This document discusses how public transportation agencies can use big data to improve performance. It provides examples of how transportation agencies can use automated vehicle location (AVL) data from buses to identify excess waiting times, busy routes, and bottlenecks. This data can then be used to optimize schedules and reduce excess waiting times. The document also discusses how AVL data combined with automatic passenger counter (APC) data can be used to analyze bus load profiles and driving patterns to find opportunities for fuel savings and safety improvements. Finally, the document presents a vision of integrating various data sources like video, sensors and customer apps to further optimize public transportation systems.

1. Using Big Data to Improve
Public Transport Performance
Roberto Baldessari
NEC Laboratories Europe
roberto.baldessari@neclab.eu
BigDataEurope Workshop
October 7th, 2015
Palais des Congrès, Bordeaux

2. 2 Using Big Data to Improve Public Transport Performance
NEC’s Transportation Business
▌Public Transportation
 Bus AVL, AFC, ETA, Passenger Info (leader in JP)
 Scheduling and communication systems
▌Road Infrastructure
 Highway traffic control
 Hybrid camera-based traffic counting / HOV
 5.8 GHz “ITS Spot” and ETC road-side systems
▌Fleet Management
 Drive recorders, ecoDriving, fleet
tracking/insurance, accident database
▌Automotive on-board
 V2X platform (HW and SW)
 Mono-vision image recognition system
 Nissan Leaf Li-Ion batteries
 76 GHz radar for Adaptive Cruise Control
▌Automotive IT
 HPC, Product Data Management & Production
Control in all continents
▌Automotive After-market
 5.8 GHz on-board ETC with card reader

3. 3 Using Big Data to Improve Public Transport Performance
What is “Big Data” for NEC
▌Platforms: parallel,
in-memory, vector
▌Acquisition: IoT,
M2M, pre-processing
▌Analytics: deep
learning, HML, SIAT
IoT
Platform

4. 4 Using Big Data to Improve Public Transport Performance
Public Transit – Quality Incentive Contracts
▌Background
Large cities adopting QICs based on KPIs
like Excess Waiting Time (EWT)
London introduced 3:2 incentive/penalty
schemes, Singapore has followed
Regularity is the goal, rather then
absolute punctuality
▌Data analytics reveal
1) Current EWT performance
2) Worst performing routes
3) Key bottlenecks on the route
4) Causes for dwell time at bottlenecks
5) Time table improvement margin
Source:
Singapore LTA

5. 5 Using Big Data to Improve Public Transport Performance
EWT Profile and Optimization from AVL Data
▌Bus operators and municipalities/
authorities often don’t know how
their public transit scores
▌Simple analytics derives hot spots
to attack in order to reduce EWT
Focus for improvement

6. 6 Using Big Data to Improve Public Transport Performance
Bus Load Profile from AVL Data
▌Load profile simply based on
AVL data (GPS + flags)
Current schedule (reverse
engineered)
▌70% accuracy vs passenger
counters
▌Surrogate / complement to
APC systems
AVL Data Current Schedule
Supervised ML
Typical
Bus Load
Profiles
Morning
peak run
Evening
peak run

7. 7 Using Big Data to Improve Public Transport Performance
Automating Schedule Coverage
▌Generate new or review
existing schedule coverage
▌Automated time-consuming,
error-prone task
▌Large KPI improvement
potential
AVL Data
Unsupervised ML
Schedule
Clusters
Optional APC
Data
Suggested
Change

8. 8 Using Big Data to Improve Public Transport Performance
Bus Driving Analysis
▌Visualization of vehicle travel path, alerts and events
▌Analysis targets
▌Planned KPI vs actual
▌Fuel consumption and other parameters

9. 9 Using Big Data to Improve Public Transport Performance
Bus Driving Analysis Benefits
Fuel saving (up to 20%) by improving drivers’ behavior
• Monitor, Analyze, Correct (counseling and training), Continuous Feedback
Safety
• Identify trend and potentially dangerous patterns
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10. 10 Using Big Data to Improve Public Transport Performance
Public Transportation Big Data Vision
▌Combine conventional
and new sources
▌Public safety and
transport
▌Value co-creation
through data stores /
IoT platforms
▌Personalized guidance
and incentive
▌Customer feedback
▌Events as a benchmark
Video-based Crowd
Behavior Analysis
Crowd Counting
AFC Tap-in & tap-out
Sensor-based Crowd
Detection
Event ticketing
Transport App