Advanced decision modeling for real time variable tolling : development and testing of a data collection platform.

Our current ability to forecast demand on tolled facilities has not kept pace with advances in decision sciences and technological innovation. The current forecasting methods suffer from lack of descriptive power of actual behavior because of the simplifications used in current economic decision models. These simplifications are in part due to the historical limitations on data collection. Today, we are seeing advances in the data collection technology that captures naturalistic behavior and this study is seeking to develop and test such technology. This will be the first phase of a naturalistic driving study on the topic of variable road tolling and decision making. This investigation extends the state of knowledge of decision modeling under risk and ambiguity by developing a mobile data collection platform for the capturing of naturalistic choice outcomes, associated environmental states, decision makers’ self-articulated perceptions of risk and assessments of ambiguity, and socio-economic attributes. This software/hardware development is the first step in advancing our ability to forecast future revenue sources in transportation. The data collection platform will be used in an extending study and will allow the study team to obtain insights for developing, testing and implementing new behavioral models that explicitly describe how we use imprecise information—in this case, ambiguous information signaled by variable toll lane charges—that are superior to those obtained to date. To support the empirical observations of driver choice of diverting to high occupancy toll (HOT) or non-HOT lanes in real-time, this study documents the development and testing of an in-vehicle perception acquisition device. The device is based on smartphone technology and its software is designed using the latest IOS release. Drivers will be able to upload the software application onto their existing smartphone or tablet. The widespread acceptance and use of smartphones and tablets allows the collection and transmission of decision data collected in real-time without installing or altering the drivers’ vehicles in any manner. This both increases the convenience—and perhaps the use of the application—and reduces the cost of data collection.

[1]  John M. Rose,et al.  Designing Stated Choice Experiments: State of the Art , 2007 .

[2]  John W. Polak,et al.  Characterizing Heterogeneity in Attitudes to Risk in Expected Utility Models of Mode and Departure Time Choice , 2008 .

[3]  Mikael B. Skov,et al.  Studying driver attention and behaviour for three configurations of GPS navigation in real traffic driving , 2010, CHI.

[4]  Jonathan M. Hankey,et al.  Voice- and Visual-Manual-Control Navigation System Evaluation Based on User Performance in Destination Entry and Navigation Tasks , 2011 .

[5]  A. Tversky,et al.  Prospect Theory : An Analysis of Decision under Risk Author ( s ) : , 2007 .

[6]  Robert B. Noland,et al.  Travel-time uncertainty, departure time choice, and the cost of morning commutes , 1995 .

[7]  John M. Rose,et al.  Applied Choice Analysis: A Primer , 2005 .

[8]  Paul Green,et al.  Safety and Usability of Speech Interfaces for In-Vehicle Tasks while Driving: A Brief Literature Review , 2006 .

[9]  J. Bates,et al.  The valuation of reliability for personal travel , 2001 .

[10]  Satoshi Fujii,et al.  Anticipated Travel Time, Information Acquisition, and Actual Experience: Hanshin Expressway Route Closure, Osaka-Sakai, Japan , 2000 .


[12]  J. Neumann,et al.  Theory of games and economic behavior , 1945, 100 Years of Math Milestones.


[14]  A. Tversky,et al.  Advances in prospect theory: Cumulative representation of uncertainty , 1992 .

[15]  A. Kudryavtsev,et al.  Description-based and experience-based decisions: individual analysis , 2012, Judgment and Decision Making.

[16]  J. Busemeyer,et al.  A contribution of cognitive decision models to clinical assessment: decomposing performance on the Bechara gambling task. , 2002, Psychological assessment.

[17]  Michel Bierlaire,et al.  Modeling Learning in Route Choice , 2007 .

[18]  W. Greene,et al.  Embedding risk attitude and decision weights in non-linear logit to accommodate time variability in the value of expected travel time savings , 2011 .

[19]  H. J. Van Zuylen,et al.  Monitoring and Predicting Freeway Travel Time Reliability: Using Width and Skew of Day-to-Day Travel Time Distribution , 2005 .