Identifying the Factors That Increase the Probability of an Injury or Fatal Traffic Crash in an Urban Context in Jordan

sustainability Article Identifying the Factors That Increase the Probability of an Injury or Fatal Traffic Crash in an Urban Context in Jordan Tasneem Miqdady and Juan de Oña * TRYSE Research Group, Department of Civil Engineering, University of Granada, ETSI Caminos, Canales y Puertos, c/Severo Ochoa, s/n, 18071 Granada, Spain; [email protected] *Correspondence: [email protected] Received: 11 July 2020; Accepted: 9 September 2020; Published: 10 September 2020   Abstract: The lack of robust studies carried out on urban roads in developing countries makes it difficult to enhance traffic safety, ensuring sustainable roads and cities. This study analyzes the contribution of a number of explanatory variables behind crashes involving injuries on arterial roads in Irbid (Jordan). Five binary logistic regression models were calibrated for a crash dataset from 2014–2018: one for the full database, and the others for the four main crash causes identified by Jordanian Traffic Police reports. The models show that whatever the crash cause, the three most significant factors linked to an injury or fatality lie in urban road sections that are in large-scale neighborhood areas, have fewer than six accesses per kilometer, and have a low traffic volume (under 500 veh/h/ln). Some of these results agree with previous studies in other countries. Jordan’s governmental agencies concerned with urban road safety might use these results to develop appropriate plans and implement priority actions for each crash cause, in addition to undertaking further research for comparative purposes. Keywords: crash severity; collision crashes; arterial roads; urban context; logistic regression; crash cause; injury; sustainable roads 1. Introduction Traffic crashes stand as the foremost obstacle to sustainable roadways. Indeed, traffic crashes are a growing worldwide problem that lead to a tremendous loss of human resources, with economic consequences as well. There were 1.35 million road deaths in 2016, and about 75% of all crash injuries occur in urban areas [ 1 ]. As urbanization has accelerated and urban traffic is more complicated, the problem of traffic crashes in urban contexts increases and it is more acute in developing countries, where around 0.5 million deaths and up to 15 million injuries are caused by urban road crashes [ 2 ]. As a case study of developing countries, Jordan traffic crashes with serious injuries and fatalities amounted to terribly tragic numbers in 2012, for which reason the Public Police Department and the ministries of Public Work and Municipalities developed a five-year safety plan (2013–2017) to decrease the figures by 20% by the end of 2017. In reality, the reduction was even greater: a 27% reduction in injuries, and a 35% reduction in fatalities. However, Jordan urban contexts specifically still suffer from a lack of safety plans to control the traffic crash problem. As successful plans for safety, and hence sustainability, depend on a profound understanding of crash causes and contexts, extensive data collection is also necessary [ 3 ]. In this respect, urban road safety studies in Jordan became more viable in 2014, when police started to report crashes directly in situ using GPS systems. In light of this improvement, many factors connected to crash location (i.e., geometric design and urban context factors) could be addressed in urban crash studies in a developing country, such as Jordan. The main contribution of this study to the literature is the possibility of analyzing the influence Sustainability 2020,12, 7464; doi:10.3390/su12187464 www.mdpi.com/journal/sustainability Sustainability 2020,12, 7464 2 of 17 of several crash location factors on the severity of traffic crashes in a developing country in an urban context. This study presents, for the first time, a clear understanding of Jordanian urban crashes and to what extent this context affects traffic crash severity. In addition, for more insight, the study hypothesizes that the heterogeneity due to crash cause could be studied and will show a variable effect, since it also reflects the driving behavior effects on Jordanian urban roads. With this intention, the analysis is replicated for each cause alone for comparison purposes. Simply, the present study aims to