TABLE 1 Summary of Existing Macro-Level Crash Frequency Studies Paper Methodological Approach Unit of Analysis Spatial Unit Crash level Independent Variable Considered Accessibility measures Exposure Measures characteristics Socioeconomic characteristics Road network characteristics Built environment Noland and Quddus (2004a) Negative binomial Pedestrian Bicyclist Standard statistical regions Fatal/Serious injury crashes, Slight injury crashes --- Yes Yes Yes Yes --- Wier et al. (2009) Ordinary least square regression Pedestrian crash Census tract Injury crashes --- Yes Yes Yes Yes Yes Ukkusuri et al. (2012) Negative binomial, Negative binomial with heterogeneity in dispersion parameter, Zero-inflated negative binomial Pedestrian crash Census tract Total crashes Yes Yes Yes Yes Yes Yes Lee et al. (2015) Multivariate Poisson lognormal conditional autoregressive model Motor vehicle, bicycle and pedestrian crashes Traffic analysis zone Total crashes Yes Yes Yes Yes Wei and Lovegrove (2013) Negative binomial Bicycle crash Traffic analysis zone Total crashes Yes Yes Yes Yes Yes Yes Siddiqui et al. (2012) Negative binomial, Bayesian log-normal model Pedestrian and bicycle crash Traffic analysis zone Total pedestrian crashes, Total bicycle crashes --- Yes Yes Yes Yes Yes Cho et al. (2009) Path analysis Pedestrian and bicycle crash Community analysis zones Total crashes Yes Yes --- --- Yes Yes MacNab (2004) Bayesian spatial and ecological regression model Young (Age 0-25) pedestrian and Bicyclist crash Local health areas Injury crashes --- Yes --- Yes Yes Yes Noland (2003) Random Effect negative binomial Crash State Fatal crashes, Injury crashes --- Yes Yes Yes Yes ---
Noland and Quddus (2004b) Negative binomial Crash County Total crashes, Fatal crashes --- Yes Yes Yes Yes --- Karlaftis et al. (1998) Cluster analysis, Negative binomial Aged driver crash Total crashes, Urban crashes, Rural crashes --- Yes --- Yes Yes --- Huang et al. (2010) Bayesian spatial model Crash County Total crashes, Severe crashes --- Yes Yes Yes Yes --- Amoros et al. (2003) Negative binomial Crash County Total crashes, Fatal crashes --- --- --- --- Yes --- Ukkusuri et al. (2011) Random parameter negative binomial Pedestrian crash Census tract Total crashes Yes Yes Yes --- Yes Yes Cottrill et al. (2010) Poisson Regression with heterogeneity Pedestrian crash Census Tract Total crashes Yes Yes Yes Yes Yes Yes Quddus (2008) Negative Binomial, Spatial autoregressive model, Bayesian hierarchical model Motorized vehicle crash, vehicle crash, Pedestrian crash Census ward Fatal crashes, Serious injury crashes, Slight Injury crashes --- Yes Yes --- Yes --- Naderan and Shahi (2010) Negative binomial Crash Traffic analysis zone Total crashes, PDO crashes, Injury crashes, Fatal crashes --- Yes --- --- --- --- Ng et al. (2002) Cluster analysis, Negative Binomial Crash Traffic analysis zone Total crashes, Fatal crashes, Pedestrian crashes --- --- --- --- --- Yes Abdel-Aty et al. (2011) Negative binomial Crash Traffic analysis zone Total crashes, Severe crashes, Peak hour crashes, Pedestrian and Bicycle crashes --- Yes --- --- Yes --- and Jovanis (2006) Full Bayes hierarchical model Crash County Fatal crashes, Injury crashes --- Yes Yes Yes --- --- Stamatiadis and Puccini 2000 Quasi induced exposure method Single vehicle crash Multi vehicle Crash State Fatal crashes --- Yes Yes Yes --- --- Noland (2003) Negative binomial Crash State Fatal injury crashes --- Yes Yes Yes Yes ---
TABLE 7 Elasticity Effects for LNB model for Montreal Island Variable Names* Segment 1 Segment 2 In-Sample STM bus stops 0.000 4.4924 4.492 STM bus route length 0.000 1.383 1.383 Transit commuters 10.764 24.055 34.819 Walk commuters 3.485 4.033 7.518 Male bike commuters 0.000 0.922 0.922 Female bike commuters 0.000 0.364 0.364 Number of vehicles 28.625 0.000 28.625 Non-permanent resident 0.000 1.870 1.870 African population 2.700 0.000 2.700 Population without income 0.000 4.669 4.669 Medium Median TAZ income 25.820 6.652 32.472 Number of one-way link 0.000 1.379 1.379 Number of bars 0.000 0.325 0.325 Lot-coverage 1.823 0.000 1.823 Presence of university 62.091 -8.011 54.081 Number of schools -7.902 3.928 -3.974 * Variable definitions are presented in Table 2
APPENDIX A Segment Characteristics and Mean Values of Segmentation Variables for LNB model for the City of Toronto Components Segments Segment 1 Segment 2 Sample shares 0.127 0.229 0.644 Observed mean of crash events 1.629 Expected mean of segment level crash events 0.363 2.500 1.231 Mean Values of Segmentation Component Variables in Each Segment Variable Overall Sample Segment 1 Segment 2 Segment 3 Ln(total number of intersection in TAZ) 3.430 2.585 3.896 3.431 Ln(total area of TAZ in Hectare) 4.401 4.905 4.106 4.406 Ln(total length of local road kilometer in TAZ) 1.364 1.313 1.257 1.412 Land use mix 0.448 0.261 0.558 0.446 Ln(Total length of bike route kilometer in TAZ) 2.131 0.990 2.127 2.357 Ln(Number of households in TAZ per hectare) 2.442 0.431 2.674 2.755