Dynamic Visualization of Volume of Traffic

Karel Jedlička, Jan Ježek, Michal Kepka, Pavel Hájek, Tomáš Mildorf, František Kolovský, Daniel Beran*

* University of West Bohemia, Pilsen, the Czech Republic

Abstract. Traffic congestions and jams are phenomena which limit everyday life in large cities. There are options how to mini-mize the delay caused by dense traffic in the age of the knowl-edge society. Most of them are based on informing drivers of the current or predicted movement of vehicles in a transportation network. Movements of vehicles in a network is often described by a Volume of Traffic - a variable which represents the number of vehicles passing a network segment in a period of time. As the vehicles’ movement is a complex spatio-temporal phenomenon, the volume of traffic varies dynamically in both space and time. Moreover, it could be differentiated according to vehicle types (cars, trucks, cyclists, pedestrians, etc.). There exist several com-prehensive visualization techniques. Their use for dynamic visu-alization, live simulation and suitable analytical applications in the entire area of interest is often problematic.The main contribution of the paper is a description of relevant data processing and visualization techniques. These techniques were applied and tested in the frame of the European project OpenTransportNet (CIP-ICT-PSP-PB 620533). There are four pi-lot regions where these techniques are being applied: the city of Antwerp, Birmingham, Issy-Les-Moulineaux and the Liberec Re-gion.The paper firstly analyses contemporary progressive methods for dynamic phenomena visualization and methods which are suit-able for traffic flow management in the area of interest. The next section of the paper explains the nature of traffic volume as a variable, the way how it can be measured on key network seg-ments and then calculated for the whole network. The following process of estimation of the traffic volume time variations includ-ing short and medium-term prediction is also outlined. The paper describes the change of geographic data structure on the exam-

ple of traffic data. This change is necessary for visualization of spatio-temporal data in contemporary clients. Finally, the se-lected visualization method is implemented in an online client.Keywords: traffic volume, transport network, dynamic, visualiza-tion, spatio-temporal dataAcknowledgements: The authors of this paper are supported by the European Union’s Competitiveness and Innovation Frame-work Programme under grant agreement no. 620533, the Open-TransportNet project.

1. IntroductionTraffic jams and traffic delays are everyday reality for many peo-ple living in large cities and conurbation areas. Several studies provide numbers of hours which a driver spends in a traffic jams per a year. E.g. the INRIX traffic scorecard website (http://www.inrix.com/scorecard/) shows data about traffic delays in Northern America and West Europe (usually approx. a day or more in traffic jams per a year for big cities). Traffic related problems are issues addressed by OpenTransport-Net EU project (CIP-ICT-PSP-PB 620533). The project is based on 4 pilot cities. All of them have to deal with traffic jams and re-lated problems. To have an imagination of time waste related to traffic, there follows examples of average time drivers spend in traffic jams in three OTN pilot cities (data from city of Liberec are not available) according to above mentioned INRIX traffic scorecard: Birmingham - 33.6 hours/year, Paris 54.0 h/y, Antwerp 76.4 h/y, (data for year 2014).This paper focuses to geoinformation technologies for decision support which can help to minimize the delay caused by dense traffic, particularly to techniques which can visualize traffic flows, traffic volumes and their relation to roads maximum capac-ity. Therefore the following Related Work chapter analyses con-temporary progressive methods for dynamic phenomena visual-ization and methods which are suitable for traffic flow manage-ment in the area of interest.