identify the main variables influencing the occurrence of traffic injuries or fatalities on urban arterial roads in Jordan. Furthermore, differences are identified according to the crash cause within several categories reported by the police. The findings contribute to Jordanian urban traffic safety studies, which are very limited in number to date (e.g., Mujalli et al. [ 4 , 5 ]). The results are compared with those of similar studies in other countries, whether developed (Das et al. [ 6 ]; Abdel-Aty and Abdelwahab [ 7 ]; Ma et al. [ 8 ]; Wang et al. [ 9 ]; Haleem and Abdel-Aty [ 10 ]; Russo et al. [ 11 ]; Garrido et al. [ 12 ]; Zhuanglin et al. [ 13 ]) or developing (Al Ghamidi [ 14 ]; Altwaijri et al. [ 15 ]; Hassan and Al-Faleh [16]; Hosseinpour et al. [17]). The paper is organized as follows: after this brief introduction, the literature review section presents existing traffic severity studies in the urban context, focusing on the main explanatory variables affecting severity; the crash data section describes the database used for analysis, the main preprocessing tasks, and some descriptive statistics; the model section presents the methodology followed; the analysis and comparison sections summarize and highlight the main outcomes obtained through the analysis; and finally, some conclusions and policy implications are offered in the last section. 2. Literature Review Although 75% of traffic crashes occur in urban infrastructures, the amount of studies examining them is much lower compared to the literature analyzing rural crashes [ 2 ], including crash severity analysis. Nevertheless, worldwide crash severity studies (in both developed and developing countries) consider several types of urban infrastructures. For example, crash severity was examined at arterial sections [ 14 , 15 , 18 – 23 ], urban signalized [ 19 , 20 ] and unsignalized [ 10 ] intersections, urban freeway segments [22], toll plazas [20], and other urban segments [17,24]. Some studies analyzing crash severity in urban contexts have considered the type of crash as one of the independent variables [ 15 , 16 , 21 , 23 ], while other studies have restricted their analysis to one type, such as head-on crash severities [ 17 ] or rollover crash severities [ 24 ]. This kind of study allows us to reduce the heterogeneity and provide more robust outputs. In addition, the effect of non-vehicle–vehicle crashes (i.e., crashes with motorcycles, bicycles, pedestrians) has also been studied in analyzing traffic behavior effects on urban crash severity [21,23,25,26]. Most of the studies that analyze crash severity in urban areas only consider as explanatory factors the variables registered in the crash reports, whatever the type of section or the crash type concerned. This paper focuses mainly on arterial sections and collision crashes (not pedestrian or roll-over crashes). In developed countries, several studies [ 20 – 23 ] found that the driver’s age and gender, vehicle type, seatbelt use, alcohol, lighting, weather and road surface conditions, and time and distance from the intersection are significant variables. Some studies in developing countries [ 14 – 16 ] have identified a similar pattern regarding age, time, road surface, lighting conditions, whether single vehicle is involved, day of the week, crash location, and head-on point of collision. Those variables were also reported as significant in defining crash severity. Regarding traffic flow and speed variables, studies in both developed and developing countries suggested that low traffic volumes (hourly or daily) are also related to severe crashes [ 22 , 23 ]. In the same way, crashes at sections with higher speed limits showed greater tendencies to involve injuries or fatalities [ 14 – 16 , 19 , 20 ]. Moreover, sharing the road with heavy vehicles and light truck vehicles was positively associated with higher severity [24]. The previous variables were widely studied in both urban and interurban sections without major differences in their effect on crash severity. However, geometric design variables have shown significant effects on crash severity at urban road sections. Previous studies [ 17 – 19 , 24 ] have considered Sustainability 2020,12, 7464 3 of 17 a great variety of geometric factors for urban roads because of their richness of design elements and characteristics. For instance, Harvey and Aultman-Hall [ 18 ] examined arterial streetscape design and traffic safety in urban areas by evaluating the effect of several street landscape design variables (i.e., width, building to building across the street; length, centerline distance between intersections; height, average building height; width–height ratio; street wall continuity; building per length; and tree coverage area) on crash severity. Ma et al. [ 19 ] also established statistical relationships to relate severe crashes to a variety of geometric design factors. They found that a higher number of severe crashes was associated with a longer length of the road segment, fewer lanes per direction, more side accesses per kilometer, and the presence of bus stops. Additionally, Hosseinpour et al. [ 17 ] analyzed the effect of road characteristics and revealed that horizontal curvature, paved shoulder width, type of terrain, and side friction were associated with more severe crashes; yet, access points and the presence of a median reduced the probability of severe head-on crashes on federal urban segments. Anarkooli et al. [ 24 ] also had the same results for single-vehicle rollover crash severity on the same segments. Not only is the design of the urban road section important, but the urban design of the region where the road section is located could also be of relevance. Previous studies [ 17 , 18 , 24 ] have also suggested that the urban context, or the landscape of a region, could have a plausible effect on traffic behavior, as well as traffic safety. Nevertheless, there is still a lack of analyses on the urban context effect on crash severity. Briefly, Harvey and Aultman-Hall [ 18 ] found that accidents in smaller, more enclosed street landscapes were less likely than those in larger, more open streetscapes to cause injury or fatal crashes. Furthermore, in-fill development and street tree planting could be used as safety countermeasures. In developing countries, Hosseinpour et al. [ 17 ] and Anarkooli et al. [ 24 ] showed that land use and the type of terrain have significant effects on crash severity. Turning to Jordan traffic safety studies, in general, most crash studies are concerned with frequency prediction (crash rates), not the severity probabilities. Moreover, many studies deal with rural areas or the whole country’s crash characteristics. By way of illustration, Al-Masaeid [ 27 ] studied traffic accident characteristics in Jordan with an extensive evaluation of the 2008 policy implementation, highlighting this law enforcement and other measures and their very positive impact on safety. Al-Omari et al. [ 28 ] studied traffic crash trends in Jordan over thirteen years (1998-2010), analyzing the distribution of crash types, severity level, age group involvement, etc. Their study also correlated traffic crashes to variables such as time, traffic speed, and pavement condition. Al-Omari et al. [ 29 ] studied the spatial–temporal incidence of crashes in Irbid City using GIS (Geographic Information Systems) and fuzzy logic to predict the riskiest spots depending on road section and intersection parameters. Finally, studying crash severity on Jordanian urban roads started with Mujalli et al. [ 4 ], who used the variables included in the Jordanian Traffic Police reports to identify factors affecting the crash severity. They identified the number of vehicles involved, accident pattern, number of directions, accident type, lighting, surface condition, and speed limit as the variables that contribute to the occurrence of high-severity crashes. Furthermore, Mujalli et al. [ 5 ] analyzed pedestrian–vehicle crashes, finding that road type, number of lanes, speed limit, lighting, and adverse weather conditions affect the risk of fatality or severe injury. This study follows the line of Mujalli et al. [ 4 ], in that, aside from defining the factors that have significant effects, it also determines to what extent these factors could increase or decrease the probability of crash injuries. Additionally, this study adds some new urban factors to the analysis (e.g., neighborhood scale, land use, on-street parking) for the first time in a traffic safety study set in Jordan. 