Population ageing and rural road accidents: Analysis of accident severity in traffic crashes with older pedestrians on Spanish crosstown roads

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Population ageing and rural road accidents: Analysis of accident severity in traffic crashes with older pedestrians on Spanish crosstown roads Natalia Casado-Sanz, Begoña Guirao , Daniel Gálvez-Pérez ⁎

Departamento de Ingeniería del Transporte, Territorio y Urbanismo, ETSI Caminos, Canales y Puertos, Universidad Politécnica de Madrid, Avda. Profesor Aranguren, s/n., 28040 Madrid, Spain

ABSTRACT

The study of the relations of dependence between population ageing and road safety are becoming increasingly important with the ageing of the population in industrialised nations, which will imply a substantial increase in the number of older drivers and also in older pedestrians and cyclists. While there is extensive literature on the decline of driving and pedestrian skills in the elderly, few studies focus on the impact of age on accident severity according to the type of road. This approach has mainly been used for high-capacity routes, without considering rural roads. Rural roads are also associated with small towns, which have a higher percentage of elderly people who are more dependent on driving due to the absence or limitations of urban public transportation. In recent years Spain has seen a sharp rise in traffic accidents, especially in rural crosstown roads, and population ageing should be analysed as a potential risk factor. This paper contributes to the limited existing literature on rural road safety by applying a logistic regression model to the accidents on Spanish crosstown roads involving one vehicle and one elderly pedestrian in the period 2006–2015. The objective of the study is to analyse the conditional probability of a fatal outcome in the case of a crash resulting in at least one severe injury. On crosstown roads, territorial indicators associated to pedestrian mobility such as the physical severance index are revealed as interesting new variables to be considered in future research.

1. Introduction In 2017 there were approximately 962 million people in the world aged 60 or over, almost more than twice as many as in 1980 when the elderly global population was 382 million. This figure is expected to double again by 2050, when it is virtually projected to reach nearly 2.1 billion (United Nations, 2017). Population ageing is occurring all over the world and was one of the key demographic phenomena of the twentieth century; it will certainly also continue to be important in the twenty-first century. For the near future, all countries will be faced with population ageing of varying intensity and in unique time frames. On 1 January 2016, the population of the European Union was approximately 510.3 million, of which 19.2% was aged 65 or over (an increase of 2.4% compared to ten years earlier) (EUROSTAT, 2018). The 65-andover age group is increasing faster than any other in this region and is expected to continue to be the fastest growing population segment for at least the next 50 years, to a figure of 30% in 2050 (EUROSTAT, 2018). This is the greatest challenge for these countries in the coming years, particularly because it has received insufficient scientific attention until now. In terms of road safety, the rapid growth of the elderly among the population as a whole is again also gaining special relevance, as the percentage of older road users (drivers, pedestrians and cyclists) in relation to the total population is rising. This trend is a cause for ⁎

significant concern as drivers aged 65 and older experience more annual driving-related fatalities per kilometre driven than any other age group (Polders et al., 2015). Additionally, older pedestrians are among the most vulnerable road users in the transportation system. Crashes involving elderly users will rise to alarming levels if nationwide actions are not taken to improve safety. The main causes of this worldwide problem can be attributed to older road users' impediments in judging the speed and intentions of other road users (Gonawala, Badami, Electicwala, & Kumar, 2013). It is also well known that sight and hearing, along with ability and reaction time, decline with advancing age (Tollazi, Rencčlj, Rodošek, & Zalar, 2010). It has been demonstrated that age-related changes in sensorial, cognitive and practical constraints, not to mention medication, can affect the driving abilities of elderly road users (Di Stefano & Macdonald, 2003). The elderly population tends to live in small and medium-sized towns (Abellán, Ayala, & Pujol, 2017). Rural areas are also associated with small towns and a greater dependence on driving due to the absence and limitations of urban public transportation. Notable dissimilarities have been found between commuters living in rural and urban areas (Yuan & Lin, 2013), and rural residents travel about 33% more than urban residents (Litman, 2017). Some authors (Marshall & Ferenchak, 2017) have confirmed that most residents of rural areas suffer higher road fatality rates than those who live in urban areas. This research (Marshall & Ferenchak, 2017) also revealed significant

Corresponding author. E-mail addresses: [email protected] (N. Casado-Sanz), [email protected] (B. Guirao), [email protected] (D. Gálvez-Pérez).

https://doi.org/10.1016/j.rtbm.2019.100377 Received 25 February 2019; Received in revised form 2 September 2019; Accepted 9 September 2019 2210-5395/ © 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: Natalia Casado-Sanz, Begoña Guirao and Daniel Gálvez-Pérez, Research in Transportation Business & Management, https://doi.org/10.1016/j.rtbm.2019.100377