2. Related worksThe visualization of a dynamic phenomena uses cartographic techniques based on temporal aspects of data. Non-temporal maps present time implicitly, but they do not provide any infor-mation about change of a feature. Temporal maps explicitly por-tray temporal aspects. These maps could be defined as a repre-sentation or abstraction of changes in geographical reality for presenting geographical information which locational and/or at-tribute components change over time (adopted from Čerba, Brašnová (2012)). In other words, maps that display spatio-tem-poral data can be either static (the display does not change) or dynamic (the display varies on a base of location and/or at-tributes of a feature, as with frames in an animation) (adopted from Kraak (2001), Buckley (2013)). It is often useful to map spatio-temporal data dynamically because the varying nature of the data is intuitively expressed in the changing display. How-ever, this intuitive understanding is countered by the increased complexity of the visualization because it changes, and this can lead to lack of understanding or misinterpretation of the mapped data, Buckley (2013). Other possible classifications of cartographic methods for de-scribing temporal aspects of spatial data can be found for exam-ple in Čerba & Brašnová (2012), Ping et al. (2008), Kraak & Klomp (1995) or Kraak & Ormeling (2010). For the purposes of this paper, the methods suitable for visual-ization of traffic flow are depicted further. There can be ways how to deal with the overwhelming of a user from displayed data on a map either with reduction and stressing of desired data or using multiple plots for showing several variables at one. Meth-ods using either the first or the second way are described fur-ther. For the 2D maps there are commonly used methods such as visu-alization of a road traffic based on a set of images (e.g. Skycomp Traffic Studies and Data Collection using Time-lapse Aerial Pho-tography (TLAP) showing traffic flow visualization from the aerial photographs; produced by Skycomp, Inc. - for more information see Skycomp (2015)), based on a color of a road defining the travel speed of vehicles in a certain moment (used in Google maps as a visualization of traffic or also ArcGIS Online provides a ready-to-use traffic map service, see e.g. Jakimavičius (2014)).

Examples of other commonly used visualizations of a traffic flow are a flow map representation which illustrates the movement of objects among locations with the reducing of a visual clutter by merging edges of the same direction (Phan et. al. (2005), Rebolj & Sturm (1999)) or a variation of a network visualization such as Becker et al. (1995). Moreover, one of the steadily developing topic of exploratory data visualization is the technique based on multiple coordinated views (MCV) given by Roberts (2007). Such a technique uses various visualization techniques for different data types, where each visualization enables interaction (such as a filtering) that is further coordinated with an other view (e.g., selecting a time interval in a bar chart triggers immediate high-lighting of relevant items in a map view). For the purpose of MCV visualization several frameworks a techniques exist (Ježek et al. (2015), Lins et al. (2013), Liu (2013)). Apart from the classical 2D methods of temporal data carto-graphic representations, there are also methods using 3D visual-ization, 4D especially, when time is the fourth dimension. One of the possible ways how to represent geodata and time in 3D is so called space-time cube (STC) method in which space is repre-sented by maps surface (x, y axis) and the vertical dimension is left to represent time (z axis). Though STC may be used not only for points but also for trajectories, filtering is necessary to main-tain good legibility while working with often crossed flows. A dis-advantage of STC is that it may took a while to find correct angle and map extent under which the figure shall convey all required information (Andrienko et al. (2013)). The extension of this method is so called Space–Time Cube and Trajectory Wall, which allows effective and efficient visualization of trajectory attributes for trajectories following similar routes (Andrienko et al. (2014)). Another solution for over plotted graphs may be an additional bar chart (e.g. Kincaid & Lam 2006). Horizontal bars represent cho-sen route segments, when one of them is selected in the chart, the appropriate segment of network is highlighted on the en-closed map. Lengths of bars may also represent time required to travel through, coloring of segments of bars can be used for ex-pressing level of fluency of traffic flow.Visualization of a variety of information about traffic can be cru-cial for a decision making during traffic jams, nature disasters and so on. Therefore lately the online systems providing the com-prehensive look into the traffic data has been developed. We de-scribe three of them here. CubeView is a web-based visualization

system (Shekhar et al. (2002)), which presents information in various formats to assist transportation managers, traffic engi-neers, travelers and commuters, and researchers and planners to observe and analyze traffic trends by showing different kind of a data visualization at a time in a form of map, graphics and trend charts. Another web-based service for traffic visualization is for example a multiple view system AITVS (Advanced Interactive Traffic Visualization System), which is defined as a system that uses two or more distinct views to support the investigation of a single conceptual entity. Multiple views can provide utility in terms of minimizing some of the cognitive overhead engendered by a single, complex view of data. The system presents informa-tion in various formats to observe and analyze traffic trends (Lu & Zheng (2006)). The last mentioned system is an AURORA sys-tem, which is an IBM Research project as well as the name of a traffic analysis and visualization system. The research project is targeted at flow-based network traffic analysis and visualization for very large networks.The representation of a spatial-temporal phenomena is also con-nected to the field of animation using semantic aspects of the efficiency of transmission (Oprah (2005)), evaluation of the carto-graphic visualization by eye-tracking methods (mentioned for example in Nossum (2014)) and cognitive representation of a temporal-changing phenomena (depicted and researched for ex-ample in Resch et al. (2014)). Authors take the existing works described above into account during the development of their first two prototypes of traffic volumes visualizations (see chapter 6). But before the data can be visualized, it has to be also under-stood. Therefore the next chapters of the paper explain the na-ture of traffic volume as a variable, the way how it can be mea-sured on key network segments and then calculated for the whole network.

