EGVIA - ERTRAC 1st European Conference Results from Road Transport Research in H2020 projects 29 November 2017 to 30 November 2017 Brussels

1. This project has received funding from the
[European Union’s Horizon 2020 research and
innovation programme under grant agreement
No 723970
XCYCLE
Luca Pietrantoni
University of Bologna
This project has received funding from the
European Union’s Horizon 2020 research
and innovation programme under grant
agreement No 635975

2. This project has received funding from the [European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 723970
XCYCLE: Advanced measures to reduce cyclists' fatalities and
increase comfort in the interaction with motorised vehicles
Related topic:
MG-3.4-2014 Traffic safety analysis and
integrated approach towards the safety of
Vulnerable Road Users
Project key words: cyclists, traffic safety,
detection systems, behavioural evaluation,
user-centred approach.
Targeted geographical areas: urban
EU funding contribution: 5009330.00 €
Duration: 42 months (May 2015 - Nov 2018)
Coordinator: University of Bologna, Italy

3. This project has received funding from the [European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 723970
Objectives of the project
Objective #1: Improvements in traffic safety analysis
Objective #2: Improvements of safety-related behavioural
analysis
Objective #3: Improvements in HMI design and acceptance of
ITS
Objective #4: Development of an in-vehicle and on-bike
system
Objective #5: Development of an infrastructure-based system

4. This project has received funding from the [European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 723970
Results - Objective #1 Traffic safety analysis
We found the main factors
contributing to Bicycle–Motorised
Vehicle (B-MV) collisions
We analyzed crash data on B-MV
collisions from 10 European
Countries
Prati, G., Marín Puchades, V., De Angelis, M., Fraboni, F., & Pietrantoni, L. (2017).
Factors contributing to bicycle–motorised vehicle collisions: a systematic
literature review. Transport Reviews, 1-25

5. This project has received funding from the [European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 723970
Results - Objective #1
Traffic safety analysis
We identified key features of cyclist crashes
using latent class analysis and association
rule mining.
19 types of bicycle crashes analysing
500.000 accidents
Prati, G., De Angelis, M., Puchades, V. M., Fraboni, F., &
Pietrantoni, L. (2017). Characteristics of cyclist crashes in Italy
using latent class analysis and association rule mining. PLoS
one, 12(2), e0171484.

6. This project has received funding from the [European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 723970
Results - Objective #1 Traffic
safety analysis
CHAID decision tree technique was employed to
establish the relationship between severity of
bicycle crashes and factors related to
• crash characteristics (type of collision and
opponent vehicle)
• infrastructure characteristics (road type,
road signage, pavement type, and type of
road segment)
• cyclists (gender and age)
• environmental factors
Prati, G., Pietrantoni, L., & Fraboni, F. (2017). Using data
mining techniques to predict the severity of bicycle
crashes. Accident Analysis & Prevention, 101, 44-54.

7. This project has received funding from the [European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 723970
Results - Objective #1 Traffic safety analysis
Bayesian network analysis
identified:
crash type (0.31),
road type (0.19),
and type of opponent vehicle
(0.18)
as the most important predictors of
severity of bicycle crashes.
Prati, G., Pietrantoni, L., & Fraboni, F. (2017). Using data mining
techniques to predict the severity of bicycle crashes. Accident
Analysis & Prevention, 101, 44-54.

8. This project has received funding from the [European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 723970
Results - Objective #1
Traffic safety analysis
We investigated the relationship
between composite indicator of
Gender Equality Index and women's
participation in transport cycling
across the 28 member states of the
European Union
The effect of gender equality varied
across different indicators, with the
strongest effect size found for time.
Prati, G. (2017). Gender equality and women's participation in
transport cycling. Journal of Transport Geography.

9. This project has received funding from the [European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 723970
Results - Objective #2 Improvements of
safety-related behavioural analysis
Puchades, V. M., Pietrantoni, L., Fraboni, F., De Angelis, M., & Prati,
G. (2017). Unsafe cycling behaviours and near crashes among Italian
cyclists. International journal of injury control and safety
promotion, 1-8.
Fraboni, F., Puchades, V. M., De Angelis, M., Prati, G., & Pietrantoni, L.
(2016). Social influence and different types of red-light behaviors among
cyclists. Frontiers in psychology, 7.
We analyzed errors and violations among cyclists and how “persuasive
design” and traffic infrastructures might reduce unsafe behaviours

10. This project has received funding from the [European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 723970
Results- Objective #3 HMI improvements and acceptance of
ITS
We identified major determinants of acceptance of Pedestrian and Cyclist Detection
System and Emergency Braking (PCDS + EBR).
According to VRUITS, “the estimates for PCDS+EBR showed the maximum reduction of
7.5% on all road fatalities and 5.8% on all road injuries which came down to an
estimate of over 2,100 fatalities and over 62,900 injuries saved per year in the EU-28”
A driving simulator in Leeds has been
programmed with common cycle-to-truck conflict scenarios.
A set of HMI recommendations has been derived covering
both visual and acoustic aspects.
Results show that design should encourage checking of the
relevant mirror when a cyclist was alongside the truck.
De Angelis, M., Puchades, V. M., Fraboni, F., Pietrantoni, L., & Prati, G. (2017). Acceptance of an In-Vehicle Cyclist
Detection System. Transportation Research Part F: Traffic Psychology and Behaviour, 49.

