We argue that Genetic Improvement can be successfully used for enhancing road traffc data mining. This would support the relevant decision makers with extending the existing network of devices that sense and control city traffc, with the end goal of improving vehicle Flow and reducing the frequency of road accidents. Our position results from a set of preliminary observations emerging from the analysis of open access road trafic data collected in real time by the Birmingham City Council

Ekárt, Anikó

Ilie-Zudor, Elisabeth

Lush, Victoria

Patelli, Alina

English

Ekárt, A

Patelli, A

Lush, V

Ilie Zudor, Angyalka

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Gaining insights into road traffic data through genetic improvement

Anikó Ekárt

Alina Patelli

Victoria Lush

Elisabeth Ilie-Zudor

Crossref

Aston Publications Explorer

Gaining Insights into Road Traic Data through GeneticImprovementAniko´ Eka´rt, Alina Patelli, Victoria LushAston Lab for Intelligent Collectives Engineering (ALICE)Aston UniversityBirmingham, UK{a.ekart,a.patelli2,v.lush1}@aston.ac.ukElisabeth Ilie-ZudorInstitute for Computer Science and ControlHungarian Academy of SciencesBudapest, Hungaryilie@sztaki.mta.huABSTRACTWe argue that Genetic Improvement can be successfully used forenhancing road trac data mining. is would support the relevantdecision makers with extending the existing network of devicesthat sense and control city trac, with the end goal of improvingvehicle ow and reducing the frequency of road accidents. Ourposition results from a set of preliminary observations emergingfrom the analysis of open access road trac data collected in realtime by the Birmingham City Council.KEYWORDSGenetic Improvement, symbolic regression, data miningACM Reference format:Aniko´ Eka´rt, Alina Patelli, Victoria Lush and Elisabeth Ilie-Zudor. 2017.Gaining Insights into Road Trac Data through Genetic Improvement. InProceedings of GECCO ’17 Companion, Berlin, Germany, July 15-19, 2017,2 pages.DOI: hp://dx.doi.org/10.1145/3067695.30825231 INTRODUCTIONe science of road trac data - from eliminating irrelevant orcorrupted records to actual data mining, such as classication andprediction - is essential to automated road trac control [2, 3].For example, extracting a model from a city’s historical data aboutvehicle speed and incident rates would make it possible to predictthe likelihood of a motor accident in a newly built junction. isinsight could inform decisions about automated road trac control,such as establishing the optimal speed limit, installing the idealnumber of speed bumps or replacing the junction with a roundabout.is could ultimately cut costs, reduce pollution and save lives.In spite of the notable progress made in terms of expandingthe network of road trac sensors, the data collected by those ischallenging to analyse eectively. One typical problem would bepredicting the occupancy of a newly built parking lot by combiningexisting models generated for similar locations, rather than waitingto collect data on the new structure. We make the case that GeneticPermission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor prot or commercial advantage and that copies bear this notice and the full citationon the rst page. Copyrights for components of this work owned by others than theauthor(s) must be honored. Abstracting with credit is permied. To copy otherwise, orrepublish, to post on servers or to redistribute to lists, requires prior specic permissionand/or a fee. Request permissions from permissions@acm.org.GECCO ’17 Companion, Berlin, Germany© 2017 Copyright held by the owner/author(s). Publication rights licensed to ACM.978-1-4503-4939-0/17/07. . .$15.00DOI: hp://dx.doi.org/10.1145/3067695.3082523Improvement (GI) is a suitable tool to address such issues. GI is asearch technique that specialises and combines existing soware toproduce new and enhanced algorithms [4, 7], as opposed to GeneticProgramming (GP) that traditionally starts either from scratch [8]or a heuristically extracted primitive set [6].We base the argument on two pivotal points:• the documented success of symbolic regression in termsof providing insight into large datasets [1] and• the promising potential of GI stemming from its capacity tocombine and exploit existing soware rather than startingsearch from scratch (a feature called “code scavenging” [7]).2 THE CHALLENGES OF MINING OPENACCESS ROAD TRAFFIC DATAe ‘Birmingham in real-time’ project is supported by the Birming-ham City Council and provides open source data1 from various roadtrac sensors, such as inductive loops, cameras feeding imagesinto automatic number plate recognition (ANPR) algorithms, etc.e Birmingham city area is 103.4 mi2 (approximately 11.5 milesin diameter) with over 400 road trac sensors installed at variousjunctions in the city. e historic data available for the past twoyears is updated periodically (at ve minute intervals) by collecting,collating and uploading new sensor readings. Output data includeaverage speed and trac ow, that is, the number of vehicles pass-ing detected by the sensor, within the ve minutes time frame, aswell as travel time between two nodes of the observed grid.Two important challenges of working with large repositories ofdata collected by physical (thus fallible) devices are accuracy andcompleteness [2]. Road trac data is no exception - we note thefollowing key issues oen recurring in the Birmingham case study:• Incorrect data. Sensors exposed to the elements are af-fected by signicant brightness and temperature variations,causing inaccuracies in the data they collect. ose valuesare dicult to tell apart from correct ones, e.g., a tracow sensor that went oine and one that monitors anarea where no car has passed during one sampling interval,will both record a value of 0. Erroneous readings may alsooriginate from incorrectly installed sensors (using one in-ductive loop to monitor multi-lane junctions is a commoninfrastructure aw).