Optimizing facility siting for probabilistic collection and distribution of information in support of urban transportation

Collecting and receiving information about the state of a transportation system is essential to effective planning for intelligent transportation systems, whether it be on the part of individual users or managers of the system. However, efforts to collect or convey information about a system’s status often require considerable investment in infrastructure/technology. Moreover, given variations in the development and use of transportation systems over time, uncertainties exist as to where and when demand for such services may be needed. To address these problems, a model for minimizing the cost of siting and/or collecting information while ensuring specified levels of demand are served at an acceptable level of reliability is proposed. To demonstrate the characteristics of the proposed formulation, it is coupled with another planning objective and applied to identify optimal sites for information provision/collection in a transportation system. Model solutions are then derived for multiple scenarios of system flow to explore how variations in the use of a transportation system can impact siting configurations.

[1]  Oded Berman,et al.  Optimal Location of Discretionary Service Facilities , 1992, Transp. Sci..

[2]  Guohui Zhang,et al.  Enhanced traffic information dissemination to facilitate toll road utilization: a nested logit model of a stated preference survey in Texas , 2014 .

[3]  Nick Hounsell,et al.  Driver response to variable message sign information in London , 2002 .

[4]  Mark S. Daskin,et al.  APPLICATION OF AN EXPECTED COVERING MODEL TO EMERGENCY MEDICAL SERVICE SYSTEM DESIGN , 1982 .

[5]  Liuqing Yang,et al.  Big Data for Social Transportation , 2016, IEEE Transactions on Intelligent Transportation Systems.

[6]  Alan T. Murray,et al.  Evaluating Vulnerability and Risk in Interstate Highway Operation , 2007 .

[7]  Fritz Busch,et al.  Optimal location of wireless charging facilities for electric vehicles: Flow-capturing location model with stochastic user equilibrium , 2015 .

[8]  Satoshi Toi,et al.  A METHOD FOR PLANNING OF ROAD SIGN SYSTEM IN HIGHWAY USING STRAYING INDEX , 2005 .

[9]  Richard L. Church,et al.  The maximal covering location problem , 1974 .

[10]  Alan T. Murray,et al.  Critical infrastructure : reliability and vulnerability , 2007 .

[11]  Erhan Erkut,et al.  A Lagrangian relaxation approach to large-scale flow interception problems , 2009, Eur. J. Oper. Res..

[12]  Hani S. Mahmassani,et al.  Finding Near-Optimal Locations for Variable Message Signs for Real-Time Network Traffic Management , 2003 .

[13]  Reginald R. Souleyrette,et al.  Pseudo-dynamic travel model application to assess traveler information , 2002 .

[14]  C. Revelle,et al.  Counterpart Models in Facility Location Science and Reserve Selection Science , 2002 .

[15]  J. Cohon,et al.  Generating multiobjective trade-offs: an algorithm for bicriterion problems , 1979 .

[16]  Juan Gomez,et al.  The influence of variable message signs on en-route diversion between a toll highway and a free competing alternative , 2019 .

[17]  Chao Yang,et al.  Models and algorithms for the screen line-based traffic-counting location problems , 2006, Comput. Oper. Res..

[18]  Michael Kuby,et al.  Comparing the p-median and flow-refueling models for locating alternative-fuel stations , 2010 .

[19]  Fang He,et al.  Optimal locations and travel time display for variable message signs , 2016 .

[20]  Timothy C. Matisziw,et al.  Maximizing Expected Coverage of Flow and Opportunity for Diversion in Networked Systems , 2018, Networks and Spatial Economics.

[21]  Mark S. Daskin,et al.  A Maximum Expected Covering Location Model: Formulation, Properties and Heuristic Solution , 1983 .

[22]  S. Travis Waller,et al.  Optimal Information Location for Adaptive Routing , 2011 .

[23]  M. J. Hodgson A Flow-Capturing Location-Allocation Model , 2010 .

[24]  Robert G. Haight,et al.  An Integer Optimization Approach to a Probabilistic Reserve Site Selection Problem , 2000, Oper. Res..

[25]  Monica Gentili,et al.  Locating sensors on traffic networks: Models, challenges and research opportunities , 2012 .

[26]  William H. K. Lam,et al.  A model for assessing the effects of dynamic travel time information via variable message signs , 2001 .

[27]  Jeffrey Henderson A Planning Model for Optimizing Locations of Changeable Message Signs , 2004 .

[28]  Kasem Choocharukul,et al.  Short-Run Route Diversion: An Empirical Investigation into Variable Message Sign Design and Policy Experiments , 2013, IEEE Transactions on Intelligent Transportation Systems.

[29]  Nathan Huynh,et al.  Location configuration design for Dynamic Message Signs under stochastic incident and ATIS scenarios , 2007 .

[30]  William H. K. Lam,et al.  EVALUATION OF COUNT LOCATION SELECTION METHODS FOR ESTIMATION OF O-D MATRICES , 1998 .

[31]  Fei-Yue Wang,et al.  Data-Driven Intelligent Transportation Systems: A Survey , 2011, IEEE Transactions on Intelligent Transportation Systems.

[32]  Bhargab Maitra,et al.  Evaluation of VMS-Based Traffic Information Using Multiclass Dynamic Traffic Assignment Model: Experience in Kolkata , 2010 .

[33]  C. Revelle,et al.  A Reliability-Constrained Siting Model with Local Estimates of Busy Fractions , 1988 .

[34]  Gang Qu,et al.  A Survey on Recent Advances in Vehicular Network Security, Trust, and Privacy , 2019, IEEE Transactions on Intelligent Transportation Systems.

[35]  Hai Yang,et al.  Evaluating the benefits of a combined route guidance and road pricing system in a traffic network with recurrent congestion , 1999 .

[36]  Alan T. Murray,et al.  Bounding Network Interdiction Vulnerability Through Cutset Identification , 2007 .

[37]  Michael Kuby,et al.  The flow-refueling location problem for alternative-fuel vehicles , 2005 .