3. Data Collection and Description of Variables Careful and extensive data collection is key to drawing sound conclusions. In Jordan, the Police Traffic Central Department is the crash database reference. Traffic policemen fill out accident reports that include crash location based on GPS coordinates, and other data (crash type, cause, time, weather, etc.). This study covers a five-year period (2014–2018), in which 21,662 traffic crashes were registered on 39 arterial road sections in Irbid City, the second most populated city in Jordan. Sustainability 2020,12, 7464 4 of 17 The original severity data consider four categories: property damage only, slight injury, serious injury, and fatality. Since urban networks generally produce a low number of injuries or fatal crashes, and to ensure a sufficient number of observations for estimation purposes and following previous studies [ 6 , 30 ], severity data are grouped into two categories: (1) property damage only (PDO) and (2) injury or fatality (INJ). Hence, in this study, the target variable of severity is binary. The crashes were also classified by type into three different categories: 20,742 collisions, 640 crashes involving pedestrians, and 280 run-off-road crashes. Only 1653 of those crashes entailed injuries, while all the rest caused property damage only (PDO). Given the low number of run-off-road and pedestrian crashes, this study focuses on collision crashes only (i.e., head-on, rear-end, sideswipe, and with fixed object crashes) and does not consider pedestrian and run-off-road crashes that should be analyzed in further studies. As mentioned previously, Jordan’s crash database provides the data adopted in this study as variables: number of vehicles involved, season, time of day, type of day, accident cause, weather conditions, and state of pavement surface. Additionally, traffic data were obtained from camera videotapes and derived from calculations following the procedures and equations of Garber and Hoel [ 31 ] and Homburger et al. [ 32 ]. These variables are hourly traffic volume, percentage of heavy vehicles, and 85th percentile of speed. In an effort to be more comprehensive and to increase the novelty of this study, several geometric and urban characteristics were considered according to the Highway Safety Manual (HSM [ 33 ]) and previous studies. The set includes: distance from the following intersection, road cross-section type (two-lane two-way or multilane two-way), geometric consistency (a qualitative evaluation by an expert of the consistency of the geometric design and construction), sufficient building setbacks (according to Jordan’s Ministry of Public Works laws), presence of on-street parking, nominal clear zone of trees (based on American Association of State Highway and Transportation Officials [ 34 ]), neighborhood scale (a qualitative measure of the massing of buildings and spaces) [ 35 ], number of accesses to the section, and land use. Afterwards, traffic, geometric, and urban characteristics were combined with the section at hand using the GIS platform, which connects the section parameters to the GPS crash coordinates. All variable categories and descriptions, along with a summary of collision data for Irbid City, are listed in Table 1. Table 1. Explanatory variable description. Variables * Variable Levels No. of Crashes INJ ** (%) Distance from intersection (DI) Heavy vehicles % (HVEH) Number of vehicles involved (VINV) For single-vehicle crash (SNG) 1018 16.9 For two-vehicle crash (TWO) 18421 3.30 For more than two-vehicle crash (MUL) 1303 7.68 Season (SEAS) if in autumn (Sep, Oct, Nov) (AUT) 2635 4.74 if in winter (Dec, Jan, Feb) (WIN) 6129 6.27 if in spring (Mar, Apr, May) (SPR) 6577 3.57 if in summer (Jun, Jul, Aug) (SUM)5401 2.50 Time of the day (TIME) if during 0:00–5:59 (0–6) 359 9.19 if during 6:00–11:59 (6–12) 3786 4.25 if during 12:00–17:59 (12–18) 9623 4.08 if during 18:00–23:59 (18–0) 6974 4.19 Day type (DAY) if during the day after the weekend (Sun) (AW) 3487 4.04 if during the day before the weekend (Thu) (BW) 3796 3.66 if during weekend holiday (Fri, Sat) (WE) 8140 4.35 if during regular workday (WD) 5319 4.60 Sustainability 2020,12, 7464 5 of 17 Table 1. Cont. Variables * Variable Levels No. of Crashes INJ ** (%) Crash cause (CAUS) if due to unsafe lane changes (LCH) 2372 3.92 if due to exceeding speed limit (LIM) 904 5.64 if due to driving without taking safety precautions (SAF) 8872 4.14 if due to failing to obey traffic control devices (TCD) 4815 4.09 If due to tailgating (TAI) 3779 4.53 Weather condition (WEAT) if in good weather (clear or cloudy) (GW) 18,178 3.16 