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regional disparities between rural and urban residents in terms of the road safety health impacts of the transportation system. A positive link has been found between urban concentration and safer urban traffic (Castro-Nuño & Arévalo-Quijada, 2018), at the expense of rural areas. These findings raise the question of equitability of impacts and highlight the need for research and infrastructure investment in rural areas to improve road safety. The term equity refers to the impartiality with which impacts are distributed (Litman, 2015), and although the problem of rural crosstown road accidents in Spain may appear to be minor in terms of road fatalities, the study of rural roads should be prioritised over urban roads following a social equity approach. In Spain, as in most developed countries, the elderly population tends to be concentrated in villages with fewer than 2000 inhabitants, where 28.2% of the population are elderly (Abellán et al., 2017). They make up only 20% of the population in Spain, but surprisingly account for around 71% of traffic fatalities (DGT, 2016). Crosstown roads assume particular importance as a type of rural road in this scenario. In these cases, small-town inhabitants are far more exposed to road crashes, and have seen their high street become absorbed in an interurban rural itinerary, producing a clash between urban mobility needs and the higher speeds demanded by interurban traffic. Pedestrian itineraries are impaired as a consequence of the road crossing the towns, marring the daily lives of their inhabitants. In these cases pedestrians are the most seriously vulnerable road user group due to their lack of protection and limited tolerance to vehicle collisions. Spanish crosstown routes saw an increasing number of fatalities from traffic crashes in the period 2011–2016, accounting for 2% of all fatalities and serious traffic injuries (DGT, 2016). The fatality rate (defined as the number of deaths per 100 victims) for 2016 was 2.3, while on other urban roads it was 0.6. In 2016 there were 1465 accidents on these roads, in which 47 people died, 149 were seriously injured and 1805 were slightly injured (DGT, 2016). These numbers represent an increase since 2015, and are still far from the figures for 2011 and 2012 (523 accidents; 37 fatalities; 113 serious traffic injuries). It is therefore very important to clarify the factors that increase the probability of a fatal outcome when a crash occurs on this type of road in order to reduce accident severity and extrapolate this experience to other developed countries. Specifically in terms of population ageing, many of the accidents recorded on Spanish crosstown roads involved at least one pedestrian (in > 30% of total accidents), and a large part of these pedestrians were elderly (42% of accidents with severe injuries and 71% of accidents with fatalities). These figures show the importance of pedestrian ageing on this type of rural road, and have led us to carry out a detailed empirical analysis of all the collisions between pedestrians and vehicles with elderly victims (over 65) on Spanish crosstown roads between 2006 and 2015. While there is extensive literature on the decline of driving and pedestrian skills in the elderly, few studies focus on the alternative factors that contribute to the increased severity of accidents involving elderly pedestrians or drivers. There are many concurrent variables affecting the severity of a road accident (road infrastructure indicators, traffic and exposure variables and socioeconomic variables), but until now the literature has concentrated on infrastructure, violation codes and exposure to risk variables, overlooking some territorial indicators. The main aim of this research is to obtain a thorough knowledge of the accidents involving elderly pedestrians on crosstown roads and to determine the key factors that increase the probability of a fatal outcome in crashes in which at least one severe injury has occurred. Territorial indicators (activity and physical severance indicators) associated to the accident spot have been measured ad-hoc for the case study (832 crosstown roads in Spain) in order to use a holistic database and not only the official national accident database. After measuring the ad-hoc territorial indicators for the 1405 accidents under study and designing the database, a sample of all the accidents involving one vehicle and one elderly pedestrian (225 accidents) was extracted and analysed in detail. The binary logistic regression was applied only to

this sample. To describe the research as a whole, this paper has been divided into the following parts: first, an introduction with the context and the main aims of the paper; second, a review of the literature focusing on publications dealing with accident severity, and a description of the other variables used in road safety databases to analyse accidents. Section 3 explains the main steps taken to design a holistic accident database of Spanish crosstown roads. Section 4 contains the methodology used, the sample characterization and the results of the analysis. Finally, the main research conclusions are described in Section 5. 2. Elderly users: ageing and other variables affecting accident severity Today's ageing population has led to a corresponding increase in the total number of older road users, either as vehicle occupants, cyclists or pedestrians (Etehad et al., 2015), and hence to a rise in the accidents involving at least one elderly person. It is therefore important to understand the different factors affecting the severity of accidents involving older users. Although there are now some examples of studies exploring the influence of age on crash severity, in their research into the history of road safety, Hagenzieker, Commandeur, and Bijleveld (2014) noted that although studies on older drivers increased in the late 1960s, they decreased from the early 1990s. We are therefore seeing the resurgence of this scientific topic due to the growing concern about the ageing process among the population as a whole. Below we classify the different approaches in the research linking road safety to pedestrian and driver ageing. One of the many lines of research on which researchers have focused concerns the visual and cognitive functions associated with advancing age and decreased mobility (Mori & Mizohata, 1995). The reduction in physical and mental faculties with advancing age leads to inappropriate behaviours in elderly drivers and pedestrians. The elderly also lose agility and muscle over the years. Studies can be found dealing with specific physical limitations such as restricted head movement (Isler, Parsonson, & Hansson, 1997). A person's health status and the relation between age and health are basic considerations when determining self-regulation (Donorfio, D'Ambrosio, Coughlin, & Mohyde, 2008). A large body of research has examined the problems of elderly people in judging other road users' speed and intention, and their deteriorated ability and reaction time (Boufous, Finch, Hayen, & Williamson, 2008). This group also tends to have difficulty in estimating the time of approaching objects (Andersen & Enriquez, 2006). Elderly crash victims have a greater risk of fatality due to an intensification of fragility caused by age-related degeneration in their physical and mental conditions (Yee, Cameron, & Bailey, 2006). For example, a study carried out in Serbia reveals that the elderly group (65 and over) had the highest percentage of fatalities due to population ageing (Jovic Vranes, Bjegovic Mikanovic, Milin Lazovic, & Kosanovic, 2018). The existing research and literature show a wide range of factors contributing to the occurrence and severity of pedestrian-vehicle crashes, not only age-related degeneration. Behavioural factors, road design and environmental conditions have been investigated by many researchers in the past. For instance, Ma, Zhao, Chien, and Dong (2015) analysed driver behaviour, vehicle characteristics and road conditions to identify their impact on road safety. Their results revealed that nine explanatory variables (including the at-fault driver's age, whether the at-fault driver had a licence or not, alcohol usage, speeding, pedestrian involvement, type of area, weather conditions, road surface and collision type) significantly affected injury severity. Lee and Abdel-Aty (2005) demonstrated that pedestrians aged between 15 and 24 are less likely to endure serious injuries when a crash has occurred, compared to older pedestrians. Ivan, Gårder, and Zajac (2000) assessed the different factors affecting accident severity in rural Connecticut and reported that vehicle type, risk factors such as alcohol and pedestrian age over 65 substantially increased pedestrian injury severity. Other factors 2

