in Sensor Systems (DCOSS), Hangzhou, 16-18 May 2012, 265272.
 Lawler, E.L. (1972) A Procedure of Computing the K Best Solutions to Discrete Optimization Problems and Its Application to the Shortest Path Problem. Management Science, 18, 401-405. http://dx.doi.org/10.1287/mnsc.18.7.401
 Krajzewicz, D., et al. (2013) SUMO-Simulation of Urban Mobility-User Documentation Publications, German Aerospace Centre, Germany. http://sumo-sim.org/userdoc/Publications.html
 Banks, J. (2002) Introduction to Transportation Engineering. McGraw-Hill, New York.
 Levingson, D. and Kumar, A. (1994) Integrating Feedback into the Transportation Planning Model: Structure and Application. Transportation Research Record, No. 1413, 70-77.
 Zhu, Z. and Yang, C. (2011) Visco Elastic Traffic Flow Model. Journal of Advanced Transformation, 47, 635-649. http://dx.doi.org/10.1002/atr.186
 Arnaout, G. and Bowling, S. (2011) Towards Reducing Traffic Congestion Using Cooperative Adaptive Cruise Control on a Freeway with a Ramp. Journal of industrial Engineering and Management, 4, 699-717. http://dx.doi.org/10.3926/Jiem.344
 Research and Innovative Technology Administration. ITS Strategic Research Plan, 2010-2014. Executive Summary. http://www.its.dot.gov/strat/pdf/ITStrategicresearch/Jan2010.pdf
 Schmied, M. (2012) Moving towards Emission Quantification in Urban Transport. Handbook on Emissions Factors in Road Transport (HBEFA), Beijing, 23 October 2012. http://www.tdm-beijing.org/files/news/Emission%20Quantification%20Workshop/Schmied_INFRAS_-_Development_of_Emission_Factors_in_Europe.pdf