1 Introduction
2 Vulnerable road user categorisation
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The amount of external protection
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Task competency, i.e., the extent to which people are able to function in risky situations
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Resilience (fragility), i.e., the extent to which people can absorb outside forces
Description | Age in years |
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Younger children | 7–12 years |
Older children; teens | 13–17 years |
Adult | 18–64 years |
Elderly | 65+ years old |
3 Safety impact assessment
3.1 Summary of state-of-the-art safety impact assessment methodologies for ITS
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Safety mechanisms: The safety mechanism approach is summarised in [14] as follows: the framework of a safety assessment of ITS should (1) cover all three dimensions of road safety − exposure, crash risk and consequence, (2) cover the effects due to behavioural adaptation in addition to the engineering effect (effect on target accident contributory factors) and (3) be compatible with the other aspects of state of the art road safety theories. This entails an estimation of the target population of the ITS and an expert evaluation of its effectiveness in preventing or mitigating accidents. A framework for assessing the road safety impacts that fulfils these requirements is the nine-point list of ITS safety mechanisms.
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Expert Questionnaires
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Accident reconstruction: this is based on case study-approach, where accident scenarios are simulated with and without the ITS present, and the outcomes are compared.
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Black box statistical analysis: a method based on artificial neural networks that assesses safety-based on information about the relevance and influence of the ITS on accident characteristics.
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Ex-post evaluation: this is based on accident data with and without the ITS.
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Field Operational Test data analysis: This approach uses Field Operational Test data to assess safety. The analysis uses data on near accidents or risky events and translates that data into an estimate on safety.
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Effectiveness methodology using a tree approach: This approach is based on mapping an accident database to a tree to classify the conditions of the injuries. The mapped accidents are multiplied by percentage of road users that didn’t not die/injured to estimate the effect of an ITS measure.
3.2 Overview of method development
3.3 Accident data
2012 | 2020 | 2030 | |
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Fatalities | 28,126 | 16,428 | 8572 |
Injuries | 1,429,888 | 1,085,888 | 762,262 |
Variable | Classification | Percentage of fatalities | Percentage of injuries |
---|---|---|---|
Collision type | Pedestrian accidents | 21% | 13% |
Single vehicle cycle accidents | 1% | 2% | |
Multiple vehicle accidents involving cycles | 7% | 10% | |
Single vehicle moped accidents | 1% | 1% | |
Multiple vehicle accidents involving mopeds | 3% | 5% | |
Single vehicle motorbike accidents | 4% | 2% | |
Multiple vehicle accidents involving motorcycles | 10% | 8% | |
Single accidents involving cars | 20% | 13% | |
Other accidents with two vehicles | 34% | 45% | |
Total | 100% | 100% |
Variable | Classification | Pedestrians | Cyclists (multiple vehicle) | Moped riders (multiple vehicle) | Motorcyclists (multiple vehicle) | ||||
---|---|---|---|---|---|---|---|---|---|
Fatalities (%) | Injuries (%) | Fatalities (%) | Injuries (%) | Fatalities (%) | Injuries (%) | Fatalities (%) | Injuries (%) | ||
Road type | Motorway | 4% | 0% | 0% | 0% | 0% | 0% | 4% | 3% |
Rural | 28% | 9% | 48% | 15% | 48% | 16% | 56% | 24% | |
Urban | 68% | 91% | 52% | 85% | 52% | 84% | 40% | 73% | |
Total
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100%
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100%
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100%
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100%
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100%
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100%
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100%
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100%
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Weather conditions | Normal | 88% | 77% | 91% | 90% | 93% | 90% | 94% | 9% |
Adverse | 12% | 23% | 9% | 10% | 7% | 10% | 6% | 91% | |
Total
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100%
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100%
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100%
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100%
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100%
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100%
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100%
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100%
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Lighting conditions | Daylight | 49% | 65% | 78% | 86% | 69% | 79% | 82% | 82% |
Night | 51% | 35% | 22% | 14% | 31% | 21% | 18% | 18% | |
Total
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100%
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100%
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100%
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100%
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100%
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100%
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100%
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100%
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Location | Intersection | 19% | 32% | 40% | 65% | 41% | 46% | 41% | 54% |
Link | 81% | 68% | 60% | 35% | 59% | 54% | 59% | 46% | |
Total
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100%
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100%
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100%
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100%
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100%
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100%
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100%
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100%
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Age | <15 years | 4% | 8% | 4% | 12% | 3% | 3% | 0% | 1% |
15–17 years | 2% | 10% | 3% | 6% | 19% | 32% | 3% | 4% | |
18—64 years | 50% | 70% | 51% | 69% | 56% | 62% | 93% | 92% | |
65+ years | 44% | 12% | 42% | 13% | 22% | 3% | 4% | 3% | |
Total
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100%
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100%
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100%
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100%
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100%
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100%
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100%
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100%
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3.4 Exposure effects
3.5 Summary
4 Mobility and comfort impact assessments
4.1 Summary of state-of-the-art mobility and comfort impact assessment methodologies for ITS
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Physiological, psychological and physical harmony (“harmony”);
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Quality and service levels; and
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Internal and external factors.