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such as the number of lanes or the lighting conditions had also an important effect on crash severity in this study, and in other research (Islam & Jones, 2014). Rothman, William Howard, Camden, and Macarthur (2012) demonstrated that the severity of pedestrian accidents occurring at uncontrolled mid-block locations was greater than those at controlled locations. Some studies have demonstrated that higher speed limits significantly increase the conditional probability of a fatal outcome in the case of a crash (Eluru, Bhat, & Hensher, 2008; Moudon, Lin, Jiao, Hurvitz, & Reeves, 2011). However, most of these studies tend to focus on infrastructure and vehicles, but do not consider the territorial factors and socioeconomic indicators of the town where the accident occurs, or exposure variables. Nearly all the international accident databases automatically compile variables such as the driver's and victims' ages and some infrastructure variables (lane width, road width, shoulder type, vehicle type, road markings, etc.) when an accident with fatalities or seriously injured victims occurs (Montella et al., 2012). Variables on territorial or exposure factors are laborious to collect, which is why most research has focused on analysing only recorded variables. In the analysis of traffic accidents, not only the methodology is important, but also the consistency of the database used. The success of any research on road safety is closely linked to the quality of the available accident database (usually designed at the national level by the authorities in each country), which serves as the input data for all statistical methods to analyse road safety. The database is compiled from a collection of road traffic crash data and enables a better understanding of operational traffic problems. It also allows the identification of risk factors and the evaluation of the efficacy of road safety programmes. Road traffic databases are helpful instruments to monitor the improvement, implementation and evaluation of safety programmes designed to decrease road traffic crashes (Abdulhafedh, 2017). Much of the research on road safety is based on the official statistical database of accidents available in each country. The quality and disaggregation of the database conditions the analytical methodology and the consistency of the results, which can assist authorities in their decisions to improve road safety and reduce accidents. It is important to note that while any methodology is constrained by the limitations of the database, there is a discrepancy in data uniformity between countries and even between local jurisdictions in the same country (Montella et al., 2012). Different road fatality and victim definitions have even been discussed in pursuit of standardisation. For example, an international comparison of different definitions of serious injury has been developed by Utriainen, Pöllänen, and Liimatainen (2018). If we compare the variables collected in the main guidelines (Montella et al., 2012) and databases around the world (US, New Zealand and Australian databases, and the requirements of Directive 2008/96/EC of the European Parliament), the age of all the people involved in the accident is automatically recorded in all databases. Compared to the other databases, the Spanish database is sufficiently consolidated to support the current research. One of the weak points of the Spanish accident database is the absence of traffic exposure data (traffic flow), road layout information, socioeconomic variables (population economic level, population ageing, unemployment rate) and territorial data (such as community severance indicators) associated to the accident location. However, all the possible factors involved must be addressed to analyse the causality of traffic accidents. Collecting these variables is very laborious, but is the only way to obtain a holistic approach for a road safety study. Considering the variables included in Table 1, all the possible factors influencing accident fatalities and severity can be classified into the following groups.

shoulder width and road markings. The Spanish accident database supplies some of these factors, although it does not collect layout variables, and the accident point is identified by the kilometric point on the road instead of GPS coordinates (as in the US), leading to further data processing problems. In the analysis of some accidents, as in the case of crosstown roads, the length of the road section where the accident took place is also important (length of the crosstown road), although this type of information must be measured ad-hoc in most cases. 2.2. Exposure indicators This group includes information on traffic flows (mobility) at the accident point, measured by the annual average daily traffic (AADT). The number of possible interactions between vehicles, defined by AADT levels, conditions the number of accidents. This type of indicators is rarely collected by official databases. AADTs are supplied by traffic gauging stations, but their location may be far from the point of the accident, requiring traffic stations to be allocated to accident points. The accident date and the climate (conditions) can also be considered as exposure indicators. 2.3. Socioeconomic variables of the town associated to the accident point (in the case of urban accidents) These include indicators such as per capita income, unemployment rate, percentage of population aged 65 and over and population size. They are not collected by official databases but can easily be found in other official databases (such as the Spanish INE statistics) and linked to each accident. 2.4. Victims' variables (fatalities or seriously injured individuals) These data are included in the national road safety database and there is no alternative way to find them. They usually include a minimum set of social data (age and gender), but also violation codes, alcohol levels and drug test results in addition to human behaviours related to the accident (pedestrian action, driver action, etc). 2.5. Territorial indicators Official databases rarely include territorial indicators associated to the accident point, and very few road safety studies deal with the relationship between accident rates and this type of indicators. Preliminary evidence (as shown in this paper) points to a territorial determinant in some types of accident locations (such as crosstown roads), but this issue remains generally unexplored in the literature on road safety. In the case of crosstown road accidents, the road clearly generates a physical severance, dividing the town into two different areas. At the local level, the location of activities in these two areas can condition the pedestrian itineraries interrupted by the crosstown road. There is therefore also a kind of “activity severance” that conditions accessibility to the main activity points. A database containing all these five types of variables would allow a holistic approach to road safety research. Researchers tend to consider only data contained in the national accident registers. The following section shows the procedure used to gather the abovementioned variables and thus consolidate the accident database for this study. 3. Designing a holistic accident database on Spanish crosstown roads A database (on Spanish crosstown roads) was first designed to be as complete as possible in order to determine the factors that increase the probability of a fatal outcome in crashes involving at least one severe injury. The accident data used in this study were obtained from the Spanish Accident Statistics database provided by the Spanish

2.1. Infrastructure factors These include layout variables (slope, minimum radius, layout consistency), road width, lane width, the presence of pavements, 3

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Table 1 Variables considered in the binary logistic regression, values and descriptive statistics. Variable

Values

% of the sample

Variable

Values

% of the sample

Roadway width

[1] [2] [3] [1] [2] [3] [1] [2] [3] [4]

22.7% 23.7% 53.6% 13.7% 61.2% 25.1% 49.4% 36.0% 12.2% 2.4%

Annual average daily traffic (AADT)

[1] 0–5000 [2] 5000–10,000 [3] > 10,000 [1] 0–5% [2] 5–8% [3] > 8% [1] 0–1000 inhabitants [2] 1000–5000 inhabitants [3] 5000–10,000 inhabitants [4] 10000–20,000 inhabitants [5] 20.000–35.000 inhabitants [6] > 35.000 inhabitants [1] 0–15% [2] 15–25% [3] > 25% [1] 0–5% [2] 5–10% [3] 10–15% [4] 15–25% [5] > 25% [1] 0–20.000 [2] 20.000–25.000 [3] > 25.000 Beginning of week (Mon) Weekday (Tue, Wed, Thu) End of week (Fri)

