Prof.dr.ir. Hans van Lint AvL Hoogleraar Traffic simulation & Computing
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1 Prof.dr.ir. Hans van Lint AvL Hoogleraar Traffic simulation & Computing
2 Plans in 14: (Real-time) diagnostics, estimation & prediction Evaluation & assessment (Open-source) Multiscale Simulation Research program Past Present Future (BIG) Data Processing Virtual Reality, Gaming Mixed Reality 2
3 February 16 (Real-time) diagnostics, estimation & prediction Evaluation & assessment (Open-source) Multiscale Simulation (BIG) Data Processing Virtual Reality, Gaming SETA Mixed Reality 3
4 Top 5 BIG PROBLEMS for modellers 1. Observability of key variables (under all sorts of circumstances) (a) density, space mean speed, queues (b)route choice and OD patterns (c) motives, activities, needs (d)underlying mental drivers and constraints (awareness, workload) 2. Validity of (practically all) our assumptions 3. Consistency of representations at different scales 4. Curse of dimensionality (methodologically and computationally) 5. Lack of expertise beyond our discipline 4
5 Are BIG DATA going to solve these problems? Real behaviour? field data Experimental control? 5
6 Are BIG DATA going to solve these problems? Real behaviour? field data lab data Works well with peds, not so much with cars or entire cities Experimental control? 6
7 Are BIG DATA going to solve these problems? Real behaviour? field data lab data Only choice for impossible or ethically unacceptable experiments (evacuations) virtual data (SP surveys to games) Experimental control? 7
8 Are BIG DATA going to solve these problems? Real behaviour? field data lab data more context new insights (and assumptions) increased observability virtual data (SP surveys to games) Experimental control? a la CSI: data fusion offers more (conclusive) evidence (1+1=3 principle) Examples extra context: Circumstances (CAN data, weather, incidentens, events, news, etc) Status netwerk & alternatives (ITS, parking, PT data) OD, route- en mode choice (GSM, Apps, Chip card) Experience, habits, attitudes (Apps, CAN data) Motives, activities, needs (social media, apps) 8
9 BIG DATA are NOT going to deliver unless we adopt a fundamentally different approach 1. Mastering more (and more complex) tools a la CSI: data fusion offers more (conclusive) evidence (1+1=3 principle) (GIS-, computing-, analysis, etc) 2. Work in interdisciplinary teams Traffic and transport scientists Social / Behavioural scientists Data & Computer scientists 3. Sharing data and expertise (dare scrutinize your stuff in the public domain) Examples extra context: Circumstances (CAN data, weather, incidentens, events, news, etc) Status netwerk & alternatives (ITS, parking, PT data) OD, route- en mode choice (GSM, Apps, Chip card) Experience, habits, attitudes (Apps, CAN data) Motives, activities, needs (social media, apps) 9
10 The 3, 4 or 5 (6, 7?) V s anything that forget about BIG DATA start thinking of (how we get to) doesn t fit in spreadsheet (oh please.) MEANINGFUL INFORMATION Challenge the future Delft University of Technology
11 Traffic observatory Coupling & fusing multi source traffic data with context: weather, events, incidents, roadworks, Fast Congestion Search Engine (CoSi) based on machine learning Multiscale estimation (& prediction?) - Densities and speeds - Critical parameters (capacities, speed distributions) - Route choice patterns, turns, splits - Network fundamental diagrams and other high level state representations The stuff we need for ex post AND ex ante evaluation (modelling) - OD Traffic demand 11
12 raw data - speeds [km/h] 6 8 Current historical database km 1 12 Limited search options 14 that supposes you know 4 where and 12 when you want to look; no context (only external meta data - difficult to connect!) 16 12: 13: 14: 15: 16: 17: 18: 19: km raw data - speeds [km/h] 12: 13: 14: 15: 16: 17: 18: 19: km minuten Filtered - speeds [km/h] 12: 13: 14: 15: 16: 12 17: 18: 19: Mild day Travel time16 12: 13: 14: 15: 16: 12 departure time 17: 18: 14 8 juni 15 km km raw data - speeds [km/h] : 13: 14: 15: 16: 17: 18: 19: Special event + heavy rain Filtered - speeds [km/h] 12: 13: 14: 15: 16: 17: 18: 19: km minuten : 13: 14: 15: 16: 17: 18: 19: Lane closure due to fire Filtered - speeds [km/h] km Travel time 12: 6 12 departure time lane closure: april 15 heavy accident 12: 13: 14: 15: 16: 17: 18: 19: km 1 1 raw data - speeds [km/h] : 13: 14: 15: 16: 17: 18: 19: Filtered - speeds [km/h] minuten 3 Travel time : 13: 14: 15: 16: departure time 17: 18: 31 mei 15 minuten 8 Travel time : 13: 14: 15: 16: departure time 17: 18: 24 maart 15 12
