Page 35 - Trafficinfratech Magazine April 2023 Digital Edition

PUBLIC TRANSPORT

Improving public transport

using data science

Providing better public transport for
the travelling public requires a good
understanding of travel demand so that
buses can cater to the demand. However,
bus networks in most cities have evolved over
the years and many routes are operated due
to legacy reasons, writes Dr Rajesh Krishnan,
CEO, ITS Planners & Engineers

arious ITS systems have Area Traffic Control mixed traffic conditions without
been deployed in our System lane discipline.
Smart Cities providing Most of the deployed ATCS
a number of datasets ATCS are adaptive traffic control
of raw, processed or systems where the signal timings systems also have the capability
Vmodelled data. Cities are adjusted automatically in to be linked with micro- simulation
software for transport modelling.
could make use of these data response to changing traffic flows. Cities or ATCS vendors develop
sets and systems to estimate Traffic detectors are installed as a base models representing typical
part of ATCS systems in order to
travel demand and improve public traffic conditions during different
detect the traffic flows. Most Smart
transport services in areas where time periods of the day and days
Cities have chosen to use modern
demand exists. Some of the traffic detectors for ATCS, such of the week and link these models
potential opportunities to do this as video based detectors or multi- with ATCS during deployment.
The base models are automatically
better by making use of systems target tracking Doppler traffic updated by the ATCS system using
that are typically deployed in Smart radars, that provide reasonably observed traffic count data. This
Cities are given below. accurate vehicle count data under essentially involves updating the
travel demand patterns in the base
model and estimating traffic flow
patterns for the updated demand.
The difference between modelled
and measured traffic counts is
reduced in the process resulting in
more accurate demand than the
base model. This means that where
base models are linked with the
ATCS, the system can be used to
obtain transport demand over the
course of a typical day. Alternatively,
the count data can be downloaded
and combined with the base
model to estimate the demand

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