In collaboration with Supporting organisations Local Hosts

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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?