Application of mobility management: a web structure for the optimisation of the mobility of working staff of big companies

This study deals with the application of a Web structure available to both Mobility Managers and employees. Its main functionalities enable the Mobility Manager to collect mobility data that can be used to design some optimal home-to-work mobility strategies and the employees to access a car pooling service. The realisation of this Web structure has been made possible by using, as support tools, Intelligent Transportation System Technologies such as Geographic Information System and Web services beside a communication network. The realisation of the application has been preceded by a pilot survey carried out within an industrial area in South Sardinia. The survey results have highlighted the need for an organised management of the home-to-work mobility, as well as the sample's willingness to use viable alternatives to private car use. In this article, the modules related to the on line questionnaires and the car pooling service are investigated. With reference to the car pooling service, the functioning of the matching algorithm has been tested by using the pilot survey results and is described in the appendix. The testing results show that the response times for the algorithm are acceptable for home-to-work trips, within the territorial context investigated in this research.

[1]  Frank Witlox,et al.  Mobility management measures by employers: overview and exploratory analysis for Belgium , 2010 .

[2]  M. J. Bianco,et al.  Effective Transportation Demand Management: Combining Parking Pricing, Transit Incentives, and Transportation Management in a Commercial District of Portland, Oregon , 2000 .

[3]  Tommy Gärling,et al.  Are effects of travel feedback programs correctly assessed? , 2009 .

[4]  Mattias Höjer Transport telematics in urban systems—a backcasting Delphi study , 1998 .

[5]  Irwin P. Levin,et al.  Ride Sharing: Psychological Factors , 1977 .

[6]  J. Anable,et al.  Smarter Choices: Assessing the Potential to Achieve Traffic Reduction Using ‘Soft Measures’ , 2008 .

[7]  Victor C. M. Leung,et al.  Next generation mobility management: an introduction , 2011, Wirel. Commun. Mob. Comput..

[8]  T. Gärling,et al.  Behaviour Theory and Soft Transport Policy Measures , 2011 .

[9]  Irwin P. Levin,et al.  MEASUREMENT OF PSYCHOLOGICAL FACTORS AND THEIR ROLE IN TRAVEL BEHAVIOR , 1977 .

[10]  Fred D. Davis,et al.  TRUST FORMATION IN NEW ORGANIZATIONAL RELATIONSHIPS , 1996 .

[11]  Gail Murray,et al.  Strategies to Assist Local Transportation Agencies in Becoming Mobility Managers , 1997 .

[12]  Susan Shaheen,et al.  Carsharing and Partnership Management: An International Perspective , 1999 .

[13]  M Hoejer URBAN TRANSPORT, INFORMATION TECHNOLOGY AND SUSTAINABLE DEVELOPMENT , 1996 .

[14]  Donald J Dailey,et al.  Seattle smart traveler: dynamic ridematching on the World Wide Web , 1999 .

[15]  Toru Nakamura WHITE PAPER, European transport policy for 2010 : time to decide , 2004 .

[16]  Ayako Taniguchi,et al.  Mobility Management in Japan , 2007 .

[17]  G. Correia,et al.  Carpooling and carpool clubs: Clarifying concepts and assessing value enhancement possibilities through a Stated Preference web survey in Lisbon, Portugal , 2011 .