Using Free and Proprietary Data to Compare Shortest-Path Lengths for Effective Pedestrian Routing in Street Networks

Ubiquitous mobile devices, such as smartphones, led to an increased popularity of pedestrian-related routing applications over the past few years. Because pedestrians typically aim to minimize their walking distance, especially in nonrecreational and multimodal trips, pedestrian routing systems will be fully used only if they can find the correct shortest path and thus help to avoid unnecessary detours. The standard equipment of car navigation systems based on the Global Positioning System several years ago led to the availability of accurate street network data for car-based routing applications. However, pedestrian routing applications should consider pedestrian-related network segments besides those used by motorized traffic, including footpaths and pedestrian bridges. The authors of this paper performed a shortest-path analysis of pedestrian routes for cities in Germany and the United States. For a set of 1,000 randomly generated origin–destination pairs, the authors compared the lengths of pedestrian routes that were computed by different freely available network sources, such as OpenStreetMap and TIGER/Line data, and proprietary data sets, such as TomTom, NAVTEQ, and ATKIS. The results showed that freely available data sources such as OpenStreetMap provided a relatively comprehensive option for cities in which commercial pedestrian data sets were not yet available.

[1]  Serge P. Hoogendoorn,et al.  Passenger Route Choice concerning Level Changes in Railway Stations , 2005 .

[2]  Reginald G. Golledge,et al.  Path Selection and Route Preference in Human Navigation: A Progress Report , 1995, COSIT.

[3]  John Morrall,et al.  Analysis of factors affecting the choice of route of pedestrians , 1985 .

[4]  James M. Keller,et al.  Automated Geospatial Conflation of Vector Road Maps to High Resolution Imagery , 2009, IEEE Transactions on Image Processing.

[5]  Serge P. Hoogendoorn,et al.  Influence of Changes in Level on Passenger Route Choice in Railway Stations , 2005 .

[6]  Stephan Winter,et al.  Datasets for pedestrian navigation services , 2001 .

[7]  A. El-geneidy,et al.  Access to Destinations: How Close Is Close Enough? Estimating Accurate Distance Decay Functions for Multiple Modes and Different Purposes , 2008 .

[8]  Hartwig H. Hochmair Optimal route selection with route planners: results of a desktop usability study , 2007, GIS.

[9]  Kyong Joo Oh,et al.  Context-aware mobile service for routing the fastest subway path , 2009, Expert Syst. Appl..

[10]  Toru Hagiwara,et al.  Overall Level of Service of Urban Walking Environment and Its Influence on Pedestrian Route Choice Behavior , 2007 .

[11]  Dennis Zielstra,et al.  Comparative Study of Pedestrian Accessibility to Transit Stations Using Free and Proprietary Network Data , 2011 .

[12]  Fang Zhao,et al.  Forecasting Transit Walk Accessibility: Regression Model Alternative to Buffer Method , 2003 .

[13]  Hartwig H. Hochmair,et al.  Grouping of Optimized Pedestrian Routes for Multi-Modal Route Planning: A Comparison of Two Cities , 2008, AGILE Conf..

[14]  Serge P. Hoogendoorn,et al.  Pedestrian route-choice and activity scheduling theory and models , 2004 .

[15]  Michael F. Goodchild Spatial Accuracy 2.0 , 2008 .

[16]  R. Bharat Rao,et al.  Evolution of mobile location-based services , 2003, CACM.

[17]  Frederick E. Petry,et al.  A Rule-based Approach for the Conflation of Attributed Vector Data , 1998, GeoInformatica.

[18]  C. Macera,et al.  Trends in Walking for Transportation in the United States, 1995 and 2001 , 2005, Preventing chronic disease.

[19]  P. Bovy,et al.  ROUTE CHOICE: WAYFINDING IN TRANSPORT NETWORKS , 1990 .

[20]  Dennis Zielstra,et al.  Quantitative Studies on the Data Quality of OpenStreetMap in Germany , 2010 .

[21]  M. Haklay How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets , 2010 .

[22]  Michael F. Goodchild,et al.  Citizens as Voluntary Sensors: Spatial Data Infrastructure in the World of Web 2.0 , 2007, Int. J. Spatial Data Infrastructures Res..

[23]  Piet Rietveld,et al.  The accessibility of railway stations: the role of the bicycle in The Netherlands , 2000 .

[24]  Joanna Hartley,et al.  Accommodating user preferences in the optimization of public transport travel , 2004 .

[25]  Dickson K. W. Chiu,et al.  A Multi-Modal Agent Based Mobile Route Advisory System for Public Transport Network , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[26]  David R. Loutzenheiser,et al.  Pedestrian Access to Transit: Model of Walk Trips and Their Design and Urban Form Determinants Around Bay Area Rapid Transit Stations , 1997 .

[27]  Simon Scheider,et al.  Specifying Essential Features of Street Networks , 2007, COSIT.

[28]  Craig A. Knoblock,et al.  Automatically Conflating Road Vector Data with Orthoimagery , 2006, GeoInformatica.

[29]  Hartwig H. Hochmair,et al.  Towards a Classification of Route Selection Criteria for Route Planning Tools , 2004, SDH.