44.9% 26.7% 28.4% 29.3% 33.8% 36.9% 14.7% 36.4% 23.6% 12.4% 9.8% 3.1% 75.1% 16.9% 8.0% 1.8% 12.9% 28.9% 41.8% 14.6% 52.9% 35.1% 12.0% 19.1% 45.8% 13.3%

Weekend (Sat, Sun) [1] Good weather [2] Intense fog [3] Light fog

21.8% 82.3% 1.9% 2.4%

[4] [5] [6] [7] [8] [9] [1] [2] [3] [4] [5] [1] [2] [3] [4] [5] [6] [7] [8] [1] [2] [1] [2] [3] [4] [5] [1] [2] [3] [4] [5] [1] [2] [3] [4] [5]

8.1% 4.3% 0.0% 0.0% 1.0% 0.0% 63.5% 4.5% 25.0% 5.5% 1.5% 21.1% 1.4% 0.5% 5.6% 3.3% 0.0% 5.2% 62.9% 94.2% 5.8% 34.2% 29.8% 16.0% 9.8% 10.2% 17.8% 21.3% 16.4% 12.0% 32.5% 16.1% 18.6% 57.8% 6.5% 1.0%

Lane width Shoulder type

<6m 6–7 m >7m > 3.75 m 3.25–3.75 m < 3.25 m Non-existent < 1.5 m 1.5–2.5 m > 2.5 m

Percentage of heavy vehicles Population size

Hard shoulder

[1] Yes [2] No

83.3% 16.7%

Residents over 65 years old

Road markings

[1] Non-existent [2] Only lane separations [3] Lanes and road margins

11.2% 14.5% 71.3%

Unemployment rate

[4] Margins of roadway [1] Yes [2] No

3.0% 54.2% 45.8%

[1] [2] [3] [4] [5] [1] [2] [3] [4] [5]

0.0–2.0 km 2.0–4.0 km 4.0–6.0 km 6.0–8.0 km > 8.0 km 0.0–0.2 0.2–0.4 0.4–0.6 0.6–0.8 0.8–1.0

65.3% 28.4% 4.9% 0.5% 0.9% 80.9% 15.6% 2.6% 0.0% 0.9%

[1] [2] [3] [4] [5] [1] [2] [3] [4] [5]

< 30 years old 30–45 years old 46–54 years old 55–65 years old > 65 years old < 30 years old 30–45 years old 46–54 years old 55–65 years old > 65 years old

17.8% 29.8% 16.8% 11.6% 24.0% 5.8% 2.2% 1.3% 5.3% 85.4%

Pavement Road length

Ratio Rmin/Rave

Driver's age

Pedestrian's age

Driver's gender Pedestrian's gender

Area of injury

[1] Male [2] Female [1] Male

84.4% 15.6% 47.6%

[2] Female

52.4%

[1] Head

28.9%

[2] Other

71.1%

Per capita income Type of day

Weather conditions

Lighting

Visibility restricted by

Type of vehicle Physical severance index

Activity severance index

Probability mixed index

Directorate General of Traffic (DGT), which includes all the accidents that occur in the national territory. The first stage consisted of extracting the accidents located on crosstown roads. Accidents with at least one serious injury or one fatality were then selected. After

Light rain Heavy rain Hail Snow Heavy wind Other Daylight Dusk Sufficient lighting Insufficient lighting Without lighting Buildings Terrain Vegetation Weather conditions Glare Dust or smoke Other Without restriction Car Motorbike 0.0–0.2 0.2–0.4 0.4–0.6 0.6–0.8 0.8–1.0 0.0–0.2 0.2–0.4 0.4–0.6 0.6–0.8 0.8–1.0 0.0–0.2 0.2–0.4 0.4–0.6 0.6–0.8 0.8–1.0

debugging the database to remove accidents with incomplete data and accidents misclassified as accidents on crosstown roads, the initial sample consisted of 1405 road accidents (260 accidents with at least one fatality, plus 1145 accidents with at least one seriously injured 4

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Fig. 1. Location of the Spanish case study (on the left) and distribution of the accidents on Spanish crosstown roads (on the right) with at least one fatality (red dots) or one seriously injured person (blue dots), 2006–2015. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

unemployment rate), variables related with the road layout (road length, road layout consistency index, relation between minimum radius and average radius) and finally, territorial factors (physical severance index and activity severance index). In relation to exposure variables, the annual average daily traffic (AADTs) associated to each accident and the percentage of heavy vehicles were compiled using the different road capacity plans in all the Spanish autonomous regions. The AADT value of the nearest station (AADT gauging station) was associated with each point where an accident occurred. The AADT value of each accident in the sample under study is shown in Fig. 3, in absolute numbers. As can be seen in this figure, crosstown roads with lower traffic volumes are far more numerous in the sample under study. Most crosstown roads in the study have a percentage of heavy vehicles between 2.5% and 7.5% (see Fig. 3). In relation to infrastructure variables, most of the crosstown roads lengths were obtained from a database provided by the Spanish Directorate General of Traffic (DGT). For towns where the length was not included in this database, a manual procedure was used to measure this variable. The road length was measured from the town entrance sign or 50 km/h speed limit signs to the end of the town or the signs indicating the end of the speed restrictions. The collection of this variable has a great importance in the analysis, since a longer crosstown road could be assumed – at least initially – to be more hazardous than a shorter one. Fig. 3 shows that 60% of crosstown roads with accidents have a road length between 0.5 and 2.5 km, and the maximum number of seriously injured victims occurs for a road length between 1 and 1.5 km. Longer crosstown roads do not imply a higher number of accidents, although the zone of interaction between pedestrian itineraries and vehicles is greater. Another fundamental variable that was compiled to achieve the objective of the study is the radius sequence along the crosstown road, required to evaluate the road consistency in some way. Due to the absence of a centralized inventory of Spanish crosstown roads, AutoTURN software was used to measure the sequence of the road layout radius. This is a CAD-based software that simulates low-speed turn manoeuvres for road vehicles. The delineated virtual route approximates the route of the road we are analysing, returning the different radiuses of the entire road layout. A horizontal alignment consistency index was prepared with the adhoc measurement of the sequence of radii of 832 crosstown roads. This index relates the minimum radius with the average radius. The expression is given by:

