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XCYCLE - Advanced measures to reduce cyclists' fatalities and increase comfort in the interaction with motorised vehicles

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

Published in: Automotive
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XCYCLE - Advanced measures to reduce cyclists' fatalities and increase comfort in the interaction with motorised vehicles

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

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