2014 ceas research poster competition 2014
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An artificial intelligent approach to develop temporal and spatial linkage between urban rail transit station demand and land-use patterns. 2014 CEAS Research Poster Competition 2014. Xin Li Ph.D Candidate, Research Assistant. Department of Civil Engineering and mechanics

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2014 CEAS Research Poster Competition 2014

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2014 ceas research poster competition 2014

An artificial intelligent approach to develop temporal and spatial linkage between urban rail transit station demand and land-use patterns

2014 CEAS Research Poster Competition

2014

Xin Li

Ph.D Candidate, Research Assistant.

Department of Civil Engineering and mechanics

University of Wisconsin - Milwaukee


2014 ceas research poster competition 2014

An artificial intelligent approach to develop temporal and spatial linkage between urban rail transit station demand and land-use patterns

Research Motivation

  • To provide demand forecasting for TOD development

    • TOD (Transit-orientated Development), is a type of community development that includes a mixture of housing, office, retail and/or other amenities integrated into a walkable neighborhood and located within a reasonable distance of transit station, especially for metro stations;

    • Various studies indicate that implementing TOD can have significant benefits to individuals, communities, regions, states, the economy and the natural environment;

    • It has been unfolding ever-increasing successful applications in the whole world. In US, we have Arlington as an excellent TOD pilot example.

  • To build a linkage between transit station demand and land-use patterns

    • Artificial Intelligence: an area of computer science that deals with the giving machines the ability to seem like they have human intelligence;

    • Unlike the traditional forecast methods, this study propose an AI-based tool to estimate the demand by taking different land-use patterns into account.


Working philosophy

An artificial intelligent approach to develop temporal and spatial linkage between urban rail transit station demand and land-use patterns

Working Philosophy

  • Data collection: (1).A two weeks Chongqing City(the biggest population city in China)metro users IC card(average 850,000 records per day and approximately 9millions records in total ); (2). 122 metros stations detailed 200m and 500m circle layers land-use information from regulatory plan;

  • Statistical tools: Canonical Correlation Analysis (CCA), is used as a procedure for assessing the relationship between two sets of random variables. In this case, CCA model is for selecting key land-use variables by imposing the assessment of the relationship between metric independent land-use variables and multiple dependent measures.

  • Artificial Intelligence: Classification model-Decision Tree Learning : is one of the most widely used and practical methods for inductive inference. It is a method for approximating discrete-valued functions that is robust to noisy data and capable of learning disjunctive expressions.

  • Advantages of Decision Tree:

  • Decision trees implicitly perform variable screening or feature selection

  • Decision trees require relatively little effort from users for data preparation

  • Nonlinear relationships between parameters do not affect tree performance

  • The best feature of using trees for analytics - easy to interpret and explain to executives.


Outcome

An artificial intelligent approach to develop temporal and spatial linkage between urban rail transit station demand and land-use patterns

Outcome

  • Seven key land use variables in total 18 land use variables have been selected by CCA check.

  • 35 training instances has been used to successfully build a decision tree model and the size of tree is 7 with 4 leaves.

  • 35 validation instances has been used to evaluate the accuracy of generated model. 33 of 35 have been correctly classified while only 2 of them failed in validation.Thevalue of four common performance measurement of decision tree, Precision, Recall, F-Measure and ROC Area, are 0.949, 0.943,0.940 and 0.914 respectively.


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