if in rainy weather (RAI) 2211 12.26 if in other bad conditions (foggy, snowy, windy, etc.) (OTH) 353 9.92 Pavement Surface (PAVE) if on dry pavement (Dry) 18449 3.70 if on wet pavement after raining (WET) 2144 7.88 if on other bad surface conditions (oily, muddy, icy, etc.) (OTH)149 18.12 Traffic volume (TRAF) if traffic volume in the segment is low (350-500 veh/h/ln) (LV) 2020 17.38 if traffic volume in the segment is moderate (501-700 veh/h/ln) (MV) 11,139 4.60 if traffic volume in the segment is high (>701 veh/h/ln) (HV) 7583 0.21 Geometric consistency (CONS) if segment curve consistency is perfect, width of lane and median is standard, perfect design of intersections, presence of safety elements (A) 1533 5.22 if segment curve consistency is good, width of lane and median is standard, good design of intersections, presence of safety elements (B) 9525 4.16 if segment curve consistency is good, width of lane and median is less than standard, bad design of intersections, presence of safety elements (C) 8689 4.14 if segment curve consistency is bad, width of lane and median is less than standard, bad design of intersections, absence of safety elements (D) 995 4.22 Cross section (SECT) if in a two-lane two-way road section (TLN) 5542 4.24 if in a multilane two-way road section (MLN) 15,200 4.24 85th-percentile speed (SP85) if in a segment of 85th % speed <50 km/h(LSP) 18,634 1.91 if in a segment of 85th % speed >50 km/h(HSP) 2108 24.86 Sufficient building setbacks (BUIL) if in a segment where on both sides buildings are settled on the standard setbacks (SUF) 11,027 3.81 if in a segment where on both sides buildings are not settled on the standard setbacks (NSUF) 9715 4.73 On-street parking (PARK) if there is on-street parking in the segment (EX) 10,119 0.85 if there is no on-street parking (NEX) 10623 7.47 Trees in nominal clear zone (TREE) if the trees in the segment curb are in the nominal clear zone (IN) 11,012 2.07 if trees exceed the nominal clear zone (EXC) 9730 16.69 Neighborhood scale (NESC) if in a large-scale neighborhood (LAR) 8104 9.79 if in a small-scale neighborhood (SMA) 12,638 0.68 Number of accesses per km (ACCE) if the number of accesses to the segment >6 (HN) 12,266 0.70 if the number of accesses to the segment ≤ 6 (LN) 8476 9.36 Sustainability 2020,12, 7464 6 of 17 Table 1. Cont. Variables * Variable Levels No. of Crashes INJ ** (%) Land use (LUSE) if in a commercial zone (COM) 9569 3.85 if in a residential zone (RES) 6360 4.61 if in an industrial zone (IND) 2155 4.87 if in a non-categorized zone (NC) 2658 4.25 Note: * All variables are categorical except DI and HVEH, which are continuous; ** INJ: injury or fatality. Crash cause data were used for segmentation purposes to identify the specific effect of the same explanatory variables for each cause, this was supposed to deal with heterogeneity of the data and consider driving behavior as much as possible. The Jordanian Traffic Police reports identify five main crash causes: unsafe lane changes (LCH), exceeding speed limit (LIM), driving without taking safety precautions (SAF), failing to obey traffic control devices (TCD), and tailgating (TAI). Table 2offers a crash data summary, classified by crash causes. Table 2. Explanatory variables and data summary of crash causes. Variables * LCH LIM SAF TCD TAI main levels total INJ total INJ total INJ total INJ total INJ DI Cont. 2372 93 904 51 8872 367 4815 197 3779 9 HVEH Cont. 2372 93 904 51 8872 367 4815 197 3779 9 VINV SNG 125 22 49 10 417 70 261 46 166 24 TWO 2099 65 776 30 7882 258 4243 127 3421 127 MUL 148 6 79 11 573 39 311 24 192 20 SEAS AUT 340 11 134 5 1107 47 583 25 471 37 WIN 643 46 271 23 2580 151 1477 98 1158 66 SPR 768 24 306 19 2853 97 1497 56 1171 39 SUM 621 12 193 4 2332 72 1276 18 979 29 TIME 0-6 53 5 16 4 140 14 74 7 76 3 6-12 475 20 178 10 1590 76 820 26 723 29 12-18 1096 36 428 16 4128 135 2281 103 1690 103 18-0 748 32 282 21 3014 142 1640 61 1290 36 DAY AW 393 22 170 10 1529 50 815 21 580 38 BW 417 19 159 6 1688 56 884 34 648 24 WE 939 39 361 14 3364 156 1888 76 1588 69 WD 623 13 214 21 2291 105 1228 66 963 40 WEAT GW 2137 61 792 33 7776 238 4207 125 3266 116 RAI 192 31 101 15 949 119 525 60 444 46 OTH 43 1 11 3 147 10 83 12 69 9 PAVE DRY 2169 78 801 37 7898 279 4248 144 3333 145 WET 174 13 93 10 904 74 547 47 426 25 OTH 29 2 10 4 70 14 20 6 20 1 TRAF LV 229 48 110 19 858 141 483 80 340 63 MV 1302 42 