individual on crosstown roads for a period of ten years (2006–2015). This represented the analysis of 832 crosstown roads (see location in Fig. 1). Of all these crosstown roads, a set of factors (including territorial, layout and exposure indicators) was obtained through an ad-hoc procedure. Subsequently, and with the aim of obtaining more detail, in the accidents in which the victims' age was repeated as a concurrent factor, the age of the victims involved in this initial sample was examined in an initial exploratory analysis. As can be seen in Fig. 2, two thirds of the victims were drivers, both in the group of accidents with at least one fatality (62.3% of the total victims) and in the group with at least one seriously injured individual (64.3% of the total victims). Many of these drivers were young people under 30 (approximately 30%). The second most affected group was passengers (19.8% accidents with at least one seriously injured individual, 18.1% accidents with fatalities). However, in terms of pedestrians, the most affected age group is the elderly (over 65). 42.4% of the total pedestrians involved in an accident with at least one seriously injured individual are over 65. This percentage rises considerably when considering accidents with at least one fatality, with 71.2% of the total number of victims. These figures are very high and show the extreme fragility of older people, given their greater comorbidity, especially in the case of pedestrians. These figures therefore show that one of the most vulnerable age groups in our sample is the elderly. This vulnerable group is mostly formed of pedestrians and, to a lesser extent, drivers, as shown in the previous figures. Therefore, starting from the initial database, the preliminary sample was filtered to include accidents with only one vehicle and one pedestrian involved, where one of the two victims was over 65 years old. As a result, the final sample consisted of 225 accidents (172 accidents involving an older pedestrian; 33 accidents involving an older driver; and 20 accidents where both the driver and pedestrian are over 65 on crosstown roads over a period of ten years (2006–2015). However, although the Spanish accident statistics database is one of the most complete worldwide, it did not include most of the variables used in the analysis, which had to be gathered using ad-hoc procedures. For example, some traffic variables such as the annual average daily traffic (AADT) at the accident point, or road layout parameters such as the minimum radius or length of the crosstown road, are not included in the Spanish database, and would be very interesting to study in order to analyse their influence on pedestrian crash severity. The following variables were measured ad-hoc: exposure indicators (annual average daily traffic), socioeconomic variables of the town (percentage of population aged 65 and over, per capita income, population size, 5

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Fig. 2. Characterization by age and type of vehicle associated with traffic accident victims on Spanish crosstown roads in the period 2006–2015.

RR =

Rminimum Raverage

measured with AutoCAD software using orthophotos of the affected towns (832 populations). These measurements were used to create an index that evaluates the physical barrier effects produced by the linear transport infrastructure. At least initially, a crosstown road in which the built-up areas are distributed along the two margins of the road is more hazardous than another in which most of the buildings are concentrated on only one side of the road, since it will potentially affect a greater percentage of the population. The index will consider the distribution of urban land as a relationship between the difference in the built areas on both sides of the road and the total built area (see Eq. (2)).

(1)

where RR is the horizontal alignment consistency index, Rminimum is the minimum radius of the section in metres and Rprom is the average radius of the section in metres. Thus the higher the value of the index, the greater the safety conditions of the crosstown road, since there will be little difference between the minimum and average radius (greater homogeneity). The exploratory analysis confirms that 80% of the accidents occurred on crosstown roads that have a horizontal alignment consistency index of < 0.20 (see Fig. 3), indicating a significant difference between both radii and the lack of homogeneity in the route of these crosstown roads. After considering the road layout variables, the following paragraphs describe the measurement of the territorial indicators. These indicators consider the community severance caused by the linear transport infrastructure (physical and psychological barrier). A new indicator has been built to measure the physical community severance (territorial factor) generated by the crosstown road. The area of the two built-up zones separated by the crosstown road were identified and

PSI =

Rss Lss Tsab

(2)

where PSI is the physical severance index, Rss is the largest built area of the crosstown road, Lss is the smallest built area and Tsab is the total built area (measured in square metres). Fig. 4 shows the different values this index can take. When this PSI value is near 0, the road crosses the territory dividing it in half (central crosstown road), which is the most unfavourable situation. In contrast, 6

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Fig. 3. New variables collected (AADT, percentage of heavy vehicles, horizontal alignment consistency index and crosstown road length) versus number of accidents involving one vehicle and one pedestrian in the period 2006–2015.

elderly people, may be much lower than the average (Burton & Mitchell, 2006). This research therefore considers only the points of interest that are 1 km away from the road axis. After analysing the points of interest in the 832 towns under study, an activity severance index was created in order to measure “community severance”. This index takes into account the number of services on each side of the crosstown road in order to analyse the most usual routes and journeys of the local inhabitants and how far the crosstown road acts as a deterrent in these pedestrian trips. The expression is given by:

a value of approximately 0.5 would imply that the crosstown road affects one third of the territory (lateral level 2). Finally, if the value obtained is close to 1, the road does not divide the population, but passes very close to it, leaving most of the buildings on one margin of the road (peri-urban crosstown road). The exploratory analysis of the sample (see Fig. 5) reveals that most of the crosstown roads (70% of the sample) with at least one fatality or seriously injured individual involving older pedestrians and drivers present low physical severance index values. This means that the accidents occurred on central crosstown roads and lateral level 1 (see Fig. 4), physically separating the municipality practically in two and affecting a greater percentage of the population. This finding is consistent with the degree of interaction between population and traffic flows and confirms the importance of this territorial factor. Not only physical severance is important, but also the location of the main activities in the community affected by the crosstown road. It was therefore considered necessary to plot the location of all the points of interest and activity centres to evaluate the attractiveness of all possible destinations for people living in a certain place. This analysis could provide knowledge of pedestrian walkability and the impact caused by the barrier effect of the crosstown road. We have called this “activity severance”. The location of the main activities was identified with LIVING MAPS (provided by ESRI). The following points of interest were considered in this analysis: schools, hospitals and health centres, grocery shops, financial institutions, public buildings and chemists. As the aim of collecting this variable was to evaluate pedestrian walkability, it is important to determine a maximum walking distance. Studies of pedestrian accessibility often consider a distance between 800 and 1000 m, depending on the type of destination. Nevertheless, the reasonable walking distance for certain population groups, especially for