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Service levels refer to the external factors. This is the external environment, and is thus the link to the physical harmony
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Quality levels refer to the internal factors. This is the physical efforts and the perception of the environment, and the link to the psychological and physiological harmony.
4.2 Development of a mobility and comfort methodology to take into account vulnerable road users
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There are very few methods available to assess mobility and comfort, and of vulnerable road users, specifically.
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There is a little or no data is available on the mobility of vulnerable road users, and even less on the comfort of vulnerable road users (discussed in section 4.3).
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The number, duration and length of journeys is covered by mechanism 6
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Modal choice is covered by mechanism 7
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Route choice is covered by mechanism 8.
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The timing of trips is covered by mechanism 6
4.3 Data
4.3.1 Data on mobility
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Region (urban, rural, etc.)
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Number of trips per person
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Mode of transport
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Main mode of transport (for a specific trip [purpose])
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Modal-Split (of all modes used on a trip, including intermodal trips)
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Trip purpose
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Trip duration
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Route choice
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Travel distance
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Travel length
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Travel speed
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Time spent on travelling, duration
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Number of journeys
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Departure time/arrival time
4.3.2 Data on comfort
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workload related to travel;
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stress related to travel;
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uncertainty related to travel;
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travelling in adverse conditions (weather etc.);
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feeling of safety in relation to traffic.
4.4 Summary
5 Application of the methodologies
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Description of the purpose and technical performance of the system
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Description of the safety, mobility and/or comfort issue addressed by the system
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Description of the type of safety, mobility and comfort aspects the system affects. For safety, these are accidents the system aims to prevent or a description of type of accidents consequences the system aims to mitigate
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Description of circumstances in which the system works or is assumed to work or does not work
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Expectation of effects on the behaviour of the driver or other road users; effects on safety, mobility and/ or comfort, such as anticipated driver reactions and vulnerable road user reactions.
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Identification of the main classifying variable: A main classifying variable is an aspect that is the most important in the effectiveness of the system. Systems can perform better under some circumstances than others, for example, a system that works only at an intersection does not have an impact at road sections that are not intersections. Because systems sometimes work in several circumstances, the most important circumstance is chosen, and made the “main classifying variable”. The main classifying variable is used as a weight when quantifying the effects under different circumstances. For example, suppose that the ITS under assessment was more effective on preventing pedestrian accidents than cyclists accidents. If the system was estimated to prevent 30% pedestrian accidents and 9% of multiple vehicle accidents involving cycles, then the overall effect was determined by multiplying the share of relevant accidents by these effect estimates, and summing the results. In the example, this would give for the overall estimate the value “30% * share of pedestrian accidents + 9% * share of multiple vehicle accidents involving cycles”. These outcomes are used in step 6, where the effectiveness per situational variable is combined with the safety, mobility or comfort data, which is split into situational variables.
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Determine the estimates per mechanism as described in step 4
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Combine the estimates per mechanism into an overall estimate: First, the estimates given in percentages were converted to coefficients of efficiency (e.g. a decrease of accidents by 10% means that the target group of accident is multiplied by coefficient 0.90). Secondly, the total effect was computed by multiplying the coefficients for each mechanism and giving this total effect as a percentage.
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Apply reduction factors for usage and penetration rate: The estimated non-usage of systems (e.g. due to annoyance) was taken into consideration together with the penetration rate, as factors reducing the effect at 100% equipment rate.