1. Introduction
Road safety has been increasingly regarded as one of the most important transportation concerns in urban areas. Over the last few decades, the development of safety performance functions has enabled traffic engineers and road safety researchers to identify important factors related to the occurrence of crashes on specific highway elements or on transportation networks [
1].
A safety performance function (SPF) is an equation used to predict the average number of crashes per year at a location as a function of exposure and, in some cases, it includes site characteristics [
2]. This type of model belongs to a family of generalized linear models (GLM) with a non-normal error structure distribution [
3].
The SPFs can be classified into two different spatial aggregation scales: micro-level and macro-level. In the first scale, the study units are based on small homogeneous road entities, such as roadway segments, ramps, and intersections [
4,
5]. The micro-level factors refer to variables aggregated at the segment/intersection level including traffic data and, geometric data (e.g., number of lanes, road functional classification). In macro-level analysis, the study units are based on some geographic areas (zonal-level) to investigate the influence of socio-economic, demographic, land use, and infrastructure-related factors on crash occurrence [
4]. Several studies have been conducted for crash modelling at a macro-level, exploring various zonal systems: block groups, census tracts, ZIP code areas and, traffic analysis zones (TAZs) [
6,
7,
8,
9,
10,
11,
12,
13]. Most of these zonal systems were developed for different specific uses. The prevalent spatial unit considered at the macro-level analysis is TAZ. A TAZ may consist of one or more census blocks, block groups, or census tracts; but usually it is a spatial aggregation of census blocks. TAZ boundaries generally coincide with identifiable physical barriers such as major streets and water bodies, and they are delineated in such a way that within each TAZ the land use activities are relatively homogeneous [
7].
The objective of these models is to establish relationships between the number of crashes per traffic analysis zone and neighbourhood traits (explanatory variables), such as traffic, road network characteristics, socioeconomic and demographic features, land use, dwelling unit, and employment type. Macro-level safety performance functions that are consistent with aggregate travel demand models have been developed to provide empirical tools for planners and engineers to conduct proactive analyses, promote more sustainable development patterns, and reduce the road crash burden on communities worldwide [
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37]. These models have great potential to promote increasingly sustainable development patterns by combining several redeeming features from pre-existing models. Specifically, improvements in land use and infrastructure efficiency, a reduction in environmental impact, an increased walkability and an improved neighbourhood social environment [
38].
Some of the dependent variables modeled in the previous studies include: total crashes; severe injury crashes; peak morning crashes; property damage only (PDO) crashes; total number of fatalities; total number of injuries; pedestrian crashes and; number of crashes involving elderly drivers [
7,
11,
20,
39,
40].
Explanatory variables used in previous studies can be grouped in four classes [
15]: (1) traffic characteristics (Exposure), (2) social demographic factors (SD), (3) roadway factors (Network), and (4) land use and travel habits (Transportation Demand Management).
Although it is not the most significant predictor of crashes, exposure is a key determinant of traffic safety. The relationship between crash occurrence and exposure is fairly straightforward. The higher the exposure, the greater the possibility for a crash to occur [
18]. The most common exposure variables, annual average daily traffic (AADT) or vehicle kilometers traveled (VKT), were used along with average zonal operating speed (SPD), and average zonal volume to capacity ratio (VC). Hadayeghi et al. [
7] found that VKT had significant effects on crash occurrence in a nonlinear relationship. Lovegrove et al. [
15,
16] confirmed earlier research regarding the dominant influence of VKT on crash predictions of all types. It also highlighted the significant influence that congestion (VC) and average zonal operating speed (SPD) play in safety evaluation.
Several studies observed that a low socioeconomic status and deprivation increase the fatality risk or the risk of being injured in traffic [
17,
18,
19]. An area’s socioeconomic deprivation level is usually measured by proxy factors such as total population (TPOP), population density (POPD), household density (NHD), and percentage employed (EMPP) within the TAZ [
9]. Ladron de Guevara et al. [
20] observed that population density and the number of employees (employment density) played a significant role in predicting crashes [
18]. Lee et al. [
24] also observed that a lower proportion of households without an available vehicle within a ZIP code was negatively associated with the risk of pedestrians being involved in a crash. Several authors have suggested factors including age, and sex to explain crash risk [
25]. Wier et al. [
26] have shown that the proportion of the population living in poverty, and the number of people aged 65 and older as percentage of the total population, were significantly good predictors of crashes. Similarly, Ukkusuri et al. [
27] found that the proportion of the uneducated (without any schooling) population had a positive effect on pedestrian crashes, while Lascala et al. [
28] concluded that the proportion of high school graduates was inversely correlated with pedestrian injury collisions.
