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Macroscopic road safety impacts of public transport: A case study of Melbourne, Australia.

Published on Nov 1, 2019in Accident Analysis & Prevention
· DOI :10.1016/J.AAP.2019.105270
Long T. Truong9
Estimated H-index: 9
(La Trobe University),
Graham Currie35
Estimated H-index: 35
(Monash University)
Abstract
Abstract Mode shift from private vehicle to public transport is often considered as a potential means of improving road safety, given public transport’s lower fatality rates. However, little research has examined how public transport travel contributes to road safety at a macroscopic level. Further, there is a limited understanding of the individual effects of different public transport modes. This paper explores the effects of commuting by public transport on road safety at a macroscopic level, using Melbourne as a case study. A random effect negative binomial (RENB) and a conditional autoregressive (CAR) model are adopted to explore links between total and severe crash data to commuting mode shares and a range of other zonal explanatory factors. Overall, results show the great potential of public transport as a road safety solution. It is evident that mode shift from private vehicle to public transport (i.e. train, tram, and bus), for commuting would reduce not only total crashes, but also severe crashes. Modelling also demonstrated that CAR models outperform RENB models. In addition, results highlight safety issues related to commuting by motorbike and active transport. Effects of sociodemographic, transport network, and land use factors on crashes at the macroscopic level are also discussed.
  • References (64)
  • Citations (2)
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References64
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#1Jesus M. Barajas (UIUC: University of Illinois at Urbana–Champaign)H-Index: 2
Historically disadvantaged populations are disproportionately represented in bicycle crashes. Previous research has found that Black and Hispanic bicyclists and areas with higher populations of non-White residents, lower median income, and high poverty experience bicycle crashes more frequently than others. Although existing research has explored the role of socioeconomic status and the built environment in predicting crash frequency, few scholars have studied how these factors account for dispa...
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#1Jaeyoung Lee (UCF: University of Central Florida)H-Index: 19
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ABSTRACTOver the last decade, bicycle ridership has been encouraged as a sustainable mode of transportation as it is economic and has less impact on the environment. Still, higher crash risk for bicyclists remains a deterrent for people to choose bicycling as their major mode of travel. As a first step in investigating bicycle safety, it is essential to identify not only the characteristics of the areas with the excessive number of bicycle crashes; but also those of the areas where crash-prone b...
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#1Jaeyoung Lee (UCF: University of Central Florida)H-Index: 19
#2Shamsunnahar Yasmin (UCF: University of Central Florida)H-Index: 14
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In traffic safety literature, crash frequency variables are analyzed using univariate count models or multivariate count models. In this study, we propose an alternative approach to modeling multiple crash frequency dependent variables. Instead of modeling the frequency of crashes we propose to analyze the proportion of crashes by vehicle type. A flexible mixed multinomial logit fractional split model is employed for analyzing the proportions of crashes by vehicle type at the macro-level. In thi...
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#1Farhana Naznin (Monash University, Clayton campus)H-Index: 6
#2Graham Currie (Monash University, Clayton campus)H-Index: 35
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Abstract Melbourne, Australia has the largest tram/streetcar network in the world including the largest mixed traffic tram operating environment. Therefore, Melbourne tram drivers are responsible for controlling one of the heaviest vehicles on road ranging from shared tram lanes to exclusive tram lanes. In addition to different tram lane configurations, tram drivers need to follow different traffic signal phases at intersections including tram priority signals as well as need to serve passengers...
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#1Qing Cai (UCF: University of Central Florida)H-Index: 12
#2Mohamed Abdel-Aty (UCF: University of Central Florida)H-Index: 56
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Abstract Introduction Macro-level traffic safety analysis has been undertaken at different spatial configurations. However, clear guidelines for the appropriate zonal system selection for safety analysis are unavailable. In this study, a comparative analysis was conducted to determine the optimal zonal system for macroscopic crash modeling considering census tracts (CTs), state-wide traffic analysis zones (STAZs), and a newly developed traffic-related zone system labeled traffic analysis distric...
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#1Richard Amoh-Gyimah (Monash University)H-Index: 8
#2Meead Saberi (Monash University)H-Index: 17
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Abstract Macroscopic safety models establish a relationship between crashes and the contributing factors in a defined spatial unit. Negative binomial (NB) and Bayesian negative binomial models with conditional autoregressive prior (CAR) are techniques widely used to establish this relationship. However, these models do not account for unobserved heterogeneity and their output is global and fixed irrespective of the spatial unit of the analysis. There is a timely need to understand how variations...
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#1Ali Pirdavani (University of Hasselt)H-Index: 10
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Abstract Increasing evidence suggests that neighborhood-based measures of socioeconomic status are correlated with traffic injury. The main objective of this study is to determine the differences in associations between predictive variables and injury crashes (i.e. including injury and fatal crashes). This study makes a novel contribution by establishing the association between traffic casualties and socio-demographic, socioeconomic characteristics, traffic exposure data and road network variabl...
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#1Jie Wang (CSU: Central South University)H-Index: 6
#2Hongwei Huang (CSU: Central South University)H-Index: 21
Last. Qiang Zeng (SCUT: South China University of Technology)H-Index: 13
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Abstract Objectives This paper aimed to (i) differentiate the effects of contributory factors on crash risks related to different transportation modes, i.e., motor vehicle, bicycle and pedestrian; (ii) explore the potential contribution of zone-level factors which are traditionally excluded or omitted, so as to track the source of heterogeneous effects of certain risk factors in crash-frequency models by different modes. Methods Two analytical methods, i.e. negative binomial models (NB) and rand...
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#1Long T. Truong (Monash University)H-Index: 9
#2Le Minh Kieu (QUT: Queensland University of Technology)H-Index: 9
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This paper investigates factors associated with traffic crash fatalities in 63 provinces of Vietnam during the period from 2012 to 2014. Random effect negative binomial (RENB) and random parameter negative binomial (RPNB) panel data models are adopted to consider spatial heterogeneity across provinces. In addition, a spatiotemporal model with conditional autoregressive priors (ST-CAR) is utilised to account for spatiotemporal autocorrelation in the data. The statistical comparison indicates the ...
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Understanding commuting patterns could provide effective support for the planning and operation of public transport systems. One-month smart card data and travel behavior survey data in Beijing were integrated to complement the socioeconomic attributes of cardholders. The light gradient boosting machine (LightGBM) was introduced to identify the commuting patterns considering the spatiotemporal regularity of travel behavior. Commuters were further divided into fine-grained clusters according to t...
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#1Juan Ruiz-RoseroH-Index: 3
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There is a large number of tools for the simulation of traffic and routes in public transport systems. These use different simulation models (macroscopic, microscopic, and mesoscopic). Unfortunately, these simulation tools are limited when simulating a complete public transport system, which includes all its buses and routes (up to 270 for the London Underground). The processing times for these type of simulations increase in an unmanageable way since all the relevant variables that are required...
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