13 An intelligent historical raw data - speeds [km/h] database km Fast search one abstraction level higher: 12 based on traffic 6 patterns and meaningful 14 4 meta data (what are you looking for?) 16 12: 13: 14: 15: 16: 17: 18: 19: km km raw data - speeds [km/h] : 13: 14: 15: 16: 17: 18: 19: Filtered - speeds [km/h] Zwaar ongeluk 12: 13: 14: 15: 16: 17: 18: 19: km minuten Filtered - speeds [km/h] 12: 13: 14: 15: 16: 17: 18: 19: lane closure: heavy accident Travel time 12: 13: 14: 15: 16: 17: 18: departure time maart 15 km km raw data - speeds [km/h] : 13: 14: 15: 16: 17: 18: 19: Filtered - speeds [km/h] Heavy accident! : 13: 14: 15: 16: 17: 18: 19: minuten Travel time : 13: 14: 15: 16: 17: 18: departure time a, 9 juni 15 minuten 7 Travel time : 13: 14: 15: 16: departure time 17: 18: a16, 9 jan 11 13
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15 Superfast searching through traffic patterns Work in progress Unknown pattern + metadata unravel in features So far so good Pattern Types X & Y Classify TODO: iterative process (1) Classify increasingly larger datasets (2) Adjust / correct manually (3) Retrain SVM classifier with increased data set (4) back to (1) until classification stable and sufficiently accurate Pattern recognition model 15
16 URBAN MOBILITY LAB THE BIGGEST MOBILITY RESEARCH QUESTIONS RELATE TO METROPOLITAN AREAS Dutch minister of transport in a self-driving car SHAREFICATION GAMEFICATION FICATION Safety Accessibility Equity Liveability Environment Economics Short-distance trips (3) Transit accessibility ()
17 EXPERIMENT: TRIPLE EVENT MARCH 28, 15 ZIGGO Dome Andre Hazes Tribute Amsterdam Arena Holland vs Turkey HMH Zha Zha Su (Caribbean Dance Event)
18 CAR TRAFFIC Parking Data many routes and intersections A dam Data all freeways, motorways in the Nederland
19 PUBLIC TRANSPORT Arrivals station Bijlmer Departures station Bijlmer
20 PEDESTRIANS (CAMERA FOOTAGE)
21 PEDESTRIANS (CAMERA FOOTAGE)
22 PEDESTRIANS (PROCESSED THROUGH PLACEMETER)
23
24 Follow up projects under way 24
25 OpenTrafficSim Great example of interdisciplinary collaboration Within TU Delft: Systems and Simulation group of Alexander Verbraeck My Transport & planning Guus Tamminga vertelt later vandaag over het N1 project NISSAN s R&D group in Silicon Valley 25
26 Central research theme OpenTrafficSim: multi scale modelling Mental processes Experience Perception Path-planning (CF + LC + GA) Planning (Activity, Destination, Route) Traffic & Transport System state Vehicular Capabilities Driving (Acceleration) On-GTU-Unit(s) (OGUs) 26
27 Effect of perception (scanning frequency) Base case: IDM+ model No reaction time Every vehicle re evaluates his surroundings every simulation timestep of.1 secs 27
28 Effect of perception (scanning frequency) Reaction time = Scanning interval + Physical reaction time Second case: IDM+ model No reaction time Mix of drivers paying attention every.1 secs and distracted drivers (re evaluating every 2 secs) 28
29 Overall DiTTLAB Architecture One integrated environment (Open) data from many H - Visualisers, analysers, exporters different sources: Traffic Transport Networks Advanced data assimilation and analytics Opensource multi-scale, multi-modaal simulation traffic and transport G - GUI s / Editors A - OpenTraffic Simulator B - OpenTrafficSim Input & toolset (Calibratie, Validatie, Identificatie, Fusie, Assimilatie tools) F - GIS (semi-static data: transport infra & built environment) C - Database (dynamic data: traffic, transport, weather, etc) E - OpenTrafficSim Ontology D - data import 29
30 Thanks! We have only JUST started and it s looking really good!
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