Forecasting of the Traffic Situation in the Hannover Region

The main requirement of road traffic participants is to know the current traffic situation. Such data is typically obtained from routing services where the time of many different individual trips is taken into account.

In the context of Data4UrbanMobility tools were developed that allow to predict the traffic situation based on such time series data. The following figure presents an interface to visualize typical time series patterns as well as outliers present in the data:

The prediction of the traffic situation is made available in the form of a map based interface for the end user:

Data4UrbanMobility Data Protection Regulation

The work on the Data4UrbanMobility data protection regulation is completed. The document is publicly available and can be found here.

First Version of MiC-App Available

A first version of the novel MiC-App (Move in the City) App is now available for D4UM-associates as well as a protected group of public users. The mobile MiC-App is a tool to gather data.

MiC was developed by the Institute for Sustainable Urbanism at the University of Braunschweig and the Projektionisten GmbH. MiC links the growing  awareness of  digital citizen rights with the potential of evaluation big datasets. Therefore MiC gives the opportunity to citizen to actively participate in a citizen science project to take part in the development of the mobility of the feature.

MiC gathers data of the users movement, where the user has the about which data should be recorded. All data is pseudonymised such that the privacy of the contributing citizen is ensured.

Current Status:

In the first version of the app, the user can easily start and end the tracking of his/her movement. It is worth to point out, that the user decides when he is tracked and when not. A summary of his/her activity is available for the user as well as the opportunity to issue feedback or even delete all of his contributed data.

Updated System with Dashboard V2

With the new version of our system,  the dashboard will provide even more insights into the impact of public events on the traffic situation.

The coloring and labels let us easily distinguish between the different type of events. By clicking on the label we show the typically affected subgraph for that event type. This allows the user to check what specific routes are typically affected by an event at that location.

Examples: Visualisation of a concert and a football game.

In addition, the graph at the top right gives additional information on how big the impact around the events start time tends to be.

{API}
We enriched the api endpoints with additional information from the data models that were developed as part of the research efforts.

D4UM App Version 1.0

We just released the first Version of the D4UM App. Every project member now has access to the application and can try out its features. Let’s quickly go over some of its main features.
The EFA integration (EFA is a routing engine covering Lower Saxony and Bremen ) allows for quick access to tip information using all available public transport options. Our focus, when designing the application, was on quick and easy navigation to provide a simple and easy to use trip planning tool.

Departures and Connections

On the departure screen we show the user the closes stops for public transportation in his immediate vicinity. On the connection screen the user can fill in his desired starting location( either an address or an existing stop ) and destination and query for what connections are available to him. The provided information contains real time data , meaning we are able to visualized delays for any given connection.

Map

On the map screen you can see and or find all available stops of public transportation. This allows for providing the user with a great way to find out what stops are available in their city. By clicking on any of the shown stops will open the departure screen and provide you with the information mentioned above. To better visualize a selected connection, we show the route you plan to travel on the map.

Menu / Settings

Additional features can be found in the settings menu of the application. Here you can find settings that allow you to customize your routing results for both the departures and connection screen. The best way to let us know what you think about the application is to use the feedback module. This can be found here as well. First click on the emoji that best describe how you feel about the app. And then put in any additional information or ideas or thoughts you may have. Now what is left is just to press send and you will send us an email.

We look forward to hearing from you.

Quantification  and Prediction of Impact of Public Events

Current Data4UrbanMobility research results allow for measuring and prediction of spatial impact on road traffic of public events. Connected, affected street segments nearby public events are identified to measure the spatial impact. The approach is depicted in the following figure:

(Karte von https://www.openstreetmap.org)

An event is marked as yellow dot, affected streets in red and the measured impact in dark blue. Moreover, an approach making use of machine learning algorithms was developed to predict the impact determined in this way, resulting an error-reduction of up to 40% when compared to existing state-of-the-art approaches.

D4UM – Platform V1 Released

The first version of the Data4UrbanMobiltiy platform has been released. The platform was designed and implemented following a 3-tier-architecture. The platform provides RESTfull Web services for mobility applications like dashboards or mobile apps. As a demonstration, an interactive map application has been developed that visualizes the spatial impact of public events. The following figure shows a screenshot of the application.

