Topic: Open Public Transport Data

from a Vehicle Centric Perspective

Alexander Seward alexander.seward at veridict.com

VERIDICTA Swedish AI company dedicated to ITS

• Real-time fleet tracking and monitoring

• Traffic management support

• Predictive analytics algorithms

• Advanced visualization and HMI

Multimodal transport:

buses, trams, railway,

subway, light rail, ferries,

autonomous vehicles, …

Alexander Seward alexander.seward at veridict.com

Different types of public transport data

Vehicle internal data Infrastructure data Transportation data

• odometer• fuel/battery level• GPS• warning lamps• occupancy level• doors status• …

• road/rail network data• speed limits• distances• traffic CCTV• traffic signals• road weather• …

• route info• time tables• trip updates• real-time delays• cancellations• accessibility info• …

Data fusion

Vehicle internal data Infrastructure data Transportation data

+ +

=Improved data model

Level of detail &quality of information

Uniform representation & overview

Planning support & predictive analytics

Multimodal transports + vehicle data

1 Real world data example clip

Direct geo-location sampling (e.g vehicle GPS position sampling)

• not always available (proprietary info / tunnels / malfunctions)

• often partial (only position - not heading, etc.)

• interim collection systems often introduce latency

• extra costs

• inaccurate – especially in dense urban areas

GPS example (latency)

Example (direct position sampling ): A vehicle moving 72 km/h (20 m/s) broadcasting GPS coordinates every 10 seconds

0 s 10 s 20 s 30 s 40 s 50 s 60 s

5 s 15 s 25 s 35 s 45 s 55 s

Mean signal transfer delay 5 s

Just before next update. Last received position is 15 seconds off. That is 300 meter error presented to user!

Vehicle/sensor side

Receiver side

Signals sampled and broadcastedat 0.1 Hz

In addition: GPS error deviation!

Aggregated tracking with raw GPS reference

2. Real world data example clip

Space-time Continuum and Polygonal chains

Y(latitude*)

X (longitude*)

* A Mercator projection is applied

Space-time Continuum and Polygonal chains

• A space-time polygonal chain (polyline) = a journey (or trip) of a vehicle between two end-points

• A polyline captures all real-time and predicted variables such as geolocation, turning, heading, speed, acceleration, deceleration, ETA (estimated time of arrival)

• By combining with planned polyline (from traffic planning) => real-time and future deviations• What is the predicted delay?• Where should a vehicle be in order to be on time?• How will a current delay evolve – diminish or increase?

• By computing polylines (in specific subparts of space-time) for all vehicles we have all the spatio-temporal information we need

Computing the polyline

• Multiple sources of data (e.g. vehicle GPS, road network data, traffic events, trip plans, delay information and vehicle observations at stops) => estimated polyline (for a vehicle)

• With the best information at hand, at any moment, the polyline can be continuously updated should the underlying information change or if new information is provided.

Toxels – The space-time building blocks

• Optimized building blocks that we can access very efficiently –hierarchical structure

• Modelling of our entire world in very high detail – meter accuracy (cf. GPS ~9 m)

• Each toxel is a single computation entity – handling a specific area of space (& time)

• High-level parallelism – GPGPU server computations – each GPU contains thousands of cores

Toxels – The space-time building blocks

t1

t0

B

A B

C D

Y(latitude*)

X (longitude*)

Toxels – The space-time building blocks

A B

DC

AA AB

AD BC

DA DB

DD

AC

BA BB

BD

DC

AAA AAB ABA

ABC

ADA

ADC ADD BCC BCD

DAB DBA

DBC

DDA DDB

AAC AAD

ABB

ABD

ADB BCA BCB

DAA

DAC DAD

DBB

DBD

DDC DDD

VERIDICT LIVEMAP

Live snapshot of Broadway, NYC

• Every vehicle: o an autonomous computational model (a-

priori and streamed real-time data)o fully interactive (display current state, plan

and predicted future whereabouts)

• Includes custom-built map engine for live traffic applications

• OpenGL optimized client software that runs everywhere - on any desktop or mobile device

• Real-time handling of diversified deep vehicle data

• Server side AI-based analytics using GPGPU optimizations

• Verified real world scalability: >1 million vehicles

Public Transport in Boston, USA

Demo (livemap.veridict.com)