492 31 4727 220 2591 114 2027 105 HV 841 3 302 1 3287 6 1741 3 1412 3 CONS A 159 7 55 4 668 32 366 19 285 18 B 1029 37 383 17 4062 171 2219 80 1732 74 C 1098 46 414 26 3705 148 2005 88 1567 70 D 86 3 52 4 437 16 225 10 195 9 SECT TLN 648 30 241 14 2372 87 1283 52 998 52 MLN 1724 63 663 37 6500 280 3532 145 2781 119 Sustainability 2020,12, 7464 7 of 17 Table 2. Cont. Variables * LCH LIM SAF TCD TAI SP85 LSP 2136 33 809 8 7995 154 4378 76 3316 84 HSP 236 60 95 43 877 213 437 121 463 87 BUIL SUF 1236 46 491 24 4719 177 2567 99 2014 74 NSUF 1136 47 413 27 4153 190 2248 98 1765 97 PARK EX 1137 7 455 5 4296 37 2350 20 1845 17 NEX 1199 86 449 46 4576 330 2465 177 1934 154 TREE IN 1200 27 455 11 4795 90 2556 58 2006 42 EXC 1172 66 449 40 4077 277 2559 139 1773 129 NESC LAR 934 78 354 48 3456 335 1887 175 1473 157 SMA 1438 15 550 3 5416 32 2928 22 2306 14 ACCE HN 1397 9 538 10 5236 31 2789 19 2306 17 LN 975 84 366 41 3636 336 2026 178 1473 154 LUSE COM 1086 38 410 25 4122 147 2204 87 1747 71 RES 746 38 302 13 2717 124 1464 62 1131 56 IND 240 6 90 9 901 46 528 21 396 23 NC 300 11 102 4 1132 50 619 27 505 21 Note: * LCH: unsafe lane changes; LIM: exceeding speed limit; SAF: driving without taking safety precautions; TCD: failing to obey traffic control devices; TAI: tailgating. 4. Methodological Approach In view of the binary categories of crash severity (dependent variables) that resulted after aggregation, and given the need to predict the probability of the outcomes rather than the outcomes themselves, a binary logistic model was chosen to represent the relationship between the level of severity (personal damage only versus injury) and the set of the explanatory variables. According to Long [ 36 ], logit and probit models provide very similar results in terms of marginal effects (i.e., the effects on the predicted mean of the outcome, keeping other covariates at the mean or averaging them over observed values) for independent variables. However, logit models have the advantage of generating coefficients that can be transformed into odds ratios. For this reason, most studies that analyze traffic crash severity use different types of logit models (e.g., binary logit, multinomial logit, ordered logit, nested logit, random parameters logit, or generalized ordered logit) [37]. This study uses a binary logit model because the dependent variable (Y) only takes two values: injury or fatal crashes (Y =1) and property damage only crashes (Y =0). The probability that an injuring or fatal crash will occur or not is modeled as a logistic distribution in Equation (1): π(x)=exp[g(x)] 1+exp[g(x)] (1) The logit of the multiple logistic regression model is given by Equation (2): g(x)=ln"π(x) 1−π(x)#=βj+ p X j=1 βjxj(2) where π(x) is the conditional probability of an injury or fatal crash, which is equal to the number of injuries or fatal crashes divided by the total number of crashes. In turn, x jis the value of the jth independent variable, with βj as the corresponding coefficient, for j=1,2, . . . p, and p is the number of independent variables. β0is the intercept. Sustainability 2020,12, 7464 8 of 17 The maximum likelihood method is employed to measure the association using the following likelihood function (Equation (3)): l(β)= n Y i=1 π(xi)yi[1−π(xi)]1−yi(3) where y i is the ith observed outcome, with the value of either 0 or 1, and i=1,2, . . . ,n, where n is the number of observations. By maximizing the log likelihood (LL) expression in Equation (4): L(β)=ln[l(β)] = n X i=1 {yiln[π(xi)]+1−yiln[1−π(xi)]}(4) the best estimate of βcan be obtained. The influence of attribute k on crash severity can be revealed by the odds ratio (OR) of Equation (5): OR =expβj(5) With a 95% confidence level, the odds ratio provides the relative amount by which the odds ratios of the crash severity increase (OR greater than 1.0) or decrease (OR less than 1.0) when the value of the predictive value is increased by 1.0 unit. Because the standard deviations among variables differ substantially, the effect of a “unit standard deviation change” can be evaluated instead of a “unit change”. In other words, Equation (6) shows that for a standard deviation change in k, the odds are expected to change by a factor, holding all other variables constant: exp(ORStdk)=exp(βj∗(SD of k)) (6) The modeling procedure began with the assessment of correlation in the dataset. The correlation matrix