Estimating Pedestrian Flows on Street Networks

Abstract

where n jk [i] is the number of shortest paths between surrounding nodes j and k that pass through i and n jk is the total number of shortest paths between the same nodes j and k. Specification of Gravity Accessibility with an Exponential Distance DecayFunction, Following Handy and Niemeier (1997) ,!]

We shape our buildings, but do they then shape us? A longitudinal analysis of pedestrian flows and development activity in Melbourne

“…Pedestrian flows between individual origin-destination (O-D) pairs in the city are modeled using a modified betweenness algorithm [ 38 ], which has been adapted to a number of different transportation applications [ 10 , 15 , 19 , 39 , 40 ]. The betweenness of a street segment is conventionally defined as the number of walks between origin-destination pairs in a network that pass through that segment.…”

“…In this study, we implemented the betweenness algorithm featured in the Urban Network Analysis toolbox [ 34 ], which includes six adjustments that make the analysis suitable to pedestrian flows [ 10 , 41 ]. First, instead of modeling interactions between all O-D pairs, as is common in social network analyses [ 42 ], users can choose which O-D locations to include.…”

“…After a century of automobile-oriented urban growth, many city governments around the world are implementing extensive pedestrian improvements on their streets, banking on a future with less space dedicated to cars and more to activities that take place on foot. Walkable cities reduce urban energy consumption [ 1 – 4 ], enhance public health [ 5 – 7 ], build community, and foster social cohesion [ 8 – 10 ]. Dense, diverse, and walkable city centers have also become a magnet for global talent, who work in knowledge-intensive jobs and cherish serendipitous face-to-face encounters that flourish in dense and liveable city environments [ 11 , 12 ].…”

“…This paper applies a recently developed pedestrian flow model [ 10 , 41 ] to examine the longitudinal impacts of changes in the built environment on pedestrian activity for the first time. As opposed to aggregate estimations of pedestrian activity with respect to broad neighborhood characteristics such as the three (or more) neighborhood D’s—density, diversity and design [ 27 ]—which much of urban walkability literature has centered on [ 28 – 31 ], our model relies on an address-level analysis of pedestrian accessibility and destination choice, combining trip-generation, trip-distribution, and route-choice into an accessibility-driven framework that some planning and mobility scholars have called for [ 32 ].…”

A Framework of Community Pedestrian Network Design Based on Urban Network Analysis

“…In this study, α was set to "1", indicating that during the simulation a specific destination would not be preset to any residents. The value of β is determined by the travel mode and destination type, In tropical areas such as Singapore, researchers have concluded that the β value is about 0.004 (in meters) [50]. Since the climate of Guangzhou is close to that in Singapore; thus, the 0.004 value is applied in this study.…”