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Introducing shared life experience metric in urban planning

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Abstract

Historically cities are formed to provide interaction and communication opportunities for communities. As cities become smarter, new forms of interactions are formed and the necessity to participate in activities such as traveling to a grocery store is replaced by submission of online order in Amazon fresh. If we move in this direction, it bears answering the question of what kinds of societal loss, or changes in social interactions should we expect in our future cities? In this paper, we develop the Shared Life Experience (SLE) metric, focusing on the interaction opportunities between people. We define this metric to be measured based on the pairwise reachability and interaction probabilities of city dwellers in the context of time and space. Furthermore, we present a framework discussing how this metric can be embedded into the design of a more dynamic urban form and how we can measure it using publicly available data. Two sets of analyses are presented. First: a bi-level model is proposed, composed of a heuristic search algorithm in the upper level to estimate the regional SLE value for a given set of parameters and finding the optimum solution. The lower level models in the bi-level structure are activity-based models producing mobility behavior of individuals in response to changes in the input parameters. Second: we present a simple methodology and discuss how to quantify the SLE index using household travel survey data collected within five boroughs of New York City. This analysis can highlight many equity-related objectives and be used as an informative tool for better decision making.

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Acknowledgements

Support for this research was provided by a PSC-CUNY Award (Grant: 61635-00 49), jointly funded by the Professional Staff Congress and The City University of New York. The paper was presented in 98th Annual Meetings of Transportation Research Board, Washington D.C. 2019.

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Contributions

MA: study conception and design, computer coding, analysis and interpretation of the results, literature review, manuscript writing, and editing. TB: data collection and analysis, manuscript writing and editing. JD; data collection and analysis, manuscript writing and editing.

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Correspondence to Mahdieh Allahviranloo.

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Appendix

Appendix

Sequence Alignment Method (SAM):

The method mostly used in text mining and also biology to compare chromosomes.

Suppose we have two strings as \( x \) and \( y \) with lengths of \( l_{x} \) and \( l_{y} \). The edit distance is computed by the following algorithm:

Initialization:

$$ \begin{array}{*{20}c} {D\left( {0,0} \right) = 0,} \\ {D\left( {i,0} \right) = i \times d,i \in \left\{ {0, \ldots ,l_{x} } \right\}} \\ {D\left( {0,j} \right) = j \times d,j \in \left\{ {0, \ldots ,l_{y} } \right\}} \\ \end{array} $$

Recurrence:

$$ D\left( {i,j} \right) = \hbox{min} \left\{ {\begin{array}{*{20}c} {D\left( {i - 1,j} \right) + d} \\ {D\left( {i,j - 1} \right) + d} \\ {D\left( {i - 1,j - 1} \right) + P\left( {x\left( i \right),y\left( j \right)} \right)} \\ \end{array} } \right.,\quad \forall i \in \left\{ {1, \ldots ,l_{x} } \right\},j \in \left\{ {1, \ldots ,l_{y} } \right\} $$

where \( d \): is a penalty associated with insertion and deletion and is set to 1. \( P \): is the penalty matrix associated with substitution and defined as follows:

$$ P\left( {x\left( i \right),y\left( j \right)} \right) = \,\left\{ {\begin{array}{*{20}c} {1\,\,\,\,\,\,if\,\,\,\,x\left( i \right) \ne y\left( j \right)} \\ {0\,\,\,\,if\,\,\,\,x\left( i \right) = y\left( j \right)\,} \\ \end{array} } \right. $$

\( D \): is the distance matrix with the size of \( \left( {l_{x + 1} } \right) \times \left( {l_{y + 1} } \right) \). The edit-distance between \( x,y \) equals \( D\left( {l_{x + 1} ,l_{y + 1} } \right) \).

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Allahviranloo, M., Bonet, T. & Diez, J. Introducing shared life experience metric in urban planning. Transportation 48, 1125–1148 (2021). https://doi.org/10.1007/s11116-020-10087-y

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