Reconstructing the Transport Network of Ancient China and its Relationship to Social Networks

This study reconstructs ancient China's transport system, exploring its intersection with societal networks. We begin by procuring population data indicative of years 2, 742, 1102, and 1522. The transportation matrix, inclusive of roads, rivers, and canals, is subsequently reconstituted. In continuation, we ascertain the transport accessibility for heavy goods. Our accessibility model reveals that history lends itself to a more coherent explanation through quantitative research. We then deploy a gravity model to examine the correlation between transport systems and social networks. The model delineates how transport, particularly travel duration, influenced societal interconnections. The implemented gravity model further highlights the steady deterrent of travel time, suggesting that transport infrastructure might sustainably govern social networks on a historic spectrum. Finally, our research draws on the analysis of five key capitals throughout history, along with other connections inadequately modeled by the gravity approach, to discuss the self-perpetuation effects of social networks. We discern that cities with elevated political stature or a sizable population/economic foundation tend to exhibit a more robust self-reinforcing influence on their social networks.

Authors

Institutions

Publication Details

Journal
Transportation research procedia
Published
2025-01-01
DOI
https://doi.org/10.1016/j.trpro.2024.12.023
Citations
2
Primary Topic
Chinese history and philosophy
Type
article
Field-Weighted Citation Impact
8.40

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Reconstructing the Transport Network of Ancient China and its Relationship to Social Networks

Jan‐Dirk Schmöcker, Wenlong Li, Liang Zhao, Abdul Sami Qureshi
2 citations
Transportation research procedia
Chinese history and philosophy
8.40
article

Reconstructing the Transport Network of Ancient China and its Relationship to Social Networks

Jan‐Dirk Schmöcker, Wenlong Li, Liang Zhao, Abdul Sami Qureshi
article en
2 citations

Abstract

This study reconstructs ancient China's transport system, exploring its intersection with societal networks. We begin by procuring population data indicative of years 2, 742, 1102, and 1522. The transportation matrix, inclusive of roads, rivers, and canals, is subsequently reconstituted. In continuation, we ascertain the transport accessibility for heavy goods. Our accessibility model reveals that history lends itself to a more coherent explanation through quantitative research. We then deploy a gravity model to examine the correlation between transport systems and social networks. The model delineates how transport, particularly travel duration, influenced societal interconnections. The implemented gravity model further highlights the steady deterrent of travel time, suggesting that transport infrastructure might sustainably govern social networks on a historic spectrum. Finally, our research draws on the analysis of five key capitals throughout history, along with other connections inadequately modeled by the gravity approach, to discuss the self-perpetuation effects of social networks. We discern that cities with elevated political stature or a sizable population/economic foundation tend to exhibit a more robust self-reinforcing influence on their social networks.

Transportation research procediaVol. 82
Kyoto University (JP)
UK Research and Innovation, Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 4%
Chinese history and philosophy
8.40
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.