Several road networks factors were considered in macroscopic studies, such as zonal lane kilometers (TLKM), percentage of each road class (ALKMP, LLKMP), intersection density (INTD), signal density (SIGD), intersection type (I3WP, IALP), and the average curvature of roadways (CRVD). Some studies have shown, that roadway density has a positive association with total crashes [
19] and fatal crashes [
12]. Hadyeghi et al. [
21] and Gomes et al. [
22] observed that intersection density, number of households, the number of major road kilometres and, the number of vehicle kilometers traveled, all had significant effects on crash occurrence. Cai et al. [
6] found that the length of sidewalks and length of bike lanes, have a positive effect on crash frequency.
Transportation Demand Management (TDM) strategies have hardly ever been implemented to improve traffic safety. Their main objectives are usually the reduction of congestion and emission, as well as travel costs and energy by means of reducing travel demand and consequently vehicle distance traveled, although their impact on traffic safety should not be neglected [
29,
30,
31]. However, different individual daily trips and land use are examined in numerous crash investigations. Wedagama et al. [
8] found that residential population density, manufacturing, retail trade and services industries were positively related to the number of road traffic crashes. Kim and Yamashita [
41] observed that areas with mixed residential and commercial land use have a higher frequency of crashes. Moreover, Pulugurtha et al. [
23] also observed that land use characteristics such as urban residential and mixed-use development are strongly associated with the number of crashes in a TAZ.
SPFs are critical to local and state transportation agencies due to their ability to identify regions with potential safety concerns [
42]. Therefore, for a jurisdiction or nation to fully benefit from applying these models, it is necessary to calibrate or recalibrate them to local conditions [
43]. This is because crash occurrence frequency, and the associated under- and over-dispersion in crash data can vary significantly across an area. The need for calibrating SPFs to specific area is clearly recognized by the American Association of State Highway and Transportation Officials (AASHTO) due to variations in factors associated with safety, such as road geometry and conditions, environmental factors, geographic characteristics, crash characteristics, reporting thresholds, all of which can be unique to a specific area [
2,
42].
Since macro-level has not been calibrated nor used until now in Italy, this paper’s aim is to fill these research gaps by developing safety performance functions to investigate the relationship between crash frequency and their contributing factors at TAZs level, using data from Naples, Italy. In this way, the paper provides Italian local and state transportation agencies with tools to conduct proactive road safety planning.
The models were developed using recorded crashes in the period of 2009–2011. To analyze different aspects of road safety, 17 dependent variables were investigated, which were divided into six main categories: (1) crash severity; (2) vehicle type; (3) crash location; (4) crash type; (5) traffic conditions; and (6) lighting conditions. There are 53 explanatory variables, which were chosen according to previous analysis of the literature, including factors describing traffic intensity, land use, employment type, socioeconomic and demographic, and traffic network characteristics.
4. Results and Discussion
The models were developed using the stepwise forward procedure, adding one explanatory variable at each step. 68 regression models were developed in order to examine the relationships between zonal crashes and a suite of factors describing traffic intensity, land use, employment type, socioeconomic and demographic, and traffic network characteristics.
Results of the stepwise procedure for all crash variables are shown in the tables below.
Table 4 shows the results of the crash severity group: total crashes (C); property damage only (PDO); severe injury crashes (C
s).
Table 5 shows the results of the crash vehicle type group: crashes where at least one car was involved (C
car); crashes where at least one truck was involved (C
truck); crashes where at least one powered two wheeler was involved (C
ptw); crashes where at least one pedestrian was involved (C
ped).
Table 6 shows the results of the crash location group: crash occur on curve or tangent elements (C
seg); crash occur within intersection (C
int).