ASI =

POIs _zA POIs _zB Tot _POIs

(3)

where ASI is the activity severance index, POIs_zA is the total number of points of interest in zone A (right side of the road), POIs_zB is the total number of points of interest in zone B (left side of the road) and Tot_POIs is the total number of points of interest in the area under study or municipality. Once the activity severance index has been calculated for all the towns with accidents involving serious injuries and fatalities on crosstown roads, the results show that 40% of crosstown roads have values close to 1, indicating that most of the services are concentrated on one side of the road. It could be assumed in this case that the conflict would depend on how many people live in the margin of the side of the road where there are no activity centres. This points to the need to design a mixed indicator that would consider not only the distribution of the activity centres but also the built areas on both sides of the crosstown road. This indicator would reflect the likelihood of the people living in the municipality crossing the crosstown road. The proposed expression is: 7

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Physical severance index 0 0.2 0.5 0.8 1

Activity severance index 0 0.2 0.5 0.8 1

Built area 1 Built area 2 50% - 50% 60% - 40% 75% - 25% 90% - 10% 100% - 0%

Type of crosstown road Central Lateral level 1 Lateral level 2 Lateral level 3 Outskirts

Distribution of the Points Of Interest (POIs) on each side of the road 50% of the POIs in zone A - 50% of the POIs in zone B 60% of the POIs in zone A - 40% of the POIs in zone B 75% of the POIs in zone A - 25% of the POIs in zone B 90% of the POIs in zone A - 10% of the POIs in zone B 100% of the POIs in zone A - 0% of the POIs in zone B

Fig. 4. Measurement of the physical and activity severance index.

I=

A1 ·P2 + A2 · P1 AT· PT

degree of significance of the variables included in the analysis of ageing on crosstown roads.

(4)

where A1 is the total area on one side of the road, A2 is the total area on the other side of the road, P1 is the total number of points of interest in area 1 (A1) and P2 is the total number of points of interest in area 2 (A2) (see Fig. 6). Once a holistic database had been designed, this research used a logistic regression to explore the determining variables affecting the severity of accidents involving elderly pedestrians and drivers. The following section describes the model finally used and the final results. Accident severity was chosen as the dependent variable to evaluate the

4. Methods and results Several statistical techniques can be applied to analyse the fundamental causes of crashes and injury severity in pedestrian-vehicle crashes; an important review can be seen in Lord and Mannering (2010) and Savolainen, Mannering, Lord, and Quddus (2011). However, the methodology used to reduce crash frequency and injury severity may be different (Savolainen et al., 2011) and in most cases these models are analysed separately. It is important to note that the factors that increase 8

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Fig. 5. Results of the explanatory data analysis of the physical and activity severance index.

the probability of a fatal outcome in the case of a crash (severity) are not necessary the same as the factors that determine the number of fatalities, which can be schematically viewed as the product of three terms: exposure, crash risk per unit of exposure, and severity (in the sense of conditional probability of a fatal crash outcome). One factor with a favourable effect on severity may therefore globally have an unfavourable influence on the number of fatalities, if it contributes to increases in exposure or crash risk. It is therefore important to collect different types of variables when analysing accident fatalities and even severity. If the aim is to understand the factors associated with crash-frequency data (for example, to explore the issues leading to a more dangerous road, in the sense of a road with more fatalities), a wide variety of methods could be applied, including the Poisson regression model, negative binomial model (Lord, 2006), Poisson-lognormal model (Lord & Miranda-Moreno, 2008) or generalized estimating equation model (Lord & Persaud, 2000), among others. On the other hand, if the purpose is to analyse crash severity, binary logit models (Sze and Wong, 2007), ordered logit or probit models (Ivan et al., 2000; Lee & Abdel-Aty, 2005), mixed logit models (Islam & Jones, 2014) or multinomial logit models (Rothman et al., 2012) could be developed. Based on this consideration, a binary logit model has been selected in this research to analyse pedestrian-vehicle crash injury severity.

the logistic regression. The objective of the logistic regression is to predict the probability of Y occurring, when the values of the variables Xs are known. The general equation is:

P(Y) =

1 1+e

(b0 + b1 X1+ b2 X2 +…+ bn Xn )

(5)

where P(Y) is the probability of Y occurring, e is the exponential function and the other coefficients are analogous to those of the linear regression. Binary logistic regression models are the most interesting, since most of the circumstances analysed in the experimental sciences respond to this model. The starting equation in the logistic regression models is:

P(Y = 1 | X) =

exp (b0 + 1 + exp (b0 +

n bx i=1 i i ) n bx i=1 i i )

(6)

where: P(Y = 1|X) is the probability that Y takes the value 1. X is the set of n covariables x1, …, xn that are part of the model. b0 is the model constant or independent term. b1 is the coefficients of the covariates. The model was applied to a final sample consisting of 225 accidents, considering 27 variables (see Table 1).

4.1. Severity model This study used logistic regression to explore the possible variables contributing to accident severity. A binary logistic regression model was applied to the sample, and accident severity was considered as the dependent variable. The dependent variable was classified into a group variable with two possible values: ‘0’ = severe injury accident group (accidents that list seriously injured individuals but no deaths) and ‘1’ = fatal accident group (accidents with at least one fatality). The method of maximum likelihood is used to estimate the parameters of

4.2. Receiver operating characteristic (ROC) curve Three models were created with different stepwise methods (Forward, Backward and Enter) according to Myers, (1990). The ROC curve was used in this study to examine the precision of the three models. Table 3 shows a confusion matrix, which will be used to construct the ROC curve. The True Positive (TP) rate is shown on the y-axis

