Smart Cities SESSION II: Lecture 2: The Smart City as a Communications Mechanism: Transit Movements

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1 Wednesday 7 October, 2015 Smart Ctes SESSION II: Lecture 2: The Smart Cty as a Communcatons Mechansm: Transt Movements Mchael Batty

2 Outlne of the Lecture 1. The Context Agan: New Data for and from the Smart Cty: Illustratng Smart Ctes wth Movement Data 2. Why London? 3. A Partal Vew of London s Network 4. Three Approaches, Three Problems a. Problem 1: Flows on the Tube Lnes by trans; some buses b. Problem 2: Representng Networks And the 3 rd problem we wll leave untl next tme c. Problem 3: Shortest Path Problems and Dsrupton

3 Our focus on transport and communcatons manly affect the routne aspects of smart ctes over short tme perods The Physcal Bult Envronment routne models real tmed streamed data computers strategc models The Abstracted Soco Physcal Cty

4 Context: New Data for and from the Smart Cty Here we are gong to move drectly to networks n the lteral sense, Remember Metcalfe s Law, so we wll look also at flows on networks Our understandng of urban flows tends to be based almost exclusvely on sngle mode systems and each mode s handled separately; arguably we need to look an ntegrated networks multplex networks The ncreasng avalablty of large behavoural data sets from Publc Transport Networks (PTNs), combned wth the ncreasng power and sophstcaton of computatonal approaches, creates new ways of explorng travel demand: real tme streamed day Greater spatal resoluton down to staton or bus stop, and greater temporal resoluton down to the mnute, and greater coverage centralsed collecton of data means a sngle data store for an entre cty PTN derved data also avods many of the prvacy ssues assocated wth moble network data because the user becomes nvsble as soon as they leave the system.

5 Why London? Transport for London s (TfL) RFID based Oyster Card s partcularly attractve because users typcally need to use ther card at both ends of a trp, provdng us wth detaled orgn and destnaton data for more than 3 mllon daly users. The system s partcularly large and complex: Approxmately 640 statons across all modes 340 statons wth Oyster Card readers served by Natonal Ral trans 80 statons served by Overground trans 270 statons served by Underground trans 45 statons served by Docklands Lght Ral 39 statons served by Tram 147 statons wth some knd of nterchange (between lne or mode) Aboveground coverage by Open Street Map (OSM) s also partcularly good, allowng us to model walkng behavour usng open source tools that respect pedestran preferences for balancng drectness wth queter streets.

6 A Partal Vew of London s Network Although many users especally vstors are used to thnkng about London n terms of the Beck schema, the combnaton of an onlne Journey Planner and regular travel on the network enable many to dentfy the quckest route even f t doesn t appear to be the most drect.

7 Three Approaches, Three Problems 1. To explore what s happenng to actual movements of trans to compute delays currently Transport for London TfL have an API for tubes and buses that enables the user to query the locaton and of all trans/buses on the network and to examne how they move from ths we can calculate delay and compute a varety of measures. Ths s what Rchard Mlton, one of my colleagues has done and I wll show some of ths t s very prelmnary 2. Use of classc graph measures to show how the network can be dsrupted ths s largely a topologcal graph/network approach that shows how betweenness centralty or accessbltes s dsturbed I am responsble for ths and wll show some work 3. Fully fledged flow and graph measures n a multmodal context tube, overground, walk so far where we are computng changes n flow ths work that Jon Reades s dong we wll look at ths next tme and focus on dsrupton.

8 Problem 1: Flows on the tube lnes by trans As we wll demonstrate, through the Trackernet system for London Underground and the Countdown system for buses, t s now possble to collect and vsualse the postons of vehcles n real tme. At peak perods there can be 7000 buses, 900 trans and 450 tubes runnng on the system. 270 underground statons. Delays for these transport systems were calculated by usng an archve of hstorc data to fnd the mean wat tme for every hour and every staton or bus stop. Ths can then be vsualsed n real tme or after the event for further analyss. We show a mx of these vsualsatons n the fgures that follow as yet we have not developed an ntegrated analyss but all the deas are there. We show the analyss frst for the tube but here s the block dagram showng how we are assemblng the data.

13 Flows Durng the Olympcs we are lookng at ths as a case study

14 Weekend Weekend Weekend

15 The Effect of Bus Strke Tuesday 22 nd May 2012, 09:00 Wednesday 23 rd May 2012, 09:00 The left mage shows the effect of the bus strke on 22 nd May 2012, whle the mage on the rght shows a normal day.

Delays from Tube, Natonal Ral and Bus Fused

showng a wat tme 20% above expected Tube

16 Delays from Tube, Natonal Ral and Bus Fused Key Natonal Ral more than 5 mnutes late Tube statons showng a wat tme 15% above expected Tuesday 9 October 10:30 Bus stops showng a wat tme 20% above expected Tube delays from the TfL status feed are also plotted as lnes

17 I want to ntroduce a lttle more background on the automated scheme n London that we are dscussng some hstory frst and then a map of the network It s qute confusng because so many networks overlap so we should be clear about the core network the underground whch has 270 statons; the Docklands Lght ralway has 45; the overground has 83 but ths extends outsde of Greater London: then natonal ral s more complcated because t dovetals wth overground and underground statons also concde but are not the same as natonal ral statons. We are manly gong to deal here wth the greater London networks but the touch card data s for a much bgger area and also for the bus system and t can also be related to natonal ral.

18 The Overground

20 Tube, DLR, Overground and Natonal Ral Networks n London

21 The smplest network for the frst problem, based on an analyss of the network, not the flows: shown below are the degrees

22 Problem 2: Representng Networks We use standard graph algebra to represent the network where we defne three ndces of centralty Degrees of the graph a a a Betweenness Centralty C k k Closeness Centralty L KD 1 K d 1

23 Representng Flows Trp Volume Entres and Exts T T T T T T T T Changes n Trp Volumes T T T T 0 Weghted Betweenness Centralty k k pk, pk 1 ~ C k T pk k k T

24 A Prelmnary Analyss (1) The Mnmal Tube Network and the Three Centralty Indces Degree Betweenness Closeness

25 A Prelmnary Analyss (2) Top Statons By Centralty

26 A Prelmnary Analyss (3) Closng Lverpool Street

27 A Prelmnary Analyss (3) Closng Green Park

Load Balancing for Hex-Cell Interconnection Network

Load Balancing for Hex-Cell Interconnection Network Int. J. Communcatons, Network and System Scences,,, - Publshed Onlne Aprl n ScRes. http://www.scrp.org/journal/jcns http://dx.do.org/./jcns.. Load Balancng for Hex-Cell Interconnecton Network Saher Manaseer,