gis in transport adam winstanley gis in transportation adam winstanley national centre for...

GIS in Transport Adam Winstanley

GIS in Transportation

Adam Winstanley

National Centre for Geocomputation

Department of Computer Science

GIS-T

GIS in Practice NCG 25/1/06

© ncg and originators of data


GIS in TransportationJianquan Cheng

Tim McCarthy

Peter Mooney

Matthew Sammon

Adam Winstanley

GIS-T


GIS in Transportation

• Cheap sensors – GPS, cameras, LIDAR• Loads of data• Data standards • Fast networks• Processing power• Software algorithms• Graphics• Web technologies


GIS-T ApplicationsLocation

Route Coverage

Journey planners

Vehicle Tracking

Location-based Services

Infrastructure management

Emergency Services

Disaster area Evacuation

Transport planning

Sustainability

Efficiency

Spatial strategy

Asset inventory

Tolling

ITS

Positioning Sensors

Network Algorithms

Spatial Analysis


Data Processing Issues

A comparison of navigation before (left image) and after (right image) data processing.This data was acquired recently in the West End, central London. Primary Navigation Used DGPS, secondary comprised optical DMI sensor & road centre line.


Sensor system

Control system

Positioning system

Sensors

AutoTramDevelopment of imaging and control technology for a public-transport vehicle in unsegregated environments

Objectives:

Sensor and control systems for rail vehicles• Collision avoidance systems • Warning and safety systems• Autonomous control system


Location-based Public Transport Information Systems

Applications using positioning systems in public-transport vehicles for passenger

information systems, vehicle tracking and fleet control.

Free drink with every meal and your tram ticket at this stop

e.g. In-vehicle multi-media passenger information system


• Tim McCarthy• IBI Group-RouteMapper

• Virtual Tolling• Noise Modelling• Asset Inventory & Infrastructure Mapping• Virtual Route Corridors (2.5-D & 3-D reconstruction)

• We design and prototype all critical modules, algorithms and sub-systems


Examples of NCG’s partnership with transportation industry

Image Mapping Systems for Asset Inventory

Ongoing partnership with UK/Canadian companies developing a range of route corridor mapping systems for transportation projects

Noise Modelling

Due to initiate new ten-month R&D project with a commercial company and governement agency. Involves development of airborne and ground based survey systems for measuring route corridor geometry. This will be used as an input to modelling noise along motorway sections in ireland. This falls under the 2007 EU directive.


Asset Inventory system

Road mapping systems use advanced camera technology together as well as navigation sensors to record multiple fields of view along route corridor at speeds of upto 100kph.

•4 X Progressive scan cameras (1024*768) at 5fps

•DGPS (1m level)

•GPS timing

•National Instrument synchronisation

•Optical DMI (0.5% error)

•Monoscopic and Stereoscopic configurations for 2-D and 3-D measurement

•Robust Datalogging PC

•Power, mount subsystems

•Data-throughput is in the order of 50 MB/s


Key Challenges – Design & construction of mobile route corrdior survey systems

Sensor Integration

•Integrating imaging, navigation, laser sensors & other third-party sub-systems

•Addressing latency and synchronisation issues

•Choosing correct navigation system

Software Engineering, algorithm design & visualisation

•Development of robust DataAcq modules, Data processing algorithms

•How do we visualise these multi-sensor datastreams


Route corrdior asset inventory can be sub-divided into three main areas;

Data Acquisition

Survey vehicle, power, camera subsystems. Primary & secondary navigation (DGPS, DMI, IMU). Additional sensors such as GPR

Data Processing

Recovering navigation and integration with images including back interpolation, data cleaning, camera calibration (monoscopic & stereoscopic)

Data Analysis

Browser providing core functionality enabling user to measure, digitise and store data. API to inetgrate with industry standard applications such as ArcGIS, MapInfo, GeoMedia.


Example of simple monoscopic calibration

IBI Group-RouteMapper


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Simplified diagram depicting a typical route corrdior data acquisition system



Data Acquisition system



Browser Interface

Multiple images

Video controls

Map controls

Image ID

Mapping (GIS layers) spatially linked to video and database



High resolution imagery

Image controls

Image clarity: 1024 x 768 resolution, non interlaced

Road Network referencing

Text overlay on image (retrieved from database)



Measurement and export

In-frame measurement

Magnifier to improve measurement accuracy

Jump to network segment or ahead/back x metres

Business systems integration: Export to HAPMS (+ GIS, database applications) IBI Group-

RouteMapper


Creation of GIS layers from VideoTracing an asset in the video creates an underlying feature in the shape file, linked to its description based on a customised asset database

For the Highways Agency, the Browser is based on the HA’s chart node referencing system

The use of standard file formats allows asset information to be exported into standard GIS software (eg Arcview) for further analysis



Integration of multiple sensorsRADAR pavement thickness data, displayed alongside video & map Change in pavement

condition, visible from GPR data & video


GIS in Transport Adam WinstanleySix camera system for street surveying



Data browsing available over the Internet



Virtual route corridors will be common place within next 10 years



The AA Route Planner

Geographical Distance Only


Interactive Trip Planning Example A (1)

Origin / destination selection• Pre-approved O/D, requiring:

– Transit stop locations

• User-entered O/D, requiring (in addition):– Text-matching of addresses / postcodes– Geocoding of addresses / postcodes

Trip constraints• Consideration of accessibility of stops

• Minimisation of trip parameters such as:– Fare cost– Number of transfers– Time:

• In transit• Walking to / between stops

Trip Planner, Regional Transportation Authority, Illinois, USA

http://tripsweb.rtachicago.com/


Trip Planner, Regional Transportation Authority, Illinois, USA

http://tripsweb.rtachicago.com/

Interactive Trip Planning Example A (2)

