Post on 22-May-2020
In collaboration with Supporting organisations Local Hosts
#UITP2017
Optimizing Mass Transportation with Artificial Intelligence & Data Mining
Dr. Luis Moreira-Matias, Senior Researcher, NEC Labs Europe
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Established July 17, 1899
98,726 Employees
217 Consolidated Subsidiaries
Business activities in over
168 countries through 237 branches
5 R&D Labs: Japan, USA, Europe, Singapore, China
Transportation Segments:
• Public transport
• Traffic management
• Automotive
• Logistics and fleets solutions
NEC GROUP OVERVIEW
More than USD 25 Billion in FY2015 sales
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Public Transportation
• Bus TMS, AVL, AFC, ETA, PIS (leader in JP)
• Train communication systems
Road Infrastructure
• Highway traffic control construction & operation
• Hybrid camera-based traffic counting / HOV
Fleet Management & Logistics
• Drive recorders, ecoDriving, fleet tracking/insurance, accident database
Automotive on-board
• V2X platform (HW and SW)
NEC’S TRANSPORTATION BUSINESS
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OUR TECHNOLOGY MAP
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TRENDS
Sources: United Nations, A.D Little, UITP
Growing Urban population Mobility demand explosion Public Transport has the
Highest Growth
APAC + Africa + LATAM = 90+% of the total growth
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TRENDS
Social Trends Policy Trends Technology Trends
▌ Millennials: Less car oriented Always connected Want real-time updates
▌ Sharing economy
▌ Autonomous driving may worsen congestion
▌ Mobility-as-a-Service
▌ City data stores/hubs
▌ Real-time info channels
▌ Congestion toll + better public transport
▌ Proactive planning
▌ Service obligation KPIs
▌ Resource re-allocation
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NEW CUSTOMER NEEDS
Award to
Public Operators
Tender to
Private Operators
Planning + Service Contracts
Monopoly
Franchises
De-regulation
London, Amsterdam, Bangalore, Montreal
Singapore, San Paulo, Santiago
Seoul, Kigali
New Customer Needs
• More accurate demand estimation (e.g. stop-level number of passengers)
• More accurate operation planning (e.g. link-level travel time prediction)
• KPI monitoring and enforcing (e.g. real-time service improvement)
Operators Competition
Time
Source: UITP Trends Report 2017
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TRANSIT CLOUD CONCEPT
• Different
pipelines for
Data… a. Collection b. Processing c. Reporting d. AI
• Support
Standard
interfaces (e.g.
GTFS/SIRI);
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DATA-DRIVEN AI FOR PLANNING AND OPERATIONS
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Proven BRIGHT applications:
• Taxi demand prediction
• Transit Travel Time prediction (for Buses, BRTS, Subways)
• Traffic Anomaly Prediction
Ongoing BRIGHT applications:
• Transit demand prediction
• Fare evasion detection (smartcards)
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CS I – TRANSIT TTP
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Stop 1
Route Forecasting
horizon:
1h-months
ahead
Stop 2 Stop n
Challenges in TTP:
• Missing Data/Noise (e.g. erroneous GPS measurements);
• Redundant Input Features (e.g. large number boarding will
be reflected on larger dwell times);
• Inadequate context representations (e.g. travel times
are lower at weekends, especially offpeaks…but no feature reflects
this directly);
AVL Counters APC Schedule
Travel Time Prediction is the task of finding a mathematical
expression that correctly models the relationship between the
travel times and a series of explanatory variables whose values
are known using a set of historical operational data.
This expression can be used to infer travel times in the future.
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CS I – TRIAL RESULTS TT 1st-5th stop TT 1st-15th stop TT 1st-25th stop
Round Trip Time
Advantages
• Accurate prediction of ETA in an horizon 1hr & ahead;
• 13% (RMSE) Better than SoA;
• Expected prediction error between 1 and 3 minutes.
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CS II – DEMAND PREDICTION
1. Demand Prediction (periodic);
2. Predictive Assignments;
3. Service Request by User;
4. Dispatching Request
5. Dispatching Decision is Locked;
6. User gets service info;
7. Service pick-up and drop-off.
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CS II – TRIAL RESULTS
Porto, Portugal New York City, USA
KPI: accuracy defined as 1-sMAPE
Competitor: SoA method (ARIMA)
Up to 10%+ Accurate
Predictions
Up to 15%+ Revenues due
to Predictive
Dispatching
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CS III – TRANSIT CONTROL All customer constraints built-in
Total computation time of 2 mins for
Network-level Control Strategy update
3x faster and 15% better than Genetic Alg.
Theoretical Gain:
Excess Waiting Time
reduced up to 35%
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QUESTIONS?