• Unavailable data. Some features, such as the geolocationdescription of vehicle ow detectors, are characterised bya signicant proportion of missing values (52% in the caseof the data from the 21st of March 2017).1hp://butc.opendata.onl/AL OpenDataGECCO ’17 Companion, July 15-19, 2017, Berlin, Germany A. Eka´rt et. al.We consider the following research questions:(1) Identify good locations for new sensors. e decisionmaking process, when it comes to extending busy roadsor seing up intermediate junctions on an existing road,should be informed by insights extracted from availabledata - a goal dicult to achieve when the reliability of thatdata is questionable.(2) Discover subtle relationships between data streams.It is sensible to expect a strong correlation between datasets recorded by two trac ow sensors in consecutivejunctions on a busy road. However, it would be even moreuseful to automatically detect understated, unexpecteddependencies between dierent sensors’ output.(3) Pre-process sensor data. e quality of data mining gen-erated models relies heavily on the accuracy and complete-ness of the input data. Automatically identifying awedrecords (transmied by faulty sensors) and dierentiatingbetween those and stand-by values (transmied by work-ing sensors recording no trac ow) would be benecial.(4) Predict useful features for existing sensors. e costof infrastructure maintenance may be reduced by havingaccess to reliable estimates of road trac density, at rushhour, next to a school or of midday occupancy in a citycentre car park, etc. Such estimates may be dicult toobtain with substantial missing data.3 GI FOR ROAD TRAFFIC PREDICTIONFor the purpose of this position paper, we focus on the rst practicalquestion of where to add new sensor nodes in a city, when extendingthe road trac sensing network, (1) above, which also involveslocating dependencies between sensors (2).When a partial network of sensors with data collected over a sub-stantial period of time (i.e. two years in our case) exists, symbolicregression can be used very eectively to produce robust predictorsof road trac ow at every node in the partial network, but varioustime series models can also be employed. ese predictors, irrespec-tive of what method was used to derive them, can then form thestarting code base for GI. As postulated by White and Singer [7],we are proposing to employ GI by using existing code, i.e., symbolicregressors and time series models created for existing nodes inthe network, as ready-made functionality for creating models forsensor nodes with similar characteristics and new sensor nodesin the network. Mutation and crossover will be used to identifythe modications and combinations of existing models that leadto robust alternative models for such similar nodes. In the case ofa new sensor node, it would be impossible to immediately modeltrac ow as actual data collected at that location usually does notexist yet. One would need to collect data over a period of time rstand then create the trac ow model for this sensor based on thecollected data. erefore, if we can establish dependencies betweensensors and similarities between groups of sensors, then, by usingGI, appropriate combinations of existing road trac ow modelscan be found to predict what the new sensor is expected to collect.Figure 1 illustrates a possible scenario using the so-called primalgraph representation for transport networks [5], where edges andvertices in the graph correspond to links (roads) and nodes (junc-tions) in the transport network. e part of the network includingnodes A-F has similar layout to the part of the network includingnodes a-f and this is indicated by the pairwise correspondence ofnodes A-a, B-b, C-c, D-d, E-e, F-f. Consider that all these nodesexcept for a are fully equipped with sensors and the introduction ofsensors at node a is being considered. Starting from the trac owmodels for the part of the network A-F, GI can be used to nd themodications (mutations) of these models to establish and validatemodels for nodes b-f and predict models for node a, where the newsensor insertion is being considered.Figure 1: New road trac sensor introduction: an exampleAs illustrated above, what-if scenarios can be provided to road tracdata collection decision makers to enable them to make informeddecisions on the placement of the new sensors.4 CONCLUSIONWe argue that applying genetic improvement to incorporate ex-isting code into automated programming, i.e., “code scavenging”,is an avenue worth following for the problem of road trac owprediction at new sensor locations, based on established models forroad trac ow at pre-existing sensor locations. is is a complexreal life problem on which the capability of GI can be demonstrated.5 ACKNOWLEDGMENTis work was supported by the European Commission through theH2020 project EXCELL (hp://excell-project.eu/), grant No. 691829.REFERENCES[1] Michael Aenzeller, Stephan M Winkler, Gabriel Kronberger, Michael Kom-menda, Bogdan Burlacu, and Stefan Wagner. 2014. Gaining deeper insights insymbolic regression. In Genetic Programming eory and Practice XI. Springer,175–190.[2] Andrew Hamilton, Ben Waterson, Tom Cherre, Andrew Robinson, and Ian Snell.2013. e evolution of urban trac control: changing policy and technology.Transportation planning and technology 36, 1 (2013), 24–43.[3] Falilat Jimoh and omas Leo McCluskey. 2016. Self-management in urbantrac control–an automated planning perspective. Springer.[4] William B. Langdon and Mark Harman. 2012. Genetically improving 50000 linesof C++. Research Note RN/12/09. (2012).[5] Stephen Marshall. 2016. Line structure representation for road network analysis.e Journal of Transport and Land Use 9, 1 (2016), 29–64.[6] Riccardo Poli, William B Langdon, Nicholas F McPhee, and John R Koza. 2008. Aeld guide to genetic programming. Lulu. com.[7] David R White and Jeremy Singer. 2015. Rethinking genetic improvementprogramming. In Proceedings of the Companion Publication of the 2015 AnnualConference on Genetic and Evolutionary Computation. ACM, 845–846.[8] John R Woodward, Colin G Johnson, and Alexander EI Brownlee. 2016. GP vsGI: if you can’t beat them, join them. In Proceedings of the 2016 on Genetic andEvolutionary Computation Conference Companion. ACM, 1155–1156.

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Gaining insights into road traffic data through genetic improvement

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