The figure shows 4 public events in the city of Hannover. The colors represent different types of public events (e.g. concerts, fairs, sport events). The circles visualize the spatial impact on road traffic caused by the public events.

Comprehensive Set of Requirements

The Data4UrbanMobility analysis of requirements includes requirements of the application partners Region Hannover (RH) and Wolfsburg AG (WAG) as well as non functional requirements. The requirements were collected by MOMA. The L3S derived research question for data analysis which are based  on the requirements of RH and WAG. The research question address especially the information needs of end-users.

The current research questions particularly include

1. Automated verification of traffic warnings and prediction of their impact
2. Identification of events and prediction of their impact
3. Investigation of correlation of road traffic data, public transportation query logs, traffic warnings and twitterfeeds
4. Determination of optimal traveling timepoints

Growing Data Collection

ISU create a comprehensive data matrix containing potential source of mobility related data. The Data4UrbanMobility data model describes all project relevant data sets and sets them into context. This makes the data available in a unified manor for both analysis and applications. The selected data sources were transformed according to the Data4UrbanMobility data model by L3S. The data quality of selected data sources (i.e. public transportation query logs and road traffic data) was examined.

Tools for extracting the relevant information from the datasets were developed to enable the integration of the datasets.

• Street and graph extraction from OpenStreetMap
• Bulkloader for public transportation queries
• Integration of “Zentrales Haltestellen Verzeichniss” (central registry of public transportation stops)

The current collection (December 12th 2017) contians

EFA-Logs: 17 million public transportation queries
Road traffic data: 174 thousand street sements with a frequency of 15 minutes
GTFS-data: 90 thousand. public transportation stops, 2.6 thousand routes
Weather: Radolan “Regenraster” (rain grid)
Twitter: 2,5 Mio. Tweets starting at June 2017

OSM: 440 thousand streets 
Events: 21 thousand public events (August 14th 2016-July 17th 2018)

Traffic warnings: 13 thousand warning (since June 2017)

Visualization of Public Transportation Information

In order to allow intuitive analytics of public transportation information, the PROJEKTIONISTEN (PROJ) developed a dashboard web application. First prototypes visualize queries addressed to the regional timetable information system EFA (www.efa.de). The prototypes serve as foundations for exploration analyses as well as the implementation of future versions of the dashboard. The following figure shows an integrated visualization of the most frequent origins and destinations of the queries.

Analysen der EFA-Logs

Analysis of EFA Public Transportation Query Logs

Analyses regarding the impact of public events on public transportation are currently conducted to address early research questions. To this extend, explorative data analyses of the impact of major public events such as football games and medium sized events such as concerts were conducted. Visual analytics were used as a first step towards comprehensive analyses, which show start-like patterns for city center which identify mobility hubs of central importance.

The figure shows the direct connection between origin and destination of public transportation queries. Darker colors correspond to more frequent queried trips. Star-like pattern identify the central train station and the central metro station.

Analyses of single stations reveal weekday dependent patterns.

The figure depicts the average number of queries with the destination “Hannover Stadionbrücke”. Differences emerge between Weekends and workdays.

The impact of public events on the queries can be visualized  as well.

The figure shows the number of queries with the Destination “Hannover Stadionbrücke” for Wednesday, April 26th 2017 (orange) as well as the average number of queries on a Wednesday for the same destination. On this day a concert took place in venue nearby. The concert start at 8 pm. The significant deviations between 5 pm and 7 pm is highly likely to be caused by visitors of the concert. This shows that public transportation queries are a valuable information source to investigate the impact of public events on mobility infrastructure.

