Travel demand modeling to simulate traffic loads for pavement deterioration curves: dealing with aggregate data at urban and regional scales

Abstract

https://mc06.manuscriptcentral.com/cjce-pubs Canadian Journal of Civil Engineering D r a f t 2Travel demand modeling to simulate traffic loads for pavement deterioration curves: dealing with aggregate data at urban and regional scales Abstract: Traditional pavement management system uses historical data on traffic volume or traffic growth rate to develop the pavement deterioration curves. This study simulates the traffic loads on regional and urban road networks to estimate the pavement deterioration curves applying travel demand models at urban and regional scales during the period of 2013-2062. Highways 1, 2, 7, 15, 16, 102 and 104 connecting the Atlantic Provinces of Canada are considered as the case study at regional scale. Arterial and local roads of both rigid and flexible pavement types in Montreal city are considered as the case study at urban scale. The TRANUS integrates spatial input-output and transportation models to simulate interprovincial freight movement on the regional road network. Urban transportation planning system (UTPS) simulates the urban traffic on the road network of Montreal city. The accumulated traffic loads (ESALs) are calculated based on the predicted AADT and locally observed truck distributions combined with truck factors. Roughness progression on regional highways and urban roads is estimated by applying regression model of international roughness index (IRI). The IRI will be 35.71, 43.33, 31.62 and 30.67 for flexible-arterial, rigid-arterial, flexible-local and rigid-local roads during the period of 2013-2062, respectively. Comparative evaluation of with and without simulated traffic reveals that the impact of simulated traffic is highest on the pavement structure of highway 2 and 1 at regional level and rigid-arterial and flexible roads of Montreal city. This study improves the traditional method of estimating pavement deterioration by incorporating the simulated traffic and traffic loads into the pavement performance function. Page 2 of 45 https://mc06.manuscriptcentral.com/cjce-pubs Canadian Journal of Civil Engineering D r a f t 3

Understanding Air Pollution from Induced Traffic during and after the Construction of a New Highway: Case Study of Highway 25 in Montreal

“…This study simulates the traffic flow on each road segment of the road network in Montreal applying a four-step travel demand model [18]. TransCAD software was used for such purpose.…”

“…TransCAD software was used for such purpose. The discrete choice model was applied to estimate the trip generations from different boroughs of the city using disaggregate household or individual level data from the 2008 origin-destination survey [18]. Individual decisions to make trips are aggregated to estimate the total number of trips produced from the boroughs of Montreal [18].…”

“…The model balances the trip productions and then factors the calculated attractions so that they normalize to the input attractions. These two steps are then enveloped in an iterative loop until the convergence has been achieved [18]. A multinomial logit (MNL) model is applied to estimate the choice of modes (car driving alone, car share, bus, metro, and bicycle) by travelers assuming that the utility of an alternative mode is a function of the choice determinants, unknown parameters, and an i.i.d.…”

“…A multinomial logit (MNL) model is applied to estimate the choice of modes (car driving alone, car share, bus, metro, and bicycle) by travelers assuming that the utility of an alternative mode is a function of the choice determinants, unknown parameters, and an i.i.d. Gumbel-distribution error term [18]. Finally, a Stochastic User Equilibrium (SUE) model simulates the traffic flow on each road segment of the network with a logit model.…”

“…The coefficients of a logistic discrete choice model of trip generation estimated the increase of working and business trips with increasing number of persons per households, but education oriented trips decreased with more people per households during both peak and off-peak hours [18]. A doubly constrained gravity model spatially distributed the trips among boroughs.…”