3. The nature of traffic volume and related parametersTraffic volume is a parameter of a road network which describes the amount of vehicles which go through a network segment in a period of time. Together with an information about the maximum capacity of network segments, it can be forecasted where the volume of traffic is going to cause traffic disruptions and traffic jams. We can distinguish three types of traffic volumes:

● annual average of daily traffic volume (AADT),● daily traffic volume (different for each day from Monday to

Sunday),● hourly variations of traffic volume (incl. peak hour traffic

volume – in the busiest hour of the day).Also a long term predictions can be made (using a mathematical traffic model) calculating the traffic volumes 10, 20 or even 30 years into the future. See more in EDIP (2012) or in Kozhukh et al. (2015).Volume of traffic has to be combined with another parameter - road capacity - to be able to describe traffic flow well. Road ca-pacity is defined as the maximum amount of cars which are able to cross a road segment during a period of time. The traffic vol-ume rises until the road capacity is reached and after that de-creases. Until the max capacity is reached, the traffic flow is un-congested (free), then it becomes congested. The transportation speed is inversely related to traffic volume and decreases rapidly when the maximum road capacity is reached. See more e.g. in Dunn Engineering (2005) for deeper description.Road capacity is usually calculated by traffic engineers taking into account many parameters, notably: road type, number of lanes, lanes width and road curvature. See more e.g. in Křivda & Frič (2005).

4. Traffic volume calculationContrariwise the road capacities, which can be calculated com-pletely from road related parameters, volumes of traffic calcula-tion depends on behavior of inhabitants in an area of interest; this behavior is described by demographic data.

4.1.Source dataIn general, there are three basic types of data necessary for traf-fic volume calculation using a mathematical traffic model, ac-cording to e.g. Kozhukh et al. (2015):

● Traffic generators - demographic data about places that are usually represented as points. These points can be cities, city districts or building blocks – it depends on the granularity of the data and the desired level of detail.

These data are used for estimation of traffic flows in the network. Distinguishing between different types of places such as living, industrial, service or shopping place is use-ful for estimation of traffic flows direction changes in time.

● Road network - well defined and topologically correct road network is the fundamental constraining graph structure, which describes the allowed movements between different places.

● Calibration measurements - physical measurements of traffic volumes (traffic census) at particular spots of the traffic network are used for calibration of calculated vol-umes.

In the case of the OTN project - Liberec Pilot, traffic generators are at the level of detail corresponding to city parts/districts. The road network has been adopted from OpenStreetMap and cali-bration measurements have been taken from the last country level traffic census1.

4.2.Calculation of daily and hourly variations of traffic vol-umes

The calculation of daily traffic volumes has been done in Trans-port Planning Software OmniTrans2. Then, hourly variations of traffic volumes have been calculated according to the experimen-tally derived curve of hourly traffic variations (see EDIP 2012 for more information about the curve). This pre-calculated curve of hourly variation of traffic volume is used to calculate the variations for each particular road segment. As it can be seen at the figure 1, the hourly variation differs for:

● Different types of roads (Highway, Speedway, European road, First class, …)

But moreover these curves can differ also for:● Four year seasons (spring, summer, autumn, winter)● Days in week (working days/weekends or even to particu-

lar weekdays Monday, Tuesday, Wednesday, Thursday, Friday, Saturday and Sunday)

1 http://scitani2010.rsd.cz/pages/informations/default.aspx 2 http://www.omnitrans-international.com/en/products/omnitr ans/product- overview

● Vehicle types (passenger cars, trucks, etc.).When a proper curve is selected for each particular road network segment, the daily volume of traffic is distributed among day

hours. Figure 1. Example graph of hourly variation of traffic volume depend-ing on road type (calculated for passenger cars, working day, autumn) (EDIP 2012).

