Multi‐temporal transport network models for accessibility studies

Abstract

Vehicle tracking is a spatio‐temporal source of high‐granularity travel time information that can be used for transportation planning. However, it is still a challenge to combine data from heterogeneous sources into a dynamic transport network, while allowing for network modifications over time. This article uses conceptual modeling to develop multi‐temporal transport networks in geographic information systems (GIS) for accessibility studies. The proposed multi‐temporal network enables accessibility studies with different temporal granularities and from any location inside the city, resulting in a flexible tool for transport and urban planning. The implemented network is tested in two case studies that focus on socially excluded people in a large global city, São Paulo, Brazil, including accessibility analyses from slum areas. It explores variations within a day and differences between transport modes across time. Case study results indicate how the accessibility is heterogeneous in low‐income regions.

Disparities in travel times between car and transit: Spatiotemporal patterns in cities

“…However, the heavy traffic also affects travel times for bus in reality. It has been shown that in São Paulo there are large inconsistencies between the scheduled PT and the actual location of their buses 43 , therefore, bus GPS data have been used to account for congestion. In this study, however, we did not include the effect of congestion on PT.…”

A high-speed railway network dataset from train operation records and weather data

“…The high-speed railway network dataset can be processed as the materials for effective methods to issue the problems in large-scale complex network, complex dynamical system, intelligent transportation, deep learning, data mining and other fields, including but not limited to complex network modeling 10 – 12 , complex dynamic system pattern mining 5 , 13 – 15 , travel demand analysis 16 , community detection and discovery 17 – 19 , urban accessibility research 20 , 21 , train delay analysis 6 , 7 , 22 – 24 , task mining on multi-scale and dynamic graphs 25 – 27 . In addition, it can be used to optimize the actual railway operation and management, such as (a) train operation scheme and schedule adjustment, (b) passenger service network improvement, (c) train speed, punctuality, capacity, and energy consumption prediction, (d) real-time dispatching, (e) intelligent driving assistance, (f) fault or accident detection and (g) maintenance plans making.…”

Measuring spatiotemporal accessibility for pediatric clinical services with multimodal transport modes: an exploratory analysis in Nanjing, China

“…Also, scholars pay attention to the impact of dynamic environmental changes on the spatiotemporal accessibility, such as the daily food and transportation environments affect grocery store accessibility (Widener et al, 2017), temporal variability accessibility to supermarkets (Farber, Morang, & Widener, 2014;Widener et al, 2015). In the perspective of the STA of health research, more comprehensive accessibility measures, including integrating time and transport modes from open data (Tenkanen, Saarsalmi, Järv, Salonen, & Toivonen, 2016), multi-temporal transport network models (Tomasiello, Giannotti, Arbex, & Davis, 2019), and multi-modal relative spatial access assessment approach (Lin et al, 2018), is becoming an important development branch of dynamic environment changes on the spatiotemporal accessibility. Considering the sociality of healthcare services, our STA research focuses on place-based STA measurements based on residents' mobility and timepoints variance.…”