showed that two variables (weather and pavement surface conditions) were strongly correlated. In order to develop meaningful model estimations, and to ensure reasonable magnitudes and signs of the coefficients, it was advisable to exclude one of those variables from the model. The selection of the variable to be removed was based on the condition that the model fit will not vary significantly, and the removed variable is the one with a higher correlation with all variables. Accordingly, weather condition was excluded from the models. A reference category was, moreover, chosen for each variable. It was the one permitting the odds for other categories of the same variable to be equal to or more than 1 (i.e., to show categories that directly increase the injury probability in the results table). Binary logit models for the entire database (general model) and for each one of the crash causes reported by the police (i.e., unsafe lane changes, LCH; exceeding speed limit, LIM; driving without taking safety precautions, SAF; failing to obey traffic control devices, TCD; and tailgating, TAI) were developed using STATA 15/MP. Because the main purpose of this study is not to predict or forecast crash severity but rather to identify the extent of the effects of all studied factors, insignificant explanatory variables are retained in the models. During application of the same fit assessment and model developing procedure on the exceeding speed limit (LIM) crashes, the model gave overestimated results. This was obviously due to the low number of observations (904 crashes), so we do not report or interpret these particular results. The fitted binary logit model for the entire database and the remaining four causes considered are shown in Table 3. Essentially, it shows the factor estimation results for the category INJ (when the crash outcome is fatality or injury) when the category property damage only (PDO) crashes is the base category. It also includes some goodness-of-fit statistics, such as number of observations, Nagelkerke R-square value, log-likelihood at convergence, log-likelihood at zero, chi-square test, and degree of freedom. Sustainability 2020,12, 7464 9 of 17 5. Analysis of the General Model Calculating the unit standard deviation change in the odds ratio for each variable when holding the other variables constant, Table 3shows the factors’ estimated effects across the entire database. Table 3. Binary logit model’s estimation of factors´ effect on injury probability. Exp. Variables General Model LCH Model TAI Model TCD Model SAF Model eˆBStdX * eˆBStdX eˆBStdX eˆBStdX eˆBStdX Continuous variables DI 1.334 1.398 1.272 1.543 1.335 HVEH 0.881 0.882 0.866 0.935 0.881 Categorical variables VINV SNG 1.513 1.653 1.480 1.693 1.418 TWO Ref. cat. Ref. cat. Ref. cat. Ref. cat. Ref. cat. MUL 1.309 1.184 1.192 1.367 1.322 SEAS AUT 1.154 1.724 1.417 1.366 0.916 WIN 1.478 2.300 1.547 1.969 1.248 SPR 1.172 1.405 1.091 1.288 1.100 SUM Ref. cat. Ref. cat. Ref. cat. Ref. cat. Ref. cat. TIME 0-6 1.140 1.251 0.902 1.083 1.220 6-12 1.038 0.837 1.245 1.058 1.039 12-18 1.006 0.735 1.547 1.230 0.811 18-24 Ref. cat. Ref. cat. Ref. cat. Ref. cat. Ref. cat. DAY AW 1.079 1.174 1.212 0.878 1.077 BW Ref. cat. Ref. cat. Ref. cat. Ref. cat. Ref. cat. WE 1.135 0.901 1.081 0.903 1.294 WD 1.073 0.641 0.959 1.117 1.126 PAVE DRY Ref. cat. Ref. cat. Ref. cat. Ref. cat. Ref. cat. WET 1.090 1.164 0.977 1.060 1.179 OTH 1.176 1.120 0.974 1.228 1.117 TRAF LV 3.767 4.044 4.092 3.744 3.871 MV 4.691 2.692 6.088 4.363 5.261 HV Ref. cat. Ref. cat. Ref. cat. Ref. cat. Ref. cat. CONS A 1.109 1.323 1.342 0.939 1.205 B1.322 2.250 1.181 1.080 1.572 C 1.310 2.208 1.362 1.075 1.457 D Ref. cat. Ref. cat. Ref. cat Ref. cat. Ref. cat. SECT TLN Ref. cat. Ref. cat. Ref. cat. Ref. cat. Ref. cat. MLN 1.090 0.928 1.130 1.076 1.175 SP85 LSP Ref. cat. Ref. cat. Ref. cat. Ref. cat. Ref. cat. HSP 2.564 2.967 2.572 2.613 2.523 BUIL SUF Ref. cat. Ref. cat. Ref. cat. Ref. cat. Ref. cat. NSUF 1.338 1.060 1.657 1.055 1.406 PARK EX Ref. cat. Ref. cat. Ref. cat. Ref. cat. Ref. cat. NEX 3.087 4.992 3.081 2.942 3.015 TREE IN Ref. cat. Ref. cat. Ref. cat. Ref. cat. Ref. cat. EXC 1.699 1.401 1.876 1.543 1.756 NESC LAR 6.740 4.342 8.087 6.857 7.951 SMA Ref. cat. Ref. cat. Ref. cat. Ref. cat. Ref. cat. ACCE HN Ref. cat. Ref. cat. Ref. cat. Ref. cat. Ref. cat. 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