5. Need of harmonized data structureData harmonization is necessary for combining data from hetero-geneous sources (e.g. regional datasets) into integrated, consis-tent and unambiguous information products (e.g. European datasets). Such datasets can be then easily used in combination with other harmonized data for viewing as well as querying and analyzing, Janečka et al. (2013).As mentioned above, the OTN project has 4 pilot cities. Birming-ham and Issy are going to use OpenStreetMap as well as the Liberec Pilot, but Antwerp uses road network from Flemish re-gional agency. Therefore a harmonized data structure has been designed and harmonization rules were developed - one set of rules for the OSM data from Liberec pilot and second set for data from Flemish region for the metropolitan area of Antwerp, see details in Jedlička et al. (2015). But harmonization rules can be developed for other data in the future as well, following a 5-step harmonization approach (see Janečka et al. (2013)). The resultant (harmonized) data structure is based on INSPIRE Data Specifica-

tion on Transport Networks (JRC (2007)) expanded on data struc-ture for traffic volume (see fig 2).

Figure 2. Harmonized data structure based on INSPIRE Data Specifi-cation on Transport Networks expanded on data structure for traffic volume. Adopted from Jedlička et al. (2015).

The figure shows just overview structure, code lists and enumer-ation are avoided. As it can be seen from the picture, each Road-Link is represented just once and there is a one-to-many relation-ship to TrafficVolume table. This data structure then serves data for both prototypes of dynamic visualizations of traffic volumes described below. PostGIS is used for the data storage.

6. Dynamic visualization of traffic volumeAs it was mentioned in the introduction, traffic volume and espe-cially its spatiotemporal changes can bring new insights about the consequences of human decisions from many areas (e.g. ur-ban planning). From that reason the main focus of visualization technique is given to the interactivity through utilizing the con-cept of Multiple Coordinated Views (as mentioned in the chapter Related Works) and dynamic queries to emphasize the impact of changes of various phenomena on the traffic volume.

First prototype of traffic volume visualization is a web based so-lution build by using WebGLayer library3 (https://github.com/jezekjan/webglayer) for a map rendering and D3.js for rendering of non-spatial data. The visualization provides these views and interactions (as depicted in fig. 3):

● Map view of road segments categorized by color that ex-press the traffic volume of the segment in particular time instance.

● Histogram visualizing intervals of traffic volumes and cor-responding summarized number of road segments.

The visualization further provide interactions and dynamic queries that instantly update both of the views. These interac-tions are:

● Time slider that enables to choose hour of the day.● Time slider that enables to choose the year.● Histogram brush, that enables to select a range of traffic

volume that is further instantly highlighted in the map view

● Map zoom and pan the instantly updates the histogram and visualize differently the data records spatially located inside the actual map viewport and the records being out-side (light blue bars).

3 For other usages of the library see Ježek et al. (2015a,b).

Figure. 3. Coordinated views and dynamic queries for traffic vol-umeUnfortunately the data from the City of Antwerp does not have information about capacity of the city streets. Even so, this visu-alization prototype shows a potential of combining geographic data with temporal information (hourly variations of traffic vol-umes, two different epochs) and non-spatial data in histogram Second introduced prototype shows visualization combining traffic volume and road capacity parameters. It is based on KML format and its styles options (Google (2015)), which is generated from the PostGIS database. The generated KML file consists of road segments with the parameters traffic volume and road ca-pacity. The visualization of the KML then provides view of road segments that are styled according to current parameter values in every defined timespan. Styles are used as follows:

● Width of the line – current traffic volume in the road seg-ment;

● Color of the line – current consumed capacity of the road segment in scale green – yellow – red.

Each geometry of the road segment is styled according to current parameter values in every defined timespan. An example of the KML visualization is shown on fig. 4 below.

