Active modes and urban mobility: outcomes from the ALLEGRO project
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The document discusses the importance of urban space for mobility, emphasizing the need to shift away from car-dependency towards active modes like walking and cycling to enhance city livability. It highlights the complexities of active modes and the lack of scientific insights and data to support effective planning and policy-making in this area. The Allegro project aims to address these gaps through innovative data collection and modeling to improve understanding and management of active transportation flows.
Active modes and urban mobility: outcomes from the ALLEGRO project
- 1. Urban space is a scarce commodity… We need space to live, work, recreate, study, but also to move around in our ever-denser cities How we move around has a major impact on the amount of space the transportation system takes up
- 2. A short round of expert elicitation… What is the average share of urban space devoted to the car? a) Less than 20% b) Between 20 and 45% c) Between 45 and 65% d) More than 65%
- 3. The battle for urban space! The modes we choose to move around has a major impact mobility spatial footprint, but also on our health, well-being, climate, etc. It is clear that active modes can play a major role in making cities liveable, either as a main mode of transport or as first-mile-last-mile option for mass PT Trends like e-bikes, bike sharing and leasing provide novel opportunities… Car 50 km/h, driver only Car Parked Tram 50 passangers Cyclist 15 km/h Bicycle Parked Pedestrian Walking Pedestrian Standing140 m2 20 m2 7 m2 5 m2 2 m2 2 m2 0.5 m2
- 4. A bicycle is not a two-wheeled car… And a pedestrian is not a cyclist who lost his bike… Aiming to make our societies less car-dependent, we are in dire need of dedicated theory, models and tools to support policy making, design, planning, and control to improve walkability and cyclability of cities My proposition: science has not yet delivered the necessary insights and tools (e.g. empirical insights, theory, models, guidelines)
- 5. A vast array of scientific challenges… Active modes are delightfully complex! The fact that active modes move in 2D opens up a range of novel challenges, behaviourally and mathematically (ask Ludo!) Interactions are multi-directional and complex
- 6. A vast array of scientific challenges… Active modes are delightfully complex! Walking and cycling do generally not only result in disutility of travel Benefits to health and well-being are valued and may yield substitution with other activities (extent in which depends on many factors)
- 7. ALLEGRO ERC Advanced Grant (November 2015) ALLEGRO provides new behavioural insights, novel theory, and models for active modes at all behaviour levels In doing so, we support control, planning and design by providing these insights, methods and tools
- 8. Traffic Operations Route Choice Mode and activity choice Wayfinding, exploring, learning Control, Planning and Design methods, tools and applications
- 9. ALLEGRO With innOvative data to a new transportation and traffic theory for pedestrians and bicycles My second proposition: the science of active mode mobility has been hampered by a serious lack of data Innovative data collection is one of ALLEGRO’s cornerstoner forming the basis of our theory, modeling, etc.
- 10. Field data collection Video, WiFi / Bluetooth, Social Data Revealed preference route choice, wayfinding data Incl. collaboration with MoBike, and The Student Hotel VR and simulators Pedestrian way finding through buildings Short-run and long-run household travel dynamics MPN longitudinal survey active mode “specials”
- 11. Controlled experiments Most comprehensive cycling experiments performed so far providing novel microscopic and macropscopic insights Microscopic data (trajectories) for 25 different scenarios, including bottlenecks, crossings, merges, mixed biketypes, etc.
- 12. Controlled experiments Most comprehensive cycling experiments performed so far providing novel microscopic and macropscopic insights Microscopic data (trajectories) for 25 different scenarios, including bottlenecks, crossings, merges, mixed biketypes, etc.
- 13. Some first results… Study reveals empirical relation between width w of cycle path and capacity Characteristics of staggered patterns (zipper effect) inside bottleneck determine capacity No clear lane regime but complex interaction of longitudinal ‘following’ and lateral distance keeping *) Fact: capacity bicycle flow is ~8 times higher than a car flow! C = 1710 + 4248 ⋅ w
- 14. Bicycle capacity drop Via our experiments we established the capacity drop for bicycle flows Once queuing occurs (e.g. at intersection), capacity reduces with 23% Finding is extremely relevant for cycling infrastructure and controller design
- 15. Patatzak Trapezium-shaped bicycle lane Capacity drop could explain why design appears to work very well Work on queue-location choice will further support intersection design
- 16. Data-inspired effort-based modelling Generalisation of principle-of-least-effort approach (game-theory) results in model qualitatively reproducing observed phenomena (self-organisation) Results where satisfactory, yet new data-driven approaches are proposed to improve predictive validity Out of time?
- 17. Traffic Operations Route Choice Mode and activity choice Wayfinding and exploring Control, Planning and Design tools and applications
- 18. Wayfinding and exploring CEG Using VR to study exploration and wayfinding throughout buildings Special attention to evacuation Validation using drills at CEG
- 19. LUCY knows tourists GPS equipped Student Hotel bikes Let’s stop here…
- 20. Traffic Operations Route Choice Mode and activity choice Wayfinding and exploring Control, Planning and Design tools and applications
- 21. Who are active mode users? In collaboration with M. de Haas (KIM) LCCA travel patterns reveals existence of traveller archetypes Car-oriented people (27%) Public- transport users (9%) Immobiles (18%) Car and bike users (13%) Bike-oriented people (12%) E-bike users (9%) Car-walk (12%) 26.5 24.6 26.5 25.4 24.9 25.6 26.8
- 22. Cofouding factors… Controlling for confounding factors yields: • Strong positive relation #trips per day / distance per bike and objective and subjective health • Positive relation #trips per day / distance per foot and objective health • Negative relation #trips per day / distance per car and objective and subjective health • For e-bike, no relation was found… • Causality researched using advanced modelling (RI-CLPM) In collaboration with M. de Haas (KIM)
- 23. Using mixed-logic to determine factors influencing active mode choice Factors influencing active mode choice Company car or PT subs. Trip purpose Built environment Season Travel Time (high variance!)
- 24. Traffic Operations Route Choice Mode and activity choice Wayfinding and exploring Control, Planning and Design tools and applications
- 25. Cooperative Bicycle Control • Application of model-based stochastic control • Optimise trade-off between missing green phase and consuming energy for traffic responsive control • Inform rider using on-board device (alt. road-side sign) • Chance to catch green phase +65% • Energy consumption -30%
- 26. Some of our achievements so far Data collection and estimation techniques and novel empirical insights Empircally underpinned models for active mode traffic flow operations on a microscopic and macroscopic level Insights into main determinants of / models for mode, activity and route choice Better empirical understanding of (differences in) wayfinding-styles and learning Novel methods for management of active mode facilities, as well as for design, and planning (e.g. novel bike-sharing strategies, planning models)