Adaptive Zoning for Transport Mode Choice Modeling

Adaptive zoning is a recently introduced method for improving computer modeling of spatial interactions and movements in the transport network. Unlike traditional zoning, where geographic locations are defined by one single universal plan of discrete land parcels or ‘zones’ for the study area, adaptive zoning establishes a compendium of different zone plans, each of which is applicable to one journey origin or destination only. These adaptive zone plans are structured to represent strong spatial interactions in proportionately more detail than weaker ones. In recent articles, it has been shown that adaptive zoning improves, by a large margin, the scalability of models of spatial interaction and road traffic assignment. This article confronts the method of adaptive zoning with an application of the scale and complexity for which it was intended, namely an application of mode choice modeling that at the same time requires a large study area and a fine-grained zone system. Our hypothesis is that adaptive zoning can significantly improve the accuracy of mode choice modeling because of its enhanced sensitivity to the geographic patterns and scales of spatial interaction. We test the hypothesis by investigating the performance of three alternative models: (1) a spatially highly detailed model that is permissible to the maximum extent by available data, but requires a high computational load that is generally out of reach for rapid turnaround of policy studies; (2) a mode choice model for the same area, but reducing the computational load by 90% by using a traditional zone system consisting of fewer zones; and (3) a mode choice model that also reduces the computational load by 90%, but based on adaptive zoning instead. The tests are carried out on the basis of a case study that uses the dataset from the London Area Transport Survey. Using the first model as a benchmark, it is found that for a given computational load, the model based on adaptive zoning contains about twice the amount of information of the traditional model, and model parameters on adaptive zoning principles are more accurate by a factor of six to eight. The findings suggest that adaptive zoning has a significant potential in enhancing the accuracy of mode choice modeling at the city or city-region scale.

[1]  Jonas Eliasson,et al.  Transport and Location Effects of Road Pricing: A Simulation Approach , 2001 .

[2]  S H Putman,et al.  Effects of Spatial System Design on Spatial Interaction Models. 1: The Spatial System Definition Problem , 1988 .

[3]  H. Miller.  Potential Contributions of Spatial Analysis to Geographic Information Systems for Transportation (GIS-T) , 2010 .

[4]  Claude E. Shannon,et al.  The mathematical theory of communication , 1950 .

[5]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[6]  Michel Bierlaire,et al.  BIOGEME: a free package for the estimation of discrete choice models , 2003 .

[7]  Stan Openshaw,et al.  An Empirical Study of Some Zone-Design Criteria , 1978 .

[8]  Daniel S. Hirschberg,et al.  Data compression , 1987, CSUR.

[9]  A S Fotheringham,et al.  The Modifiable Areal Unit Problem in Multivariate Statistical Analysis , 1991 .

[10]  Stan Openshaw,et al.  Optimal Zoning Systems for Spatial Interaction Models , 1977 .

[11]  H. Miller,et al.  Exact Computational Methods for Calculating Distances Between Objects in a Cartographic Database , 1996 .

[12]  A. Curtis,et al.  The Zone Definition Problem in Location-Allocation Modeling , 2010 .

[13]  E. Hillsman,et al.  Errors in measuring distances from populations to service centers , 1978 .

[14]  Brent R. Moulton.  An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Unit , 1990 .

[15]  Ann M Hartell,et al.  Methodological Challenges of Environmental Justice Assessments for Transportation Projects , 2007 .

[16]  Daniel McFadden,et al.  The behavioral science of transportation , 2007 .

[17]  José Manuel Viegas,et al.  Effects of the Modifiable Areal Unit Problem on the Delineation of Traffic Analysis Zones , 2009 .

[18]  Ying Jin,et al.  A New Method of Adaptive Zoning for Spatial Interaction Models , 2012 .

[19]  Alex Hagen-Zanker,et al.  Improving geographic scalability of traffic assignment through adaptive zoning. , 2011 .

[20]  H. Landau.  Sampling, data transmission, and the Nyquist rate , 1967 .

[21]  Y Jin,et al.  A NEW LAND USE AND TRANSPORT INTERACTION MODEL FOR LONDON AND ITS SURROUNDING REGIONS , 2002 .

[22]  R. Keane,et al.  Determining landscape extent for succession and disturbance simulation modeling , 2007, Landscape Ecology.

[23]  J. Current,et al.  Analysis of Errors Due to Demand Data Aggregation in the Set Covering and Maximal Covering Location Problems , 2010 .

[24]  Santiago Saura,et al.  Sensitivity of landscape pattern metrics to map spatial extent , 2001 .

[25]  Stan Openshaw,et al.  A geographical solution to scale and aggregation problems in region-building, partitioning and spatial modelling , 1977 .

[26]  Elisabete A. Silva,et al.  Modifiable areal unit problem (MAUP) effects on traffic analysis zones (TAZ) delineation , 2005 .

[27]  Hjp Harry Timmermans,et al.  A learning-based transportation oriented simulation system , 2004 .

[28]  C Ding.  The GIS-Based Human-Interactive TAZ Design Algorithm: Examining the Impacts of Data Aggregation on Transportation-Planning Analysis , 1998 .

[29]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[30]  Kang-Tsung Chang,et al.  Effects of Zoning Structure and Network Detail on Traffic Demand Modeling , 2002 .

[31]  José Manuel Viegas,et al.  A traffic analysis zone definition: a new methodology and algorithm , 2009 .

[32]  W. Tobler.  Frame independent spatial analysis , 1989 .

[33]  J. Pucher,et al.  Infrastructure, programs, and policies to increase bicycling: an international review. , 2010, Preventive medicine.

[34]  R. Cervero,et al.  TRAVEL DEMAND AND THE 3DS: DENSITY, DIVERSITY, AND DESIGN , 1997 .