Abstract
In order to prevent taxi speeding by utilizing road characteristics, GPS trajectory data of taxis in Chengdu city area were gathered to identify their speeding behavior, and road characteristics were extracted as well. Then, with speeding frequency and average speeding severity of each road as speeding characteristics, global Moran's I and four kinds of spatial regression models were adopted to analyze spatial autocorrelation of speeding characteristics and road factors and to explore significant influencing factors of the former. The results reveal that obvious spatial autocorrelation exists between taxi speeding and road characteristics. Spatial Autocorrelation Model (SAC) and Spatial Durbin Model (SDM) are the best for fitting of speeding frequency and average speeding severity estimation, respectively. Number of connected road, access number and lane number evidently increase taxi speeding frequency while road length and lane number significantly increase average speeding severity. Whereas, work zone and one-way roads are unrelated with speeding characteristics.
| Translated title of the contribution | Road factor analysis of taxi speeding behavior considering spatial effect |
|---|---|
| Original language | Chinese (Traditional) |
| Pages (from-to) | 162-170 |
| Number of pages | 9 |
| Journal | China Safety Science Journal |
| Volume | 31 |
| Issue number | 3 |
| DOIs | |
| State | Published - Mar 2021 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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