Fig. 6. Examples of application of the Probability Mixed Indicator (PMI). 9

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are 1130.0% higher than the baseline condition. For the other levels of this variable, the results are far from statistical significance and no firm conclusion can be drawn. Therefore, as a conclusion of the influence of this variable, the severity is possibly lower when the crosstown road divides the village in two equal halves (due to lower speeds), although the number of pedestrians who cross the roadway (and thus expose themselves to an accident) may be considerably higher in this situation (as compared to a ‘lateral’ or ‘outskirts’ crosstown road). As a result, although the severity is lower, we cannot rule out the possibility that the number of fatalities may be higher when the crosstown road divides the town in two equal halves. With regards to activity severance, the model suggests that the location of the different services is not substantially relevant in fatal crashes involving elderly pedestrians. However, the likelihood of the people living in the municipality crossing the crosstown road is very significant in the model (probability mixed index). In the same way as for the physical severance index, the severity of an accident occurring on a crosstown road that separates a town into two equal halves, and where the services and activities are favourably distributed throughout the town, is lower than in towns where the services are located on only one side of the road (the odds ratios decrease as the probability of crossing increases). In regard to infrastructure factors, the odds ratio estimated for a roadway width of between 6 and 7 m was 0.374 (e−0.984), and 0.221 (e−1.512) for a roadway width of > 7 m. These results suggest that a wider crosstown road decreases the conditional probability of a fatal outcome in the case of a crash involving at least one seriously injured individual. At first glance, this result could not be expected, since according to previous literature, a wide roadway may encourage higher speeds and therefore may increase the severity (Fitzpatrick, Carlson, & Brewer, 2001; Ma, Yang, & Zeng, 2009). However, another interpretation would be that wider roadways correspond to more urban environments (densely built and populated environments), where the roadway has been widened in order to give space for car parking, for example. And for a number of reasons the speeds and severity are lower in more dense urban areas. A wide roadway in densely urbanised environment may correspond to a cross-section with more lanes, or two lanes in addition to lateral spaces dedicated to car parking and with narrower lane widths. This interpretation would be consistent with the results concerning the effect of lane width: the severity is lower for lanes of < 3.25 m. In relation to visibility, visibility restricted by building very significantly reduces the accident severity on crosstown roads involving a pedestrian. Restricted visibility may play a complex role in terms of severity: it reduces the time available for braking for example (leading to a higher impact speed and a higher severity). Furthermore, visibility restricted by building may correspond to denser urban environments, where speeds and severity are lower for a number of other reasons. The vehicle type is very significant to the pedestrian's level of injury when the collision occurs. Fatal outcomes are less frequent when the vehicle hitting the pedestrian is a motorbike rather than a car on this particular type of road. However, the overall probability of causing a fatal accident is not necessarily lower for a motorbike, since compared to cars, motorcyclists may have a higher risk of colliding with a pedestrian, according to Clabaux et al., (2014). Regarding exposure indicators, the odds ratio decreases as the annual average daily traffic increases. Crosstown roads with a low volume of traffic lead to an increased severity of pedestrian crashes than those with higher traffic volumes. The probability of receiving fatal injuries on these crosstown roads is higher when the pedestrian is less exposed to traffic. Higher traffic volumes may also correspond to more densely urbanised environments, with a large local traffic, and where accident severity is low for a number of reasons. They could also correspond to a more congested traffic, with lower speeds. Finally, in relation to the pedestrian injury site, as can be expected, the factor for other injury sites is 0.298 (e−1.209), indicating that

Fig. 7. ROC curves for the different models.

of the ROC curve graph, and represents all the correctly classified fatal injuries (TP/Total fatal injuries). The false positive (FP) rate is shown on the x-axis of the ROC curve, and represents the false predicted fatal injuries (FP/Total number non-fatal injuries). A curve closer to the topleft corner indicates a better performance. The area under the ROC curve (AUC) is assessed to evaluate the performance of the algorithms (see Fig. 7). The method chosen was the one with the biggest area under the ROC curve (Backward method). 4.3. Results This section discusses the results of the binary logit model developed for pedestrian crashes. The analysis was done with the R-studio software and the MASS statistical package. As the main objective of this study is to determine the key factors that increase the probability of a fatal outcome in the case of a crash involving at least one seriously injured individual, three different models were estimated for pedestrian crashes using the 27 explanatory variables given in Table 1. The three different criteria (area under the ROC curve, the Akaike information criterion and the Bayesian information criterion) were compared to determine the most appropriate model. The final model selected was the one calculated with the Backward method. The binary logit models were estimated for all the data using the maximization of log-likelihood method (Table 2). The estimated coefficients reveal the effects of a contributing variable on the conditional probability of a fatal outcome in the case of a crash involving at least one severe injury. The estimation results of the model can be seen in Table 2. The chosen model reflects the influence of a total of 12 variables. The coefficients with positive values indicate an increase in the probability of a fatal outcome in the case of a collision involving a pedestrian. The variables of physical severance index [2, 3, 5], driver aged between 30 and 45 and driver aged between 55 and 65, visibility restricted by terrain or vegetation, AADT between 5000 and 10,000 vehicles/day, and a probability mixed index between 0.8 and 1, are found to increase the probability of a fatal outcome. The results of the physical severance index revealed that the territorial variable is very significant. The odds ratio estimated for a physical severance index value between 0.2 and 0.4 was 2.983 (e1.093). This analysis suggests that the probability of a fatal outcome on a lateral crosstown road is 198.2% higher than the baseline condition of a central crosstown road. The odds ratio estimated for a physical severance index value between 0.8 and 1 was 12.300 (e2.510). This analysis suggests that the odds of a fatal outcome on a peri-urban crosstown road 10

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Table 2 Main determinants of the severity model for pedestrian crashes. Variable

Coeff.

