Name
Abstract | ||
|---|---|---|
Using a unique data set containing about 15.06 million truck transportation records in five months, we investigate the highway freight transportation diversity of 338 Chinese cities based on the truck transportation probability pij from one city to another. The transportation probabilities are calculated from the radiation model based on the geographic distance and its cost-based version based on the driving distance as the proxy of cost. For each model, we consider both the population and the gross domestic product (GDP), and find quantitatively very similar results. We find that the transportation probabilities have nice power-law tails with the tail exponents close to 0.5 for all the models. The two transportation probabilities in each model fall around the diagonal p(ij)=p(ji) but are often not the same. In addition, the corresponding transportation probabilities calculated from the raw radiation model and the cost-based radiation model also fluctuate around the diagonal p(ij)(geo)=p(ij)(cost). We calculate four sets of highway truck transportation diversity according to the four sets of transportation probabilities that are found to be close to each other for each city pair. It is found that the population, the gross domestic product, the in-flux, and the out-flux scale as power laws with respect to the transportation diversity in the raw and cost-based radiation models. It implies that a more developed city usually has higher diversity in highway truck transportation, which reflects the fact that a more developed city usually has a more diverse economic structure. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/e23050637 | ENTROPY |
Keywords | DocType | Volume |
econophysics, highway freight transportation, radiation model, transportation network, network diversity, power law, economic development | Journal | 23 |
Issue | ISSN | Citations |
5 | 1099-4300 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Wang | 1 | 18 | 6.14 |
| Jun-Chao Ma | 2 | 0 | 0.34 |
| Zhi-Qiang Jiang | 3 | 68 | 5.20 |
| Wanfeng Yan | 4 | 0 | 0.34 |
| Wei-Xing Zhou | 5 | 206 | 15.05 |