Measuring Urban Motorized Passenger Mobility: Evidence from Sample Selection Poisson Regression Model in the Case of Istanbul

Enver Cenan İnce ,Hüseyin Murat Çelik

doi:10.13189/cea.2019.070404

Abstract

Unlike any previous researches of urban passenger mobility demand on behalf of the travel demand behaviors of the individuals, this study firstly proposes a measurement focusing specifically on the interrelation between the travel times of the individuals and the number of motorized trips they exhibit in a day. It is the first time in the literature that the related measurement is focusing on the additional number of daily motorized trips -instead of focusing on measuring vehicle miles traveled- as a result of decrease in daily motorized travel time. In this sense, a single equation Sample Selection Poisson Regression Model (SSPRM) seems a good candidate to measure the motorized passenger mobility and to integrate it into a trip generation model. The proposed model has shown that a 26 percent decrease in average motorized travel time per capita in Istanbul will make the motorized trip making increase by 6.5%. Non-linear structure of elasticity estimate of SSPRM may further allow us to estimate the spatial variation of generative impact of induced urban passenger mobility within the trip generation models since it is possible to account for the individual characteristics in the estimation of elasticities as long as we have disaggregate spatial data. By the way, these measures of induced passenger motorized mobility might be used in formulating and in assessing any travel demand management policies so as to minimize the motorized trip makings within the urban spaces.

Highlights

After many years of continuing investment in highway network to solve traffic congestion in urban areas, transportation professionals came to the idea that the efforts to solve the traffic congestion by constructing new highways might be a futile effort since these new highways are re-congested in a relatively short period of time confirming the contention that “you cannot build your way out of traffic congestion”
The results partially confirmed the initial Even though we modeled the characteristics of trip expectations in linear regression models (LRM), Poisson Regression Model (PRM), and Negative Binomial Regression Model (NBRM)
When we look at the motorized trip making as a proxy for the measurement of result by Sample Selection Poisson Regression Model (SSPRM) showed that all variables are positively induced urban motorized passenger mobility, and these contributes to total daily trip making

Summary

Introduction

After many years of continuing investment in highway network to solve traffic congestion in urban areas, transportation professionals came to the idea that the efforts to solve the traffic congestion by constructing new highways might be a futile effort since these new highways are re-congested in a relatively short period of time confirming the contention that “you cannot build your way out of traffic congestion”. The newly created capacities to solve an existing congestion problem stimulate the suppressed demands and cause shifts in time, route and mode of daily travels as called “triple divergence” by Downs [1]. Emergence of the suppressed demand accompanying with these convergences as a consequence of the constructing a new highway facility is called as “induced urban passenger mobility” as the empirical framework of urban passenger mobility. By the notion called induced urban passenger mobility, it is explicitly referred to the additional daily number of motorized trips that is caused by some amount of decrease in daily motorized travel time of an individual. While some part of the induced urban passenger mobility comes from diversion of the existing demand, some other parts are newly generated traffic. While divergence and suppressed demand effects are short term immediate effects, developmental effects are generally realized in the long term

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Results

Conclusion