Overview

The EUSka (Elektronische Unfalltypen-Steckkarte), an electronic map of accident types, including the classification of accident data and the analysis procedures for local accident investigations has been developed and replaced paper maps, which helps to analyse accidents more systematically and easily. For example, in Figure 12, a location distribution of pedestrian accidents can be clearly seen that helps traffic engineers to focus on accident black spots.

Figure 12: Example of EUSka displaying pedestrian accidents (source: PTV, 2009)

Microscopic level is more suitable for studying pedestrian accident analysis at signalised intersections, since valuable information can be acquired mainly from accident lists and accident diagrams.

Figure 13: Accident diagram at Rheinstraße-Neckarstraße in Darmstadt

3.2 Pedestrian accident analysis 23 An accident diagram (Figure 13) takes the intersection layout plan as the background, regulated symbols are used to describe accidents happened at the intersection in one year normally. Following information can be acquired with reference to pedestrian accidents:

• exact location of pedestrian accidents

• lighting and pavement conditions

• age of involved pedestrians

• pedestrian crossed on Red or not

• type of involved vehicles (passenger car /heavy vehicle/bus/tram)

• severity of pedestrian accidents (fatality/injury)

An accident list contains general information about background conditions, road and traffic conditions, layout and signal control conditions of the intersection, take accident database of Darmstadt for example, it contains following information:

• names and grades of intersecting roads

• intersection location in the road network

• reconstructed or not

• land use

• traffic related facilities in a vicinity of the intersection (such as train station, hotel, etc)

• trams or busses passing the intersection

• tram stations or bus stops nearby the intersection

• speed limitation of intersecting roads

• general description of pedestrian and cycle traffic (existence/volume is high or low)

• signal control strategy ( within the green wave or not)

• transit signal priority

• red light monitoring

• signal for sight-handicapped

• information about traffic signal controller

Generally, urban traffic accidents are classified into seven main types according to “Merkblatt für die Auswertung von Straßenverkehrsunfällen” (FGSV, 1998), among which “accidents related with turning traffic (type 2)” and “street crossing accidents (type 4)” are more related to pedestrian accidents. The main types are classified into several sub-types, for example, regarding one of the main accident types named “street crossing accidents” (type 2), it is classified into several subtypes basically according to the location of accidents(in front , behind or inside of the intersection) and crossing directions of pedestrians( from left or from right), shown in Figure 14. Complete sub-types with reference to pedestrian accidents can be found in Appendix A.

In front of the intersection pedestrians from left

without visibility problems pedestrians from left with visibility problems pedestrians from right

Behind the intersection pedestrians from left

pedestrians from right

with special sign regulating right-of-way

Inside the intersection

Figure 14: Pedestrian street crossing accidents (Type 2)

(source: “Merkblatt für die Auswertung von Straßenverkehrsunfällen”, FGSV, 1998)

Detailed accident analysis of Darmstadt (Germany) (2001~2005) is carried out following the methods mentioned above, see Appendix A.

3.2.1.2 Accident and risk

In pedestrian safety, risk is defined as “the probability of pedestrian collision/injury/fatality per unit of exposure” (e.g. Keall, 1995; Pucher and Dijkstra, 2000, 2003; etc.). In another word, the risk is derived from accident, but as a function of “exposure". According to different measures of exposure, the risk can be measured in macro level and micro level.

Macro risk

Measures of exposure used in the U.S. include “pedestrian distance travelled”, “pedestrian trips made” (Pucher and Dijkstra, 2000, 2003) and “the number of streets crossed” (Roberts et al., 1996).

In Europe, the most common measures include “the number of pedestrian trips made”, “time spent walking” and “distance walked” (ETSC, 1999). By using the concept of “risk”, firstly, at-risk groups can be identified, for whom their behaviour can then be investigated and attempts to modify it;

secondly, locations where pedestrians are more at risk can be recognised and this can lead to the development of appropriate countermeasures (TRL, 1986).

For example, when the raw accident data are presented as a function of exposure, measured as the hours spent walking, a very different picture emerges (Figure 15). It shows that the age categories

3.2 Pedestrian accident analysis 25 15-20 do not have elevated risk levels; rather, the high numbers of fatalities in this category are due to the fact that adolescents spend more time walking than other age groups.

(a) raw accident data of casualties

(b) risk data of casualties per million hours spent walking

Figure 15: Example of comparison of raw accident data and risk data (source: Keall, 1995)

Micro risk

Micro risk is more focused on a given location, e.g. a certain intersection, measures of exposure include “pedestrian volume” (Davis et al., 1988); “the product of pedestrian and vehicle volumes at an intersection” (Older and Grayson, 1972), or the square root of that product (TRL, 2001).

For instance,

PV

R= A (Older and Grayson, 1972) where,

R: pedestrian risk at a specific location

A: the number of pedestrian accidents in a given time period P: pedestrian flow over the same, or another time period V: vehicle flow over the same, or another time period 3.2.1.3 Drawbacks of accident analysis

Accident analysis is thought to be the most direct and objective way to estimate traffic safety, however, restricted by features of accident itself and complicated work of accident data registration and management, this method has several drawbacks (e.g. Korda, 1999; Lord, 1996; etc.).

• Accidents are always rare in a certain site, especially pedestrian accidents. Too small number could be misleading.

• Long time-span is required to collect enough accident data, especially for pedestrian accidents. Before-and-After analysis isn’t suitable to solve urgent problems.

• Accident analysis is based on accident data with high quality and quantity, lack of a well developed registration and management system hampers accident analysis.

• Provision of detailed accident data is charged by certain authorities and not always available.

• Dark figure exists, which means unreported cases of accidents are the biggest limitation of accident analysis, especially for pedestrian accidents.

• Differences in exposure (the amount of walking) population profile, modal split, and other factors may explain many of the differences and need to be taken into account when making comparisons in different areas.