Development of an User Equilibrium Assignment Model for an Urban Road Network Including Expressways

Matsui, Hiroshi; Fujita, Motohiro

doi:10.2208/jscej.2000.653_85

Cited by 5 publications

(1 citation statement)

References 7 publications

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“…We use this extended model (hereafter TUE) as the OD-modification approach. For comparison, we also adopt the TUE with fixed hourly demand (TUE-f) to eliminate the residual demand in TUE and assign hourly OD demand separately in each hour, Journal of Advanced Transportation 5 which is the same model as a day-long UE assignment with toll road [32] when using day-long OD demand.…”

Section: Formulation Of Od Demand-modification Approach Andmentioning

confidence: 99%

Time Coefficient Estimation for Hourly Origin-Destination Demand from Observed Link Flow Based on Semidynamic Traffic Assignment

Fujita

Yamada²,

Murakami

2017

Journal of Advanced Transportation

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Day-long origin-destination (OD) demand estimation for transportation forecasting is advantageous in terms of accuracy and reliability because it is not affected by hourly variations in the OD distribution. In this paper, we propose a method to estimate the time coefficient of day-long OD demand to estimate hourly OD demand and to predict hourly traffic for urban transportation planning of a large-scale road network that lacks discrete-time rich traffic data. The model proposed estimates the time coefficients from observed link flows given a proven day-long OD demand based on a bilevel formulation of the generalized least square and semidynamic traffic assignment (OD-modification approach). The OD-modification approach is formulated as a static user-equilibrium assignment with elastic demand, based on the residual demand at the end of each period. Our model does not require setting many parameters regarding the OD demand matrices and the discrete-time dynamic traffic assignments. Applying the model to large-scale road network demonstrates that it efficiently improves estimation accuracy because the 24-hour time coefficients of survey data are slightly biased and may be modified properly. In addition, the methods that partially relax the assumption of OD-modification approach and transform the estimated demand into demand based on departure time are examined.

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Section: Formulation Of Od Demand-modification Approach Andmentioning

confidence: 99%

Time Coefficient Estimation for Hourly Origin-Destination Demand from Observed Link Flow Based on Semidynamic Traffic Assignment

Fujita

Yamada²,

Murakami

2017

Journal of Advanced Transportation

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Applied study of price discrimination implemented on a two-path route expressway

Shi

2006

Tinshhua Sci. Technol.

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Study on Macroscopic Relation between Environmental Noise and Road Traffic Flow in Urban Areas Based on Grid Square Data Using GIS

et al. 2009

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We have proposed a simple model to predict environmental noise in urban areas. In the model, the sound power level per unit area is estimated from the local traffic volume and the law of the conservation of energy is applied to derive the average value of LAeq in an area.LAeq that represents environmental noise in the area is shown by the following expression according to the model.Wμ is the sound power level per unit area in the area, and ΔR is an increase of the noise by the reflection such as buildings. Wμ has been found to be expressed by the following equation from the result of the survey in Japan. Wμ = 10 log 10 (Σ ql /S φ ) + 22 [dB/m 2 ] • • • • • • • • • • (2)

show abstract

Development of an User Equilibrium Assignment Model for an Urban Road Network Including Expressways

Cited by 5 publications

References 7 publications

Time Coefficient Estimation for Hourly Origin-Destination Demand from Observed Link Flow Based on Semidynamic Traffic Assignment

Time Coefficient Estimation for Hourly Origin-Destination Demand from Observed Link Flow Based on Semidynamic Traffic Assignment

Applied study of price discrimination implemented on a two-path route expressway

Study on Macroscopic Relation between Environmental Noise and Road Traffic Flow in Urban Areas Based on Grid Square Data Using GIS

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