Figure. 4. Visualization of traffic volumes in KML format4

The visualization in form of generating of KML files provides pos-sibility of taking snapshots of the traffic volume for defined time range. Further interaction of the KML visualization depends on viewer that is used for opening of the KML file. However, in most cases a time slider is provided to specify narrower time spans and zooming is provided to look at the road network in detail. The live example is available online at:http://odh.isaf2014.info/projects/open-data-hackathon/wiki/Traf-fic_network_with_traffic_volumes or directly here: http://gis.zcu.cz/projekty/OTN/TrafficVolumesExample.html

7. SummaryThe paper is focused on dynamic visualization of volume of traf-fic. Authors start with analysis of contemporary works dealing with a visualization of a dynamic phenomena using cartographic 4 http://gis.zcu.cz/projekty/OTN/TrafficVolumesExample.html

techniques having regard to the temporal aspects. Then they shortly introduce the nature of traffic volume and related param-eters and describe the process of traffic volume calculation. After that, a data structure for traffic volume visualization is designed. Finally, as the main contribution of this paper, two prototypes for dynamic visualizations of traffic volumes are implemented and described. For the future work, the authors will apply next ver-sions of prototype visualizations also to the rest of OTN pilot cities. The functionality of the prototypes will be further enriched by additional features such as visual comparison of regions of interest, by an opportunity of reporting a blockage of a road by a user and consecutive recalculation of Volume of traffic or by an option of choosing from Traffic generators to calculate the short-est path between them based on traffic volume attribute.

ReferencesAndrienko, G., Andrienko, N., Bak, P., Keim, D., & Wrobel, S. (2013).

Visual analytics of movement. Springer Science & Business Media.Andrienko, G., Andrienko, N., Schumann, H., & Tominski, C. (2014).

Visualization of Trajectory Attributes in Space–Time Cube and Trajec-tory Wall. In Cartography from Pole to Pole (pp. 157-163). Springer Berlin Heidelberg.

Becker, R. A., Eick, S. G., & Wilks, A. R. (1995). Visualizing network data. Visualization and Computer Graphics, IEEE Transactions on, 1(1), 16-28.

Buckley, A. (2013). Guidelines for the Effective Design of Spatio-Tempo-ral Maps. In 26th International Cartographic Conference, ICC.

Čerba, O., Brašnová, K. (2012) Cartographic Visualization of Temporal Aspect of Spatial Data. In Proceedings of AutoCarto 2012. Columbus, Ohio: CaGIS, 2012. p. 1-9.

Dunn Engineering. (2005). Traffic control systems handbook. Dunn En-gineering Asociates. Available online at: http://ops.fhwa.dot.gov/publi-cations/fhwahop06006/fhwa_hop_06_006.pdf

EDIP. (2012). TP 189 Stanovení intenzit automobilové dopravy, [Deter-mination of automobile volume of traffic], 2012, ISBN: 978-80-87394-06-9

Google (2015) KML Reference, Google, Inc. Available online at https://developers.google.com/kml/documentation/kmlreference

JRC. (2007). D2.8.I.7 Data Specification on Transport Networks – Tech-nical Guidelines. European Commission Joint Research Centre. Avail-

able online at: http://inspire.ec.europa.eu/documents/Data_Specifica-tions/INSPIRE_DataSpecification_TN_v3.0.pdf

Jakimavičius, M. (2014) Traffic flows analysis and visualization based on data from an advanced Vilnius traveller’s information system, In The 9th International Conference “ENVIRONMENTAL ENGINEERING” Selected Papers, eISSN 2029-7092 / eISBN 978-609-457-640-9. Avail-able online at http://enviro.vgtu.lt

Janečka, K., Cerba, O., Jedlicka, K., & Jezek, J. (2013). TOWARDS IN-TEROPERABILITY OF SPATIAL PLANNING DATA 5-STEPS HARMO-NIZATION FRAMEWORK. 13th SGEM GeoConference on Informatics, Geoinformatics And Remote Sensing, 1(SGEM2013), 1005-1016.

Jedlička, K., Ježek, J., Mildorf, T. (2015). OTN D4.4 Data harmonization and integration. OpenTransportNet – Spatially Referenced Data Hubs for Innovation in the Transport. Section. CIP-ICT-PSP-PB 620533. On-line: http://www.opentransportnet.eu/otn/sites/default/files/OTN D4.4 Data Harmonisation and Integration_v1.0.compressed.pdf

Ježek, J., Bernard, J., Kolingerová, I. (2015a) WebGLayer for Advanced Spatial Data Exploratory Visualization on the Web, under review of Geoinformatica journal, Springer.