Route selection• Requires path-finding algorithm and vector

data:– for entire transit network, at minimum– for entire street network, where user enters O/D

• Allows (inter-stop) walking/cycling distance/time

Arrival / departure time reporting• Requires detailed regular schedule information

Mode / service, direction and stop reporting• Requires:

– Names of modes / services and stops– Mode / service branding with colours / symbology– Understanding of travel direction WRT stops– Knowledge of transfer stops

Fare reporting• Requires fare and passenger-type information


Journey Planner, Transport for London, UK

http://journeyplanner.tfl.gov.uk

Interactive Trip Planning Example B (1)

Additional trip constraints• Allow user to select preferred modes





Enhanced accessibility constraints• User can specify mobility impairment type, not merely

indicate such impairment• Requires more information for each stop

Specification of non O / D via-location• Additional location must be incorporated into trip

Enhanced walking / cycling parameters• Requires:

• Additional data about stops / services, e.g.• Which services allow bicycles• Which stops have bicycle parks

• Information on walking / cycling travel speeds





Indication of total trip time

Listing of alternative routes• Enhances user choice by

displaying no. of transfers per route





Detailed description of route• Allows route to be printed and followed

Account taken of congestion• For on-street transit, user notified of

maximum journey time, given prevailing conditions

• Requires congestion data for each street

Incorporation of real-time information• User notified of service delays and

emergencies• Alternative routing suggested• Requires live updates from services





Relevant map for each stop• Shows:

• Route (with mode changes)• Nearby:

• Landmarks• Transit stops


Interactive Trip Planning Example C (1)

Google Transit Trip Planner

http://www.google.com/transit

Experimental trip planning by Google• User can enter free-text description of:

• Origin• Destination• Proposed arrival / departure time

• Trip duration, categorized by:• In transit time• Walking time

• Cost comparison:• Public transit• Car

• Simultaneously:• Describes route• Maps route

• Incorporates data provided by local public transit companies


Interactive Trip Planning Example C (2)

Google Transit Trip Planner

http://www.google.com/transit

Experimental trip planning by Google• Also maps route by car, for comparison


Considerations

• Example (1): We often wish to optimise (minimise) overall travel time – but are willing to tradeoff some travel time – for a journey that does not require us to change trains/buses more than once

• Example (2): Searching for the optimal route (based on criteria X) and ignoring all other optimisation criteria Y may return a route with minimal X but very high Y values (ie cost, risk, etc)


Using an EA for Journey Planning

• Research carried out at CS Dept/NCG evaluated the implementation of an Evolutionary Algorithm (EA) - within a GIS - to ‘evolve’ routes on transportation networks exhibiting optimal tradeoffs between the N criteria considered

• Users choose what they perceive as the most important set of criteria to optimise

• The EA searches for valid routes on the GIS representation of the transportation network

• The GIS displays the route options to the user


Automatic Vehicle Location (AVL) (1)

• Transport for London’s iBus system for London Buses• To be rolled out over 4 years - trials started Dec 2005• £177m on AVL and telecoms technology• ~15 private companies under contract• 8000+ buses• 6.3m daily passengers• 17,500 stops• 700 routes• 2000+ wayside signs• Technology from Siemens



• Transport for London’s iBus system for London Buses• Vehicle position determined by GPS

– Useful for emergencies– Useful for bus performance monitoring

• Position relayed by GPRS to central computer• Predicted arrival times sent by:

– GPRS to potential passengers’ mobile phones– GPRS / ISDN to wayside signs– GPRS to buses, so that drivers can:

• Know headway info (time gap between consecutive buses)

• Can make decisions to maintain regularity and prevent “bunching”


• Chicago Transit Authority• Automated Voice Annunciation System (AVAS) • System automatically:

– Displays and announces the next stop to passengers– Announces at each bus stop to those waiting:

• Bus route• Destination

• Position determined using:– GPS– odometer inputs that provide distance travelled– gyroscope that observes changes in direction

• Technology from CleverDevices



• Where change occurs it may be possible to:– Determine the beneficiaries, using GIS– Tax them– Use the revenue to pay for the transit system

• Croydon Tramlink, UK– Consortium led by Atisreal (2004) found increases in

property values within central Croydon

• Orange Line, Chicago– McMillen & McDonald (2004) showed that value

increases prior to construction were caused by the expectation of improved accessibility

Transport Interactions with Property Values (1)


Major raw data sources: Amsterdam Region

Inhabitants Employment

Car network Transit network


Focal points of Employment


TAZ from Neighbourhood units

GIS in Transport Adam WinstanleyTravel time- OD Matrix

Car route

Transit route


Virtual Network


Car Corridor


Transit Corridor


Low (< average)Vir

tual

spe

edClassification

1

2

3

4

High (> average)

Low (<average) High (>average)

Public Transport share

Visual speed = Distance /( t-time * share + c-time*(1-share))


Spatial strategy


Ratio of employment to inhabitants

Ratio of job opportunity to inhabitants

Different spatial patterns


Job accessibility/(un-sustainability)

Spatial conflicts


Example of strategy

Sustainable accessibility

203.4 205.1


Thank you

• Jianquan Cheng

• Tim McCarthy

• Peter Mooney

• Matthew Sammon

• Adam Winstanley

National Centre for Geocomputation,

John Hume Building,

National University of Ireland, Maynooth,

Maynooth,

Co. Kildare,

Ireland.

[email protected]

© ncg and originators of data

gis in transport adam winstanley gis in transportation adam winstanley national centre for...

Documents

originators of data

mbs slide

publictransport vehicles

control systems

data processing algorithms

data cleaning

store data

sensor datastreams slide