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• Minimality Attack in Privacy Preserving Data Publishing. Wong, Raymond Chi-Wing; Fu, Ada Wai-Chee; Wang, Ke; Pei, Jian C. Koch, J. Gehrke, M. N. Garofalakis, D. Srivastava, K. Aberer, A. Deshpande, D. Florescu, C. Y. Chan, V. Ganti, C.-C. Kanne, W. Klas, E. J. Neuhold (eds.) (2007). 543–554.
• A Review of Two Industrial Deployments of Multi-criteria Decision-making Systems at General Electric. Subbu, Raj; Bonissone, Piero P.; Bollapragada, Srinivas; Chalermkraivuth, Kete Charles; Eklund, Neil H. W.; Iyer, Naresh; Shah, Rasik; Xue, Feng; Yan, Weizhong (2007). 136–145.
• Probabilistic Skylines on Uncertain Data. Pei, Jian; Jiang, Bin; Lin, Xuemin; Yuan, Yidong C. Koch, J. Gehrke, M. N. Garofalakis, D. Srivastava, K. Aberer, A. Deshpande, D. Florescu, C. Y. Chan, V. Ganti, C.-C. Kanne, W. Klas, E. J. Neuhold (eds.) (2007). 15–26.
• Multi-Dimensional Analysis of Data Streams Using Stream Cubes. Han, Jiawei; Cai, Yandong; Chen, Yixin; Dong, Guozhu; Pei, Jian; Wah, Benjamin W.; Wang, Jianyong C. C. Aggarwal (ed.) (2007). (Vol. 31) 103–125.
• Parametric model-based anomaly detection for locomotive subsystems. Xue, Feng; Yan, Weizhong (2007). 3074–3079.
• Achieving k-Anonymity by Clustering in Attribute Hierarchical Structures. Li, Jiuyong; Wong, Raymond Chi-Wing; Fu, Ada Wai-Chee; Pei, Jian in Lecture Notes in Computer Science, A. M. Tjoa, J. Trujillo (eds.) (2006). (Vol. 4081) 405–416.
• Using High Dimensional Indexes to Support Relevance Feedback Based Interactive Images Retrival. Zhang, Junqi; Zhou, Xiangdong; Wang, Wei; Shi, Baile; Pei, Jian U. Dayal, K.-Y. Whang, D. B. Lomet, G. Alonso, G. M. Lohman, M. L. Kersten, S. K. Cha, Y.-K. Kim (eds.) (2006). 1211–1214.
• Minimum Description Length Principle: Generators Are Preferable to Closed Patterns. Li, Jinyan; Li, Haiquan; Wong, Limsoon; Pei, Jian; Dong, Guozhu (2006). 409–414.
• Efficiently Mining Frequent Closed Partial Orders. Pei, Jian; Liu, Jian; Wang, Haixun; Wang, Ke; Yu, Philip S.; Wang, Jianyong (2005). 753–756.
• Catching the Best Views of Skyline: A Semantic Approach Based on Decisive Subspaces. Pei, Jian; Jin, Wen; Ester, Martin; Tao, Yufei K. Böhm, C. S. Jensen, L. M. Haas, M. L. Kersten, P.- Åke Larson, B. C. Ooi (eds.) (2005). 253–264.
• Mining Most General Multidimensional Summarization of Probably Groups in Data Warehouses. Yu, Hui; Pei, Jian; Tang, Shiwei; Yang, Dongqing J. Frew (ed.) (2005). 185–194.
• GraphMiner: a structural pattern-mining system for large disk-based graph databases and its applications. Wang, Wei; Wang, Chen; Zhu, Yongtai; Shi, Baile; Pei, Jian; Yan, Xifeng; Han, Jiawei F. Özcan (ed.) (2005). 879–881.
• Data Mining: The Next Generation. Ramakrishnan, Raghu; Agrawal, Rakesh; Freytag, Johann-Christoph; Bollinger, Toni; Clifton, Christopher W.; Dzeroski, Saso; Hipp, Jochen; Keim, Daniel A.; Kramer, Stefan; Kriegel, Hans-Peter; Leser, Ulf; Liu, Bing; Mannila, Heikki; Meo, Rosa; Morishita, Shinichi; Ng, Raymond T.; Pei, Jian; Raghavan, Prabhakar; Spiliopoulou, Myra; Srivastava, Jaideep; Torra, Vicenç in Dagstuhl Seminar Proceedings, R. Agrawal, J.-C. Freytag, R. Ramakrishnan (eds.) (2004). (Vol. 04292)
