Investigating Acceptable Level of Travel Demand before Capacity Enhancement for Signalized Urban Road Networks

Year 2020, Volume: 31 Issue: 2, 9897 - 9917, 01.03.2020

Özgür Başkan , Hüseyin Ceylan , Cenk Ozan

https://doi.org/10.18400/tekderg.464260

Cited By: 4

Abstract

Increasing travel demand in urban areas triggers traffic congestion and increases delay in road networks. In this context, local authorities that are responsible for traffic operations seek to strike a balance between traffic volume and capacity to reduce total travel time on road networks. Since intersections are the most critical components of road networks in terms of safety and operational issues, adjusting intersection signal timings becomes an effective method for authorities. When this tool remains incapable of overcoming traffic congestions, authorities take expensive measures such as increasing link capacities, lane additions or applying grade-separated junctions. However, it may be more useful to handle road networks as a whole by investigating the effects of optimizing signal timings of all intersections in the network. Therefore, it would be useful to investigate the right time for physical improvements on the road network to avoid premature investments considering limited resources of local authorities. In this study, effects of increasing travel demand on Total Travel Cost (TTC) is investigated by developing a bi-level programming model, called TRAvel COst Minimizer (TRACOM), in which the upper level minimizes the TTC subject to the stochastic user equilibrium link flows determined at the lower level. The TRACOM is applied to Allsop and Charlesworths’ network for different origin-destination demand matrix multipliers. Results revealed that TTC values showed an approximate linear increase while the travel demand is increased up to 16%. After this value, TTC showed a sudden spike although the travel demand was linearly increased that means optimizing signal timings must be supported by applying psychical improvements.

Keywords

Total travel cost, urban road networks, signal timing optimization, investment decisions

Investigating Acceptable Level of Travel Demand Before Capacity Enhancement for Signalized Urban Road Networks

Abstract

Increasing travel demand in urban areas triggers
traffic congestion and increases delay in road networks. In this context, local
authorities that are responsible for traffic operations seek to strike a
balance between traffic volume and capacity to reduce total travel time on road
networks. Since intersections are the most critical components of road networks
in terms of safety and operational issues, adjusting intersection signal
timings becomes an effective method for authorities. When this tool remains
incapable of overcoming traffic congestions, authorities take expensive
measures such as increasing link capacities, lane additions or applying
grade-separated junctions. However, it may be more useful to handle road
networks as a whole by investigating the effects of optimizing signal timings
of all intersections in the network. Therefore, it would be useful to
investigate the right time for physical improvements on the road network to
avoid premature investments considering limited resources of local authorities.
In this study, effects of increasing travel demand on Total Travel Cost (TTC)
is investigated by developing a bi-level programming model, called TRAvel COst
Minimizer (TRACOM), in which the upper level minimizes the TTC subject to the
stochastic user equilibrium link flows determined at the lower level. The
TRACOM is applied to Allsop and Charlesworths’ network for different
origin-destination demand matrix multipliers. Results revealed that TTC values
showed an approximate linear increase while the travel demand is increased up
to 16%. After this value, TTC showed a sudden spike although the travel demand
was linearly increased that means optimizing signal timings must be supported
by applying psychical improvements.

Keywords

Total travel cost, urban road networks, signal timing optimization, investment decisions

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