Table 7 shows the results of the vehicle type group are reported: single vehicle is a type of road traffic crash in which only one vehicle is involved (C
sv); multi-vehicle is a road traffic collision involving more than one vehicle (C
mv).
Table 8 shows the results of the traffic conditions group are reported: peak day crashes occur in the part of the day during which traffic congestion on roads is highest (from 7 a.m. to 10 a.m.) (C
peakday); peak night crashes occur in the part of the night during which traffic congestion on roads is highest (from 4 p.m. to 9 p.m.) (C
peaknight); off-peak day crashes occur in the part of the day during which traffic congestion on roads is lower (from 10 a.m. to 4 p.m.) (C
off-peak-day); off-peak night crashes occur in the part of the night during which traffic congestion on roads is lower (from 9 p.m. to 4 a.m.) (C
off-peak-night).
Table 9 shows the results of the crash lighting condition group: crashes during the day (C
day); crashes during the night (C
night).
Analysis of the results shows that the goodness of fit of the models improves with decreasing the number of TAZ, particularly
increases and AIC decreases, except for C
ptw, C
off-peak day, and C
off-peak night, where going from 208 to 107 TAZ
decreases. Observing the parameters of good fit, the best TAZ is 208. This finding is consistent with previous studies. Xu et al. [
85,
86] observed that zoning schemes with the higher number of zones tend to have an increasing number of significant variables, more stable coefficient estimation, smaller standard error, but worse model performance. Moreover, Lee et al. [
47] confirmed that a higher level of aggregation of TAZ provides the best estimation models with less dispersion, but also demonstrated that if the zone is too large it may lose many local features.
For all models, exposure variables were the most significant predictors and positively associated with the number of crashes in each TAZ, as suggested and frequently shown in the literature [
9]. In all models, exposure variables such as the length of the road network (TRKM), the average congestion level (V/C) and the average speed (SPD) all gave significant results. These outcomes are consistent with the literature. Lovegrove et al. [
87] and Wei and Lovegrove [
88] found that regional congestion levels (V/C) were directly associated with the crash prediction model, and estimated that decreasing V/C values would result in decreasing crash estimates. This suggested that the average weighted V/C value for a given traffic zone could be used as a surrogate indicator of road safety. Xie et al. [
89] found that street length has a positive impact on crash occurrence.
In the model, the statistically significant demographic variables are resident population (POP), population aged 65 and above (Pop ≥ 65), male population (MaPop), and population aged 25 to 45 (25 ≤ Pop < 45). In particular, Pop ≥ 65 is associated with eight dependent variables: C, PDO, C
s, C
car, C
ped, C
seg, C
sv and C
off-peak day. These results are in line with other studies such as Montella et al. [
57], in which the older population showed greater propensity toward fatal crashes. The studies conducted by Noland and Quddus [
19] and Aguero-Valverde and Jovanis [
12] showed that a higher percentage of the elderly population are associated with a higher number of road crashes, while, according to Amoh-Gyimah et al. [
9] the elderly population percentage was positively associated with minor injury pedestrian crashes. A possible explanation is that the elderly may have weak eyesight and might usually take longer to cross a street, thus increasing their exposure to vehicle traffic [
90]. C
peakday is associated with resident population (POP), which was consistent with the research of Abdel-Aty et al. [
10,
91], Hadayeghi et al. [
21], and Xie et al. [
89]. The male population (MaPop) variable affects CPTW. Montella et al. [
56] found that male PTW drivers, in combination with other variables, was significantly correlated with fatal crashes. Interaction male population and population aged 25 to 44 (MaPop*25 ≤ Pop < 45) variable affects C
off-peak night and C
night. These outcomes are consistent with many studies, which have shown that as you get older, aggressive driving tendencies decrease and driver gender is correlated directly with aggressive driving [
92,
93]
The results also showed that increased crashes were associated with increases in workers per residents (WKGD). In particular, workers per residents (WKGD) is associated with C
car, C
truck, C
seg, C
peaknight and C
day. These results confirm earlier research by Lovegrove et al. [
15] and Kim et al. [
39,
94]. The difference between C
peaknight and C
peakday is related to the different activities carried out during the hours of the day. During the peak night hours, work-related trips prevail, while during the hours of the morning trips are more diverse, such as travel to school or shops.
