Abstract:
The simulation-based prediction of traffic conditions based on current and
past traffic observations is an important component in the intelligent transportation
system (ITS) applications. Infrastructure, in the form of toll plazas, is inevitable for
collection of revenue after the development of National Highways in India. Intelligent transportation systems utilize the advanced technologies and employ them in the
field of transportation. The implementation of advanced traffic management systems
(ATMS) at toll plazas will improve the toll plaza operations. A simulation model can
help in the evaluation and optimization of toll operations of existing toll plazas as
well as in the planning and design of similar systems. With this motivation, a lanewise classified vehicle count prediction algorithm, which can simulate traffic conditions at any time interval, has been developed in this study based on Monte Carlo
simulation (MCS). Vehicle arrival was modeled by assuming Poisson’s distribution, followed by classification. Lane selection was done using the probability-based
discrete random number generation. Radio-frequency identification (RFID)-based
electronic toll collection (ETC) system gives timely varying traffic counts observed
at the toll plaza, which has been utilized to develop and validate the simulation model.
The flexibility with respect to the probabilities of the proposed algorithm makes it
more applicable in the area of ITS. The observed vehicle count for each lane has
been compared with the simulated values. The results of statistical tests show that
there is no significant difference between actual and simulated traffic for each lane.
