Lane changing control to reduce traffic load effect on long-span bridges

Caprani, Colin C.; Enright, Bernard; Carey, Colm

doi:10.1201/b12352-434

Cited by 3 publications

(2 citation statements)

References 12 publications

(17 reference statements)

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“…The lesser percentage is adopted as a common truck percentage on a busy commercial route, while the greater percentage accounts for slow lanes of multi-lane carriageways, which typically have a greater truck percentage in the slow lane. Note also that such a high percentage is possible when the flow is low, typically in the early morning or at night [3,7,66,67]. Table 4 gives the dynamic capacity Q out for the mixed traffic conditions and the relevant heavyvehicle adjustment factors f hv for Q out , as well as the static capacity Q max (which can be attained only in uncongested traffic).…”

Section: Multi-class Simulationsmentioning

confidence: 99%

Micro-simulation of single-lane traffic to identify critical loading conditions for long-span bridges

O’Brien

Lipari

Caprani

2015

Engineering Structures

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Section: Multi-class Simulationsmentioning

confidence: 99%

Micro-simulation of single-lane traffic to identify critical loading conditions for long-span bridges

O’Brien

Lipari

Caprani

2015

Engineering Structures

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“…Further work has shown that the vehicle arrival process for congested traffic is different to free-flowing traffic. Car drivers tend to move to the fast lane during the breakdown from free-flowing to congested traffic, resulting in a higher percentage of truck platoons in congested traffic compared with free-flowing traffic (OBrien et al 2010, Caprani et al 2012a, Enright et al 2012. These truck platoons have a significant impact on traffic loading on long-span bridges (Enright et al 2012) and are hence important factors in the accurate calculation of traffic loading.…”

Section: )mentioning

confidence: 99%

A pseudo-microsimulation approach for modelling congested traffic loading on long-span bridges

Carey¹,

Caprani

Enright³

2017

Structure and Infrastructure Engineering

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This paper investigates congested traffic loading on long-span bridges through the use of traffic microsimulation. Six months of Weigh-In-Motion (WIM) free-flow traffic data (including cars) are used as input for the microsimulation of congested traffic. Key parameters that affect traffic loading are identified in the output of the microsimulation, and these parameters form the basis for a more computationally-efficient 'pseudo-microsimulation of congested traffic' (PMCT) model. This PMCT model is shown to replicate the traffic loading from full microsimulation accurately and allows longrun simulations, equivalent to 1000 years of congested traffic, to be performed with an acceptably short duration. This reduces the significant uncertainties associated with extrapolating short-run simulation results to long return periods. The 1000-year simulated results from the PMCT are compared with the extrapolated results from full microsimulation, and with the traffic loading from some design codes, for different bridge lengths. Both types of microsimulation are also applied to calculate maximum lifetime loading for two typical long-span bridgesone cable-stayed and one suspension bridgeusing influence lines determined from finite element models.

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Traffic Microsimulation for Bridge Loading Assessment and Management

Caprani¹

2013

IABSE Conference, Rotterdam 2013: Assessment, Upgrading and Refurbishment of Infrastructures

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<p>Traffic load is a highly variable parameter in the safety assessment of bridges. Its accurate assessment is important to avoid unnecessary and expensive rehabilitation measures for existing structures. Traffic microsimulation offers a comprehensive approach to the modelling of traffic. It is based on individual vehicle behaviour, with parameters that account for differences in vehicle type and driver behaviour. It inherently captures both free-flowing and congested traffic states, offering a more realistic picture of these states and their consequent loading on bridges. Further, microsimulation enables potential traffic management methods to be examined to estimate their effectiveness in mitigating critical loading scenarios. This work summarizes some recent results from the use of traffic microsimulation. Comparison with the Eurocode for bridge loading codes is made and various traffic management strategies are investigated for their effect on loading. The possibility of bridge-to-vehicle communication is explored, as is the use of lane change restrictions zones before the bridge. A bridge load control system is also examined. The results are persuasive: it is shown that microsimulation can be an important tool in more accurate assessment of the actual loading experienced by bridges, as well as providing a means to explore load control through traffic management. Given the ageing bridge stock and increasing financial limitations of the developed world, this work offers bridge owners a means of ensuring safety, whilst reducing costs.</p>

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Lane changing control to reduce traffic load effect on long-span bridges

Cited by 3 publications

References 12 publications

Micro-simulation of single-lane traffic to identify critical loading conditions for long-span bridges

Micro-simulation of single-lane traffic to identify critical loading conditions for long-span bridges

A pseudo-microsimulation approach for modelling congested traffic loading on long-span bridges

Traffic Microsimulation for Bridge Loading Assessment and Management

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