Ježek, J., Jedlička, K., Martolos, J. (2015b). Visual Analytics of Traffic-Related Open Data and VGI. ICIST 2015 Conference.

Kincaid, R., & Lam, H. (2006, May). Line graph explorer: scalable dis-play of line graphs using Focus+ Context. In Proceedings of the work-ing conference on Advanced visual interfaces (pp. 404-411). ACM.

Kraak, M-J (2001) Settings and Needs for Web Cartography, in Web Cartography: Developments and Prospects, Kraak M-J, Brown A, edi-tors, 1–7

Kraak, M.-J. and Ormeling, F. (2010) Cartography. Visualization of Spa-tial Data. 3rd edition. Pearson Education Limited.

Kraak, M.J. and Klomp, A. (1995) A Classification of Cartographic Ani-mations: Towards a Tool for the Design of Dynamic Maps in a GIS Environment. The Seminar on Teaching Animated Cartography, Spain. Available online at http://cartography.geog.uu.nl/ica/Madrid/kraak.html

Kozhukh, D., Jedlička, K., Mildorf, T., Charvát, K., Charvát K., Jr, Marto-los, J., Šťastný, J. (2015). Benefits of Using Traffic Volumes Described on Examples in the Open Transport Net Project Pilot Regions. AGRIS on-line Papers in Economics and Informatics. ISSN 1804-1930. (under review).

Křivda, V., Frič, J. (2005). Organizace a řízení dopravy. [Organization and management of traffic]. Institut dopravy. Fakulta strojní. VŠB -

Technická univerzita Ostrava. Available online at http://kds.vsb.cz/ord/

Lins, L., Klosowski, J. T., & Scheidegger, C. (2013). Nanocubes for real-time exploration of spatiotemporal datasets. Visualization and Com-puter Graphics, IEEE Transactions on, 19(12), 2456-2465.

Liu, Z., Jiang, B., & Heer, J. (2013). imMens: Real‐time Visual Querying of Big Data. In Computer Graphics Forum (Vol. 32, No. 3pt4, pp. 421-430). Blackwell Publishing Ltd.

Lu, C. T., Boedihardjo, A. P., Zheng, J. (2006). Aitvs: Advanced interac-tive traffic visualization system. In Data Engineering, 2006. ICDE'06. Proceedings of the 22nd International Conference on (pp. 167-167). IEEE.

Nossum, A. S. (2014). Exploring Eye Movement Patterns on Carto-graphic Animations Using Projections of a Space-Time-Cube. The Car-tographic Journal, 51(3).

Opach, T. (2005). Semantic and pragmatic aspect of transmitting infor-mation by animated maps. In Proceedings of the XXII rd ACI/ICA In-ternational Cartographic Conference.

Phan, D., Xiao, L., Yeh, R., & Hanrahan, P. (2005). Flow map layout. In Information Visualization, 2005. INFOVIS 2005. IEEE Symposium on (pp. 219-224). IEEE.

Ping, Y., Xinming, T., & Shengxiao, W. (2008). Dynamic cartographic representation of spatiotemporal data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sci-ences, 37, 7-12.

Rebolj, D., Sturm, P. J. (1999). A GIS based component-oriented inte-grated system for estimation, visualization and analysis of road traffic air pollution. Environmental Modelling & Software, 14(6), 531-539.

Resch, B., Hillen, F., Reimer, A., & Spitzer, W. (2013). Towards 4D Car-tography-Four-dimensional Dynamic Maps for Understanding Spatio-temporal Correlations in Lightning Events. The Cartographic Journal, 50(3), 266-275.

Roberts, J. C. (2007). State of the art: Coordinated & multiple views in exploratory visualization. In Coordinated and Multiple Views in Ex-ploratory Visualization, 2007. CMV'07. Fifth International Conference on (pp. 61-71). IEEE.

Shekhar, S., Lu, C. T., Liu, R., & Zhou, C. (2002). CubeView: a system for traffic data visualization. In Intelligent Transportation Systems, 2002. Proceedings. The IEEE 5th International Conference on (pp. 674-678). IEEE.

Skycomp (2015) Aerial Traffic Observation, Data Collection and O-D Studies, Skycomp, Inc., Available online at http://www.skycomp.com/