Exploring the temporal correlations of factors affecting traffic safety on mountain freeways: Through new crash frequency modelling methods
Liang Zhang, Zhongxiang Huang, Aiwu Kuang, Jie Yu, Lei Zhu, Songtao Yang
TL;DR
This study introduces new crash modeling methods to better understand traffic safety on mountain freeways in China, considering seasonal and temporal factors.
Contribution
The study proposes two novel crash modeling methods that incorporate temporal correlations for mountain freeway safety analysis in China.
Findings
New models outperformed seven existing methods in goodness-of-fit and prediction accuracy.
Special road sections like interchanges and tunnels are linked to higher crash risks.
Moderate rainfall increases crash risks, while heavy rainfall reduces them due to altered travel plans.
Abstract
The potential factors contributing to safety risks on mountainous freeways exhibit significant seasonal clustering and temporal correlations. However, these temporal characteristics have not been accurately captured by existing crash modeling methods, which severely compromise model fit and may lead to erroneous conclusions. This study makes three major contributions. Firstly, a multidimensional crash dataset involving design features, traffic conditions, pavement performance, and weather conditions was established based on eight quarterly datasets of mountain freeways in China. Secondly, two new crash modeling methods considering temporal correlations were proposed. The first model embedded an autoregressive structure and a time linear trend function within a Poisson model, while the second model incorporated an autoregressive structure and time-varying regression coefficients within a…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26
Figure 27
Figure 28
Figure 29
Figure 30
Figure 31
Figure 32
Figure 33
Figure 34
Figure 35
Figure 36
Figure 37
Figure 38
Figure 39
Figure 40
Figure 41
Figure 42
Figure 43
Figure 44
Figure 45
Figure 46
Figure 47
Figure 48
Figure 49
Figure 50Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTraffic and Road Safety · Infrastructure Maintenance and Monitoring · Traffic Prediction and Management Techniques