S.E.⁎

Z-value

p

Odds ratio

(Intercept) Physical severance index [2] (Ref. Physical severance index [1]) Physical severance index [3] (Ref. Physical severance index [1]) Physical severance index [4] (Ref. Physical severance index [1]) Physical severance index [5] (Ref. Physical severance index [1]) Type of vehicle, motorbike (Ref. Type of vehicle, car) Driver's age 30–45 years old (Ref. < 30 years old) Driver's age 46–54 years old (Ref. < 30 years old) Driver's age 55–65 years old (Ref. < 30 years old) Driver's age > 65 years old (Ref. < 30 years old) Female driver (Ref. Male driver) Roadway width 6–7 m (Ref. < 6 m) Roadway width > 7 m (Ref. < 6 m) Lane width 3.25–3.75 m (Ref. > 3.75 m) Lane width < 3.25 m (Ref. > 3.75 m) No pavement (Ref. pavement) Visibility restricted by building (Ref. No restriction) Visibility restricted by terrain (Ref. No restriction) Visibility restricted by vegetation (Ref. No restriction) Visibility restricted by climatology (Ref. No restriction) Visibility restricted by glare (Ref. No restriction) Visibility restricted by dust (Ref. No restriction) Other visibility restrictions (Ref. No restriction) AADT 5000–10,000 vehicles/day (Ref. < 5000 vehicles/day) AADT > 10,000 vehicles/day (Ref. < 5000 vehicles/day) Probability mixed index [2] (Ref. Probability mixed index [1]) Probability mixed index [3] (Ref. Probability mixed index [1]) Probability mixed index [4] (Ref. Probability mixed index [1]) Probability mixed index [5] (Ref. Probability mixed index [1]) Other injury zone (Ref. Head injury) Log likelihood at zero Log likelihood at convergence

−14.156 1.093 0.536 −0.507 2.510 −1.204 1.357 −0.292 1.080 −2.165 −0.927 −0.984 −1.512 −0.905 −1.811 −0.776 −2.925 0.217 20.583 −1.501 −3.015 −0.609 −1.973 0.185 −1.327 −0.922 −1.041 −3.221 0.655 −1.209 −134.348 −84.872

950.656 0.579 0.754 0.900 1.046 0.826 0.607 0.714 0.711 1.179 0.615 0.628 0.639 0.654 0.805 0.454 0.798 1.486 3956.180 0.958 1.368 1.616 1.190 0.482 0.601 0.757 0.727 1.234 1.851 0.435

−0.015 1.888 0.711 −0.562 2.399 −1.459 2.237 −0.409 1.517 −1.836 −1.508 −1.569 −2.366 −1.383 −2.25 −1.711 −3.666 0.146 0.005 −1.566 −2.203 −0.377 −1.657 0.383 −2.207 −1.218 −1.432 −2.609 0.354 −2.773

0.988 0.059 0.477 0.574 0.016 0.145 0.025 0.683 0.129 0.066 0.132 0.117 0.018 0.167 0.024 0.087 0.000 0.884 0.996 0.117 0.028 0.706 0.097 0.702 0.027 0.223 0.152 0.009 0.724 0.006

0.000 2.983 1.710 0.603 12.300 0.300 3.885 0.756 2.945 0.115 0.396 0.374 0.221 0.404 0.163 0.460 0.054 1.243 8.69e8 0.223 0.049 0.544 0.139 1.203 0.265 0.398 0.353 0.040 1.925 0.298

Sig. .

⁎ ⁎

.

⁎ ⁎ . ⁎⁎⁎

⁎ . ⁎

⁎⁎ ⁎⁎

Signif. codes: 0 ‘⁎⁎⁎’ 0.001 ‘⁎⁎’ 0.01 ‘⁎’ 0.05 ‘.’ 0.1 ‘ ‘ 1. ⁎ S.E. (Standard Error).

have a significant impact on the severity of accidents involving the elderly and must be taken into account when analysing road safety. Further research is needed to analyse the mixed indicator in depth; it would also be interesting to combine new statistical methods (such as the Poisson regression model) to explore alternative factors contributing to pedestrian fatalities, and not only the study of injury severity levels. From a policy perspective, strategic action plans for rural roads should be created for each region affected. Specific action guidelines based on equity criteria should be devised for each type of rural road. New strategies are needed to integrate crosstown roads into the urban structure when they become obsolete, taking population ageing into account in their design: part of the solution could be redundant signalling and the implementation of alternative traffic calming devices. The main pedestrian itineraries traversing the crosstown should also be analysed considering the main purpose of the pedestrian trip (doctor's visit, simply walking, shopping, etc.). When planning road traffic areas for elderly users, the infrastructure must serve its function, namely that road users should be able to predict the responses of other drivers or pedestrians, especially on crosstown roads. From the driver's point of view, crosstown roads cannot be studied independently of the road network as a whole, so the entire car trip itinerary involving crosstown roads should be studied. When a rural road crosses a town in an isolated manner, the effect on driving may not be the same as in the opposite situation, in which there are a series of consecutive crosstown roads separated by only a few kilometres. There is clearly a need to model this type of itinerary effects that may imply a higher fatality rate on crosstown roads. In terms of national policy, it is important to clarify the evidence of the benefits of retraining programs for older drivers and even road education for pedestrians. The current conclusions of this study are simply a starting point for a future line of research intended to

Table 3 Sensitivity and specificity.

Test as fatal Test as non-fatal

Fatal

Non-fatal

True positive (sensitivity) False negative (missed)

False positive (false alarm) True negative (specificity)

compared to head injuries, pedestrians are 70.0% less likely to receive fatal injuries when the injury is in an area other than the head. 5. Conclusions and policy recommendations The literature has shown that the rapid growth of the elderly as a group among the whole population is a cause for significant concern due to the increasing numbers of elderly drivers and pedestrians. An initial exploratory analysis of the Spanish accident database revealed that ageing is also becoming a serious problem in the Spanish road network. As this population group in Spain is mainly located in small and medium-sized towns, the study of crosstown roads was considered a good starting point to address this issue. Accidents on crosstown roads were extracted and examined in detail, with the conclusion that most of this vulnerable group were pedestrians and drivers. The initial sample was therefore filtered to identify accidents involving only one vehicle and one pedestrian where one of the two victims was over 65, producing a final sample of 225 accidents with fatalities or seriously injured individuals. A logistic binary regression was also applied to study injury severity. The database was built through an ad-hoc collection of territorial, exposure and infrastructure indicators. The results show that most variables such as visibility conditions, the physical division of the town caused by the road, the probability of crossing and the distribution of the services and activities in the town 11

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investigate in greater depth the casuistry of accidents involving the ageing population. The crosstown road scenario is only one example of the rural road situation: urban and interurban scenarios also remain unexplored. As conventional roads (rather than high-capacity roads) concentrate the majority of fatalities, future research lines should have the ultimate aim of achieving the target of “zero fatalities” on the road network as a whole.

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