11. This project has received funding from the [European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 723970
Results- Objective #4 Development of in-vehicle and
on-bike system
We developed sensor requirements on a truck based
on right turn scenarios simulation.
We developed a UWB
localization system consisting
of active tags mounted on
bikes and anchor nodes
mounted on a vehicle.
An on-bike device with audio
warnings was was tested in a
semi-controlled field study in
Bologna.
Dardari, D., Decarli, N., Guerra, A., Al-Rimawi, A., Puchades, V. M., Prati, G., ... & Pietrantoni, L.
(2017). High-accuracy tracking using ultrawideband signals for enhanced safety of
cyclists. Mobile information systems, 2017

12. This project has received funding from the [European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 723970
Results- Objective #5 Development of an
infrastructured-based system

13. This project has received funding from the [European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 723970
Results- Objective #5
Development of an
infrastructured-based system
Video recordings and trajectory data have
been analysed in Braunschweig
Goal: prediciting critical situations between
right-turning motorists and crossing cyclists
before they occur
Results are basis for detection of atypical
behaviour and patterns in data
material àinput for development of
predictive algorithms

14. This project has received funding from the [European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 723970
Results- Objective #5 Development of an
infrastructured-based system
Blokpoel, R., & Niebel, W. (2017). Advantage of cooperative traffic light control algorithms. IET Intelligent
Transport Systems.
We developed adaptive traffic controller
algorithm for ”green wave for cyclists”
Green wave reduces risk of red light violation for bicycles
Increases comfort and therefore stimulates modal shift
Bicycles will form platoons, which increases visibility

15. This project has received funding from the [European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 723970

16. This project has received funding from the [European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 723970
Expected or potential impact of the project
• Reduction of B-MV collisions
Ø Reducing critical scenarios in the urban environment (blind spots in right and left turn,
waiting time at intersections and red-light skipping behaviours by cyclists)
Ø Enabling the users to undertake the right pattern of actions in critical situations: multi-
modality warnings, intuitive HMI designs (e.g., bird-eyed view)
• Effective tools for education and training aimed at changing behaviours and risk perceptions
of different type of road users (cyclists, car/HGV drivers) in a changing context (e-bikes, CAD)
• Innovative analytical methods to investigate contributing factors of collisions involving VRUs in
different European countries > new challenges (interactions between cyclists and electric or
autonomous vehicles)
• Environmental, health and economic impact - cycling as an active and sustainable mode
(promotion of cycling by increasing “perception of safety”)

17. This project has received funding from the [European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 723970
Achievements of the project /1
Indicator Units used
Project
ref.
Project objective Current achievements
Types of road
users
Testing procedure
improvements
Number of crashes
which can be
represented more
realistically
D3.2
Develop an innovative in-vehicle
system
A system has been developed
for automatic testing of
defined cyclist scenarios, with
robot steering and a bicyclist
on a rail.
Capable of running defined
BAST scenarios.
Cyclists
Testing Procedure /
Equipment
improvement
Precision for
bicyclist detection
D3.2
D3.4
Increase precision for cyclists
detection in terms of decimeter-
level positioning accuracy (relative
between users including vehicles,
infrastructure and bicyclists),
higher update rate than GNSS (10
Hz max).
More precision obtained:
decimeter-level positioning
accuracy between bicyclists
and infrastructure, passenger
vehicle
20 Hz update rate
Cyclists,
motorists

18. This project has received funding from the [European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 723970
Achievements of the project /2
Indicator Units used
Project
ref.
Project objective Current achievements
Types of road
users
HMI improvements and
better vehicles control
strategies
Risk
assessment
strategy
objective
levels
D3.1
D3.3
Optimize HMI strategy for warning driver
about the presence of cyclists
Design HMI with discrete levels of
urgency (Information, Warning,
Assistance, and Intervention).
A driving simulator has been programmed with
common cycle-to-truck conflict scenarios.
A set of HMI recommendations has been derived
covering both visual and acoustic aspects. Design
should encourage checking of the relevant mirror
when a cyclist was alongside the truck.
Heavy Vehicle
Drivers
Avoidance of
fatalities/accidents
Estimated
number of lives
saved
Estimated
number of
accidents to be
avoided
D3.1
D3.2
D5.1
Reduce the number of fatalities and
accidents in B-MV collisions
In 2014, 22% of the 3863 people killed in
Heavy Vehicle accidents in Europe
involved cyclists (850 deaths).
Preventing 1% of these accidents would
save about 9 lives, preventing 10% of
them would save about 85 lives.
Deployed and tested infrastructure on a truck that
expands the sensory horizon, as well as
established and tested a generic communication
protocol (V2X facilities layer messages) for IS that
can serve as a platform for new development with
arbitrarily advanced sensors.
On-site tests show that of the most common
scenarios can be mitigated or prevented using only
the on-vehicle sensors.
Cyclists
Increased resilience of
the transport system
(avoidance of
congestion)
Cost savings
due to traffic
congestion
D3.1
Reduce traffic congestions testing an
innovative “green wave system” for
cyclists
Microsimulation
Development of an algorithm for ”green wave” for
cyclists
Development of a hybrid “green wave” system for
cyclists
Cyclists

19. This project has received funding from the [European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 723970