Crowdsourcing information for knowledge-based design of routes for unscheduled public transport trips

Purpose – The purpose of this paper is to devise a crowdsourcing methodology for acquiring and exploiting knowledge to profile unscheduled transport networks for design of efficient routes for public transport trips. Design/methodology/approach – This paper analyzes daily travel itineraries within Mexico City provided by 610 public transport users. In addition, a statistical analysis of quality-of-service parameters of the public transport systems of Mexico City was also conducted. From the statistical analysis, a knowledge base was consolidated to characterize the unscheduled public transport network of Mexico City. Then, by using a heuristic search algorithm for finding routes, public transport users are provided with efficient routes for their trips. Findings – The findings of the paper are as follows. A crowdsourcing methodology can be used to characterize complex and unscheduled transport networks. In addition, the knowledge of the crowds can be used to devise efficient routes for trips (using public...

[1]  Fawzy Soliman,et al.  Strategies for implementing knowledge management: role of human resources management , 2000, J. Knowl. Manag..

[2]  Daren C. Brabham.  Crowdsourcing the Public Participation Process for Planning Projects , 2009 .

[3]  Gaetano Fusco,et al.  User Empowerment and Advanced Public Transport Solutions , 2013 .

[4]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[5]  Jeff Howe,et al.  Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business , 2008, Human Resource Management International Digest.

[6]  Jerald Jariyasunant,et al.  Algorithm for Finding Optimal Paths in a Public Transit Network with Real-Time Data , 2011 .

[7]  Nathan Eagle,et al.  txteagle: Mobile Crowdsourcing , 2009, HCI.

[8]  Robert B. Noland,et al.  Current map-matching algorithms for transport applications: State-of-the art and future research directions , 2007 .

[9]  Henry A. Kautz,et al.  Learning and inferring transportation routines , 2004, Artif. Intell..

[10]  Laurent Siklóssy,et al.  Searching time-table networks , 1991, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[11]  James A. Landay,et al.  UbiGreen: investigating a mobile tool for tracking and supporting green transportation habits , 2009, CHI.

[12]  Richard L. Church,et al.  Finding shortest paths on real road networks: the case for A* , 2009, Int. J. Geogr. Inf. Sci..

[13]  Tim Kraska,et al.  CrowdDB: answering queries with crowdsourcing , 2011, SIGMOD '11.

[14]  Steffen Fritz,et al.  Geo-Wiki.Org: The Use of Crowdsourcing to Improve Global Land Cover , 2009, Remote. Sens..

[15]  Osvaldo Cairó,et al.  The KAMET II methodology: Knowledge acquisition, knowledge modeling and knowledge generation , 2012, Expert Syst. Appl..

[16]  Leenard Baas,et al.  Lessons from the spread of Bus Rapid Transit in Latin America , 2013 .

[17]  Richard T. Herschel,et al.  Tacit to explicit knowledge conversion: knowledge exchange protocols , 2001, J. Knowl. Manag..

[18]  Katharina Morik,et al.  Heterogeneous Stream Processing and Crowdsourcing for Traffic Monitoring: Highlights , 2014, ECML/PKDD.

[19]  Gwen Kash,et al.  The promise and challenges of integrating public transportation in Bogotá, Colombia , 2014, Public Transport.

[20]  Bernd Ludwig.  Using the Internet in a Mobile Public Transport Navigation System , 2008, FIS.

[21]  Jennifer E. Rowley.  Knowledge organisation for a new millennium: principles and processes , 2000, J. Knowl. Manag..

[22]  Ariel Orda,et al.  Shortest-path and minimum-delay algorithms in networks with time-dependent edge-length , 1990, JACM.

[23]  Lloyd Wright,et al.  Mass transit options , 2003 .

[24]  Franco Zambonelli.  Pervasive urban crowdsourcing: Visions and challenges , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[25]  Osvaldo Cairó Battistutti,et al.  Unscheduled Public Transport Intelligent Navigation System , 2014, KES.

[26]  G. McLean,et al.  Linking organizational culture, structure, strategy, and organizational effectiveness: Mediating role of knowledge management , 2010 .

[27]  Jung-Yuan Kung,et al.  The fuzzy shortest path length and the corresponding shortest path in a network , 2005, Comput. Oper. Res..

[28]  M. Schteingart.  The environmental problems associated with urban development in Mexico City , 1989 .

[29]  Daren C. Brabham.  Crowdsourcing as a Model for Problem Solving , 2008 .

[30]  Lloyd Wright,et al.  Sustainable Transport: A Sourcebook for Policy-makers in Developing Cities. Module 3a: Mass Transit Options , 2003 .

[31]  Alan Borning,et al.  OneBusAway: results from providing real-time arrival information for public transit , 2010, CHI.