The children and young people included in socio-educational projects (MinRe-edu) variable negatively affects the frequency of crashes. In particular MinRe-edu is associated with C, PDO, C
s, C
ptw, C
ped, C
peakday, C
off-peak day, C
off-peak night, C
day and C
night. These projects provide care to children from disadvantaged neighbourhoods, and organize after school educational activities and workshops which include music, art, cooking, sports, games and leisure activities. This variable shows that these projects also promote less aggressive driving habits in young people. The results of the present study confirmed the positive effects of an active learning-based educational program [
95]. This result highlight that the road user is the first link in the road safety chain. Whatever the technical measures in place, the effectiveness of a road safety policy depends ultimately on the users’ behaviour. For this reason, education, training and enforcement are essential [
58].
Regarding the transportation network, it was observed that as the number of trips increases, crashes also tend to increase. In particular, total trips (TRIP
t) is associated with C, PDO, C
car, C
truck, C
ptw, C
sv, C
mv and C
day. TRIP
p is associated with C
seg, TRIP
a, C
s, C
ped and C
int. These results confirm earlier research by Abdel-Aty et al. [
91]; Dong et al. [
96], Naderan and Shahi [
97]. A certain TDM scenario may be developed to reduce trips of a specific purpose, and the related number of crashes could be predicted. Hbus is the number of bus stops served in one hour in the area and it is an indirect measure of bus stop capacity. Hbus is a proxy for pedestrian traffic, so the positive sign indicates a growing correlation with crashes. The association between increased collisions and increased bus stops (BS) is consistent with researches of Kim et al. [
39,
94], Wei et al. [
88] and Rhee et al. [
98]. A larger number of subway stations were found to increase traffic crashes. Bus stops attract pedestrian activities, and an increase of such nodes would increase the possibility of conflict between pedestrians and vehicle traffic, and at bus stops, between buses, other vehicles and pedestrians [
89]. Pedestrian traffic is most likely unprotected, and therefore pedestrian routes must be improved.
5. Conclusions
Incorporating safety considerations into the transportation planning process in a comprehensive way has emerged as a strategy for improving transportation safety in recent years.
Macro-level safety performance functions were developed in this study to provide decision support tools for planners to consider safety in the transportation planning process, and to promote more sustainable land use and transport patterns. The objective of this study was to develop a series of macro-level safety performance functions that are consistent with aggregate travel demand models. It might be very helpful for administrations which do not have quality crash data to identify the area which has the highest number of crashes.
68 models were developed using recorded crashes in the period 2009–2011 in the city of Naples. To analyze different aspects of road safety, 17 dependent variables were investigated for four TAZ levels. The first result obtained highlights that, observing parameters of good fit of 68 models, the optimal scale was the TAZ with 208 zones. This result shows that using traditional zoning schemes might not be the optimal systems for regional safety analysis. In this study, the optimal zoning was obtained by aggregating contiguous small areas with similar crash characteristics.
The main significant variables were: children and young people included in socio-educational projects, population, population aged 65 and above, population aged 25 to 44, male population, total vehicle kilometers traveled, average congestion level, average speed, number of trips originating in the TAZ, number of trips ending in the TAZ, number of total trips and, number of bus stops served per hour.
Most of these variables are consistent with the literature, except for the MinRe-edu variable (children and young people included in socio-educational projects). Although a large number of road safety education programs exist, very few studies use crashes as an evaluation criterion—most use intermediate variables such as knowledge, attitudes and (self-reported) safe behaviour [
95,
99]. This study highlights the positive influence of socio-educational projects, connecting the presence of such projects to a reduction in crashes.
The findings of this study highlight that road safety management must take a more comprehensive approach with broader range of policy tools that can be applied to a wider range of component parts that comprise the road system. This approach must recognize that the road user is the first link in the road safety chain, and to achieve greater safety, socio-educational projects have to be included. This is in line with the first objective of the Road Safety Action Program 2011–2020 proposed by European Commission: Improve education and training of road users [
58].