• Preface to CoMWIM 2004. Yang, Dongqing; Tang, Shiwei; Pei, Jian; Wang, Tengjiao; Gao, Jun in Lecture Notes in Computer Science, S. Wang, D. Yang, K. Tanaka, F. Grandi, S. Zhou, E. E. Mangina, T. W. Ling, I.-Y. Song, J. Guan, H. C. Mayr (eds.) (2004). (Vol. 3289) 197.
• Data Mining for Intrusion Detection: Techniques, Applications and Systems. Pei, Jian; Upadhyaya, Shambhu J.; Farooq, Faisal; Govindaraju, Venugopal Z. M. Özsoyoglu, S. B. Zdonik (eds.) (2004). 877.
• A Fast Algorithm for Subspace Clustering by Pattern Similarity. Wang, Haixun; Chu, Fang; Fan, Wei; Yu, Philip S.; Pei, Jian (2004). 51–60.
• Mining Confident Colocation Rules without A Support Threshold. Huang, Yan; Xiong, Hui; Shekhar, Shashi; Pei, Jian G. B. Lamont, H. Haddad, G. A. Papadopoulos, B. Panda (eds.) (2003). 497–501.
• SOCQET: Semantic OLAP with Compressed Cube and Summarization. Lakshmanan, Laks V. S.; Pei, Jian; Zhao, Yan A. Y. Halevy, Z. G. Ives, A. Doan (eds.) (2003). 658.
• ApproxMAP: Approximate Mining of Consensus Sequential Patterns. Kum, Hye-Chung; Pei, Jian; Wang, Wei; Duncan, Dean D. Barbará, C. Kamath (eds.) (2003). 311–315.
• MaPle: A Fast Algorithm for Maximal Pattern-based Clustering. Pei, Jian; Zhang, Xiaoling; Cho, Moonjung; Wang, Haixun; Yu, Philip S. (2003). 259–266.
• QC-Trees: An Efficient Summary Structure for Semantic OLAP. Lakshmanan, Laks V. S.; Pei, Jian; Zhao, Yan A. Y. Halevy, Z. G. Ives, A. Doan (eds.) (2003). 64–75.
• Online Analytical Processing Stream Data: Is It Feasible?. Chen, Yixin; Dong, Guozhu; Han, Jiawei; Pei, Jian; Wah, Benjamin W.; Wang, Jianyong (2002).
• On Computing Condensed Frequent Pattern Bases. Pei, Jian; Dong, Guozhu; Zou, Wei; Han, Jiawei (2002). 378–385.
• Quotient Cube: How to Summarize the Semantics of a Data Cube. Lakshmanan, Laks V. S.; Pei, Jian; Han, Jiawei (2002). 778–789.
• Fault-Tolerant Frequent Pattern Mining: Problems and Challenges. Pei, Jian; Tung, Anthony K. H.; Han, Jiawei (2001).
• Mining Multi-Dimensional Constrained Gradients in Data Cubes. Dong, Guozhu; Han, Jiawei; Lam, Joyce M. W.; Pei, Jian; Wang, Ke P. M. G. Apers, P. Atzeni, S. Ceri, S. Paraboschi, K. Ramamohanarao, R. T. Snodgrass (eds.) (2001). 321–330.
• Multi-Dimensional Sequential Pattern Mining. Pinto, Helen; Han, Jiawei; Pei, Jian; Wang, Ke; Chen, Qiming; Dayal, Umeshwar (2001). 81–88.
• PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth. Pei, Jian; Han, Jiawei; Mortazavi-Asl, Behzad; Pinto, Helen; Chen, Qiming; Dayal, Umeshwar; Hsu, Meichun D. Georgakopoulos, A. Buchmann (eds.) (2001). 215–224.
• FreeSpan: frequent pattern-projected sequential pattern mining. Han, Jiawei; Pei, Jian; Mortazavi-Asl, Behzad; Chen, Qiming; Dayal, Umeshwar; Hsu, Meichun R. Ramakrishnan, S. J. Stolfo, R. J. Bayardo, I. Parsa (eds.) (2000). 355–359.
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